文章：

通过基线及适应性模拟计算机断层扫描预测咽癌患者放疗结果的深度学习模型

Predicting Radiotherapy Outcomes with Deep Learning Models Through Baseline and Adaptive Simulation Computed Tomography in Patients with Pharyngeal Cancer

原文发布日期：30 October 2025

DOI: 10.3390/cancers17213492

类型: Article

开放获取: 是

英文摘要：

Background/Objectives: The implementation of adaptive radiation therapy (ART) is increasingly becoming widely available in the clinical practice of radiotherapy (RT). For patients with pharyngeal cancer receiving RT, this study aimed to develop a deep learning (DL) model by merging baseline and ART simulation computed tomography (CT) images to predict treatment outcomes.Methods: Clinical and imaging data from 162 patients of newly diagnosed oropharyngeal or hypopharyngeal cancer were analyzed. All completed definitive treatment and their baseline and ART non-contrast simulation CTs were utilized for training. After augmentation of the CT images, a deep contrastive learning model was employed to predict the occurrence of local recurrence (LR), neck lymph node relapse (NR), and distant metastases (DM). Receiver operating characteristic curve analysis was conducted to evaluate the model’s performance.Results: Over a median follow-up period of 34 months, 53 (32.7%), 36 (22.2%), and 23 (14.0%) patients developed LR, NR, and DM, respectively. Following the integration of prediction results from baseline and ART simulation CTs, the area under the curve for predicting the occurrence of LR, NR, and DM reached 0.773, 0.747, and 0.793. At the same time, the accuracy for the three endpoints was 72.4%, 74.7%, and 75.7%, respectively.Conclusions: For patients with pharyngeal cancer ready to receive RT-based treatment, our proposed models can predict the development of LR, NR, or DM through baseline and ART simulation CTs. External validation needs to be conducted to confirm the model’s performance.

摘要翻译： 

背景/目的：自适应放射治疗（ART）的实施在放射治疗（RT）临床实践中日益普及。本研究旨在针对接受RT的咽癌患者，通过融合基线及ART模拟计算机断层扫描（CT）图像开发深度学习（DL）模型，以预测治疗结局。方法：研究分析了162例新诊断口咽癌或下咽癌患者的临床及影像数据。所有患者均完成根治性治疗，其基线及ART非增强模拟CT图像被用于模型训练。对CT图像进行数据增强后，采用深度对比学习模型预测局部复发（LR）、颈部淋巴结复发（NR）及远处转移（DM）的发生。通过受试者工作特征曲线分析评估模型性能。结果：在中位34个月的随访期内，分别有53例（32.7%）、36例（22.2%）和23例（14.0%）患者发生LR、NR和DM。整合基线及ART模拟CT的预测结果后，模型预测LR、NR和DM发生的曲线下面积分别达到0.773、0.747和0.793，对三个终点的预测准确率分别为72.4%、74.7%和75.7%。结论：对于拟接受RT治疗的咽癌患者，本研究提出的模型可通过基线及ART模拟CT预测LR、NR或DM的发生。需开展外部验证以确认模型性能。

原文链接：

Predicting Radiotherapy Outcomes with Deep Learning Models Through Baseline and Adaptive Simulation Computed Tomography in Patients with Pharyngeal Cancer