Publications by authors named "Luca Boldrini"

57 Publications

Personalized automation of treatment planning in head-neck cancer: A step forward for quality in radiation therapy?

Phys Med 2021 Jan 25;82:7-16. Epub 2021 Jan 25.

Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Italy; DIMES, Alma Mater Studiorum Bologna University, Italy.

Purpose: To perform a comprehensive dosimetric and clinical evaluation of the new Pinnacle Personalized automated planning system for complex head-and-neck treatments.

Methods: Fifteen consecutive head-neck patients were enrolled. Radiotherapy was prescribed using VMAT with simultaneous integrated boost strategy. Personalized planning integrates the Feasibility engine able to supply an "a priori" DVH prediction of the achievability of planning goals. Comparison between clinically accepted manually-generated (MP) and automated (AP) plans was performed using dose-volume histograms and a blinded clinical evaluation by two radiation oncologists. Planning time between MP and AP was compared. Dose accuracy was validated using the PTW Octavius-4D phantom together with the 1500 2D-array.

Results: For similar targets coverage, AP plans reported less irradiation of healthy tissue, with significant dose reduction for spinal cord, brainstem and parotids. On average, the mean dose to parotids and maximal doses to spinal cord and brainstem were reduced by 13-15% (p < 0.001), 9% (p < 0.001) and 16% (p < 0.001), respectively. The integral dose was reduced by 16% (p < 0.001). The dose conformity for the three PTVs was significantly higher with AP plans (p < 0.001). The two oncologists chose AP plans in more than 80% of cases. Overall planning times were reduced to <30 min for automated optimization. All AP plans passed the 3%/2 mm γ-analysis by more than 95%.

Conclusion: Complex head-neck plans created using Personalized automated engine provided an overall increase of plan quality, in terms of dose conformity and sparing of normal tissues. The Feasibility module allowed OARs dose sparing well beyond the clinical objectives.
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http://dx.doi.org/10.1016/j.ejmp.2020.12.015DOI Listing
January 2021

Delta Radiomics Analysis for Local Control Prediction in Pancreatic Cancer Patients Treated Using Magnetic Resonance Guided Radiotherapy.

Diagnostics (Basel) 2021 Jan 5;11(1). Epub 2021 Jan 5.

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

The aim of this study is to investigate the role of Delta Radiomics analysis in the prediction of one-year local control (1yLC) in patients affected by locally advanced pancreatic cancer (LAPC) and treated using Magnetic Resonance guided Radiotherapy (MRgRT). A total of 35 patients from two institutions were enrolled: A 0.35 Tesla T2*/T1 MR image was acquired for each case during simulation and on each treatment fraction. Physical dose was converted in biologically effective dose (BED) to compensate for different radiotherapy schemes. Delta Radiomics analysis was performed considering the gross tumour volume (GTV) delineated on MR images acquired at BED of 20, 40, and 60 Gy. The performance of the delta features in predicting 1yLC was investigated in terms of Wilcoxon Mann-Whitney test and area under receiver operating characteristic (ROC) curve (AUC). The most significant feature in predicting 1yLC was the variation of cluster shade calculated at BED = 40 Gy, with a -value of 0.005 and an AUC of 0.78 (0.61-0.94). Delta Radiomics analysis on low-field MR images might play a promising role in 1yLC prediction for LAPC patients: further studies including an external validation dataset and a larger cohort of patients are recommended to confirm the validity of this preliminary experience.
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http://dx.doi.org/10.3390/diagnostics11010072DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7824764PMC
January 2021

MRI-guided stereotactic radiation therapy for hepatocellular carcinoma: a feasible and safe innovative treatment approach.

J Cancer Res Clin Oncol 2021 Jan 4. Epub 2021 Jan 4.

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

Purpose: Hepatocellular carcinoma (HCC) in early stages benefits from local ablative treatments such as radiofrequency ablation (RFA) or transarterial chemoembolization (TACE). In this context, radiotherapy (RT) has shown promising results but has not been thoroughly evaluated. Magnetic resonance-guided RT (MRgRT) may represent a paradigm shifting improvement in stereotactic body radiotherapy (SBRT) for liver tumors.

Methods: We retrospectively evaluated HCC patients treated on a hybrid low-tesla MRgRT unit. A total biologically effective dose (BED) > 100 Gy was delivered in 5 consecutive fractions, respecting the appropriate organs-at-risk constraints. Hybrid MR scans were used for treatment planning and cine MR was used for delivery gating. Patients were followed up for toxicity and treatment-response assessment.

Results: Ten patients were enrolled, with a total of 12 lesions. All the lesions were irradiated with no interruptions. Six patients had already performed previous local therapies. Median follow-up after SBRT was 6.5 months (1-25). Two cases of acute toxicity were reported (G ≤ 2 according to CTCAE v4.0). At the time of the analysis, 90% of the population presented local control. Child-Pugh before and after treatment remained unchanged in all but one patient.

Conclusion: MRgRT is a feasible and safe option showing favorable toxicity profile for HCC treatment.
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http://dx.doi.org/10.1007/s00432-020-03480-8DOI Listing
January 2021

Case Report: First in Human Online Adaptive MR Guided SBRT of Peritoneal Carcinomatosis Nodules: A New Therapeutic Approach for the Oligo-Metastatic Patient.

Front Oncol 2020 15;10:601739. Epub 2020 Dec 15.

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

Peritoneal carcinosis (PC) is characterized by poor prognosis. PC is currently treated as a locoregional disease and the possibility to perform very precise treatments such as stereotactic body radiation therapy (SBRT) has opened up new therapeutic perspectives. More recently, the introduction of Magnetic Resonance-guided Radiation Therapy (MRgRT) allowed online adaptation (OA) of treatment plan to optimize daily dose distribution based on patient's anatomy. The aim of this study is the evaluation of the effectiveness of SBRT OA workflow in an oligometastatic patient affected by PC. We report the clinical case of a patient affected by PC originating from colon cancer, previously treated with chemotherapy and surgery, addressed to OA SBRT treatment on a single chemoresistant PC nodule, delivered with a 0.35 T MR Linac. Treatment was delivered using gating approach in deep inspiration breath hold condition in order to reduce intrafraction variability. Prescription dose was 35 Gy in 5 fractions. The PTV V95% of the original plan was 96.6%, while the predicted values for the following fractions were 11.9, 56.4, 0, 0, and 61%. Similarly, the small bowel V19.5 Gy of the original plan was 4.63 cc, while the predicted values for the following fractions were 3.7, 8.6, 10.7, 1.96, 3.7 cc. Thanks to the OA approach, the re-optimized PTV V95% coverage improved to 96.1, 89.0, 85.5, 94.5, and 94%; while the small bowel V19.5 Gy to 3.36; 3.28; 1.84; 2.62; 2.6 cc respectively. After the end of RT, the patient was addressed to follow-up, and the re-evaluation F-FDG PET-CT was performed after 10 months from irradiation showed complete response. No acute or late toxicities were recorded. MRgRT with OA approach in PC patients is technically and clinically feasible with clean toxicity result. Online adaptive SBRT for oligometastases opens up new therapeutic scenarios in the management of this category of patients.
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http://dx.doi.org/10.3389/fonc.2020.601739DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7770165PMC
December 2020

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

Case Report: A Case Report of Stereotactic Ventricular Arrhythmia Radioablation (STAR) on Large Cardiac Target Volume by Highly Personalized Inter- and Intra-fractional Image Guidance.

Front Cardiovasc Med 2020 23;7:565471. Epub 2020 Nov 23.

Dipartimento di Scienze Cardiovascolari, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.

Although catheter ablation is the current gold standard treatment for refractory ventricular arrhythmias, sometimes its efficacy is not optimal and it's associated with high risks of procedural complication and death. Stereotactic arrhythmia radioablation (STAR) is increasingly being adopted for such clinical presentation, considering its efficacy and safety. We do report our experience managing a case of high volume of left ventricle for refractory ventricular tachycardia in advanced heart failure patient, by delivering a single fraction of STAR through an highly personalization of dose delivery applying repeated inter- and continuous intra-fraction image guidance. According to the literature reports, we recommend considering increasing as much as possible the personalization features and safety technical procedure as long as that is not significantly affecting the STAR duration. Moreover, the duration in itself shouldn't be the main parameter, but balanced into the frame of possibly obtainable outcome improvement. At best of our knowledge, this is the first report applying such specific technology onto this clinical setting. Future studies will clarify these issues.
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http://dx.doi.org/10.3389/fcvm.2020.565471DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7719630PMC
November 2020

Artificial intelligence (AI) and interventional radiotherapy (brachytherapy): state of art and future perspectives.

J Contemp Brachytherapy 2020 Oct 30;12(5):497-500. Epub 2020 Oct 30.

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

Purpose: Artificial intelligence (AI) plays a central role in building decision supporting systems (DSS), and its application in healthcare is rapidly increasing. The aim of this study was to define the role of AI in healthcare, with main focus on radiation oncology (RO) and interventional radiotherapy (IRT, brachytherapy).

Artificial Intelligence In Interventional Radiation Therapy: AI in RO has a large impact in providing clinical decision support, data mining and advanced imaging analysis, automating repetitive tasks, optimizing time, and modelling patients and physicians' behaviors in heterogeneous contexts. Implementing AI and automation in RO and IRT can successfully facilitate all the steps of treatment workflow, such as patient consultation, target volume delineation, treatment planning, and treatment delivery.

Conclusions: AI may contribute to improve clinical outcomes through the application of predictive models and DSS optimization. This approach could lead to reducing time-consuming repetitive tasks, healthcare costs, and improving treatment quality assurance and patient's assistance in IRT.
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http://dx.doi.org/10.5114/jcb.2020.100384DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7701925PMC
October 2020

Prognostic Factors and Long-Term Survival in Locally Advanced NSCLC with Pathological Complete Response after Surgical Resection Following Neoadjuvant Therapy.

Cancers (Basel) 2020 Nov 30;12(12). Epub 2020 Nov 30.

Università Cattolica del Sacro Cuore, 00168 Rome, Italy.

Outcomes for locally advanced NSCLC with pathological complete response (pCR), i.e., pT0N0 after induction chemoradiotherapy (IT), have been seldom investigated. Herein, long-term results, in this highly selected group of patients, have been evaluated with the aim to identify prognostic predictive factors. Patients affected by locally advanced NSCLC (cT1-T4/N0-2/M0) who underwent IT, possibly following surgery, from January 1992 to December 2019, were considered for this retrospective analysis. Survival rates and prognostic factors have been studied with Kaplan-Meier analysis, log-rank and Cox regression analysis. Three-hundred and forty-three consecutive patients underwent IT in the considered period. Out of them, 279 were addressed to surgery; among them, pCR has been observed in 62 patients (18% of the total and 22% of the operated patients). In the pCR-group, clinical staging was IIb in 3 (5%) patients, IIIa in 28 (45%) patients and IIIb in 31 (50%). Surgery consisted of (bi)lobectomy in the majority of cases (80.7%), followed by pneumonectomy (19.3%). Adjuvant therapy was administered in 33 (53.2%) patients. Five-year overall survival and disease-free survival have been respectively 56.18% and 48.84%. The relative risk of death, observed with the Cox regression analysis, was 4.4 times higher (95% confidence interval (CI): 1.632-11.695, = 0.03) for patients with N2 multi-station disease, 2.6 times higher (95% CI: 1.066-6.407, = 0.036) for patients treated with pneumonectomy and 3 times higher (95% CI: 1.302-6.809, = 0.01) for patients who did not receive adjuvant therapy. Rewarding long-term results could be expected in locally advanced NSCLC patients with pCR after IT followed by surgery. Baseline N2 single-station disease and adjuvant therapy after surgery seem to be associated with better prognosis, while pneumonectomy is associated with poorer outcomes.
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http://dx.doi.org/10.3390/cancers12123572DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7759985PMC
November 2020

Quantitative analysis of MRI-guided radiotherapy treatment process time for tumor real-time gating efficiency.

J Appl Clin Med Phys 2020 Nov 22;21(11):70-79. Epub 2020 Oct 22.

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

Purpose: Magnetic Resonance-guided radiotherapy (MRgRT) systems allow continuous monitoring of therapy volumes during treatment delivery and personalized respiratory gating approaches. Treatment length may therefore be significantly affected by patient's compliance and breathing control. We quantitatively analyzed treatment process time efficiency (T ) using data obtained from real-world patient treatment logs to optimize MRgRT delivery settings.

Methods: Data corresponding to the first 100 patients treated with a low T hybrid MRI-Linac system, both in free breathing (FB) and in breath hold inspiration (BHI) were collected. T has been computed as the percentage difference of the actual single fraction's total treatment time and the predicted treatment process time, as computed by the TPS during plan optimization. Differences between the scheduled and actual treatment room occupancy time were also evaluated. Finally, possible correlations with planning, delivery and clinical parameters with T were also investigated.

Results: Nine hundred and nineteen treatment fractions were evaluated. T difference between BHI and FB patients' groups was statistically significant and the mean T were 42.4%, and -0.5% respectively. No correlation was found with T for BHI and FB groups. Planning, delivering and clinical parameters classified BHI and FB groups, but no correlation with T was found.

Conclusion: The use of BHI gating technique can increase the treatment process time significantly. BHI technique could be not always an adequate delivery technique to optimize the treatment process time. Further gating techniques should be considered to improve the use of MRgRT.
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http://dx.doi.org/10.1002/acm2.13030DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7701108PMC
November 2020

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

Radiother Oncol 2020 Dec 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

Germline BRCA 1-2 status prediction through ovarian ultrasound images radiogenomics: a hypothesis generating study (PROBE study).

Sci Rep 2020 10 5;10(1):16511. Epub 2020 Oct 5.

Dipartimento per le Scienze della salute della donna, del bambino e di sanità pubblica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Gynecologic Oncology, Rome, Italy.

Radiogenomics is a specific application of radiomics where imaging features are linked to genomic profiles. We aim to develop a radiogenomics model based on ovarian US images for predicting germline BRCA1/2 gene status in women with healthy ovaries. From January 2013 to December 2017 a total of 255 patients addressed to germline BRCA1/2 testing and pelvic US documenting normal ovaries, were retrospectively included. Feature selection for univariate analysis was carried out via correlation analysis. Multivariable analysis for classification of germline BRCA1/2 status was then carried out via logistic regression, support vector machine, ensemble of decision trees and automated machine learning pipelines. Data were split into a training (75%) and a testing (25%) set. The four strategies obtained a similar performance in terms of accuracy on the testing set (from 0.54 of logistic regression to 0.64 of the auto-machine learning pipeline). Data coming from one of the tested US machine showed generally higher performances, particularly with the auto-machine learning pipeline (testing set specificity 0.87, negative predictive value 0.73, accuracy value 0.72 and 0.79 on training set). The study shows that a radiogenomics model on machine learning techniques is feasible and potentially useful for predicting gBRCA1/2 status in women with healthy ovaries.
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http://dx.doi.org/10.1038/s41598-020-73505-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7536234PMC
October 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

Convolutional Neural Network Based on Fluorescein Angiography Images for Retinopathy of Prematurity Management.

Transl Vis Sci Technol 2020 07 7;9(2):37. Epub 2020 Jul 7.

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

Purpose: The purpose of this study was to explore the use of fluorescein angiography (FA) images in a convolutional neural network (CNN) in the management of retinopathy of prematurity (ROP).

Methods: The dataset involved a total of 835 FA images of 149 eyes (90 patients), where each eye was associated with a binary outcome (57 "untreated" eyes and 92 "treated"; 308 "untreated" images, 527 "treated"). The resolution of the images was 1600 and 1200 px in 20% of cases, whereas the remaining 80% had a resolution of 640 and 480 px. All the images were resized to 640 and 480 px before training and no other preprocessing was applied. A CNN with four convolutional layers was trained on 90% of the images ( = 752) randomly chosen. The accuracy of the prediction was assessed on the remaining 10% of images ( = 83). Keras version 2.2.0 for R with Tensorflow backend version 1.11.0 was used for the analysis.

Results: The validation accuracy after 100 epochs was 0.88, whereas training accuracy was 0.97. The receiver operating characteristic (ROC) presented an area under the curve (AUC) of 0.91.

Conclusions: Our study showed, we believe for the first time, the applicability of artificial intelligence (CNN) technology in the ROP management driven by FA. Further studies are needed to exploit different fields of applications of this technology.

Translational Relevance: This algorithm is the basis for a system that could be applied to both ROP as well as experimental oxygen induced retinopathy.
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http://dx.doi.org/10.1167/tvst.9.2.37DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7424905PMC
July 2020

Characterization of an inorganic scintillator for small-field dosimetry in MR-guided radiotherapy.

J Appl Clin Med Phys 2020 Sep 25;21(9):244-251. Epub 2020 Aug 25.

Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.

Introduction: Aim of this study is to dosimetrically characterize a new inorganic scintillator designed for magnetic resonance-guided radiotherapy (MRgRT) in the presence of 0.35 tesla magnetic field (B).

Methods: The detector was characterized in terms of signal to noise ratio (SNR), reproducibility, dose linearity, angular response, and dependence by energy, field size, and B orientation using a 6 MV magnetic resonance (MR)-Linac and a water tank. Field size dependence was investigated by measuring the output factor (OF) at 1.5 cm. The results were compared with those measured using other detectors (ion chamber and synthetic diamond) and those calculated using a Monte Carlo (MC) algorithm. Energy dependence was investigated by acquiring a percentage depth dose (PDD) curve at two field sizes (3.32 × 3.32 and 9.96 × 9.96 cm ) and repeating the OF measurements at 5 and 10 cm depths.

Results: The mean SNR was 116.3 ± 0.6. Detector repeatability was within 1%, angular dependence was <2% and its response variation based on the orientation with respect to the B lines was <1%. The detector has a temporal resolution of 10 Hz and it showed a linear response (R  = 1) in the dose range investigated. All the OF values measured at 1.5 cm depth using the scintillator are in accordance within 1% with those measured with other detectors and are calculated using the MC algorithm. PDD values are in accordance with MC algorithm only for 3.32 × 3.32 cm field. Numerical models can be applied to compensate for energy dependence in case of larger fields.

Conclusion: The inorganic scintillator in the present form can represent a valuable detector for small-field dosimetry and periodic quality controls at MR-Linacs such as dose stability, OFs, and dose linearity. In particular, the detector can be effectively used for small-field dosimetry at 1.5 cm depth and for PDD measurements if the field dimension of 3.32 × 3.32 cm is not exceeded.
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http://dx.doi.org/10.1002/acm2.13012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7497936PMC
September 2020

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

Radiol Med 2020 Aug 24. 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
August 2020

External Validation of Early Regression Index (ERI) as Predictor of Pathologic Complete Response in Rectal Cancer Using Magnetic Resonance-Guided Radiation Therapy.

Int J Radiat Oncol Biol Phys 2020 Dec 3;108(5):1347-1356. Epub 2020 Aug 3.

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

Purpose: Tumor control probability (TCP)-based early regression index (ERI) is a radiobiological parameter that showed promising results in predicting pathologic complete response (pCR) on T2-weighted 1.5 T magnetic resonance (MR) images of patients with locally advanced rectal cancer. This study aims to validate the ERI in the context of low-tesla MR-guided radiation therapy, using images acquired with different magnetic field strength (0.35 T) and image contrast (T2/T1). Furthermore, the optimal timing for pCR prediction was estimated, calculating the ERI index at different biologically effective dose (BED) levels.

Methods And Materials: Fifty-two patients with locally advanced rectal cancer treated with neoadjuvant chemoradiation therapy were enrolled in this multi-institutional retrospective study. For each patient, a 0.35 T T2/T1-weighted MR image was acquired during simulation and on each treatment day. Gross tumor volume was contoured according to International Commission on Radiation Units Report 83 guidelines. According to the original definition, ERI was calculated considering the residual tumor volume at BED = 25 Gy. ERI was also calculated in correspondence with several BED levels: 13, 21, 32, 40, 46, 54, 59, and 67. The predictive performance of the different ERI indices were evaluated in terms of receiver operating characteristic curve. The robustness of ERI with respect to the interobserver variability was also evaluated considering 2 operators and calculating the intraclass correlation index.

Results: Fourteen patients showed pCR. ERI correctly 47 of 52 cases (accuracy = 90%), showing good results in terms of sensitivity (86%), specificity (92%), negative predictive value (95%), and positive predictive value (80%). The analysis at different BED levels shows that the best predictive performance is obtained when this parameter is calculated at BED = 25 Gy (area under the curve = 0.93). ERI results are robust with respect to interobserver variability (intraclass correlation index = 0.99).

Conclusions: This study confirmed the validity and the robustness of ERI as a pCR predictor in the context of low-tesla MR-guided radiation therapy and indicate 25 Gy as the best BED level to perform predictions.
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http://dx.doi.org/10.1016/j.ijrobp.2020.07.2323DOI Listing
December 2020

On-line adaptive MR guided radiotherapy for locally advanced pancreatic cancer: Clinical and dosimetric considerations.

Tech Innov Patient Support Radiat Oncol 2020 Sep 2;15:15-21. Epub 2020 Jul 2.

Università Cattolica del Sacro Cuore, Largo Agostino Gemelli 8, 00168 Rome, Italy.

Introduction: Magnetic Resonance-guided Radiation Therapy (MRgRT) allows online adaptations (OA) of the treatment plan to optimize daily dose distribution based on patient's anatomy, just before fraction delivery. The aim of this study is to evaluate feasibility and the dosimetric improvement of the OA workflow implemented in our institution for locally advanced pancreatic cancer (LAPC) patients, in terms of target coverage and organs at risk (OARs) sparing.

Methods: We retrospectively analysed 8 LAPC patients treated with MRgRT in combination with the OA approach, using video-assisted inspiratory breath-hold for a total of 38 fractions with a dose ranging from 30 Gy to 40 Gy in 5 fractions.Dose distribution of the baseline plan was first calculated based on daily anatomy, obtaining a "predicted" plan to assess the dosimetric improvement. If the dose distribution did not meet the constraints set in the planning phase, PTV, GTV and OARs were re-contoured within a distance of 3 cm from the PTV external edge and a new online "adaptive" plan was generated. Other clinical and planning parameters were also evaluated to assess the feasibility and the dosimetic benefit of the online adaptive workflow.

Results: Out of 38 total fractions, 26 (68.4%) were adapted online and 12 (31.6%) were delivered using the baseline plan. The use of the adaptive workflow resulted to be feasible in our clinical practice and advantageous in all the patients: mean PTV V95% increased by 10.8% (5.7-20.8) while mean CTV V98% of 12.6% (7.3-17.7). Also OARs V33 and V25 showed a positive trend avoiding unnecessary irradiation.

Conclusion: OA workflow improves the dosimetric benefit of MRgRT, preventing the occurrence of high-doses to OARs and increasing the safety of stereotactic treatment for LAPC, without any drawback for our daily clinical practice routine.
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http://dx.doi.org/10.1016/j.tipsro.2020.06.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334416PMC
September 2020

Conducting research in Radiation Oncology remotely during the COVID-19 pandemic: Coping with isolation.

Clin Transl Radiat Oncol 2020 Sep 18;24:53-59. Epub 2020 Jun 18.

European Society for Radiotherapy & Oncology (ESTRO) Young Committee, Brussels, Belgium.

Introduction: With the COVID-19 pandemic, individuals have been forced to follow strict social isolation guidelines. While crucial to control the pandemic, isolation might have a significant impact on productivity and mental health. Especially for researchers working in healthcare, the current situation is complex. We therefore carried out a survey amongst researchers in the field of radiation oncology to gain insights on the impact of social isolation and working from home and to guide future work.

Materials And Methods: An online survey was conducted between March 27th and April 5th, 2020. The first part contained 14 questions intended to capture an overview of the specific aspects related to research while in isolation. The second (optional) part of the questionnaire was the validated Hospital Anxiety and Depression Scale (HADS), a self-reported measure used to assess levels of anxiety and depressive symptoms.

Results: From 543 survey participants, 48.8% reported to work full-time from home. The impact on perceived productivity, with 71.2% of participants feeling less productive, caused 58% of participants to feel some level of guilt.Compared to normative data, relatively high levels of anxiety and depressive symptoms were recorded for the 335 participants who filled out the HADS questionnaire. Group comparisons found the presence of a supportive institutional program as the sole factor of statistical significance in both anxiety and depressive symptom levels. People having to work full-time on location showed higher depressive symptom levels than those working from home. Anxiety scores were negatively correlated with the number of research years.

Conclusion: Results of the survey showed there is a non-negligible impact on both productivity and mental health. As the radiation oncology research community was forced to work from home during the COVID-19 pandemic, lessons can be learned to face future adverse situations but also to improve work-life balance in general.
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http://dx.doi.org/10.1016/j.ctro.2020.06.006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7299875PMC
September 2020

Paget's disease of scrotum and penis case report of a re-irradiation and review of the literature.

Dermatol Ther 2020 11 25;33(6):e13890. Epub 2020 Jul 25.

Villa Santa Teresa, Radioterapia Oncologica, Palermo, Italy.

Extramammary Paget's disease (EMPD) is a rare cutaneous adenocarcinoma generally arising in the anogenital region. Surgery is still considered the treatment of choice for patients with EMPD, while Radiotherapy is a common alternative for inoperable cases and it's necessary in case of lack of surgical radicality. In this article, we described our experience and a review of the literature, with a particular focus on radiation-induced toxicity and on the feasibility of re-irradiation. A 70-year-old patient with EPMD underwent adjuvant radiotherapy in 2015. After 28 months for recurrence another radiant treatment was performed. No G3 (CTCAE v4) toxicity were recorded. In the last follow-up visit at 18 months, no signs of relapse were reported. A search strategy of the bibliographic database PubMed was performed. The inclusion criteria for the articles were case report, clinical prospective, or retrospective studies with histological confirmation of EMPD of scrotum and penis; studies with patients undergoing RT; studies in the past 30 years. In most of the 14 reported studies, RT was overall well tolerated. The major observed toxicity was G3 skin toxicity in one study. To our knowledge, there are no other cases of EPMD re-irradiation in literature. Our patient showed an excellent response and tolerated very well the high doses of both the radiation treatments. This suggests that the tolerance of skin to re-irradiation following a long period between the two treatments may be comparable to the normal constraints.
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http://dx.doi.org/10.1111/dth.13890DOI Listing
November 2020

Oncotype DX Predictive Nomogram for Recurrence Score Output: The Novel System ADAPTED01 Based on Quantitative Immunochemistry Analysis.

Clin Breast Cancer 2020 10 5;20(5):e600-e611. Epub 2020 May 5.

UOC di Radioterapia Oncologica, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Dipartimento di Scienze della Salute della Donna e del Bambino e di Sanità Pubblica, Rome, Italy; Università Cattolica del Sacro Cuore, Istituto di Radiologia, Rome, Italy.

Purpose: Oncotype DX (ODX) predicts breast cancer recurrence risk, guiding the choice of adjuvant treatment. In many countries, access to the test is not always available. We used correlation between phenotypical tumor characteristics, quantitative classical immunohistochemistry (IHC), and recurrence score (RS) assessed by ODX to develop a decision supporting system for clinical use.

Patients And Methods: Breast cancer patients who underwent ODX testing between 2014 and 2018 were retrospectively included in the study. The data selected for analysis were age, menopausal status, and pathologic and IHC features. IHC was performed with standardized quantitative methods. The data set was split into two subsets: 70% for the training set and 30% for the internal validation set. Statistically significant features were included in logistic models to predict RS ≤ 25 or ≤ 20. Another set was used for external validation to test reproducibility of prediction models.

Results: The internal set included 407 patients. Mean (range) age was 53.7 (31-80) years, and 222 patients (54.55%) were > 50 years old. ODX results showed 67 patients (16.6%) had RS between 0 and 10, 272 patients between 11 and 25 (66.8%), and 68 patients > 26 (16.6%). Logistic regression analysis showed that RS score (for threshold ≤ 25) was significantly associated with estrogen receptor (P = .004), progesterone receptor (P < .0001), and Ki-67 (P < .0001). Generalized linear regression resulted in a model that had an area under the receiver operating characteristic curve (AUC) of 92.2 (sensitivity 84.2%, specificity 80.1%) and that was well calibrated. The external validation set (183 patients) analysis confirmed the model performance, with an AUC of 82.3 and a positive predictive value of 91%. A nomogram was generated for further prospective evaluation to predict RS ≤ 25.

Conclusion: RS was related to quantitative IHC in patients with RS ≤ 25, with a good performance of the statistical model in both internal and external validation. A nomogram for enhancing clinical approach in a cost-effective manner was developed. Prospective studies must test this application in clinical practice.
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http://dx.doi.org/10.1016/j.clbc.2020.04.012DOI Listing
October 2020

Reliability of ITV approach to varying treatment fraction time: a retrospective analysis based on 2D cine MR images.

Radiat Oncol 2020 Jun 12;15(1):152. Epub 2020 Jun 12.

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

Background: Internal Target Volume (ITV) is one of the most common strategies to passively manage tumour motion in Radiotherapy (RT). The reliability of this approach is based on the assumption that the tumour motion estimated during pre-treatment 4D Computed Tomography (CT) acquisition is representative of the motion during the whole RT treatment. With the introduction of Magnetic Resonance-guided RT (MRgRT), it has become possible to monitor tumour motion during the treatment and verify this assumption. Aim of this study was to investigate the reliability of the ITV approach with respect to the treatment fraction time (TFT) in abdominal and thoracic lesions.

Methods: A total of 12 thoracic and 15 abdominal lesions was analysed. Before treatment, a 10-phase 4DCT was acquired and ITV margins were estimated considering the envelope of the lesion contoured on the different 4DCT phases. All patients underwent MRgRT treatment in free-breathing, monitoring the tumour position on a sagittal plane with 4 frames per second (sec). ITV margins were projected on the tumour trajectory and the percentage of treatment time in which the tumour was inside the ITV (%TT) was measured to varying of TFT. The ITV approach was considered moderately reliable when %TT ≥ 90% and strongly reliable when %TT ≥ 95%. Additional ITV margins required to achieve %TT ≥ 95% were also calculated.

Results: In the analysed cohort of patients, ITV strategy can be considered strongly reliable only for lung lesions with TFT ≤ 7 min (min). The ITV strategy can be considered only moderately reliable for abdominal lesions, and additional margins are required to obtain %TT ≥ 95%. Considering a TFT ≤ 4 min, additional margins of 2 mm in cranio-caudal (CC) and 1 mm in antero-posterior (AP) are suggested for pancreatic lesions, 3 mm in CC and 2 mm in AP for renal and liver ones.

Conclusions: On the basis of the analysed cases, the ITV approach appears to be reliable in the thorax, while it results more challenging in the abdomen, due to the higher uncertainty in ITV definition and to the observed larger intra and inter-fraction motion variability. The addition of extra margins based on the TFT may represent a valid tool to compensate such limitations.
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http://dx.doi.org/10.1186/s13014-020-01530-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7291491PMC
June 2020

Role of radiation oncology in modern multidisciplinary cancer treatment.

Mol Oncol 2020 07 22;14(7):1431-1441. Epub 2020 Jun 22.

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

Cancer care is moving from a disease-focused management toward a patient-centered tailored approach. Multidisciplinary management that aims to define individual, optimal treatment strategies through shared decision making between healthcare professionals and patient is a fundamental aspect of high-quality cancer care and often includes radiation oncology. Advances in technology and radiobiological research allow to deliver ever more tailored radiation treatments in an ever easier and faster way, thus improving the efficacy, safety, and accessibility of radiation therapy. While these changes are improving quality of cancer care, they are also enormously increasing complexity of decision making, thus challenging the ability to deliver quality affordable cancer care. In this review, we provide an updated outline of the role of radiation oncology in the modern multidisciplinary treatment of cancer. Particularly, we focus on the way some developments in key areas of cancer management are challenging multidisciplinary cancer care in the different clinical settings of early, locally advanced, and metastatic disease, thus highlighting some priority areas of research.
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http://dx.doi.org/10.1002/1878-0261.12712DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7332217PMC
July 2020

Magnetic resonance-guided radiotherapy feasibility in elderly cancer patients: proposal of the MASTER scoring system.

Tumori 2021 Feb 15;107(1):26-31. Epub 2020 May 15.

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

Background: Elderly patients are often excluded from advanced treatments owing to clinical complexity or frailty. Magnetic resonance-guided radiotherapy (MRgRT) represents a new frontier of radiotherapy delivery that can play an important role in the management of these patients.

Aim: To assess MRgRT feasibility in elderly patients, describe their compliance with this treatment, and provide a scoring system for elderly patient selection.

Methods: Patients aged >75 years were enrolled. No restrictions on tumor site, staging, or treatment intent were applied. Patients underwent joint radiation oncology-geriatrics visits to assess the feasibility of MRgRT and to identify the most significant items (i.e. clinical variables) for the setup of a scoring system. The proposed scoring system was then internally validated on a prospectively enrolled cohort of elderly patients who were candidates for MRgRT.

Results: Thirty patients were enrolled between February and March 2018. Their mean age was 81.4 ± 3.4 years (range 75-88). Radiotherapy intent was curative in 26 patients; 14 patients were considered frail at screening tests before radiotherapy. Twelve items were identified as clinically significant for the setup of the MASTER score (MRI-Guided Radiotherapy Selection Elderly Score) score. Validation of the score showed 100% reliability, with no patient discharged after selection.

Conclusions: MRgRT appears to be feasible in elderly patients and the MASTER score is proposed to support clinical decision-making in recommending elderly patients for this technology.
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http://dx.doi.org/10.1177/0300891620920709DOI Listing
February 2021

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

Low Tesla magnetic resonance guided radiotherapy for locally advanced cervical cancer: first clinical experience.

Tumori 2020 Dec 17;106(6):497-505. Epub 2020 Feb 17.

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

Objective: Magnetic resonance-guided radiotherapy (MRgRT) represents an innovative approach for personalized radiotherapy treatments and its applications are being explored in various anatomical sites to fully understand its potential advantages. This study describes the first clinical experience of MRgRT application in patients with locally advanced cervical cancer (LACC) undergoing neoadjuvant chemoradiotherapy. The feasibility of the technique is evaluated and its toxicity profile and clinical outcomes are reported.

Methods: Patients with LACC (International Federation of Gynecology and Obstetrics stage IIA-IVA) undergoing neoadjuvant chemoradiotherapy (CRT) on a 0.35T Tri-60-Co hybrid unit (ViewRay) were retrospectively compared with randomly selected patients treated with a standard linear accelerator. Total prescribed dose was 50.6 Gy (2.3 Gy/fraction) to planning target volume 1 (PTV1) and 39.6 Gy (1.8 Gy/fraction) to PTV2, delivered using a simultaneous integrated boost. Surgery was performed 8 weeks after the end of CRT. The effect of magnetic resonance guidance on replanning approaches, treatment-related toxicities, and pathologic response were assessed for each patient. Patient outcomes were noted and dosimetric comparisons performed between the 2 arms.

Results: Nine patients with LACC treated from May 2018 to November 2018 were retrospectively enrolled and their records compared with the records of an equivalent cohort of randomly selected patients. Five replanning cases were performed in the MRgRT group and 0 in the linear accelerator group. Acute G1-G2 gastrointestinal toxicities were observed in 33.3% of MRgRT patients and in 55.5% of linear accelerator patients; acute G1-G2 genitourinary toxicities in 22.2% and 33.3%, respectively. No G3 toxicity was found except for neutropenia in 2 patients. No differences were observed in pathologic response between the 2 groups.

Conclusions: Despite the retrospective nature of the observations and the low number of enrolled patients, the application of MRgRT in LACC appears to be safe and feasible with a favorable toxicity profile and response rates comparable to gold standard, supporting the setup of larger prospective studies to investigate the potentialities of this new technology.
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http://dx.doi.org/10.1177/0300891620901752DOI Listing
December 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

Deep Learning: A Review for the Radiation Oncologist.

Front Oncol 2019 1;9:977. Epub 2019 Oct 1.

CRUK/MRC Oxford Institute for Radiation Oncology, University of Oxford, Oxford, United Kingdom.

Deep Learning (DL) is a machine learning technique that uses deep neural networks to create a model. The application areas of deep learning in radiation oncology include image segmentation and detection, image phenotyping, and radiomic signature discovery, clinical outcome prediction, image dose quantification, dose-response modeling, radiation adaptation, and image generation. In this review, we explain the methods used in DL and perform a literature review using the Medline database to identify studies using deep learning in radiation oncology. The search was conducted in April 2018, and identified studies published between 1997 and 2018, strongly skewed toward 2015 and later. A literature review was performed using PubMed/Medline in order to identify important recent publications to be synthesized into a review of the current status of Deep Learning in radiation oncology, directed at a clinically-oriented reader. The search strategy included the search terms "radiotherapy" and "deep learning." In addition, reference lists of selected articles were hand searched for further potential hits of relevance to this review. The search was conducted in April 2018, and identified studies published between 1997 and 2018, strongly skewed toward 2015 and later. Studies using DL for image segmentation were identified in Brain ( = 2), Head and Neck ( = 3), Lung ( = 6), Abdominal ( = 2), and Pelvic ( = 6) cancers. Use of Deep Learning has also been reported for outcome prediction, such as toxicity modeling ( = 3), treatment response and survival ( = 2), or treatment planning ( = 5). Over the past few years, there has been a significant number of studies assessing the performance of DL techniques in radiation oncology. They demonstrate how DL-based systems can aid clinicians in their daily work, be it by reducing the time required for or the variability in segmentation, or by helping to predict treatment outcomes and toxicities. It still remains to be seen when these techniques will be employed in routine clinical practice.
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http://dx.doi.org/10.3389/fonc.2019.00977DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6779810PMC
October 2019