Physics and Imaging in Radiation Oncology最新文献

{"title":"Prediction of Grade 4 radiation-induced lymphopenia during chemoradiation therapy for lung cancer patients: Insights from two past trials","authors":"Serena Monti ,&nbsp;Giuseppe Palma ,&nbsp;Ting Xu ,&nbsp;Radhe Mohan ,&nbsp;Zhongxing Liao ,&nbsp;Laura Cella","doi":"10.1016/j.phro.2025.100782","DOIUrl":"10.1016/j.phro.2025.100782","url":null,"abstract":"<div><h3>Background and Purpose</h3><div>Radiation-induced lymphopenia (RIL) is a significant side effect associated with radiation therapy (RT) with important prognostic implications. We developed and tested a normal tissue complication probability (NTCP) model for Grade 4 (G4) RIL in patients with locally advanced Non-Small-Cell Lung Cancer (NSCLC) who underwent concurrent chemotherapy and RT, analyzing data from patients enrolled in two clinical trials.</div></div><div><h3>Materials and Methods</h3><div>We retrospectively analyzed the data from NCT00915005 (MDA-cohort) and NCT00533949 (RTOG0617-cohort) trials. After finding the candidate predictors of G4-RIL, defined as absolute lymphocyte count (ALC) at nadir &lt; 0.2*10⁹ cells/l during RT, we trained an NTCP model on the MDA-cohort and tested it on the RTOG-cohort, based on common available variables in the two cohorts. Model performance was assessed in terms of discrimination and calibration.</div></div><div><h3>Results</h3><div>In the MDA-cohort, 55 out of 161 (34%) patients developed G4-RIL, while in the RTOG-cohort 16 out of 227 (7%) developed this condition. The relative volume of healthy lungs receiving at least 5 Gy (V_5Gy) and baseline ALC were selected as predictors in an NTCP model, with good discriminative performances (cross validated ROC-AUC: 0.68). The predictive value of V_5Gy was confirmed in the RTOG0917-cohort (ROC-AUC: 0.67), although its validation was limited with suboptimal calibration, potentially due to discrepancies between cohorts.</div></div><div><h3>Conclusions</h3><div>Baseline ALC and lung V_5Gy were identified as predictors for G4-RIL, consistent with findings from previous studies. Treatment plan optimization aiming at reducing low-dose bath in the lungs could be an effective strategy for severe RIL mitigation.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100782"},"PeriodicalIF":3.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144123137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving outcome prediction in oropharyngeal carcinoma through the integration of diffusion-weighted magnetic resonance imaging radiomics","authors":"Heleen Bollen ,&nbsp;Rüveyda Dok ,&nbsp;Frederik De Keyzer ,&nbsp;Sarah Deschuymer ,&nbsp;Annouschka Laenen ,&nbsp;Johannes Devos ,&nbsp;Vincent Vandecaveye ,&nbsp;Sandra Nuyts","doi":"10.1016/j.phro.2025.100759","DOIUrl":"10.1016/j.phro.2025.100759","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Locoregional recurrence (LRR) is the primary pattern of failure in head and neck cancer (HNC) following radiation treatment (RT). Predicting an individual patient’s LRR risk is crucial for pre-treatment risk stratification and treatment adaptation during RT. This study aimed to evaluate the feasibility of integrating pre-treatment and mid-treatment diffusion-weighted (DW)-MRI radiomic parameters into multivariable prognostic models for HNC.</div></div><div><h3>Materials and methods</h3><div>A total of 178 oropharyngeal cancer (OPC) patients undergoing (chemo)radiotherapy (CRT) were analyzed on DW-MRI scans. 105 radiomic features were extracted from ADC maps. Cox regression models incorporating clinical and radiomic parameters were developed for pre-treatment and mid-treatment phases. The models’ discriminative ability was assessed with the Harrel C-index after 5-fold cross-validation.</div></div><div><h3>Results</h3><div>Gray Level Co-occurrence Matrix (GLCM)-correlation emerged as a significant pre-treatment radiomic predictor of locoregional control (LRC) with a C-index (95 % CI) of 0.66 (0.57–0.75). Significant clinical predictors included HPV status, stage, and alcohol use, yielding a C-index of 0.70 (0.62–0.78). Combining clinical and radiomic data resulted in a C-index of 0.72 (0.65–0.80), with GLCM-correlation, disease stage and alcohol use as significant predictors. The mid-treatment model, which included delta (Δ) mean ADC, stage, and additional chemotherapy, achieved a C-index of 0.74 (0.65–0.82). Internal cross-validation yielded C-indices of 0.60 (0.51–0.69), 0.56 (0.44–0.66), and 0.63 (0.54–0.73) for the clinical, combined, and mid-treatment models, respectively.</div></div><div><h3>Conclusion</h3><div>The addition of Δ ADC improves the clinical model, highlighting the potential complementary value of radiomic features in prognostic modeling.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100759"},"PeriodicalIF":3.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Organs at risk proximity in central lung stereotactic ablative radiotherapy: A comparison of four-dimensional computed tomography and magnetic resonance-guided breath-hold delivery techniques","authors":"Nicolas Giraud ,&nbsp;Hilâl Tekatli ,&nbsp;Famke L. Schneiders ,&nbsp;John R. van Sornsen de Koste ,&nbsp;Marco Marzo ,&nbsp;Miguel A. Palacios ,&nbsp;Suresh Senan","doi":"10.1016/j.phro.2025.100761","DOIUrl":"10.1016/j.phro.2025.100761","url":null,"abstract":"<div><div>Higher toxicity rates are associated with stereotactic ablative radiotherapy (SABR) to central lung tumors. Breath-hold (BH) magnetic resonance-guided SABR (MR-SABR) can reduce doses to organs at risk (OAR). We quantified the planning target volumes (PTV) to OAR distance in 45 lesions treated using MR-SABR and generated a corresponding four-dimensional computed tomography (4D-CT) based PTV (motion-encompassing internal target volume plus 5 mm). For lesions located ≦3 cm from airways, BH MR-SABR increased the median PTV distance to OAR by 3.7 mm. For lesions ≦3 cm from pericardium, median PTV-OAR separation increased by 2.0 mm with BH. These findings highlight the advantage of BH SABR for central lung tumors.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100761"},"PeriodicalIF":3.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multicentre prospective risk analysis of a fully automated radiotherapy workflow","authors":"Geert De Kerf ,&nbsp;Ana Barragán-Montero ,&nbsp;Charlotte L. Brouwer ,&nbsp;Pietro Pisciotta ,&nbsp;Marie-Claude Biston ,&nbsp;Marco Fusella ,&nbsp;Geoffroy Herbin ,&nbsp;Esther Kneepkens ,&nbsp;Livia Marrazzo ,&nbsp;Joshua Mason ,&nbsp;Camila Panduro Nielsen ,&nbsp;Koen Snijders ,&nbsp;Stephanie Tanadini-Lang ,&nbsp;Aude Vaandering ,&nbsp;Tomas M. Janssen","doi":"10.1016/j.phro.2025.100765","DOIUrl":"10.1016/j.phro.2025.100765","url":null,"abstract":"<div><h3>Background and Purpose</h3><div>Fully automated workflows (FAWs) for radiotherapy treatment preparation are feasible, but remain underutilized in clinical settings. A multicentre prospective risk analysis was conducted to support centres in managing FAW-related risks and to identify workflow steps needing improvement.</div></div><div><h3>Material and Methods</h3><div>Eight European radiotherapy centres performed a failure mode and effect analysis (FMEA) on a hypothetical FAW, with a manual review step at the end. Centres assessed occurrence, severity and detectability of provided, or newly added, failure modes to obtain a risk score. Quantitative analysis was performed on curated data, while qualitative analysis summarized free text comments.</div></div><div><h3>Results</h3><div>Manual review and auto-segmentation were identified as the highest-risk steps and the highest scoring failure modes were associated with inadequate manual review (high detectability and severity score), incorrect (i.e. outside of intended use) application of the FAW (high severity score) and protocol violations during patient preparation (high occurrence score). The qualitative analysis highlighted amongst others the risk of deviation from protocol and the difficulty for manual review to recognize automation errors. The risk associated with the technical parts of the workflow was considered low.</div></div><div><h3>Conclusions</h3><div>The FMEA analysis highlighted that points where people interact with the FAW were considered higher risk than lack of trust in the FAW itself. Major concerns were the ability of people to correctly judge output in case of low generalizability and increasing skill degradation. Consequently, educational programs and interpretative tools are essential prerequisites for widespread clinical application of FAWs.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100765"},"PeriodicalIF":3.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of the failed-tolerance causes of electronic-portal-imaging-device-based in vivo dosimetry using machine learning for volumetric-modulated arc therapy: A feasibility study","authors":"Nipon Saiyo ,&nbsp;Hironori Kojima ,&nbsp;Kimiya Noto ,&nbsp;Naoki Isomura ,&nbsp;Kosuke Tsukamoto ,&nbsp;Shotaro Yamaguchi ,&nbsp;Yuto Segawa ,&nbsp;Junya Kohigashi ,&nbsp;Akihiro Takemura","doi":"10.1016/j.phro.2025.100785","DOIUrl":"10.1016/j.phro.2025.100785","url":null,"abstract":"<div><h3>Background and Purpose</h3><div>When electronic-portal-imaging-device (EPID)-based <em>in vivo</em> dosimetry (IVD) identifies dose tolerance failures, the cause of the failures should be evaluated. This study aimed to develop a machine-learning (ML) model to classify the cause of EPID-based IVD failures in volumetric-modulated arc therapy (VMAT) treatment.</div></div><div><h3>Materials and Methods</h3><div>Twenty-three prostate VMAT plans were used to recalculate the dose distribution in homogeneous phantom images as no-error (NE) plans. Errors in the randomized multileaf collimator (RMLC) position, monitor unit (MU) variation, lateral position, pitch rotation, and roll rotation were simulated. The IVD results of the NE plans and introduced errors were obtained using EPIgray software. Support vector machines (SVMs) were used to develop ML models for each error. The accuracy percentage, F1-score, and area under the receiver operating characteristic (ROC) curve (AUC) were used to evaluate models’ performances. The models were verified using five additional plans with an Alderson Rando phantom.</div></div><div><h3>Results</h3><div>The models obtained accuracies of over 90% and F1-scores of 0.9 for the RMLC position and MU variation. For lateral position, pitch rotation, and roll rotation errors, the accuracies were 66.1%, 65.2%, and 66.8%, and the F1-scores were 0.66, 0.65, and 0.67, respectively. The AUCs for all the errors were over 0.7. Additionally, the model verification results consistently classified EPIgray data for all the error types.</div></div><div><h3>Conclusion</h3><div>The developed ML models classified the causes of the failed tolerance of the EPID-based IVD.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100785"},"PeriodicalIF":3.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Breakthroughs in modern brachytherapy are transforming cancer care","authors":"Georgina Fröhlich,&nbsp;Alfonso Gomez-Iturriaga,&nbsp;Gemma Eminowicz,&nbsp;Christoph Bert,&nbsp;Åsa Carlsson Tedgren,&nbsp;Alexandra J. Stewart,&nbsp;Bernd Wisgrill,&nbsp;Ludvig P. Muren","doi":"10.1016/j.phro.2025.100783","DOIUrl":"10.1016/j.phro.2025.100783","url":null,"abstract":"","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100783"},"PeriodicalIF":3.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time target localization on 1.5 T magnetic resonance imaging linac orthogonal cine images using transfer learning","authors":"Yiling Wang ,&nbsp;Elia Lombardo ,&nbsp;Jie Wang ,&nbsp;Yu Fan ,&nbsp;Yue Zhao ,&nbsp;Stefanie Corradini ,&nbsp;Claus Belka ,&nbsp;Marco Riboldi ,&nbsp;Christopher Kurz ,&nbsp;Guillaume Landry","doi":"10.1016/j.phro.2025.100789","DOIUrl":"10.1016/j.phro.2025.100789","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Deep learning-based tumor tracking is promising for real-time magnetic-resonance-imaging (MRI)-guided radiotherapy. We investigate the applicability of a tumor tracking model developed for 0.35 T MRI-linac sagittal cine-MRI for 1.5 T interleaved orthogonal cine-MRI and implement transfer learning to further improve its performance.</div></div><div><h3>Materials and methods</h3><div>We collected 3600 cine-MRI frames in sagittal, coronal and axial planes from 24 patients (validation 10, testing 14) treated on a 1.5 T MRI-linac, where two expert clinicians manually segmented target labels. A transformer-based deformation model trained on 0.35T MRI-linac images (baseline model, BL) was evaluated and used as a starting point to train patient-specific (PS) models. The Dice similarity coefficient (DSC) and the surface distance (50th and 95th percentiles, SD50%, SD95%) were used to compare the obtained target segmentations with the ground truth labels. The percentage of negative Jacobian determinant values (NegJ), accounting for the folding pixel ratio, was determined.</div></div><div><h3>Results</h3><div>Outperformed by all the PS models, the BL model averaged in a DSC of 0.85, SD50% of 1.9 mm, SD95% of 5.9 mm and NegJ of 0.45 % in testing. The best PS model averaged in a DSC of 0.90, SD50% of 1.3 mm, SD95% of 3.9 mm and NegJ of 0.02 % in testing.</div></div><div><h3>Conclusion</h3><div>We have found the 0.35 T model trained on sagittal cine-MRIs cannot be directly applied to a 1.5 T interleaved orthogonal cine-MRI system. However, PS transfer learning could improve the target tracking performance and reach an accuracy comparable to the inter-observer variability.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100789"},"PeriodicalIF":3.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144203432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing patient-specific deep learning based segmentation for abdominal magnetic resonance imaging-guided radiation therapy: A framework conditioned on prior segmentation","authors":"Francesca De Benetti ,&nbsp;Nikolaos Delopoulos ,&nbsp;Claus Belka ,&nbsp;Stefanie Corradini ,&nbsp;Nassir Navab ,&nbsp;Thomas Wendler ,&nbsp;Shadi Albarqouni ,&nbsp;Guillaume Landry ,&nbsp;Christopher Kurz","doi":"10.1016/j.phro.2025.100766","DOIUrl":"10.1016/j.phro.2025.100766","url":null,"abstract":"<div><h3>Background and purpose:</h3><div>Conventionally, the contours annotated during magnetic resonance-guided radiation therapy (MRgRT) planning are manually corrected during the RT fractions, which is a time-consuming task. Deep learning-based segmentation can be helpful, but the available patient-specific approaches require training at least one model per patient, which is computationally expensive. In this work, we introduced a novel framework that integrates fraction MR volumes and planning segmentation maps to generate robust fraction MR segmentations without the need for patient-specific retraining.</div></div><div><h3>Materials and methods:</h3><div>The dataset included 69 patients (222 fraction MRs in total) treated with MRgRT for abdominal cancers with a 0.35 T MR-Linac, and annotations for eight clinically relevant abdominal structures (aorta, bowel, duodenum, left kidney, right kidney, liver, spinal canal and stomach). In the framework, we implemented two alternative models capable of generating patient-specific segmentations using the planning segmentation as prior information. The first one is a 3D UNet with dual-channel input (i.e. fraction MR and planning segmentation map) and the second one is a modified 3D UNet with double encoder for the same two inputs.</div></div><div><h3>Results:</h3><div>On average, the two models with prior anatomical information outperformed the conventional population-based 3D UNet with an increase in Dice similarity coefficient <span><math><mrow><mo>&gt;</mo><mn>4</mn><mspace></mspace><mtext>%</mtext></mrow></math></span>. In particular, the dual-channel input 3D UNet outperformed the one with double encoder, especially when the alignment between the two input channels is satisfactory.</div></div><div><h3>Conclusion:</h3><div>The proposed workflow was able to generate accurate patient-specific segmentations while avoiding training one model per patient and allowing for a seamless integration into clinical practice.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100766"},"PeriodicalIF":3.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reconstructed dose and geometric coverage for tight margins using intrafraction re-planning on an integrated magnetic resonance imaging and linear accelerator system for prostate cancer patients","authors":"Ingeborg van den Berg ,&nbsp;Cornel Zachiu ,&nbsp;Eline N. de Groot-van Breugel ,&nbsp;Thomas Willigenburg ,&nbsp;Gijsbert H. Bol ,&nbsp;Jan J.W. Lagendijk ,&nbsp;Bas W. Raaymakers ,&nbsp;Harm H.E. van Melick ,&nbsp;Cornelis A.T. van den Berg ,&nbsp;Jochem R.N. van der Voort van Zyp ,&nbsp;Johannes C.J. de Boer","doi":"10.1016/j.phro.2025.100776","DOIUrl":"10.1016/j.phro.2025.100776","url":null,"abstract":"<div><h3>Background and purpose</h3><div>A sub-fractionation workflow enables a substantial reduction in planning target volume (PTV) margin in prostate cancer (PCa) patients by reducing systematic motion during magnetic resonance (MR)-guided radiotherapy. This study assessed geometric and reconstructed dose outcomes in patients treated with a tight-margin sub-fractionation workflow on a combined linear accelerator with a 1.5 T MRI scanner (MR-Linac).</div></div><div><h3>Materials and methods</h3><div>We evaluated the sub-fractionation workflow with tight margins (2–3 mm) on 128 PCa patients who completed treatment with 5 × 7.25 Gy (36.25 Gy total dose). A traffic light protocol was applied based on residual motions to detect patients with unexpectedly large motions. When ’red’ traffic light criteria were met, plans with larger margins (5 mm isotropic) were adopted for subsequent fractions. Intra- and inter-fraction dose accumulation was performed via an in-house developed deformable image registration algorithm.</div></div><div><h3>Results</h3><div>A total of 89 % (114/128) of patients completed treatment with the initial tight margins. The mean 3D intrafraction shifts were 1.0 mm (SD: 0.6 mm) in the group with the tight margins and 1.9 mm (SD: 1.5 mm) in the patient group who switched to large margins. The median accumulated D99% was 34.9 Gy (interquartile range: 34.0–35.3 Gy) for patients with prostate shifts who switched to larger margins. In 57 % (8/14) of these patients, the accumulated D99% was above the threshold of 34.4 Gy.</div></div><div><h3>Conclusions</h3><div>Tight margins of 2–3 mm can be safely applied for at least 95 % (122/128) of the PCa patients undergoing a sub-fractionation workflow on a 1.5 T MR-linac.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100776"},"PeriodicalIF":3.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncertainties in outcome modelling in radiation oncology","authors":"Lukas Dünger ,&nbsp;Emily Mäusel ,&nbsp;Alex Zwanenburg ,&nbsp;Steffen Löck","doi":"10.1016/j.phro.2025.100774","DOIUrl":"10.1016/j.phro.2025.100774","url":null,"abstract":"<div><div>Outcome models predicting e.g. survival, tumour control or radiation-induced toxicities play an important role in the field of radiation oncology. These models aim to support the clinical decision making and pave the way towards personalised treatment. Both validity and reliability of their output are required to facilitate clinical integration. However, models are influenced by uncertainties, arising from data used for model development and model parameters, among others. Therefore, quantifying model uncertainties and addressing their causes promotes the creation of models that are sufficiently reliable for clinical use. This topical review aims to summarise different types and possible sources of uncertainties, presents uncertainty quantification methods applicable to various modelling approaches, and highlights central challenges that need to be addressed in the future.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100774"},"PeriodicalIF":3.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}