Radiation Oncology最新文献

{"title":"Evaluating large language models as an educational tool for meningioma patients: patient and clinician perspectives.","authors":"Diana-Coralia Dehelean, Sebastian H Maier, Alev Altay-Langguth, Alexander Nitschmann, Michael Schmeling, Daniel F Fleischmann, Paul Rogowski, Christian Trapp, Stefanie Corradini, Claus Belka, Stephan Schönecker, Sebastian N Marschner","doi":"10.1186/s13014-025-02671-2","DOIUrl":"10.1186/s13014-025-02671-2","url":null,"abstract":"<p><strong>Background: </strong>The study explores the potential of ChatGPT, an advanced large language model (LLM) by OpenAI, in educating patients about meningioma, a common type of brain tumor. While ChatGPT has generated significant debate regarding its utility and ethics, its growing popularity suggests that patients may increasingly use such tools for medical information. The study specifically examines how patients who have undergone radiation therapy for meningioma perceive the information generated by ChatGPT, integrating both patient feedback and clinical assessment.</p><p><strong>Methods: </strong>Eight meningioma-related questions on diagnosis, treatment options, and radiation therapy were posed to ChatGPT 4. A questionnaire with these responses and feedback items was developed to assess utility, accuracy, clarity, and alignment with patients' experiences. Nine clinicians first rated each response's relevance, correctness, and completeness on a five-point Likert scale. Subsequently, 28 patients with meningioma completed the questionnaire during their first follow-up visit (three months post-radiation therapy). Finally, the same questions were presented to three other large language models (ChatGPT 4o mini, Gemini Free, Gemini Advanced), and seven blinded clinicians rated each model's responses before selecting the most accurate, eloquent, and comprehensive overall.</p><p><strong>Results: </strong>The study cohort included 28 meningioma patients, mostly female, with a median age of 60 years. Most patients found the information clear, accurate, and reflective of their experiences, with 60% willing to use ChatGPT for future inquiries. Clinicians rated the relevance and correctness of the information highly, although completeness was rated slightly lower, particularly for questions about specific radiation therapy details and side effects. ChatGPT 4 and its newer version ChatGPT 4o mini received the highest, nearly identical scores among the four LLMs evaluated, while Gemini Free scored the lowest in clinician assessments.</p><p><strong>Conclusions: </strong>ChatGPT demonstrates potential as a supplementary educational tool for meningioma patients, though some areas may require improvement, particularly in providing comprehensive information. The study highlights the potential for integrating AI in patient education, while also noting the need for clinical oversight to ensure accuracy and completeness.</p><p><strong>Trial registration: </strong>LMU ethics vote nr.: 23-0742.</p>","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":"20 1","pages":"101"},"PeriodicalIF":3.3,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12167587/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144295224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond the ratio of tumor reduction: residual volume after induction chemotherapy emerges as the optimal biomarker in advanced nasopharyngeal cancer.","authors":"Sixue Dong, Guangsen Pan, Zhen Zhang, Weigang Hu, Ruiping Zhai, Hongmei Ying, Jiazhou Wang","doi":"10.1186/s13014-025-02677-w","DOIUrl":"10.1186/s13014-025-02677-w","url":null,"abstract":"","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":"20 1","pages":"100"},"PeriodicalIF":3.3,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12167576/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144295223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation of ultra-hypofractionated radiotherapy for breast cancer in the Netherlands in 2020-2023, using registry data and questionnaires.","authors":"Anouk H Eijkelboom, Eva J A van Beek, Marcel R Stam, Paulien Westhoff, Marissa C van Maaren, Margriet G A Sattler, Enja J Bantema-Joppe, Marcel Verheij, Desirée H J G van den Bongard, Sabine Siesling","doi":"10.1186/s13014-025-02669-w","DOIUrl":"10.1186/s13014-025-02669-w","url":null,"abstract":"<p><strong>Background: </strong>This study investigated the implementation of ultra-hypofractionated radiotherapy (i.e. 5 fractions) in DCIS and early-stage breast cancer, factors associated with its use, and variation across radiotherapy institutes.</p><p><strong>Methods: </strong>Registry and questionnaire data were used. Registry data included data from the Netherlands Cancer Registry and the NABON Breast Cancer Audit-Radiotherapy (NBCA-R). Women eligible for 5 fractions were included. Trends and variation were visualised using trendlines and case-mix adjusted boxplots. Logistic regression was applied to investigate which factors were associated with the use of 5 fractions. In April 2024 a questionnaire was distributed among radiotherapy institutes to identify facilitators and barriers for implementation.</p><p><strong>Results: </strong>The current study included 16,115 women. In 2020, 18.5% of the eligible women received 5 fractions, compared to 60.8% in 2023. The lowest variation between radiotherapy institutes was found in 2023 (median: 60.4%, interquartile range: 53.3-70.6%). Age, tumour grade, multifocality, (y)pT, (y)pN, radiotherapy target volume, type of radiotherapy institute, and start year of radiation were associated with the chance of receiving 5 fractions. Sixteen out of the 19 radiotherapy institutes completed the questionnaire, showing variation in age and radiotherapy target volume for which the schedule was used. Most institutes mentioned no barriers for using 5 fractions. Questionnaire data confirmed the trendline finding that national consensus meetings were essential for largescale implementation.</p><p><strong>Conclusions: </strong>The use of ultra-hypofractionated radiotherapy has increased during the past four years, with reduced variation across Dutch institutes. Registry and questionnaire data indicated that national consensus meetings were instrumental in driving implementation.</p>","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":"20 1","pages":"99"},"PeriodicalIF":3.3,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12164123/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144286940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long-term performance evaluation of a 1.5T MR-Linac using statistical process control techniques.","authors":"Qing Xiao, Mengdie Shen, Guangjun Li, Shipai Zhu, Jinrong He, Qiang Wang, Guyu Dai, Hang Yu, Jialu Lai, Renming Zhong, Sen Bai","doi":"10.1186/s13014-025-02670-3","DOIUrl":"10.1186/s13014-025-02670-3","url":null,"abstract":"<p><strong>Background: </strong>The integration of magnetic resonance imaging with linear accelerators (Linacs) enhances adaptive radiotherapy by providing real-time imaging for improved treatment precision. However, the long-term performance of MR-Linac systems, particularly in clinical settings, remains insufficiently studied. Traditional quality assurance (QA) methods, relying on binary pass/fail criteria, may overlook critical system variations. This study applies statistical process control (SPC) techniques to evaluate the long-term performance of a 1.5T MR-Linac, focusing on optimization in beam quality, MR-to-MV alignment, MR imaging, and geometric distortion.</p><p><strong>Methods: </strong>A dual-phase SPC framework was applied to 1 year of daily and weekly QA data from an Elekta Unity MR-Linac. Phase I established performance benchmarks, while Phase II monitored deviations online. Evaluated parameters included beam output, symmetry, MR-to-MV alignment, signal-to-noise ratio (SNR), spatial linearity, slice profile, and geometric distortion across spherical volumes (DSVs). Stability and variability were quantified using control charts and process performance indices (Ppk).</p><p><strong>Results: </strong>Beam quality was stable overall (Ppk ≥ 1.33), though output dose and transverse symmetry showed increased variability in Phase II, with dose Ppk declining from 3.13 to 1.33. MR-to-MV alignment was consistent, but Phi rotational and Z translational offsets showed variability after system upgrades. Imaging metrics, including SNR and spatial linearity, achieved A + performance (Ppk ≥ 1.67) in Phase II, while vertical spatial resolution was lower (Ppk 1.04-1.10). Geometric distortion was well-controlled, though larger DSVs (≥ 500 mm) showed increased AP-axis distortion (2.44 mm) compared to RL (1.37 mm) and FH (0.93 mm).</p><p><strong>Conclusions: </strong>SPC techniques dynamically identified stable parameters and areas for improvement. Key recommendations include enhanced alignment protocols for beam quality and MR-to-MV offsets, as well as targeted strategies to address geometric distortion in larger volumes and along the AP axis.</p>","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":"20 1","pages":"98"},"PeriodicalIF":3.3,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12145638/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144250554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The effect of training data size on real-time respiration prediction using long short-term memory model.","authors":"Wenzheng Sun, Jun Dang, Lei Zhang, Qichun Wei, Chao Li, Ye Liu, Huang Jing, Kanghua Huang, Yuanpeng Zhang, Bing Li","doi":"10.1186/s13014-025-02676-x","DOIUrl":"10.1186/s13014-025-02676-x","url":null,"abstract":"<p><strong>Aim: </strong>To investigate the optimal training dataset size (TDS) for respiration prediction accuracy using a long short-term memory (LSTM) model.</p><p><strong>Methods: </strong>The respiratory signals of 151 patients acquired with the real-time position management system were retrospectively included in this study. Among the dataset, 101 respiratory signals were utilized to evaluate the impact of the TDS on prediction accuracy, while the remaining 50 signals were employed for setting the default hyperparameters. The prediction accuracy of the LSTM model using eight different TDSs (10 s, 20 s, 30 s, 60 s, 90 s, 110 s, 130 s, and 150 s) was examined and evaluated by the root mean square error (RMSE) between the real and predicted respiratory signals. The interplay effects of the main hyperparameters, the ahead time and the different testing data lengths using different TDSs were also measured.</p><p><strong>Results: </strong>For the 520 ms ahead time, the root mean square error values of the LSTM model using the eight different training data sizes listed above were 0.146 cm, 0.137 cm, 0.134 cm, 0.125 cm, 0.120 cm, 0.121 cm, 0.121 cm, and 0.119 cm, respectively. The LSTM model achieved the highest prediction accuracy when the TDS was 150 s. The prediction accuracy was stable when the TDS exceeded 90 s.</p><p><strong>Conclusions: </strong>TDS selection could influence the respiration signal prediction accuracy of the LSTM model. The relationship between TDS and the prediction accuracy of the LSTM model was not linear. The 90 s seemed to be an optimal TDS for the respiration signal prediction tasks using the LSTM model, as it was the shortest time at which a favorable prediction accuracy was maintained in this study.</p>","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":"20 1","pages":"97"},"PeriodicalIF":3.3,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12144701/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144250555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic profiles of early biological responses to predict the treatment efficacy of proton therapy in liver cancer assessed with in vivo kinetic [18F]-FDG PET/MRI.","authors":"Yi-Hsiu Chung, I-Chun Cho, Fujie Jhang, Chi-Chang Weng, Gigin Lin, Ching-Fang Yu, Fang-Hsin Chen","doi":"10.1186/s13014-025-02673-0","DOIUrl":"10.1186/s13014-025-02673-0","url":null,"abstract":"<p><strong>Background: </strong>Proton beam therapy is an advanced treatment for patients with unresectable hepatocellular carcinoma. However, evaluating the response to treatment with tumor size alone is insufficient. Herein, we used kinetic [18F]-FDG PET and diffusion-weighted MR imaging to monitor the biological responses to proton beam therapy in hepatocellular carcinoma mice to assess treatment efficacy. Murine BNL HCC cells were orthotopically implanted into the livers of 8-week-old male BALB/c mice, which received 20 Gy of the single dose in proton beam therapy. The biological responses to proton beam therapy were assessed on pre-treatment and post-treatment days 1, 3, and 7.</p><p><strong>Results: </strong>Compared with the not-receiving proton beam therapy group, the treated group led to an increasing trend in tumor K1 values and constant relative SUVs within 7 days on the dynamic PET imaging. On diffusion-weighted MR imaging, the tumor relative apparent diffusion coefficient values significantly increased post-treatment days 3 and 7. Significantly decreased tumor proliferation, cellular density, and cellular uptake of [18F]-FDG on days 1 and/or 3 post-treatment, with a rebound on day 7, were observed in the dynamic profiling of tumor cells ex vivo and in vitro. Vascular remodeling and elevated macrophage infiltrates in the tumor microenvironment were associated with proton beam therapy. However, there were no significant changes in tumor size between the treated and non-treated groups after treatment until day 7.</p><p><strong>Conclusions: </strong>In vivo kinetic [18F]-FDG PET/MRI techniques can provide a feasible means to assess early liver tumor response to proton beam therapy and predict treatment outcomes.</p>","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":"20 1","pages":"96"},"PeriodicalIF":3.3,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12144789/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144250553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic cervical tumors segmentation in PET/MRI by parallel encoder U-net.","authors":"Shuai Liu, Zheng Tan, Tan Gong, Xiaoying Tang, Hongzan Sun, Fei Shang","doi":"10.1186/s13014-025-02664-1","DOIUrl":"10.1186/s13014-025-02664-1","url":null,"abstract":"<p><strong>Background: </strong>Automatic segmentation of cervical tumors is important in quantitative analysis and radiotherapy planning.</p><p><strong>Methods: </strong>A parallel encoder U-Net (PEU-Net) integrating the multi-modality information of PET/MRI was proposed to segment cervical tumor, which consisted of two parallel encoders with the same structure for PET and MR images. The features of the two modalities were extracted separately and fused at each layer of the decoder. Res2Net module on skip connection aggregated the features of various scales and refined the segmentation performance. PET/MRI images of 165 patients with cervical cancer were included in this study. U-Net, TransUNet, and nnU-Net with single or multi-modality (PET or/and T2WI) input were used for comparison. The Dice similarity coefficient (DSC) with volume data, DSC and the 95th percentile of Hausdorff distance (HD95) with tumor slices were calculated to evaluate the performance.</p><p><strong>Results: </strong>The proposed PEU-Net exhibited the best performance (DSC_3d: 0.726 ± 0.204, HD₉₅: 4.603 ± 4.579 mm), DSC_2d (0.871 ± 0.113) was comparable to the best result of TransUNet with PET/MRI (0.873 ± 0.125).</p><p><strong>Conclusions: </strong>The networks with multi-modality input outperformed those with single-modality images as input. The results showed that the proposed PEU-Net could use multi-modality information more effectively through the redesigned structure and achieved competitive performance.</p>","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":"20 1","pages":"95"},"PeriodicalIF":3.3,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12143082/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144235731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiation-induced cardiac substructure damage and dose constraints: a review.","authors":"Xinyi Li, Yajing Wu, Qingyu Wang, Bingyue Li, Jun Wang","doi":"10.1186/s13014-025-02668-x","DOIUrl":"10.1186/s13014-025-02668-x","url":null,"abstract":"<p><p>The field of radiation-induced cardiac damage (RIHD) is garnering increasing attention. The application of advanced radiotherapy reduces the cardiac radiation dose. Still, challenges remain in the uneven dose distribution, the different sensitivity among cardiac substructures (CSs), and the delineation of target areas within these substructures. This article encompasses cardiac substructures, including atria and ventricles, coronary arteries, pulmonary vasculars and superior vena cava (SVC), cardiac conduction system, heart valves and heart base. This review will provide better understanding of RIHD as it firstly summarizes dose limitation of CSs, as well as the risk of cardiac toxicities and its impact on survival following the comprehensive search.</p>","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":"20 1","pages":"94"},"PeriodicalIF":3.3,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12139131/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144235732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A nomogram integrating clinical stage and pre-EBV DNA to identify the cycles of induction chemotherapy for locoregionally advanced nasopharyngeal carcinoma.","authors":"Sunqin Cai, Zongwei Huang, Zihan Chen, Ying Li, Jingjing Su, Ronghui Chen, Siqi Xu, Jing Wang, Sufang Qiu","doi":"10.1186/s13014-025-02672-1","DOIUrl":"10.1186/s13014-025-02672-1","url":null,"abstract":"<p><strong>Objective: </strong>This research focused on determining the optimal cycles of induction chemotherapy (IC) in high-risk locoregionally advanced nasopharyngeal carcinoma (LA-NPC).</p><p><strong>Methods: </strong>The retrospective analysis was conducted on 885 patients. Potential bias was minimized by propensity score matching (PSM). Overall survival (OS) served as the primary endpoint. Survival outcomes were analyzed using Kaplan-Meier curves, with statistical comparisons performed via the log-rank test. Prognostic determinants were identified through multivariate cox regression analysis. A nomogram model was constructed to quantify individualized prognosis.</p><p><strong>Results: </strong>Patients were divided into 2/3-cycle (IC = 2/3) and 4-cycle IC (IC = 4) groups. After PSM, 446 patients remained and were categorized into distinct risk groups according to independent predictors, including clinical stage and pre-treatment Epstein-Barr virus DNA (pre-EBV DNA). For the high-risk cohort (stage IVa with pre-EBV DNA ≥ 4000 copies/mL), the IC = 4 regimen showed higher 5-year OS (70.4% vs. 54.7%, P = 0.036) than the IC = 2/3 regimen. In the low- and middle-risk cohorts, the IC = 2/3 regimen exhibited OS comparable to the IC = 4 regimen. The established nomogram model demonstrated superior prognostic power compared to individual factors. Given the adverse effects, the IC = 4 regimen was associated with significantly higher rates of grade 3-4 neutropenia (24.6% vs. 15.5%, P = 0.017) and thrombocytopenia (8.0% vs. 3.7%, P = 0.049) compared to the IC = 2/3 regimen.</p><p><strong>Conclusion: </strong>The developed nomogram offers personalized guidance on selecting individual IC cycles for LA-NPC patients.</p>","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":"20 1","pages":"93"},"PeriodicalIF":3.3,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12139057/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144227381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the impact of pelvic radiotherapy dose distribution on lymphocyte counts: a voxel-based analysis.","authors":"Sanghyeok Lee, Seohan Kim, Sangseok Ha, Kyu-Hye Choi, Wonmo Sung","doi":"10.1186/s13014-025-02652-5","DOIUrl":"10.1186/s13014-025-02652-5","url":null,"abstract":"<p><strong>Background and purpose: </strong>This study aimed to evaluate the impact of pelvic radiation therapy (RT) on the occurrence of severe radiation-induced lymphopenia (SRIL) and identify its clinical and dosimetric predictors using voxel-wise analysis. Understanding these impacts is crucial for improving patient outcomes and optimizing treatment protocols in radiation oncology.</p><p><strong>Materials and methods: </strong>A retrospective analysis was conducted on 122 patients who underwent pelvic RT. Absolute lymphocyte counts (ALC) were measured before treatment and within one month of RT initiation. Patients were classified into SRIL and non-SRIL groups on the basis of their lowest recorded ALC during treatment. The associations between SRIL and clinical/dosimetric parameters were assessed via univariable (UVA) and multivariate (MVA) analysis. The influence of regionally detailed dose was assessed by voxel-based analysis (VBA) on spatially normalized 3D dose maps and CT images, focusing on the sacrum, femoral heads, and pelvic bones.</p><p><strong>Results: </strong>SRIL was associated with clinical and dosimetric factors. The baseline ALC was the most significant clinical predictor, with a lower baseline ALC increasing SRIL risk (OR = 0.996, p = 0.001). VBA further revealed localized highly related regional dose patterns, with 92.17% of the left femoral head and 91.32% of the right femoral head showing significant SRIL associations, whereas the associations were significantly lower in the sacrum (10.39%) and pelvic bones (left: 30.01%, right: 31.52%).</p><p><strong>Conclusion: </strong>This study identified key clinical and dosimetric factors influencing SRIL in patients undergoing pelvic radiotherapy. Baseline ALC was the most significant clinical factor, and VBA showed that regional dose pattern changes within the femoral head were significantly associated with SRIL.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":"20 1","pages":"92"},"PeriodicalIF":3.3,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12135238/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144217376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}