Radiation Oncology最新文献

{"title":"Preliminary results from a phase II trial of spatially fractionated radiotherapy combined with immunotherapy and anti-angiogenic therapy in patients with bulky solid tumors: early evidence of promising efficacy and favorable safety.","authors":"Yuntao Zhou, Yanwei Li, Hui Zhu, Siyi Yang, Chengwen Yang, Shengpeng Jiang, Shiping Shen, Lujun Zhao, Jing Luo, Zhiyong Yuan, Ningbo Liu","doi":"10.1186/s13014-026-02841-w","DOIUrl":"https://doi.org/10.1186/s13014-026-02841-w","url":null,"abstract":"","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147822909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Elective nodal irradiation of high-risk regions is superior to involved field radiotherapy for limited-stage small cell lung cancer: a propensity score-matched retrospective study.","authors":"Zheng Zhang, Meng Yan, Jiakun Gao, Gengmin Niu, Yihan Guo, Boyu Qian, Ming Li, Kai Ren, Xue Li, Lujun Zhao","doi":"10.1186/s13014-026-02845-6","DOIUrl":"https://doi.org/10.1186/s13014-026-02845-6","url":null,"abstract":"","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147787395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SHAP-based interpretable machine learning with longitudinal delta-radiomics across seven weeks of treatment for xerostomia prediction in head-and-neck cancer.","authors":"Damilola Oluwafemi Samson, Mahayu Ismail, Mohd Ariff Mohamed Hanifa, Eznal Izwadi Mohd Mahidin, Hanani Abdul Manan, Noorazrul Yahya","doi":"10.1186/s13014-026-02846-5","DOIUrl":"https://doi.org/10.1186/s13014-026-02846-5","url":null,"abstract":"","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147787404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying intrafractional colon tumor motion on 1.5 T MR-linac cine-MRI and applying anisotropic residual-motion margins for SBRT.","authors":"Guoqing Liu, Min Liu, Li Pan, Bisheng Liu, Na Huang, Yanhua Liu, Gang Niu, Shuanghong Wu, Xianliang Wang, Bo Song, Qian Peng","doi":"10.1186/s13014-026-02818-9","DOIUrl":"https://doi.org/10.1186/s13014-026-02818-9","url":null,"abstract":"<p><strong>Purpose: </strong>To noninvasively quantify the intrafraction motion of unresectable locally advanced colon cancer (UNLACC) via 1.5 T magnetic resonance imaging and linear accelerator (MR-linac) cine-MRI and assess the preliminary feasibility of translating motion envelopes into individualized, anisotropic residual-motion margins for stereotactic body radiotherapy (SBRT) workflow.</p><p><strong>Methods: </strong>This prospective, single‑centre, observational cross‑sectional study serves as a preliminary test to validate the workflow of a phase I single-arm clinical study and falls under the preliminary feasibility verification phase (Clinical Trials.gov NCT06244537; Initial Release Time: 01/25/2024). We first performed internal validation of a rolling-ball-based motion extraction (RBME) algorithm using a dynamic phantom on the same 1.5 T MR linac, which demonstrated negligible systematic error with a Bland‒Altman mean bias of 0.01 mm (standard deviation 0.49 mm) and 95% limits of agreement of -0.98~0.95 mm. We then prospectively accrued 24 patients for a simulated MR-linac workflow and applied RBME to cine-MRI to generate patient- and axis-specific motion envelopes. Intrafraction motion was analysed in a repeated-measures framework to test axis-by-site effects. Finally, we conducted a planning simulation prescribing 25 Gy in 5 fractions; no patients yet treated according to the proposed strategy. The simulated planning target volume (PTV) is the gross tumor volume (GTV) plus an RBME-derived anisotropic residual-motion margin and an additional 3 mm setup margin.</p><p><strong>Results: </strong>Phantom testing supported the accuracy of RBME ( ~ ± 1 mm agreement). In patients, the mean excursions peak in the caudal/right/anterior directions and are smallest in the cranial/left/posterior directions, with the average of means centered at approximately 4 ~ 5 mm. Across all lesions, mean root-mean-square (RMS) displacement was 2.64 ± 1.32 mm cranio-caudal, 2.91 ± 1.05 mm anterior-posterior, and 2.75 ± 1.43 mm left-right. The corresponding 95th percentile (P95) excursions were 4.49 ± 2.43 mm, 4.97 ± 1.74 mm, and 4.76 ± 2.62 mm. Axis-by-segment interactions were significant for RMS (χ² 19.93, df 8, P = 0.011) and P95 (χ² 20.44, df 8, P = 0.008), indicating location-dependent anisotropy.</p><p><strong>Conclusion: </strong>A phantom-validated RBME algorithm was used to characterise irregular intrafraction motion for UNLACC on a 1.5T MR-linac. The resulting motion envelopes should be regarded as hypothesis-generating and intended to inform further method development and protocol design.</p>","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147700150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A deep learning-driven automated treatment planning framework for cervical cancer patients treated with volumetric modulated arc therapy.","authors":"Boda Ning, Xiuyan Liang, Zhenguo Cui, Yingfa Li, Qi Liu, Shuaining Ma, Xiting Chen, Shanshan Yang, Yanling Bai, Deyang Yu","doi":"10.1186/s13014-026-02842-9","DOIUrl":"https://doi.org/10.1186/s13014-026-02842-9","url":null,"abstract":"<p><strong>Background and purpose: </strong>The rapid and efficient generation of high-quality, dose-consistency volumetric modulated arc therapy (VMAT) plans remains challenging in radiotherapy. This study proposes a deep learning (DL) end-to-end (E2E) auto-planning framework and validate its practicality and feasibility for clinical implementation.</p><p><strong>Materials and methods: </strong>A total of 458 cervical cancer VMAT plans were enrolled and split into training, validation, and test cohorts. An E2E auto-planning framework with a two-stage cascaded DL network was developed: Stage 1 predicted coarse dose from CT and structure masks, and Stage 2 refined it using four beam-band priors and a composite loss. Dose-volume histogram (DVH) endpoints from refined predicted dose were converted into Monaco objectives via a scripting module for iterative optimization. Performance was evaluated with Dose, DVH, and snDVH scores, ablations, and comparisons with manual plans in terms of quality, clinical evaluation and deliverability.</p><p><strong>Results: </strong>The proposed DL method achieved the best performance, with Dose score, DVH score and snDVH score of 2.114 ± 0.218 Gy, 1.194 ± 0.295 Gy and 2.027 ± 0.586, respectively. Compared with manual plans, E2E auto-plans preserved target volume coverage while reducing all DVH metrics for bladder, rectum, small intestine, and spinal cord by 2% - 35% (all p < 0.05). The gamma passing rate of E2E auto-plans was higher than manual plans in the 3%/3 mm gamma criterion (98.1% vs. 97.9%).</p><p><strong>Conclusion: </strong>The proposed auto-planning framework demonstrated a high level of automation and clinical applicability, offering a reliable and promising tool to support radiotherapy workflows.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147692490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning CT-to-electron-density mapping for radiotherapy treatment planning using neural networks.","authors":"Shaohua Yan, Heling Zhu, Ying Zhou, Qizhen Zhu, Yiming Zhang, Wenbo Li, Lu Bai, Shaobin Wang, Bo Yang, Jie Qiu","doi":"10.1186/s13014-026-02838-5","DOIUrl":"https://doi.org/10.1186/s13014-026-02838-5","url":null,"abstract":"","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147663454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning approach to estimation of in vivo measured dose and treatment planning for total body irradiation.","authors":"Sangmin Lee, Jung-In Kim, Seonghee Kang, Jaeman Son, Hyeongmin Jin, Chang Heon Choi, Jong Min Park, Joo Ho Lee, Ji Hyun Chang, Hyun-Cheol Kang, Seongmoon Jung","doi":"10.1186/s13014-026-02829-6","DOIUrl":"https://doi.org/10.1186/s13014-026-02829-6","url":null,"abstract":"","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147663447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation of cellular senescence in the mouse liver caused by low dose fractionated X-ray therapy.","authors":"Xin Lan, Lina Cai, Lingyu Zhang, Yashi Cai, Linqian Zhou, Weiyi Ke, Guanyou Chen, Yanting Chen, Xiaoman Zhou, Weixu Huang, Jianming Zou, Huifeng Chen","doi":"10.1186/s13014-026-02837-6","DOIUrl":"https://doi.org/10.1186/s13014-026-02837-6","url":null,"abstract":"","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147663500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dosimetric advantages and clinical feasibility of a novel lateral decubitus position for left breast cancer patients undergoing PORT.","authors":"Yingying Zhou, Jinfeng Xu, Yuan Deng, Lisheng Pan, Sufen Deng, Huali Li, Mengqi Yuan, Shaojun Mai, Huanhuan Liu, Bo Chen, Longmei Cai, Lin Yang, Ting Song, Xin Zhen, Hongmei Wang","doi":"10.1186/s13014-026-02836-7","DOIUrl":"https://doi.org/10.1186/s13014-026-02836-7","url":null,"abstract":"","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147655182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Practice of radiation therapy for squamous cell esophageal cancer in Austria - a survey on behalf of the ÖGRO-GIT.","authors":"Sabine Gerum, Patrick Clemens, Johanna Salinger, Philipp Harl, Robert Jaeger, Oxana Komina, Michael Kopp, Karin Peterka, Irene Reiter, Rainer Schmid, Susanne Schwaiger, Heidi Stranzl-Lawatsch, Clemens Venhoda, Falk Roeder","doi":"10.1186/s13014-026-02808-x","DOIUrl":"https://doi.org/10.1186/s13014-026-02808-x","url":null,"abstract":"","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147617019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}