Publications by authors named "Janita E van Timmeren"

14 Publications

  • Page 1 of 1

Quantification of the spatial distribution of primary tumors in the lung to develop new prognostic biomarkers for locally advanced NSCLC.

Sci Rep 2021 Oct 22;11(1):20890. Epub 2021 Oct 22.

Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland.

The anatomical location and extent of primary lung tumors have shown prognostic value for overall survival (OS). However, its manual assessment is prone to interobserver variability. This study aims to use data driven identification of image characteristics for OS in locally advanced non-small cell lung cancer (NSCLC) patients. Five stage IIIA/IIIB NSCLC patient cohorts were retrospectively collected. Patients were treated either with radiochemotherapy (RCT): RCT1* (n = 107), RCT2 (n = 95), RCT3 (n = 37) or with surgery combined with radiotherapy or chemotherapy: S1* (n = 135), S2 (n = 55). Based on a deformable image registration (MIM Vista, 6.9.2.), an in-house developed software transferred each primary tumor to the CT scan of a reference patient while maintaining the original tumor shape. A frequency-weighted cumulative status map was created for both exploratory cohorts (indicated with an asterisk), where the spatial extent of the tumor was uni-labeled with 2 years OS. For the exploratory cohorts, a permutation test with random assignment of patient status was performed to identify regions with statistically significant worse OS, referred to as decreased survival areas (DSA). The minimal Euclidean distance between primary tumor to DSA was extracted from the independent cohorts (negative distance in case of overlap). To account for the tumor volume, the distance was scaled with the radius of the volume-equivalent sphere. For the S1 cohort, DSA were located at the right main bronchus whereas for the RCT1 cohort they further extended in cranio-caudal direction. In the independent cohorts, the model based on distance to DSA achieved performance: AUC [95% CI] = 0.67 [0.55-0.78] and AUC = 0.59 [0.39-0.79] for RCT patients, but showed bad performance for surgery cohort (AUC = 0.52 [0.30-0.74]). Shorter distance to DSA was associated with worse outcome (p = 0.0074). In conclusion, this explanatory analysis quantifies the value of primary tumor location for OS prediction based on cumulative status maps. Shorter distance of primary tumor to a high-risk region was associated with worse prognosis in the RCT cohort.
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http://dx.doi.org/10.1038/s41598-021-00239-0DOI Listing
October 2021

Machine learning for grading and prognosis of esophageal dysplasia using mass spectrometry and histological imaging.

Comput Biol Med 2021 Oct 4;138:104918. Epub 2021 Oct 4.

Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, The Netherlands; Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6202 AZ, Maastricht, The Netherlands.

Background: Barrett's esophagus (BE) is a precursor lesion of esophageal adenocarcinoma and may progress from non-dysplastic through low-grade dysplasia (LGD) to high-grade dysplasia (HGD) and cancer. Grading BE is of crucial prognostic value and is currently based on the subjective evaluation of biopsies. This study aims to investigate the potential of machine learning (ML) using spatially resolved molecular data from mass spectrometry imaging (MSI) and histological data from microscopic hematoxylin and eosin (H&E)-stained imaging for computer-aided diagnosis and prognosis of BE.

Methods: Biopsies from 57 patients were considered, divided into non-dysplastic (n = 15), LGD non-progressive (n = 14), LGD progressive (n = 14), and HGD (n = 14). MSI experiments were conducted at 50 × 50 μm spatial resolution per pixel corresponding to a tile size of 96x96 pixels in the co-registered H&E images, making a total of 144,823 tiles for the whole dataset.

Results: ML models were trained to distinguish epithelial tissue from stroma with area-under-the-curve (AUC) values of 0.89 (MSI) and 0.95 (H&E)) and dysplastic grade (AUC of 0.97 (MSI) and 0.85 (H&E)) on a tile level, and low-grade progressors from non-progressors on a patient level (accuracies of 0.72 (MSI) and 0.48 (H&E)).

Conclusions: In summary, while the H&E-based classifier was best at distinguishing tissue types, the MSI-based model was more accurate at distinguishing dysplastic grades and patients at progression risk, which demonstrates the complementarity of both approaches. Data are available via ProteomeXchange with identifier PXD028949.
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http://dx.doi.org/10.1016/j.compbiomed.2021.104918DOI Listing
October 2021

Margin calculation for multiple lung metastases treated with single-isocenter SBRT.

Radiother Oncol 2021 Sep 10;162:105-111. Epub 2021 Jul 10.

Department of Radiation Oncology, University Hospital Zürich and University of Zürich, Switzerland.

Background And Purpose: A single-isocenter stereotactic body radiotherapy (SBRT) approach for multiple lung metastases has the potential to lower cumulative patient dose and reduce overall treatment time. However, the magnitude of inter-lesion position variation is currently unknown and not incorporated in margin calculations. The aim of this study was to quantify inter-lesion position variation and calculate safety margins for single-isocenter lung SBRT.

Materials And Methods: A total of 83 pairs of pulmonary metastases from 42 NSCLC patients were used to calculate relative inter-lesion position variation by lesion-based registration of planning CT and verification CBCT. Furthermore, β-value assessment of van Herk's margin formula was performed by evaluating the distance between planned and blurred dose profiles of simulated spherical lesions, to evaluate its validity for heterogeneously planned dose distributions. Population-based ITV to PTV margins were calculated using the entire dataset and using subgroups with significant differences in relative inter-lesion position variation.

Results: The mean ± SD inter-lesion position variation was 1.2 ± 1.1 mm as 3D-vector. Inter-lesion position variation was significantly increased if ≥1 lesion was not attached to the pleura or lesions were distant. The simulation showed that the combined SD of the random errors contributed to the margin only in the SI direction with 0.25∙σ for a 65% dose prescription. When incorporating inter-lesion position variation, the safety margins increased from 5.6, 5.8, 5.2 mm (AP, SI, LR) to 6.0, 6.6, 5.5 mm for the entire cohort.

Conclusion: Relative inter-lesion position variation is influenced by inter-target distance and location and can be compensated with additional safety margins of <1 mm using single-isocenter SBRT.
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http://dx.doi.org/10.1016/j.radonc.2021.07.001DOI Listing
September 2021

A Prospectively Validated Prognostic Model for Patients with Locally Advanced Squamous Cell Carcinoma of the Head and Neck Based on Radiomics of Computed Tomography Images.

Cancers (Basel) 2021 Jun 29;13(13). Epub 2021 Jun 29.

The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands.

Background: Locoregionally advanced head and neck squamous cell carcinoma (HNSCC) patients have high relapse and mortality rates. Imaging-based decision support may improve outcomes by optimising personalised treatment, and support patient risk stratification. We propose a multifactorial prognostic model including radiomics features to improve risk stratification for advanced HNSCC, compared to TNM eighth edition, the gold standard.

Patient And Methods: Data of 666 retrospective- and 143 prospective-stage III-IVA/B HNSCC patients were collected. A multivariable Cox proportional-hazards model was trained to predict overall survival (OS) using diagnostic CT-based radiomics features extracted from the primary tumour. Separate analyses were performed using TNM8, tumour volume, clinical and biological variables, and combinations thereof with radiomics features. Patient risk stratification in three groups was assessed through Kaplan-Meier (KM) curves. A log-rank test was performed for significance (-value < 0.05). The prognostic accuracy was reported through the concordance index (CI).

Results: A model combining an 11-feature radiomics signature, clinical and biological variables, TNM8, and volume could significantly stratify the validation cohort into three risk groups ( < 0∙01, CI of 0.79 as validation).

Conclusion: A combination of radiomics features with other predictors can predict OS very accurately for advanced HNSCC patients and improves on the current gold standard of TNM8.
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http://dx.doi.org/10.3390/cancers13133271DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8269129PMC
June 2021

Systematic Review on the Association of Radiomics with Tumor Biological Endpoints.

Cancers (Basel) 2021 Jun 16;13(12). Epub 2021 Jun 16.

Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland.

Radiomics supposes an alternative non-invasive tumor characterization tool, which has experienced increased interest with the advent of more powerful computers and more sophisticated machine learning algorithms. Nonetheless, the incorporation of radiomics in cancer clinical-decision support systems still necessitates a thorough analysis of its relationship with tumor biology. Herein, we present a systematic review focusing on the clinical evidence of radiomics as a surrogate method for tumor molecular profile characterization. An extensive literature review was conducted in PubMed, including papers on radiomics and a selected set of clinically relevant and commonly used tumor molecular markers. We summarized our findings based on different cancer entities, additionally evaluating the effect of different modalities for the prediction of biomarkers at each tumor site. Results suggest the existence of an association between the studied biomarkers and radiomics from different modalities and different tumor sites, even though a larger number of multi-center studies are required to further validate the reported outcomes.
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http://dx.doi.org/10.3390/cancers13123015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8234501PMC
June 2021

Comparison of beam segment versus full plan re-optimization in daily magnetic resonance imaging-guided online-adaptive radiotherapy.

Phys Imaging Radiat Oncol 2021 Jan 14;17:43-46. Epub 2021 Jan 14.

Department of Radiation Oncology, University Hospital Zürich and University of Zürich, Zürich, Switzerland.

The optimal approach for magnetic resonance imaging-guided online adaptive radiotherapy is currently unknown and needs to consider patient on-couch time constraints. The aim of this study was to compare two different plan optimization approaches. The comparison was performed in 238 clinically applied online-adapted treatment plans from 55 patients, in which the approach of re-optimization was selected based on the physician's choice. For 33 patients where both optimization approaches were used at least once, the median treatment planning dose metrics of both target and organ at risk differed less than 1%. Therefore, we concluded that beam segment weight optimization was chosen adequately for most patients without compromising plan quality.
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http://dx.doi.org/10.1016/j.phro.2021.01.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8058019PMC
January 2021

Head and neck radiotherapy on the MR linac: a multicenter planning challenge amongst MRIdian platform users.

Strahlenther Onkol 2021 Apr 23. Epub 2021 Apr 23.

Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland.

Purpose: Purpose of this study is to evaluate plan quality on the MRIdian (Viewray Inc., Oakwood Village, OH, USA) system for head and neck cancer (HNC) through comparison of planning approaches of several centers.

Methods: A total of 14 planners using the MRIdian planning system participated in this treatment challenge, centrally organized by ViewRay, for one contoured case of oropharyngeal carcinoma with standard constraints for organs at risk (OAR). Homogeneity, conformity, sparing of OARs, and other parameters were evaluated according to The International Commission on Radiation Units and Measurements (ICRU) recommendations anonymously, and then compared between centers. Differences amongst centers were assessed by means of Wilcoxon test. Each plan had to fulfil hard constraints based on dose-volume histogram (DVH) parameters and delivery time. A plan quality metric (PQM) was evaluated. The PQM was defined as the sum of 16 submetrics characterizing different DVH goals.

Results: For most dose parameters the median score of all centers was higher than the threshold that results in an ideal score. Six participants achieved the maximum number of points for the OAR dose parameters, and none had an unacceptable performance on any of the metrics. Each planner was able to achieve all the requirements except for one which exceeded delivery time. The number of segments correlated to improved PQM and inversely correlated to brainstem D and to Planning Target Volume1 (PTV) D. Total planning experience inversely correlated to spinal canal dose.

Conclusion: Magnetic Resonance Image (MRI) linac-based planning for HNC is already feasible with good quality. Generally, an increased number of segments and increasing planning experience are able to provide better results regarding planning quality without significantly prolonging overall treatment time.
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http://dx.doi.org/10.1007/s00066-021-01771-8DOI Listing
April 2021

MR-Guided Radiotherapy for Head and Neck Cancer: Current Developments, Perspectives, and Challenges.

Front Oncol 2021 19;11:616156. Epub 2021 Mar 19.

Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland.

Based on the development of new hybrid machines consisting of an MRI and a linear accelerator, magnetic resonance image guided radiotherapy (MRgRT) has revolutionized the field of adaptive treatment in recent years. Although an increasing number of studies have been published, investigating technical and clinical aspects of this technique for various indications, utilizations of MRgRT for adaptive treatment of head and neck cancer (HNC) remains in its infancy. Yet, the possible benefits of this novel technology for HNC patients, allowing for better soft-tissue delineation, intra- and interfractional treatment monitoring and more frequent plan adaptations appear more than obvious. At the same time, new technical, clinical, and logistic challenges emerge. The purpose of this article is to summarize and discuss the rationale, recent developments, and future perspectives of this promising radiotherapy modality for treating HNC.
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http://dx.doi.org/10.3389/fonc.2021.616156DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8017313PMC
March 2021

Distance to isocenter is not associated with an increased risk for local failure in LINAC-based single-isocenter SRS or SRT for multiple brain metastases.

Radiother Oncol 2021 06 30;159:168-175. Epub 2021 Mar 30.

Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Switzerland.

Purpose: To evaluate the impact of the distance between treatment isocenter and brain metastases on local failure in patients treated with a frameless linear-accelerator-based single-isocenter volumetric modulated arc (VMAT) SRS/SRT for multiple brain metastases.

Methods And Materials: Patients treated with SRT for brain metastases (BM) between April 2014 and May 2019 were included in this retrospective study. BM treated with a single-isocenter multiple-target (SIMT) SRT were evaluated for local recurrence-free intervals in dependency to their distance to the treatment isocenter. A Cox-regression model was used to investigate different predictor variables for local failure. Results were compared to patients treated with a single-isocenter-single-target (SIST) approach.

Results: In total 315 patients with a cumulative number of 1087 BM were analyzed in this study of which 140 patients and 708 BM were treated with SIMT SRS/SRT. Median follow-up after treatment was 13.9 months for SIMT approach and 11.9 months for SIST approach. One-year freedom from local recurrence was 87% and 94% in the SIST and SIMT group, respectively. Median distance to isocenter (DTI) was 4.7 cm (range 0.2-10.5) in the SIMT group. Local recurrence-free interval was not associated with the distance to the isocenter in univariable or multivariable Cox-regression analysis. Multivariable analysis revealed only volume as an independent significant predictor for local failure (p-value <0.05).

Conclusion: SRS/SRT using single-isocenter VMAT for multiple targets achieved high local metastases control rates irrespective of distance to the isocenter, supporting efficacy of single-isocenter stereotactic radiation therapy for multiple brain metastases.
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http://dx.doi.org/10.1016/j.radonc.2021.03.022DOI Listing
June 2021

Cochlea sparing optimized radiotherapy for nasopharyngeal carcinoma.

Radiat Oncol 2021 Apr 1;16(1):64. Epub 2021 Apr 1.

Department of Radiation Oncology, University Hospital Zurich (USZ), University of Zurich (UZH), Rämistrasse 100, 8091, Zurich, Switzerland.

Background: Definitive chemoradiotherapy (CRT) is standard of care for nasopharyngeal carcinoma. Due to the tumor localization and concomitant platinum-based chemotherapy, hearing impairment is a frequent complication, without defined dose-threshold. In this study, we aimed to achieve the maximum possible cochleae sparing.

Materials And Methods: Treatment plans of 20 patients, treated with CRT (6 IMRT and 14 VMAT) based on the QUANTEC organs-at-risk constraints were investigated. The cochleae were re-delineated independently by two radiation oncologists, whereas target volumes and other organs at risk (OARs) were not changed. The initial plans, aiming to a mean cochlea dose < 45 Gy, were re-optimized with VMAT, using 2-2.5 arcs without compromising the dose coverage of the target volume. Mean cochlea dose, PTV coverage, Homogeneity Index, Conformity Index and dose to other OAR were compared to the reference plans. Wilcoxon signed-rank test was used to evaluate differences, a p value < 0.05 was considered significant.

Results: The re-optimized plans achieved a statistically significant lower dose for both cochleae (median dose for left and right 14.97 Gy and 18.47 Gy vs. 24.09 Gy and 26.05 Gy respectively, p < 0.001) compared to the reference plans, without compromising other plan quality parameters. The median NTCP for tinnitus of the most exposed sites was 11.3% (range 3.52-91.1%) for the original plans, compared to 4.60% (range 1.46-90.1%) for the re-optimized plans (p < 0.001). For hearing loss, the median NTCP of the most exposed sites could be improved from 0.03% (range 0-99.0%) to 0.00% (range 0-98.5%, p < 0.001).

Conclusions: A significantly improved cochlea sparing beyond current QUANTEC constraints is feasible without compromising the PTV dose coverage in nasopharyngeal carcinoma patients treated with VMAT. As there appears to be no threshold for hearing toxicity after CRT, this should be considered for future treatment planning.
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http://dx.doi.org/10.1186/s13014-021-01796-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8017833PMC
April 2021

PET-Plan: potential for dose escalation by target volume reduction in locally advanced NSCLC.

Transl Lung Cancer Res 2020 Aug;9(4):1595-1598

Department of Radiation Oncology, University Hospital Zürich and University of Zürich, Zürich, Switzerland.

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http://dx.doi.org/10.21037/tlcr-20-653DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7481637PMC
August 2020

Treatment plan quality during online adaptive re-planning.

Radiat Oncol 2020 Aug 21;15(1):203. Epub 2020 Aug 21.

Department of Radiation Oncology, University Hospital Zürich and University of Zürich, Rämistrasse 100, 8091, Zürich, Switzerland.

Background: Online adaptive radiotherapy is intended to prevent plan degradation caused by inter-fractional tumor volume and shape changes, but time limitations make online re-planning challenging. The aim of this study was to compare the quality of online-adapted plans to their respective reference treatment plans.

Methods: Fifty-two patients treated on a ViewRay MRIdian Linac were included in this retrospective study. In total 238 online-adapted plans were analyzed, which were optimized with either changing of the segment weights (n = 85) or full re-optimization (n = 153). Five different treatment sites were evaluated: prostate, abdomen, liver, lung and pelvis. Dosimetric parameters of gross tumor volume (GTV), planning target volume (PTV), 2 cm ring around the PTV and organs at risk (OARs) were considered. The Wilcoxon signed-rank test was used to assess differences between online-adapted and reference treatment plans, p < 0.05 was considered significant.

Results: The average duration of the online adaptation, consisting of contour editing, plan optimization and quality assurance (QA), was 24 ± 6 min. The GTV was slightly larger (average ± SD: 1.9% ± 9.0%) in the adapted plans than in the reference plans (p < 0.001). GTV-D exhibited no significant changes when considering all plans, but GTV-D increased by 0.40% ± 1.5% on average (p < 0.001). There was a very small yet significant decrease in GTV-coverage for the abdomen plans. The ring D increased on average by 1.0% ± 3.6% considering all plans (p < 0.001). There was a significant reduction of the dose to the rectum of 4.7% ± 16% on average (p < 0.001) for prostate plans.

Conclusions: Dosimetric quality of online-adapted plans was comparable to reference treatment plans and OAR dose was either comparable or decreased, depending on treatment site. However, dose spillage was slightly increased.
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http://dx.doi.org/10.1186/s13014-020-01641-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7441614PMC
August 2020

Radiomics in medical imaging-"how-to" guide and critical reflection.

Insights Imaging 2020 Aug 12;11(1):91. Epub 2020 Aug 12.

Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland.

Radiomics is a quantitative approach to medical imaging, which aims at enhancing the existing data available to clinicians by means of advanced mathematical analysis. Through mathematical extraction of the spatial distribution of signal intensities and pixel interrelationships, radiomics quantifies textural information by using analysis methods from the field of artificial intelligence. Various studies from different fields in imaging have been published so far, highlighting the potential of radiomics to enhance clinical decision-making. However, the field faces several important challenges, which are mainly caused by the various technical factors influencing the extracted radiomic features.The aim of the present review is twofold: first, we present the typical workflow of a radiomics analysis and deliver a practical "how-to" guide for a typical radiomics analysis. Second, we discuss the current limitations of radiomics, suggest potential improvements, and summarize relevant literature on the subject.
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http://dx.doi.org/10.1186/s13244-020-00887-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7423816PMC
August 2020

Radiomics: from qualitative to quantitative imaging.

Br J Radiol 2020 Apr 26;93(1108):20190948. Epub 2020 Feb 26.

The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.

Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes. As a result of advances in both computational hardware and machine learning algorithms, computers are making great strides in obtaining quantitative information from imaging and correlating it with outcomes. Radiomics, in its two forms "handcrafted and deep," is an emerging field that translates medical images into quantitative data to yield biological information and enable radiologic phenotypic profiling for diagnosis, theragnosis, decision support, and monitoring. Handcrafted radiomics is a multistage process in which features based on shape, pixel intensities, and texture are extracted from radiographs. Within this review, we describe the steps: starting with quantitative imaging data, how it can be extracted, how to correlate it with clinical and biological outcomes, resulting in models that can be used to make predictions, such as survival, or for detection and classification used in diagnostics. The application of deep learning, the second arm of radiomics, and its place in the radiomics workflow is discussed, along with its advantages and disadvantages. To better illustrate the technologies being used, we provide real-world clinical applications of radiomics in oncology, showcasing research on the applications of radiomics, as well as covering its limitations and its future direction.
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http://dx.doi.org/10.1259/bjr.20190948DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7362913PMC
April 2020
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