Prediction of Radiation Treatment Response for Locally Advanced Rectal Cancer via a Longitudinal Trend Analysis Framework on Cone-Beam CT.
Prediction of Radiation Treatment Response for Locally Advanced Rectal Cancer via a Longitudinal Trend Analysis Framework on Cone-Beam CT.
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DOI：
10.3390/cancers15215142
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发表时间：
2023-10-25
期刊：
影响因子：
5.2
通讯作者：
Qi, X. Sharon
中科院分区：
文献类型：
--
作者：
Li, Zirong;Raldow, Ann C.;Weidhaas, Joanne B.;Zhou, Qichao;Qi, X. Sharon
关键词：
局部晚期直肠癌 (LARC) 通常采用新辅助放化疗 (CRT) 和全直肠系膜切除术 (TME) 进行治疗。由于患者间和患者内部放射敏感性的差异，患者对 CRT 的反应不同。 15-27%的患者对CRT完全缓解并达到病理完全缓解（pCR），而很大一部分患者只有部分缓解或不良缓解。 TME 是一种高度侵入性的手术，可能会导致过度治疗，导致获得 pCR 的 LARC 患者发病甚至死亡。这项工作引入了一个综合框架，通过在治疗过程中对纵向锥形束计算机断层扫描 (CBCT) 进行系统分析，为临床决策提供信息。反应预测模型的有效性和稳健性基于从治疗前和治疗期间图像的目标体积衍生的定量成像特征，并在 LARC 患者的回顾性队列中进行了个性化治疗的验证。局部晚期直肠癌 (LARC) 在治疗管理方面提出了重大挑战，特别是在识别可能对个体化放射治疗 (RT) 产生反应的患者方面。由于患者之间和患者内部放射敏感性的差异，患者对相同放射治疗疗程的反应不同。室内体积锥形束计算机断层扫描 (CBCT) 广泛用于确保正确对准，但也使我们能够评估治疗过程中的肿瘤反应。在这项工作中，我们提出了一个纵向放射学趋势 (LRT) 框架，使用每日 CBCT 扫描进行准确和稳健的治疗反应评估，以早期检测患者反应。 LRT框架由四个模块组成：（1）CBCT扫描到规划CT的自动配准和评估； (2)特征提取和归一化； (3)纵向趋势分析； (4)特征缩减和模型创建。该框架的有效性通过留一法交叉验证 (LOOCV) 进行了验证，对 LARC 患者回顾性队列使用了总共 840 次 CBCT 扫描。趋势模型显示了响应组与无响应组之间的显着差异，曲线下面积 (AUC) 为 0.98，这允许在 RT 治疗过程中系统监测和早期预测患者反应，以实现潜在的适应性管理。
Locally advanced rectal cancer (LARC) is commonly treated with neoadjuvant chemoradiation (CRT) followed by total mesorectal excision (TME). Patients respond to the CRT differently due to inter- and intra- patient variability in radiosensitivity. 15–27% of patients completely respond to the CRT and achieve pathologic complete response (pCR), while a large proportion of patients have a partial or poor response. TME is a highly invasive procedure, which can cause overtreatment, leading to morbidity and even mortality for the LARC patients who had a pCR. This work introduces an integrated framework to inform clinical decision making through systematical analysis of longitudinal Cone Beam Computed Tomography (CBCT) during a treatment course. The effectiveness and robustness of the response prediction model, based on quantitative imaging features derived from targeted volume from pre- and during-treatment images, were validated on a retrospective cohort of LARC patients towards personalized treatment. 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.
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影响因子：
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DOI：
10.1016/j.ijrobp.2021.01.042
发表时间：
2021-07-01
期刊：
International journal of radiation oncology, biology, physics
影响因子：
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