Locally advanced rectal cancer (LARC) presents a significant challenge in terms of treatment management, particularly with regards to identifying patients who are likely to respond to radiation therapy (RT) at an individualized level. Patients respond to the same radiation treatment course differently due to inter- and intra-patient variability in radiosensitivity. In-room volumetric cone-beam computed tomography (CBCT) is widely used to ensure proper alignment, but also allows us to assess tumor response during the treatment course. In this work, we proposed a longitudinal radiomic trend (LRT) framework for accurate and robust treatment response assessment using daily CBCT scans for early detection of patient response. The LRT framework consists of four modules: (1) Automated registration and evaluation of CBCT scans to planning CT; (2) Feature extraction and normalization; (3) Longitudinal trending analyses; and (4) Feature reduction and model creation. The effectiveness of the framework was validated via leave-one-out cross-validation (LOOCV), using a total of 840 CBCT scans for a retrospective cohort of LARC patients. The trending model demonstrates significant differences between the responder vs. non-responder groups with an Area Under the Curve (AUC) of 0.98, which allows for systematic monitoring and early prediction of patient response during the RT treatment course for potential adaptive management.
局部进展期直肠癌的治疗管理面临重大挑战，尤其在个体化识别可能对放射治疗产生应答的患者方面。由于患者间及患者个体内的放射敏感性差异，相同放疗方案会产生不同疗效。室内锥形束计算机断层扫描虽广泛用于确保精准定位，同时也可用于评估治疗过程中的肿瘤应答情况。本研究提出一种基于每日CBCT扫描的纵向影像组学趋势分析框架，旨在实现精准稳健的治疗应答评估及早期患者应答识别。该框架包含四个模块：(1) CBCT与计划CT的自动配准与评估；(2)特征提取与标准化处理；(3)纵向趋势分析；(4)特征降维与模型构建。通过对局部进展期直肠癌患者回顾性队列的840次CBCT扫描进行留一法交叉验证，该框架有效性得到证实。趋势模型显示应答组与非应答组存在显著差异，曲线下面积达0.98，可在放疗过程中实现系统性监测与早期应答预测，为适应性治疗管理提供可能。
肿瘤(癌症)患者之家