基于CT的影像组学在预测接受新辅助化疗的晚期胃癌肿瘤退缩分级中的泛化表现
CT-Based Radiomics Showing Generalization to Predict Tumor Regression Grade for Advanced Gastric Cancer Treated With Neoadjuvant Chemotherapy.
作者信息
Chen Yong, Xu Wei, Li Yan-Ling, Liu Wentao, Sah Birendra Kumar, Wang Lan, Xu Zhihan, Wels Michael, Zheng Yanan, Yan Min, Zhang Huan, Ma Qianchen, Zhu Zhenggang, Li Chen
机构信息
Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Department of General Surgery, Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
出版信息
Front Oncol. 2022 Feb 25;12:758863. doi: 10.3389/fonc.2022.758863. eCollection 2022.
OBJECTIVE
The aim of this study was to develop and validate a radiomics model to predict treatment response in patients with advanced gastric cancer (AGC) sensitive to neoadjuvant therapies and verify its generalization among different regimens, including neoadjuvant chemotherapy (NAC) and molecular targeted therapy.
MATERIALS AND METHODS
A total of 373 patients with AGC receiving neoadjuvant therapies were enrolled from five cohorts. Four cohorts of patients received different regimens of NAC, including three retrospective cohorts (training cohort and internal and external validation cohorts) and a prospective Dragon III cohort (NCT03636893). Another prospective SOXA (apatinib in combination with S-1 and oxaliplatin) cohort received neoadjuvant molecular targeted therapy (ChiCTR-OPC-16010061). All patients underwent computed tomography before treatment, and thereafter, tumor regression grade (TRG) was assessed. The primary tumor was delineated, and 2,452 radiomics features were extracted for each patient. Mutual information and random forest were used for dimensionality reduction and modeling. The performance of the radiomics model to predict TRG under different neoadjuvant therapies was evaluated.
RESULTS
There were 28 radiomics features selected. The radiomics model showed generalization to predict TRG for AGC patients across different NAC regimens, with areas under the curve (AUCs) (95% interval confidence) of 0.82 (0.760.90), 0.77 (0.630.91), 0.78 (0.660.89), and 0.72 (0.660.89) in the four cohorts, with no statistical difference observed (all p > 0.05). However, the radiomics model showed poor predictive value on the SOXA cohort [AUC, 0.50 (0.27~0.73)], which was significantly worse than that in the training cohort (p = 0.010).
CONCLUSION
Radiomics is generalizable to predict TRG for AGC patients receiving NAC treatments, which is beneficial to transform appropriate treatment, especially for those insensitive to NAC.
目的
本研究旨在开发并验证一种放射组学模型，以预测对新辅助治疗敏感的晚期胃癌（AGC）患者的治疗反应，并验证其在不同治疗方案（包括新辅助化疗（NAC）和分子靶向治疗）中的通用性。
材料与方法
从五个队列中纳入了373例接受新辅助治疗的AGC患者。四个队列的患者接受了不同方案的NAC，包括三个回顾性队列（训练队列以及内部和外部验证队列）和一个前瞻性的Dragon III队列（NCT03636893）。另一个前瞻性SOXA（阿帕替尼联合S-1和奥沙利铂）队列接受了新辅助分子靶向治疗（ChiCTR-OPC-16010061）。所有患者在治疗前均接受了计算机断层扫描，此后评估肿瘤退缩分级（TRG）。勾勒出原发肿瘤，并为每位患者提取2452个放射组学特征。使用互信息和随机森林进行降维和建模。评估了放射组学模型在不同新辅助治疗下预测TRG的性能。
结果
共选择了28个放射组学特征。放射组学模型显示出可预测不同NAC方案下AGC患者TRG的通用性，四个队列中的曲线下面积（AUC）（95%区间置信度）分别为0.82（0.760.90）、0.77（0.630.91）、0.78（0.660.89）和0.72（0.660.89），未观察到统计学差异（所有p>0.05）。然而，放射组学模型在SOXA队列中的预测价值较差[AUC，0.50（0.27~0.73）]，显著低于训练队列（p = 0.010）。
结论
放射组学可用于预测接受NAC治疗的AGC患者的TRG，这有助于转变合适的治疗方案，尤其是对于那些对NAC不敏感的患者。
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