Publications by authors named "Nicola Dinapoli"

63 Publications

Delivery of online adaptive magnetic resonance guided radiotherapy based on isodose boundaries.

Phys Imaging Radiat Oncol 2021 Apr 7;18:78-81. Epub 2021 Jun 7.

Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, 00168 Rome, Italy.

Magnetic Resonance-guided Radiotherapy (MRgRT) allows direct monitoring of treated volumes. The aim of this study was to investigate the feasibility of a new gating strategy consisting in using an isodose as boundary. Forty-four patients treated for thoracic and abdominal lesions using MRgRT were enrolled. The accuracy of the new strategy was compared to the conventional one in terms of area improvement available for gating without compromising target coverage. A mean increase of 24% for lung, 15% for liver and 11% for pancreas was observed, demonstrating how the new method can be useful in challenging situations with low dose conformality.
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http://dx.doi.org/10.1016/j.phro.2021.05.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8254198PMC
April 2021

Personalized Treatment Planning Automation in Prostate Cancer Radiation Oncology: A Comprehensive Dosimetric Study.

Front Oncol 2021 1;11:636529. Epub 2021 Jun 1.

Radiation Oncology Unit, Gemelli Molise Hospital-Università Cattolica del Sacro Cuore, Campobasso, Italy.

Background: In radiation oncology, automation of treatment planning has reported the potential to improve plan quality and increase planning efficiency. We performed a comprehensive dosimetric evaluation of the new Personalized algorithm implemented in Pinnacle for full planning automation of VMAT prostate cancer treatments.

Material And Methods: Thirteen low-risk prostate (without lymph-nodes irradiation) and 13 high-risk prostate (with lymph-nodes irradiation) treatments were retrospectively taken from our clinical database and re-optimized using two different automated engines implemented in the Pinnacle treatment system. These two automated engines, the currently used Autoplanning and the new Personalized are both template-based algorithms that use a wish-list to formulate the planning goals and an iterative approach able to mimic the planning procedure usually adopted by experienced planners. In addition, the new Personalized module integrates a new engine, the Feasibility module, able to generate an "" DVH prediction of the achievability of planning goals. Comparison between clinically accepted manually generated (MP) and automated plans generated with both Autoplanning (AP) and Personalized engines (Pers) were performed using dose-volume histogram metrics and conformity indexes. Three different normal tissue complication probabilities (NTCPs) models were used for rectal toxicity evaluation. The planning efficiency and the accuracy of dose delivery were assessed for all plans.

Results: For similar targets coverage, Pers plans reported a significant increase of dose conformity and less irradiation of healthy tissue, with significant dose reduction for rectum, bladder, and femurs. On average, Pers plans decreased rectal mean dose by 11.3 and 8.3 Gy for low-risk and high-risk cohorts, respectively. Similarly, the Pers plans decreased the bladder mean doses by 7.3 and 7.6 Gy for low-risk and high-risk cohorts, respectively. The integral dose was reduced by 11-16% with respect to MP plans. Overall planning times were dramatically reduced to about 7 and 15 min for Pers plans. Despite the increased complexity, all plans passed the 3%/2 mm γ-analysis for dose verification.

Conclusions: The Personalized engine provided an overall increase of plan quality, in terms of dose conformity and sparing of normal tissues for prostate cancer patients. The Feasibility "" DVH prediction module provided OARs dose sparing well beyond the clinical objectives. The new Pinnacle Personalized algorithms outperformed the currently used Autoplanning ones as solution for treatment planning automation.
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http://dx.doi.org/10.3389/fonc.2021.636529DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8204695PMC
June 2021

The Multidimensional Assessment for Pediatric Patients in Radiotherapy (M.A.P.-RT) Tool for Customized Treatment Preparation: RADAR Project.

Front Oncol 2021 29;11:621690. Epub 2021 Mar 29.

UOC di Radioterapia Oncologica, Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.

Pediatric patients may experience considerable distress during radiotherapy. Combining psychological interventions with standard therapies can reduce the need for sedation. The RADAR Project aims to use a systematic method of recording data that can reveal patients' difficulties and fragility during treatment. In this context, the aim of our study was to investigate the ability of a multidimensional assessment tool (M.A.P.-RT schedule) to predict the need for sedation during radiotherapy. The schedule, which is administered during the first evaluation, was created to collect information on patients and their families in a standardized way. The study enrolled pediatric patients (aged 0-18 years or 18-21 with cognitive impairment). Data were collected by means of the M.A.P.-RT module; this explores various thematic areas, and is completed by the radiation oncologist, psychologist and nurse during their first evaluation. Features were selected by means of the Boruta method (random forest classifier), and the totals of the significant partial scores on each subsection of the module were inserted into a logistic model in order to test for their correlation with the use of anesthesia and with the frequency of psychological support. The results of logistic regression (LR) were used to identify the best predictors. The AUC was used to identify the best threshold for the scores in the evaluation. A total of 99 patients were considered for this analysis. The feature that best predicted both the need for anesthesia and the frequency of psychological support was the total score (TS), the AUC of the ROC being 0.9875 for anesthesia and 0.8866 for psychological support. During the first evaluation, the M.A.P.-RT form can predict the need for anesthesia in pediatric patients, and is a potential tool for personalizing therapeutic and management procedures.
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http://dx.doi.org/10.3389/fonc.2021.621690DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8039366PMC
March 2021

Pretreatment MRI Radiomics Based Response Prediction Model in Locally Advanced Cervical Cancer.

Diagnostics (Basel) 2021 Mar 31;11(4). Epub 2021 Mar 31.

Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168 Roma, Italy.

The aim of this study was to create a radiomics model for Locally Advanced Cervical Cancer (LACC) patients to predict pathological complete response (pCR) after neoadjuvant chemoradiotherapy (NACRT) analysing T2-weighted 1.5 T magnetic resonance imaging (MRI) acquired before treatment start. Patients with LACC and an International Federation of Gynecology and Obstetrics stage from IB2 to IVA at diagnosis were retrospectively enrolled for this study. All patients underwent NACRT, followed by radical surgery; pCR-assessed on surgical specimen-was defined as absence of any residual tumour. Finally, 1889 features were extracted from MR images; features showing statistical significance in predicting pCR at the univariate analysis were selected following an iterative method, which was ad-hoc developed for this study. Based on this method, 15 different classifiers were trained considering the most significant features selected. Model selection was carried out using the area under the receiver operating characteristic curve (AUC) as target metrics. One hundred eighty-three patients from two institutions were analysed. The model, showing the highest performance with an AUC of 0.80, was the random forest method initialised with default parameters. Radiomics appeared to be a reliable tool in pCR prediction for LACC patients undergoing NACRT, supporting the identification of patient risk groups, which paves treatment pathways tailored according to the predicted outcome.
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http://dx.doi.org/10.3390/diagnostics11040631DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8066099PMC
March 2021

Personalised support of brain tumour patients during radiotherapy based on psychological profile and quality of life.

Support Care Cancer 2021 Aug 22;29(8):4555-4563. Epub 2021 Jan 22.

UOC di Radioterapia Oncologica, Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy.

Purpose: Psychological distress in primary malignant brain tumour (PMBT) patients is associated with poorer outcomes. Radiotherapy (RT) often induces side effects that significantly influence patients' quality of life (QoL), with potential impact on survival. We evaluated distress, anxiety, depression, and QoL over time to identify patients with difficulties in these areas who required more intense psychological support.

Methods: Psychological questionnaires-Distress Thermometer (DT), Hospital Anxiety and Depression Scale (HADS), and Functional Assessment of Cancer Therapy (FACT-G and FACT-Br)-were completed at the beginning (T0), in the middle (T1), directly after RT (T2), and 3 months after RT (T3). We personalised the psychological support provided for each patient with a minimum of three sessions ('typical' schedule) and a maximum of eight sessions ('intensive' schedule), depending on the patients' psychological profiles, clinical evaluations, and requests. Patients' survival was evaluated in the glioblastoma multiforme (GBM) patients, with an explorative intent.

Results: Fifty-nine consecutive PMBT patients receiving post-operative RT were included. For patients who were reported as 'not distressed' at T0, no statistically significant changes were noted. In contrast, patients who were 'distressed' at T0 showed statistically significant improvements in DT, HADS, FACT-G, and FACT-Br scores over time. 'Not distressed' patients required less psychological sessions over the study duration than 'distressed' patients. Interestingly, 'not distressed' GBM patients survived longer than 'distressed' GBM patients.

Conclusions: Increased psychological support improved distress, mood, and QoL for patients identified as 'distressed', whereas psychological well-being was maintained with typical psychological support in patients who were identified as being 'not distressed'. These results encourage a standardisation of psychological support for all RT patients.
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http://dx.doi.org/10.1007/s00520-021-06000-7DOI Listing
August 2021

Predicting Radiotherapy Impact on Late Bladder Toxicity in Prostate Cancer Patients: An Observational Study.

Cancers (Basel) 2021 Jan 6;13(2). Epub 2021 Jan 6.

UOC Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia,Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168 Roma, Italy.

Background And Purpose: The aim of our study was to elaborate a suitable model on bladder late toxicity in prostate cancer (PC) patients treated by radiotherapy with volumetric technique.

Materials And Methods: PC patients treated between September 2010 and April 2017 were included in the analysis. An observational study was performed collecting late toxicity data of any grade, according to RTOG and CTCAE 4.03 scales, cumulative dose volumes histograms were exported for each patient. Vdose, the value of dose to a specific volume of organ at risk (OAR), impact was analyzed through the Mann-Whitney rank-sum test. Logistic regression was used as the final model. The model performance was estimated by taking 1000 samples with replacement from the original dataset and calculating the AUC average. In addition, the calibration plot (Hosmer-Lemeshow goodness-of-fit test) was used to evaluate the performance of internal validation. RStudio Software version 3.3.1 and an in house developed software package "Moddicom" were used.

Results: Data from 175 patients were collected. The median follow-up was 39 months (min-max 3.00-113.00). We performed Mann-Whitney rank-sum test with continuity correction in the subset of patients with late bladder toxicity grade ≥ 2: a statistically significant -value with a Vdose of 51.43 Gy by applying a logistic regression model (coefficient 4.3, value 0.025) for the prediction of the development of late G ≥ 2 GU toxicity was observed. The performance for the model's internal validation was evaluated, with an AUC equal to 0.626. Accuracy was estimated through the elaboration of a calibration plot.

Conclusions: Our preliminary results could help to optimize treatment planning procedures and customize treatments.
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http://dx.doi.org/10.3390/cancers13020175DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7825573PMC
January 2021

Delta Radiomics Can Predict Distant Metastasis in Locally Advanced Rectal Cancer: The Challenge to Personalize the Cure.

Front Oncol 2020 3;10:595012. Epub 2020 Dec 3.

Dipartimento Diagnostica per Immagini, Radioterapia oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.

Purpose: Distant metastases are currently the main cause of treatment failure in locally advanced rectal cancer (LARC) patients. The aim of this research is to investigate a correlation between the variation of radiomics features using pre- and post-neoadjuvant chemoradiation (nCRT) magnetic resonance imaging (MRI) with 2 years distant metastasis (2yDM) rate in LARC patients.

Methods And Materials: Diagnostic pre- and post- nCRT MRI of LARC patients, treated in a single institution from May 2008 to June 2015 with an adequate follow-up time, were retrospectively collected. Gross tumor volumes (GTV) were contoured by an abdominal radiologist and blindly reviewed by a radiation oncologist expert in rectal cancer. The dataset was firstly randomly split into 90% training data, for features selection, and 10% testing data, for the validation. The final set of features after the selection was used to train 15 different classifiers using accuracy as target metric. The models' performance was then assessed on the testing data and the best performing classifier was then selected, maximising the confusion matrix balanced accuracy (BA).

Results: Data regarding 213 LARC patients (36% female, 64% male) were collected. Overall 2yDM was 17%. A total of 2,606 features extracted from the pre- and post- nCRT GTV were tested and 4 features were selected after features selection process. Among the 15 tested classifiers, logistic regression proved to be the best performing one with a testing set BA, sensitivity and specificity of 78.5%, 71.4% and 85.7%, respectively.

Conclusions: This study supports a possible role of delta radiomics in predicting following occurrence of distant metastasis. Further studies including a consistent external validation are needed to confirm these results and allows to translate radiomics model in clinical practice. Future integration with clinical and molecular data will be mandatory to fully personalized treatment and follow-up approaches.
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http://dx.doi.org/10.3389/fonc.2020.595012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7744725PMC
December 2020

A deep learning approach to generate synthetic CT in low field MR-guided adaptive radiotherapy for abdominal and pelvic cases.

Radiother Oncol 2020 12 17;153:205-212. Epub 2020 Oct 17.

Fondazione Policlinico Universitario ''Agostino Gemelli'' IRCCS, Rome, Italy; Università Cattolica del Sacro Cuore, Rome, Italy.

Purpose: Artificial intelligence (AI) can play a significant role in Magnetic Resonance guided Radiotherapy (MRgRT), especially to speed up the online adaptive workflow. The aim of this study is to set up a Deep Learning (DL) approach able to generate synthetic computed tomography (sCT) images from low field MR images in pelvis and abdomen.

Methods: A conditional Generative Adversarial Network (cGAN) was used for sCT generation: a total of 120 patients treated on pelvic and abdominal sites were enrolled and divided in training (80) and test sets (40). Intensity modulated radiotherapy (IMRT) treatment plans were calculated on sCT and original CT and then compared in terms of gamma analysis and differences in Dose Volume Histogram (DVH). The two one-sided test for paired samples (TOST-P) was used to evaluate the equivalence among different DVH parameters calculated for target and organs at risks (OAR) on CT and sCT images.

Results: Using a CPU architecture, the mean time required by the neural network to generate a synthetic CT was 175 ± 43 seconds (s) for pelvic cases and 110 ± 40 s for abdominal ones. Mean gamma passing rates for the three tolerance criteria analysed (1%/1 mm, 2%/2 mm and 3%/3 mm) were respectively 90.8 ± 4.5%, 98.7 ± 1.1% and 99.8 ± 0.2% for abdominal cases; 89.3 ± 4.8%, 99.0 ± 0.7% and 99.9 ± 0.2% for pelvic ones, while equivalence within 1% was observed among the DVH indicators.

Conclusion: This study demonstrated that sCT generation using a DL approach is feasible for low field MR images in pelvis and abdomen, allowing a reliable calculation of IMRT plans in MRgRT.
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http://dx.doi.org/10.1016/j.radonc.2020.10.018DOI Listing
December 2020

Hypofractionated sequential radiotherapy boost: a promising strategy in inoperable locally advanced pancreatic cancer patients.

J Cancer Res Clin Oncol 2021 Mar 1;147(3):661-667. Epub 2020 Oct 1.

Università Cattolica del Sacro Cuore, Roma, Italy.

Purpose: To investigate the potential benefits of a hypofractionated radiotherapy boost (HRB) after chemotherapy (CT) and concomitant chemoradiotherapy (CRT) in locally advanced pancreatic cancer (LAPC) patients. Primary endpoints were early and late toxicity, local control (LC) and pain-free progression (PFP) assessment. Two-years overall survival (OS), metastasis-free survival (MFS) and disease-free survival (DFS) were secondary endpoints.

Materials And Methods: Patients (pts) affected by unresectable non-metastatic LAPC, previously treated with CT and CRT in upfront or sandwich setting, were selected for sequential HRB. Total prescribed dose was 30 Gy in 5 fractions (fr) to pancreatic primary lesion. Dose de-escalation was allowed in case of failure in respecting organs at risk constraints. Early and late toxicity were assessed according to CTCAE v.4.0 classification. The Kersh-Hazra scale was used for pain assessment. Local Control, PFP, MFS and DFS were calculated from the date of HRB to the date of relapse or the date of the last follow-up.

Results: Thirty-one pts affected by unresectable, non-metastatic LAPC were consecutively enrolled from November 2004 to October 2019. All pts completed the planned HRB. Total delivered dose varied according to duodenal dose constraint: 20 Gy in 5 fr (N: 6; 19.4%), 20 Gy in 4 fr (N: 5; 16.2%), 25 Gy in 5 fr (N: 18; 58.0%) and 30 Gy in 6 fr (N: 2; 6.4%). Early and late toxicity were assessed in all pts: no Grade 3 or 4 acute gastrointestinal toxicity and no late gastrointestinal complications occurred. Median LC was 19 months (range 1-156) and 1- and 2-year PFP were 85% and 62.7%, respectively (median 28 months; range 2-139). According to the Kersh-Hazra scale, four pts had a Grade 3 and four pts had a Grade 1 abdominal pain before HRB. At the last follow-up only 3/31 pts had residual Grade 1 abdominal pain.Median MFS was 18 months (range 1-139). The 2-year OS after HRB was 57.4%, while 2-year OS from diagnosis was 77.3%.

Conclusion: Treatment intensification with hypofractionated radiotherapy boost is well tolerated in pts affected by unresectable LAPC previously treated with CT/CRT. Its rates of local and pain control are encouraging, supporting its introduction in clinical practice. Timing, schedule and dose of HRB need to be further investigated to personalize therapy and optimize clinical advantages.
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http://dx.doi.org/10.1007/s00432-020-03411-7DOI Listing
March 2021

A field strength independent MR radiomics model to predict pathological complete response in locally advanced rectal cancer.

Radiol Med 2021 Mar 24;126(3):421-429. Epub 2020 Aug 24.

Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy.

Purpose: Aim of this study was to develop a generalised radiomics model for predicting pathological complete response after neoadjuvant chemo-radiotherapy in locally advanced rectal cancer patients using pre-CRT T2-weighted images acquired at a 1.5 T and a 3 T scanner.

Methods: In two institutions, 195 patients were scanned: 136 patients were scanned on a 1.5 T MR scanner, 59 patients on a 3 T MR scanner. Gross tumour volumes were delineated on the MR images and 496 radiomic features were extracted, applying the intensity-based (IB) filter. Features were standardised with Z-score normalisation and an initial feature selection was carried out using Wilcoxon-Mann-Whitney test: The most significant features at 1.5 T and 3 T were selected as main features. Several logistic regression models combining the main features with a third one selected by those resulting significant were elaborated and evaluated in terms of area under curve (AUC). A tenfold cross-validation was repeated 300 times to evaluate the model robustness.

Results: Three features were selected: maximum fractal dimension with IB = 0-50, energy and grey-level non-uniformity calculated on the run-length matrix with IB = 0-50. The AUC of the model applied to the whole dataset after cross-validation was 0.72, while values of 0.70 and 0.83 were obtained when 1.5 T and 3 T patients were considered, respectively.

Conclusions: The model elaborated showed good performance, even when data from patients scanned on 1.5 T and 3 T were merged. This shows that magnetic field intensity variability can be overcome by means of selecting appropriate image features.
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http://dx.doi.org/10.1007/s11547-020-01266-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7937600PMC
March 2021

SKIN-COBRA (Consortium for Brachytherapy data Analysis) ontology: The first step towards interdisciplinary standardized data collection for personalized oncology in skin cancer.

J Contemp Brachytherapy 2020 Apr 30;12(2):105-110. Epub 2020 Apr 30.

Fondazione Policlinico Universitario "A. Gemelli" IRCCS, UOC Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Roma, Italy.

Purpose: The primary objective of the SKIN-COBRA (Consortium for Brachytherapy data Analysis) ontology is to define a specific terminological system to standardize data collection for non-melanoma skin cancer patients treated with brachytherapy (BT, interventional radiotherapy). Through ontological characterization of information, it is possible to find, isolate, organize, and integrate its meaning.

Material And Methods: SKIN-COBRA is a standardized data collection consortium for non-melanoma skin patients treated with BT, including 8 cancer centers. Its ontology was firstly defined by a multicentric and multidisciplinary working group and evaluated by the consortium, followed by a multi-professional technical commission involving a mathematician, an engineer, a physician with experience in data storage, a programmer, and a software expert.

Results: Two hundred and ninety variables were defined in 10 input forms. There are 3 levels, with each offering a specific type of analysis: 1. Registry level (epidemiology analysis); 2. Procedures level (standard oncology analysis); 3. Research level (radiomics analysis). The ontology was approved by the technical commission and consortium, and an ad-hoc software system was defined to be implemented in the SKIN-COBRA consortium.

Conclusions: Large databases are natural extension of traditional statistical approaches, a valuable and increasingly necessary tool for modern healthcare system. Future analysis of the collected multinational and multicenter data will show whether the use of the system can produce high-quality evidence to support multidisciplinary management of non-melanoma skin cancer and utilizing this information for personalized treatment decisions.
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http://dx.doi.org/10.5114/jcb.2020.94579DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7207239PMC
April 2020

Radiotherapy imaging: An unexpected ally in fighting COVID 19 pandemic.

Radiother Oncol 2020 07 25;148:223-224. Epub 2020 Apr 25.

Fondazione Policlinico Universitario ''A. Gemelli'' IRCCS, UOC Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Roma, Italy.

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http://dx.doi.org/10.1016/j.radonc.2020.04.036DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7194723PMC
July 2020

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping.

Radiology 2020 05 10;295(2):328-338. Epub 2020 Mar 10.

From OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Fetscherstr 74, PF 41, 01307 Dresden, Germany (A.Z., S. Leger, E.G.C.T., C.R., S. Löck); National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany (A.Z.); Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden and Helmholtz Association/Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany (A.Z., S. Leger, E.G.C.T.); German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany (A.Z., S. Leger, E.G.C.T., C.R., S. Löck); Medical Physics Unit, McGill University, Montréal, Canada (M.V., I.E.N.); Image Response Assessment Team Core Facility, Moffitt Cancer Center, Tampa, Fla (M.A.A.); Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Harvard University, Boston, Mass (H.J.W.L.A.); Institute of Information Systems, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland (V.A., A.D., H.M.); Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY (A.A.); Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Md (S.A.); Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Md (S.A., A.R.); Center for Biomedical image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pa (S.B., C.D., S.M.H., S.P.); Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (S.B., C.D., S.M.H., S.P.); Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (S.B.); Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen (UMCG), Groningen, the Netherlands (R.J.B., R.B., E.A.G.P.); Radiology and Nuclear Medicine, VU University Medical Centre (VUMC), Amsterdam, the Netherlands (R.B.); Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland (M.B., M.Guckenberger, S.T.L.); Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy (L.B., N.D., R.G., J.L., V.V.); Laboratoire d'Imagerie Translationnelle en Oncologie, Université Paris Saclay, Inserm, Institut Curie, Orsay, France (I.B., C.N., F.O.); Cancer Imaging Dept, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom (G.J.R.C., V.G., M.M.S.); Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, Lausanne, Switzerland (A.D.); Laboratory of Medical Information Processing (LaTIM)-team ACTION (image-guided therapeutic action in oncology), INSERM, UMR 1101, IBSAM, UBO, UBL, Brest, France (M.C.D., M.H., T.U.); Department of Radiation Oncology, the Netherlands Cancer Institute (NKI), Amsterdam, the Netherlands (C.V.D.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (S.E., S.N.); Department of Radiation Oncology, Physics Division, University of Michigan, Ann Arbor, Mich (I.E.N., A.U.K.R.); Surgical Planning Laboratory, Brigham and Women's Hospital and Harvard Medical School, Harvard University, Boston, Mass (A.Y.F.); Department of Cancer Imaging and Metabolism, Moffitt Cancer Center, Tampa, Fla (R.J.G.); Department of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (M. Götz, F.I., K.H.M.H., J.S.); The D-Lab, Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands (P.L., R.T.H.L.); Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany (F.L., J.S.F., D.T.); Department of Clinical Medicine, University of Bergen, Bergen, Norway (A.L.); Department of Radiation Oncology, University of California, San Francisco, Calif (O.M.); University of Geneva, Geneva, Switzerland (H.M.); Department of Electrical Engineering, Stanford University, Stanford, Calif (S.N.); Department of Medicine (Biomedical Informatics Research), Stanford University School of Medicine, Stanford, Calif (S.N.); Departments of Radiology and Physics, University of British Columbia, Vancouver, Canada (A.R.); Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Mich (A.U.K.R.); Department of Radiation Oncology, University of Groningen, University Medical Center Groningen (UMCG), Groningen, the Netherlands (N.M.S., R.J.H.M.S., L.V.v.D.); School of Engineering, Cardiff University, Cardiff, United Kingdom (E.S., P.W.); Department of Medical Physics, Velindre Cancer Centre, Cardiff, United Kingdom (E.S.); Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany (E.G.C.T., C.R., S. Löck), Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiooncology-OncoRay, Dresden, Germany (E.G.C.T., C.R.); Department of Nuclear Medicine, CHU Milétrie, Poitiers, France (T.U.); Department of Radiology, the Netherlands Cancer Institute (NKI), Amsterdam, the Netherlands (J.v.G.); GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, the Netherlands (J.v.G.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (J.v.G.); and Department of Radiology, Leiden University Medical Center (LUMC), Leiden, the Netherlands (F.H.P.v.V.).

Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 See also the editorial by Kuhl and Truhn in this issue.
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http://dx.doi.org/10.1148/radiol.2020191145DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7193906PMC
May 2020

Template-based automation of treatment planning in advanced radiotherapy: a comprehensive dosimetric and clinical evaluation.

Sci Rep 2020 01 16;10(1):423. Epub 2020 Jan 16.

Radiation Oncology Unit, Fondazione di Ricerca e Cura Giovanni Paolo II - Università Cattolica del Sacro Cuore, Campobasso, Italy.

Despite the recent advanced developments in radiation therapy planning, treatment planning for head-neck and pelvic cancers remains challenging due to large concave target volumes, multiple dose prescriptions and numerous organs at risk close to targets. Inter-institutional studies highlighted that plan quality strongly depends on planner experience and skills. Automated optimization of planning procedure may improve plan quality and best practice. We performed a comprehensive dosimetric and clinical evaluation of the Pinnacle AutoPlanning engine, comparing automatically generated plans (AP) with the historically clinically accepted manually-generated ones (MP). Thirty-six patients (12 for each of the following anatomical sites: head-neck, high-risk prostate and endometrial cancer) were re-planned with the AutoPlanning engine. Planning and optimization workflow was developed to automatically generate "dual-arc" VMAT plans with simultaneously integrated boost. Various dose and dose-volume parameters were used to build three metrics able to supply a global Plan Quality Index evaluation in terms of dose conformity indexes, targets coverage and sparing of critical organs. All plans were scored in a blinded clinical evaluation by two senior radiation oncologists. Dose accuracy was validated using the PTW Octavius-4D phantom together with the 1500 2D-array. Autoplanning was able to produce high-quality clinically acceptable plans in all cases. The main benefit of Autoplanning strategy was the improvement of overall treatment quality due to significant increased dose conformity and reduction of integral dose by 6-10%, keeping similar targets coverage. Overall planning time was reduced to 60-80 minutes, about a third of time needed for manual planning. In 94% of clinical evaluations, the AP plans scored equal or better to MP plans. Despite the increased fluence modulation, dose measurements reported an optimal agreement with dose calculations with a γ-pass-rate greater than 95% for 3%(global)-2 mm criteria. Autoplanning engine is an effective device enabling the generation of VMAT high quality treatment plans according to institutional specific planning protocols.
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http://dx.doi.org/10.1038/s41598-019-56966-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6965209PMC
January 2020

On the accuracy of bulk synthetic CT for MR-guided online adaptive radiotherapy.

Radiol Med 2020 Feb 8;125(2):157-164. Epub 2019 Oct 8.

Fondazione Policlinico Universitario A. Gemelli, IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy.

Purpose: MR-guided radiotherapy (MRgRT) relies on the daily assignment of a relative electron density (RED) map to allow the fraction specific dose calculation. One approach to assign the RED map consists of segmenting the daily magnetic resonance image into five different density levels and assigning a RED bulk value to each level to generate a synthetic CT (sCT). The aim of this study is to evaluate the dose calculation accuracy of this approach for applications in MRgRT.

Methods: A planning CT (pCT) was acquired for 26 patients with abdominal and pelvic lesions and segmented in five levels similar to an online approach: air, lung, fat, soft tissue and bone. For each patient, the median RED value was calculated for fat, soft tissue and bone. Two sCTs were generated assigning different bulk values to the segmented levels on pCT: The sCT uses the RED values recommended by ICRU46, and the sCT uses the median patient-specific RED values. The same treatment plan was calculated on two the sCTs and the pCT. The dose calculation accuracy was investigated in terms of gamma analysis and dose volume histogram parameters.

Results: Good agreement was found between dose calculated on sCTs and pCT (gamma passing rate 1%/1 mm equal to 91.2% ± 6.9% for sCT and 93.7% ± 5.3% b or sCT). The mean difference in estimating V95 (PTV) was equal to 0.2% using sCT and 1.2% using sCT, respect to pCT values CONCLUSIONS: The bulk sCT guarantees a high level of dose calculation accuracy also in presence of magnetic field, making this approach suitable to MRgRT. This accuracy can be improved by using patient-specific RED values.
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http://dx.doi.org/10.1007/s11547-019-01090-0DOI Listing
February 2020

A new frontier of image guidance: Organs at risk avoidance with MRI-guided respiratory-gated intensity modulated radiotherapy: Technical note and report of a case.

J Appl Clin Med Phys 2019 Jun 4;20(6):194-198. Epub 2019 May 4.

Dipartimento di Diagnostica per Immagini, UOC di Radioterapia Oncologica, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy.

The case of a 50-year-old man affected by a rhabdomiosarcoma metastatic lesion in the left flank Is reported. The patient was addressed to 50.4 Gy radiotherapy with concomitant chemotherapy in order to locally control the lesion. A Tri-60-Co magnetic resonance hybrid radiotherapy unit was used for treatment delivery and a respiratory gating protocol was applied for the different breathing phases (Free Breathing, Deep Inspiration Breath Hold and Final Expiration Breath Hold). Three intensity modulated radiation therapy (IMRT) plans were calculated and Final Expiration Breath Hold plan was finally selected due to the absence of PTV coverage differences and better organs at risk sparing (i.e. kidneys). This case report suggests that organs at risk avoidance with MRI-guided respiratory-gated Radiotherapy is feasible and particularly advantageous whenever sparing the organs at risk is of utmost dosimetric or clinical importance.
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http://dx.doi.org/10.1002/acm2.12575DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6560311PMC
June 2019

Efficacy of an eye movement desensitization and reprocessing (EMDR) intervention for a head and neck cancer patient with intolerable anxiety undergoing radiotherapy.

Psychooncology 2019 03 6;28(3):647-649. Epub 2019 Feb 6.

Dipartimento di Scienze Radiologiche, Radioterapiche ed Ematologiche, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italia.

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http://dx.doi.org/10.1002/pon.5000DOI Listing
March 2019

Delta radiomics for rectal cancer response prediction with hybrid 0.35 T magnetic resonance-guided radiotherapy (MRgRT): a hypothesis-generating study for an innovative personalized medicine approach.

Radiol Med 2019 Feb 29;124(2):145-153. Epub 2018 Oct 29.

Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Istituto di Radiologia, Fondazione Policlinico A. Gemelli IRCCS - Università Cattolica Sacro Cuore, Largo A. Gemelli, 8, 00168, Rome, Italy.

The aim of this study was to evaluate the variation of radiomics features, defined as "delta radiomics", in patients undergoing neoadjuvant radiochemotherapy (RCT) for rectal cancer treated with hybrid magnetic resonance (MR)-guided radiotherapy (MRgRT). The delta radiomics features were then correlated with clinical complete response (cCR) outcome, to investigate their predictive power. A total of 16 patients were enrolled, and 5 patients (31%) showed cCR at restaging examinations. T2*/T1 MR images acquired with a hybrid 0.35 T MRgRT unit were considered for this analysis. An imaging acquisition protocol of 6 MR scans per patient was performed: the first MR was acquired at first simulation (t0) and the remaining ones at fractions 5, 10, 15, 20 and 25. Radiomics features were extracted from the gross tumour volume (GTV), and each feature was correlated with the corresponding delivered dose. The variations of each feature during treatment were quantified, and the ratio between the values calculated at different dose levels and the one extracted at t0 was calculated too. The Wilcoxon-Mann-Whitney test was performed to identify the features whose variation can be predictive of cCR, assessed with a MR acquired 6 weeks after RCT and digital examination. The most predictive feature ratios in cCR prediction were the L_least and glnu ones, calculated at the second week of treatment (22 Gy) with a p value = 0.001. Delta radiomics approach showed promising results and the quantitative analysis of images throughout MRgRT treatment can successfully predict cCR offering an innovative personalized medicine approach to rectal cancer treatment.
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http://dx.doi.org/10.1007/s11547-018-0951-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6373341PMC
February 2019

Towards a modular decision support system for radiomics: A case study on rectal cancer.

Artif Intell Med 2019 05 4;96:145-153. Epub 2018 Oct 4.

Polo Scienze Oncologiche ed Ematologiche, Fondazione Policlinico Universitario Agostino Gemelli, Largo A. Gemelli, 8, 00168 Rome, Italy.

Following the personalized medicine paradigm, there is a growing interest in medical agents capable of predicting the effect of therapies on patients, by exploiting the amount of data that is now available for each patient. In disciplines like oncology, where images and scans are available, the exploitation of medical images can provide an additional source of potentially useful information. The study and analysis of features extracted by medical images, exploited for predictive purposes, is termed radiomics. A number of tools are available for supporting some of the steps of the radiomics process, but there is a lack of approaches which are able to deal with all the steps of the process. In this paper, we introduce a medical agent-based decision support system capable of handling the whole radiomics process. The proposed system is tested on two independent data sets of patients treated for rectal cancer. Experimental results indicate that the system is able to generate highly performant centre-specific predictive model, and show the issues related to differences in data sets collected by different centres, and how such issues can affect the performance of the generated predictive models.
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http://dx.doi.org/10.1016/j.artmed.2018.09.003DOI Listing
May 2019

Hypofractionated stereotactic radiotherapy for oligometastatic patients: developing of a response predictive model.

Med Oncol 2018 Sep 14;35(11):146. Epub 2018 Sep 14.

Department of Radiation Oncology, Università Cattolica del Sacro Cuore, Largo A. Gemelli, 1, Rome, Italy.

Objectives: Treatment of oligometastatic patients is a current challenge in radiation oncology. Aim of this study is to define a dose-response relationship for hypofractionated radiotherapy of oligometastases.

Methods: Retrospective analysis of metastases treated by hypofractionated stereotactic radiotherapy was performed. Delivered dose was calculated both as biological effective dose (BED), and as ratio between BED and the logarithm of metastasis volume (BED logVolume Ratio, BVR). Two dose-response models were defined by logistic regression. The fitted outcome was the Metastases Complete Response (MCR). Performances of the models were assessed by area under the receiver operating curve (AUC) and by bootstrap calibration of original data. BED and BVR impact on survival outcomes has been evaluated.

Results: Fifty-three patients with 79 metastases were analyzed. AUC and calibration of BVR-based logistic model showed better accuracy in predicting MCR with respect to BED-based model. No significant difference between the two ROCs was observed (De Long test p value > 0.05), but significant discordance in calibration resulted in the BED model (p value < 0.05 in Hosmer-Lemeshow Goodness of fit test). BVR returned also better results in multivariate analyses for survival outcomes.

Conclusions: The ratio between BED and the logarithm of metastasis volume (BVR), as a corrective factor for fitting the probability of metastases response to stereotactic radiotherapy, could be a tool for evaluating and prescribing treatments for oligometastatic disease. BVR can be useful for producing more reliable survival statistics too.
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http://dx.doi.org/10.1007/s12032-018-1206-4DOI Listing
September 2018

Identification of the most significant magnetic resonance imaging (MRI) radiomic features in oncological patients with vertebral bone marrow metastatic disease: a feasibility study.

Radiol Med 2019 Jan 6;124(1):50-57. Epub 2018 Sep 6.

Department of Radiation Oncology - Gemelli-ART, Catholic University of Rome, School of Medicine, Foundation University Hospital "A. Gemelli", Largo A. Gemelli 8, 00168, Rome, Italy.

Objectives: Recently, radiomic analysis has gained attention as a valuable instrument for the management of oncological patients. The aim of the study is to isolate which features of magnetic resonance imaging (MRI)-based radiomic analysis have to be considered the most significant predictors of metastasis in oncological patients with spinal bone marrow metastatic disease.

Materials And Methods: Eight oncological patients (3 lung cancer; 1 prostatic cancer; 1 esophageal cancer; 1 nasopharyngeal cancer; 1 hepatocarcinoma; 1 breast cancer) with pre-radiotherapy MR imaging for a total of 58 dorsal vertebral bodies, 29 metastatic and 29 non-metastatic were included. Each vertebral body was contoured in T1 and T2 weighted images at a radiotherapy delineation console. The obtained data were transferred to an automated data extraction system for morphological, statistical and textural analysis. Eighty-nine features for each lesion in both T1 and T2 images were computed as the median of by-slice values. A Wilcoxon test was applied to the 89 features and the most statistically significant of them underwent to a stepwise feature selection, to find the best performing predictors of metastasis in a logistic regression model. An internal cross-validation via bootstrap was conducted for estimating the model performance in terms of the area under the curve (AUC) of the receiver operating characteristic.

Results: Of the 89 textural features tested, 16 were found to differ with statistical significance in the metastatic vs non-metastatic group. The best performing model was constituted by two predictors for T1 and T2 images, namely one morphological feature (center of mass shift) (p value < 0.01) for both datasets and one histogram feature minimum grey level (p value < 0.01) for T1 images and one textural feature (grey-level co-occurrence matrix joint variance (p value < 0.01) for T2 images. The internal cross-validation showed an AUC of 0.8141 (95% CI 0.6854-0.9427) in T1 images and 0.9116 (95% CI 0.8294-0.9937) in T2 images.

Conclusions: The results suggest that MRI-based radiomic analysis on oncological patients with bone marrow metastatic disease is able to differentiate between metastatic and non-metastatic vertebral bodies. The most significant predictors of metastasis were found to be based on T2 sequence and were one morphological and one textural feature.
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http://dx.doi.org/10.1007/s11547-018-0935-yDOI Listing
January 2019

ENT COBRA ONTOLOGY: the covariates classification system proposed by the Head & Neck and Skin GEC-ESTRO Working Group for interdisciplinary standardized data collection in head and neck patient cohorts treated with interventional radiotherapy (brachytherapy).

J Contemp Brachytherapy 2018 Jun 30;10(3):260-266. Epub 2018 Jun 30.

Interdisciplinary Brachytherapy Unit, University of Lübeck - University Hospital S-H, Campus Lübeck, Germany.

Purpose: Clinical data collecting is expensive in terms of time and human resources. Data can be collected in different ways; therefore, performing multicentric research based on previously stored data is often difficult. The primary objective of the ENT COBRA (COnsortium for BRachytherapy data Analysis) ontology is to define a specific terminological system to standardized data collection for head and neck (H&N) cancer patients treated with interventional radiotherapy.

Material And Methods: ENT-COBRA is a consortium for standardized data collection for H&N patients treated with interventional radiotherapy. It is linked to H&N and Skin GEC-ESTRO Working Group and includes 11 centers from 6 countries. Its ontology was firstly defined by a multicentric working group, then evaluated by the consortium followed by a multi-professional technical commission involving a mathematician, an engineer, a physician with experience in data storage, a programmer, and a software expert.

Results: Two hundred and forty variables were defined on 13 input forms. There are 3 levels, each offering a specific type of analysis: 1. Registry level (epidemiology analysis); 2. Procedures level (standard oncology analysis); 3. Research level (radiomics analysis). The ontology was approved by the consortium and technical commission; an ad-hoc software architecture ("broker") remaps the data present in already existing storage systems of the various centers according to the shared terminology system. The first data sharing was successfully performed using COBRA software and the ENT COBRA Ontology, automatically collecting data directly from 3 different hospital databases (Lübeck, Navarra, and Rome) in November 2017.

Conclusions: The COBRA Ontology is a good response to the multi-dimensional criticalities of data collection, retrieval, and usability. It allows to create a software for large multicentric databases with implementation of specific remapping functions wherever necessary. This approach is well-received by all involved parties, primarily because it does not change a single center's storing technologies, procedures, and habits.
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http://dx.doi.org/10.5114/jcb.2018.76982DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6052377PMC
June 2018

Magnetic Resonance, Vendor-independent, Intensity Histogram Analysis Predicting Pathologic Complete Response After Radiochemotherapy of Rectal Cancer.

Int J Radiat Oncol Biol Phys 2018 11 4;102(4):765-774. Epub 2018 May 4.

Fondazione Policlinico A. Gemelli IRCCS - Università Cattolica Sacro Cuore, Dipartimento Scienze Radiologiche, Radioterapiche ed Ematologiche, Istituto di Radiologia, Roma, Italia.

Purpose: The objective of this study is finding an intensity based histogram (IBH) signature to predict pathologic complete response (pCR) probability using only pre-treatment magnetic resonance (MR) and validate it externally in order to create a workflow for the external validation of an MR IBH signature and to apply the model out of the environment where it has been tuned. The impact of pCR and the final predictors on the survival outcome were also evaluated.

Methods And Materials: Three centers using different MR scanners were involved in this retrospective study. The first center recruited 162 patients for model training, and the second and third centers provided 34 plus 25 patients for external validation. Patients provided written consent. Accrual period was from May 2008 to December 2014. After surgery pathologic response was defined. T2-weighted MR scans acquired before chemoradiation therapy (CRT) were used for analysis addressed on primary lesions. Images were pre-processed using Laplacian of Gaussian (LoG) filter with multiple σ, and first order intensity histogram-based features (kurtosis, skewness, and entropy) were extracted. Features selection was performed using Mann-Whitney test. Tumor staging (cT, cN) was added to build a logistic regression model and predict pCR. Model performance was evaluated with internal and external validation using area under the curve (AUC) of the receiver operator characteristic (ROC) and calibration with Hosmer-Lemeshow test. The linear cross-correlation matrix (Pearson's coefficient) and the variance inflation factor (VIF) were used to check the correlation and the co-linearity among the final predictors. The amount of the information added through the radiomics features was estimated by using the DeLong's test, and the impact of pCR and the final predictors on survival outcomes were evaluated through the Kaplan-Meier curves by using the log-rank test and the multivariate Cox model.

Results: Candidate-to-analysis features were skewness (σ = 0.485, P value = .01) and entropy (σ = 0.344, P value < .05). Logistic regression analysis showed as significant covariates cT (P value < .01), skewness-σ = 0.485 (P value = .01), and entropy-σ = 0.344 (P value < .05). Model AUCs were 0.73 (internal) and 0.75 (external).

Conclusions: This MR-based, vendor-independent model can be helpful for predicting pCR probability in locally advanced rectal cancer (LARC) patients only using pre-treatment imaging.
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http://dx.doi.org/10.1016/j.ijrobp.2018.04.065DOI Listing
November 2018

Hybrid Tri-Co-60 MRI radiotherapy for locally advanced rectal cancer: An evaluation.

Tech Innov Patient Support Radiat Oncol 2018 Jun 31;6:5-10. Epub 2018 Mar 31.

Polo Scienze Oncologiche ed Ematologiche, Istituto di Radiologia, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario Agostino Gemelli, Largo Francesco Vito, 1 - 00168 Roma, Italy.

Introduction: Aim of this paper is to investigate the plan quality of a tri-Co-60 MRI-Hybrid system for intensity-modulated radiation therapy (IMRT) in patients affected by locally advanced rectal cancer (LARC) undergoing neo-adjuvant radiotherapy.

Materials And Methods: Ten consecutive LARC patients were selected. Tri-Co-60 step and shoot IMRT plans were generated simulating the presence of the magnetic field (B) or not (B) with the dedicated treatment planning system (TPS).The total planned dose was 45 Gy in 25 fractions to the mesorectum and the pelvic nodes (planning target volume 2, PTV2) and 55 Gy to the tumor and correspondent mesorectum (PTV1) through simultaneous integrated boost (SIB). Tri-Co-60 IMRT plans were compared with Volumetric Modulated Arc Therapy (VMAT) and IMRT plans for Linear Accelerator (Linac).

Results: B and B tri-Co-60 IMRT plans showed no relevant differences. Mean values of PTV1 and PTV2 receiving at least 95% of the D (V) were higher than 95% in all treatment plans. All plans met the V constraint for the PTV1. Mean values of V for the PTV2 were 14.8, 5.0, and 7.3% respectively for tri-Co-60, VMAT and IMRT. Mean Wu's HI values were similar in all plans (7.4-7.8%). All plans met the V constraint for small bowel, but mean V value was higher with tri-Co-60.Bladder irradiation was comparable and always lower than the chosen D max 65 Gy constraint.Mean values of V and V to the body and median skin doses were higher with tri-Co-60 plans.

Discussion: Treatment plans with Tri-Co-60 step and shoot IMRT met the dose-volume objectives in patients with LARC. Nevertheless, a larger volume of normal tissue received low-moderate doses when compared with Linac based VMAT and IMRT.
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http://dx.doi.org/10.1016/j.tipsro.2018.02.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7033778PMC
June 2018

A new standardized data collection system for interdisciplinary thyroid cancer management: Thyroid COBRA.

Eur J Intern Med 2018 07 21;53:73-78. Epub 2018 Feb 21.

Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy.

The big data approach offers a powerful alternative to Evidence-based medicine. This approach could guide cancer management thanks to machine learning application to large-scale data. Aim of the Thyroid CoBRA (Consortium for Brachytherapy Data Analysis) project is to develop a standardized web data collection system, focused on thyroid cancer. The Metabolic Radiotherapy Working Group of Italian Association of Radiation Oncology (AIRO) endorsed the implementation of a consortium directed to thyroid cancer management and data collection. The agreement conditions, the ontology of the collected data and the related software services were defined by a multicentre ad hoc working-group (WG). Six Italian cancer centres were firstly started the project, defined and signed the Thyroid COBRA consortium agreement. Three data set tiers were identified: Registry, Procedures and Research. The COBRA-Storage System (C-SS) appeared to be not time-consuming and to be privacy respecting, as data can be extracted directly from the single centre's storage platforms through a secured connection that ensures reliable encryption of sensible data. Automatic data archiving could be directly performed from Image Hospital Storage System or the Radiotherapy Treatment Planning Systems. The C-SS architecture will allow "Cloud storage way" or "distributed learning" approaches for predictive model definition and further clinical decision support tools development. The development of the Thyroid COBRA data Storage System C-SS through a multicentre consortium approach appeared to be a feasible tool in the setup of complex and privacy saving data sharing system oriented to the management of thyroid cancer and in the near future every cancer type.
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http://dx.doi.org/10.1016/j.ejim.2018.02.012DOI Listing
July 2018

Fractal-based radiomic approach to predict complete pathological response after chemo-radiotherapy in rectal cancer.

Radiol Med 2018 Apr 11;123(4):286-295. Epub 2017 Dec 11.

Polo Scienze Oncologiche ed Ematologiche, Istituto di Radiologia, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario Agostino Gemelli, Largo Francesco Vito 1, 00168, Rome, Italy.

The aim of this study was to propose a methodology to investigate the tumour heterogeneity and evaluate its ability to predict pathologically complete response (pCR) after chemo-radiotherapy (CRT) in locally advanced rectal cancer (LARC). This approach consisted in normalising the pixel intensities of the tumour and identifying the different sub-regions using an intensity-based thresholding. The spatial organisation of these subpopulations was quantified using the fractal dimension (FD). This approach was implemented in a radiomic workflow and applied to 198 T2-weighted pre-treatment magnetic resonance (MR) images of LARC patients. Three types of features were extracted from the gross tumour volume (GTV): morphological, statistical and fractal features. Feature selection was performed using the Wilcoxon test and a logistic regression model was calculated to predict the pCR probability after CRT. The model was elaborated considering the patients treated in two institutions: Fondazione Policlinico Universitario "Agostino Gemelli" of Rome (173 cases, training set) and University Medical Centre of Maastricht (25 cases, validation set). The results obtained showed that the fractal parameters of the subpopulations have the highest performance in predicting pCR. The predictive model elaborated had an area under the curve (AUC) equal to 0.77 ± 0.07. The model reliability was confirmed by the validation set (AUC = 0.79 ± 0.09). This study suggests that the fractal analysis can play an important role in radiomics, providing valuable information not only about the GTV structure, but also about its inner subpopulations.
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http://dx.doi.org/10.1007/s11547-017-0838-3DOI Listing
April 2018

Radiomics based analysis to predict local control and survival in hepatocellular carcinoma patients treated with volumetric modulated arc therapy.

BMC Cancer 2017 Dec 6;17(1):829. Epub 2017 Dec 6.

Department of Radiation Oncology, Cheng-Ching General Hospital, Taichung, Taiwan.

Background: To appraise the ability of a radiomics based analysis to predict local response and overall survival for patients with hepatocellular carcinoma.

Methods: A set of 138 consecutive patients (112 males and 26 females, median age 66 years) presented with Barcelona Clinic Liver Cancer (BCLC) stage A to C were retrospectively studied. For a subset of these patients (106) complete information about treatment outcome, namely local control, was available. Radiomic features were computed for the clinical target volume. A total of 35 features were extracted and analyzed. Univariate analysis was used to identify clinical and radiomics significant features. Multivariate models by Cox-regression hazards model were built for local control and survival outcome. Models were evaluated by area under the curve (AUC) of receiver operating characteristic (ROC) curve. For the LC analysis, two models selecting two groups of uncorrelated features were analyzes while one single model was built for the OS analysis.

Results: The univariate analysis lead to the identification of 15 significant radiomics features but the analysis of cross correlation showed several cross related covariates. The un-correlated variables were used to build two separate models; both resulted into a single significant radiomic covariate: model-1: energy p < 0.05, AUC of ROC 0.6659, C.I.: 0.5585-0.7732; model-2: GLNU p < 0.05, AUC 0.6396, C.I.:0.5266-0.7526. The univariate analysis for covariates significant with respect to local control resulted in 9 clinical and 13 radiomics features with multiple and complex cross-correlations. After elastic net regularization, the most significant covariates were compacity and BCLC stage, with only compacity significant to Cox model fitting (Cox model likelihood ratio test p < 0.0001, compacity p < 0.00001; AUC of the model is 0.8014 (C.I. = 0.7232-0.8797)).

Conclusion: A robust radiomic signature, made by one single feature was finally identified. A validation phases, based on independent set of patients is scheduled to be performed to confirm the results.
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http://dx.doi.org/10.1186/s12885-017-3847-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5718116PMC
December 2017

Beyond geometrical overlap: a Dosimetrical Evaluation of automated volumes Adaptation (DEA) in head and neck replanning.

Tech Innov Patient Support Radiat Oncol 2017 Sep-Dec;3-4:1-6. Epub 2017 Jul 21.

Gemelli Advanced Radiation Therapy Center, Fondazione Policlinico Universitario "A. Gemelli", Università Cattolica del Sacro Cuore, Rome, Italy.

Introduction: Automated target volumes adaptation could be useful in H&N replanning, but its dosimetric impact has not been analyzed.Primary aim of this investigation is dose coverage assessment in fully automated and edited PTV adaptation settings, compared to manual benchmark.

Materials And Methods: Ten IMRT patients were selected and replanning CTs were acquired.A deformable registration with PTV adaptation was performed defining PTVA.PTV B was obtained through manual editing and a benchmark PTV C was manually segmented by a delineation team.The Dice Similarity Index (DSI) and the mean Hausdorff Distance (mHD) were calculated between PTV A and PTV C, and between PTV B and PTV C.One IMRT plan was realized for each PTV: the plans optimized on PTV A and PTV B were proposed on PTV C to evaluate their dosimetric reliability compared to the benchmark plan in terms of PTV V95% dose coverage.

Results: The comparisons between PTV A with PTV C and PTV B with PTV C showed that the better DSI (high) and mHD values (low) are, the smaller difference when compared to PTV C V95% is described.Evaluating plan A and B, PTV C V95% reduced by 6.1 ± 3.0% and by 4.1 ± 2.3% respectively when compared to plan C PTV C V95%.PTV B reaches acceptable dose coverage values (PTV V95% >95%) when DSI is >0.91 and a mHD < 0.17 mm and it has better results when compared to PTV A in 70%.

Discussion: The results show a correlation between the DSI-mHD and the PTV V95% variation, in the comparisons PTV A and PTV B vs PTV C.Furthermore, we observed that PTV V95% coverage is higher in PTV B than in PTV A: the use of automated propagation may not be definitive and requires manual correction.
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http://dx.doi.org/10.1016/j.tipsro.2017.06.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7033794PMC
July 2017

ENT COBRA (Consortium for Brachytherapy Data Analysis): interdisciplinary standardized data collection system for head and neck patients treated with interventional radiotherapy (brachytherapy).

J Contemp Brachytherapy 2016 Aug 26;8(4):336-43. Epub 2016 Aug 26.

Department of Radiation Oncology - Gemelli-ART, Catholic University, Italy.

Purpose: Aim of the COBRA (Consortium for Brachytherapy Data Analysis) project is to create a multicenter group (consortium) and a web-based system for standardized data collection.

Material And Methods: GEC-ESTRO (Groupe Européen de Curiethérapie - European Society for Radiotherapy & Oncology) Head and Neck (H&N) Working Group participated in the project and in the implementation of the consortium agreement, the ontology (data-set) and the necessary COBRA software services as well as the peer reviewing of the general anatomic site-specific COBRA protocol. The ontology was defined by a multicenter task-group.

Results: Eleven centers from 6 countries signed an agreement and the consortium approved the ontology. We identified 3 tiers for the data set: Registry (epidemiology analysis), Procedures (prediction models and DSS), and Research (radiomics). The COBRA-Storage System (C-SS) is not time-consuming as, thanks to the use of "brokers", data can be extracted directly from the single center's storage systems through a connection with "structured query language database" (SQL-DB), Microsoft Access(®), FileMaker Pro(®), or Microsoft Excel(®). The system is also structured to perform automatic archiving directly from the treatment planning system or afterloading machine. The architecture is based on the concept of "on-purpose data projection". The C-SS architecture is privacy protecting because it will never make visible data that could identify an individual patient. This C-SS can also benefit from the so called "distributed learning" approaches, in which data never leave the collecting institution, while learning algorithms and proposed predictive models are commonly shared.

Conclusions: Setting up a consortium is a feasible and practicable tool in the creation of an international and multi-system data sharing system. COBRA C-SS seems to be well accepted by all involved parties, primarily because it does not influence the center's own data storing technologies, procedures, and habits. Furthermore, the method preserves the privacy of all patients.
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http://dx.doi.org/10.5114/jcb.2016.61958DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5018530PMC
August 2016
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