S.P.M. de Vette , H. Neh , L. van der Hoek , D.C. MacRae , H. Chu , A. Gawryszuk , R.J.H.M. Steenbakkers , P.M.A. van Ooijen , C.D. Fuller , K.A. Hutcheson , J.A. Langendijk , N.M. Sijtsema , L.V. van Dijk
{"title":"基于3D剂量、CT和分割的头颈癌放疗后晚期吞咽困难深度学习NTCP模型","authors":"S.P.M. de Vette , H. Neh , L. van der Hoek , D.C. MacRae , H. Chu , A. Gawryszuk , R.J.H.M. Steenbakkers , P.M.A. van Ooijen , C.D. Fuller , K.A. Hutcheson , J.A. Langendijk , N.M. Sijtsema , L.V. van Dijk","doi":"10.1016/j.radonc.2025.111169","DOIUrl":null,"url":null,"abstract":"<div><h3>Background & purpose</h3><div>Late radiation-associated dysphagia after head and neck cancer (HNC) significantly impacts patient’s health and quality of life. Conventional normal tissue complication probability (NTCP) models use discrete dose parameters to predict toxicity risk but fail to fully capture the complexity of this side effect. Deep learning (DL) offers potential improvements by incorporating 3D dose data for all anatomical structures involved in swallowing. This study aims to enhance dysphagia prediction with 3D DL NTCP models compared to conventional NTCP models.</div></div><div><h3>Materials & methods</h3><div>A multi-institutional cohort of 1484 HNC patients was used to train and validate a 3D DL model (Residual Network) incorporating 3D dose distributions, organ-at-risk segmentations, and CT scans, with or without patient- or treatment-related data. Predictions of grade ≥ 2 dysphagia (CTCAEv4) at six months post-treatment were evaluated using area under the curve (AUC) and calibration curves. Results were compared to a conventional NTCP model based on pre-treatment dysphagia, tumour location, and mean dose to swallowing organs. Attention maps highlighting regions of interest for individual patients were assessed.</div></div><div><h3>Results</h3><div>DL models outperformed the conventional NTCP model in both the independent test set (AUC = 0.80–0.84 versus 0.76) and external test set (AUC = 0.73–0.74 versus 0.63) in AUC and calibration. Attention maps showed a focus on the oral cavity and superior pharyngeal constrictor muscle.</div></div><div><h3>Conclusion</h3><div>DL NTCP models performed significantly better than the conventional NTCP model, suggesting the benefit of using 3D-input over the conventional discrete dose parameters. Attention maps highlighted relevant regions linked to dysphagia, supporting the utility of DL for improved predictions.</div></div>","PeriodicalId":21041,"journal":{"name":"Radiotherapy and Oncology","volume":"213 ","pages":"Article 111169"},"PeriodicalIF":5.3000,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep learning NTCP model for late dysphagia after radiotherapy for head and neck cancer patients based on 3D dose, CT and segmentations\",\"authors\":\"S.P.M. de Vette , H. Neh , L. van der Hoek , D.C. MacRae , H. Chu , A. Gawryszuk , R.J.H.M. Steenbakkers , P.M.A. van Ooijen , C.D. Fuller , K.A. Hutcheson , J.A. Langendijk , N.M. Sijtsema , L.V. van Dijk\",\"doi\":\"10.1016/j.radonc.2025.111169\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background & purpose</h3><div>Late radiation-associated dysphagia after head and neck cancer (HNC) significantly impacts patient’s health and quality of life. Conventional normal tissue complication probability (NTCP) models use discrete dose parameters to predict toxicity risk but fail to fully capture the complexity of this side effect. Deep learning (DL) offers potential improvements by incorporating 3D dose data for all anatomical structures involved in swallowing. This study aims to enhance dysphagia prediction with 3D DL NTCP models compared to conventional NTCP models.</div></div><div><h3>Materials & methods</h3><div>A multi-institutional cohort of 1484 HNC patients was used to train and validate a 3D DL model (Residual Network) incorporating 3D dose distributions, organ-at-risk segmentations, and CT scans, with or without patient- or treatment-related data. Predictions of grade ≥ 2 dysphagia (CTCAEv4) at six months post-treatment were evaluated using area under the curve (AUC) and calibration curves. Results were compared to a conventional NTCP model based on pre-treatment dysphagia, tumour location, and mean dose to swallowing organs. Attention maps highlighting regions of interest for individual patients were assessed.</div></div><div><h3>Results</h3><div>DL models outperformed the conventional NTCP model in both the independent test set (AUC = 0.80–0.84 versus 0.76) and external test set (AUC = 0.73–0.74 versus 0.63) in AUC and calibration. Attention maps showed a focus on the oral cavity and superior pharyngeal constrictor muscle.</div></div><div><h3>Conclusion</h3><div>DL NTCP models performed significantly better than the conventional NTCP model, suggesting the benefit of using 3D-input over the conventional discrete dose parameters. Attention maps highlighted relevant regions linked to dysphagia, supporting the utility of DL for improved predictions.</div></div>\",\"PeriodicalId\":21041,\"journal\":{\"name\":\"Radiotherapy and Oncology\",\"volume\":\"213 \",\"pages\":\"Article 111169\"},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2025-09-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Radiotherapy and Oncology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0167814025051734\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Radiotherapy and Oncology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167814025051734","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
背景与目的:头颈癌(HNC)后晚期放射相关吞咽困难显著影响患者的健康和生活质量。传统的正常组织并发症概率(NTCP)模型使用离散剂量参数来预测毒性风险,但不能完全捕获这种副作用的复杂性。深度学习(DL)通过整合与吞咽有关的所有解剖结构的3D剂量数据,提供了潜在的改进。与传统的NTCP模型相比,本研究旨在增强3D DL NTCP模型对吞咽困难的预测。材料和方法:采用1484例HNC患者的多机构队列来训练和验证3D DL模型(残差网络),该模型包含3D剂量分布、器官危险分割和CT扫描,有或没有患者或治疗相关数据。使用曲线下面积(AUC)和校准曲线评估治疗后6个月对 ≥ 2级吞咽困难(CTCAEv4)的预测。结果与基于治疗前吞咽困难、肿瘤位置和对吞咽器官的平均剂量的传统NTCP模型进行比较。对个别患者感兴趣区域的注意图进行了评估。结果:DL模型在独立测试集(AUC = 0.80-0.84 vs 0.76)和外部测试集(AUC = 0.73-0.74 vs 0.63)的AUC和校准上都优于传统的NTCP模型。注意图显示口腔和咽上缩肌为焦点。结论:DL NTCP模型的表现明显优于传统的NTCP模型,表明使用3d输入优于传统的离散剂量参数。注意图突出了与吞咽困难相关的相关区域,支持深度学习改进预测的效用。
Deep learning NTCP model for late dysphagia after radiotherapy for head and neck cancer patients based on 3D dose, CT and segmentations
Background & purpose
Late radiation-associated dysphagia after head and neck cancer (HNC) significantly impacts patient’s health and quality of life. Conventional normal tissue complication probability (NTCP) models use discrete dose parameters to predict toxicity risk but fail to fully capture the complexity of this side effect. Deep learning (DL) offers potential improvements by incorporating 3D dose data for all anatomical structures involved in swallowing. This study aims to enhance dysphagia prediction with 3D DL NTCP models compared to conventional NTCP models.
Materials & methods
A multi-institutional cohort of 1484 HNC patients was used to train and validate a 3D DL model (Residual Network) incorporating 3D dose distributions, organ-at-risk segmentations, and CT scans, with or without patient- or treatment-related data. Predictions of grade ≥ 2 dysphagia (CTCAEv4) at six months post-treatment were evaluated using area under the curve (AUC) and calibration curves. Results were compared to a conventional NTCP model based on pre-treatment dysphagia, tumour location, and mean dose to swallowing organs. Attention maps highlighting regions of interest for individual patients were assessed.
Results
DL models outperformed the conventional NTCP model in both the independent test set (AUC = 0.80–0.84 versus 0.76) and external test set (AUC = 0.73–0.74 versus 0.63) in AUC and calibration. Attention maps showed a focus on the oral cavity and superior pharyngeal constrictor muscle.
Conclusion
DL NTCP models performed significantly better than the conventional NTCP model, suggesting the benefit of using 3D-input over the conventional discrete dose parameters. Attention maps highlighted relevant regions linked to dysphagia, supporting the utility of DL for improved predictions.
期刊介绍:
Radiotherapy and Oncology publishes papers describing original research as well as review articles. It covers areas of interest relating to radiation oncology. This includes: clinical radiotherapy, combined modality treatment, translational studies, epidemiological outcomes, imaging, dosimetry, and radiation therapy planning, experimental work in radiobiology, chemobiology, hyperthermia and tumour biology, as well as data science in radiation oncology and physics aspects relevant to oncology.Papers on more general aspects of interest to the radiation oncologist including chemotherapy, surgery and immunology are also published.