A Machine Learning Model for Predicting a Major Response to Neoadjuvant Chemotherapy in Advanced Gastric Cancer.
A Machine Learning Model for Predicting a Major Response to Neoadjuvant Chemotherapy in Advanced Gastric Cancer.
复制标题
预测进展期胃癌新辅助化疗主要反应的机器学习模型。
DOI：
10.3389/fonc.2021.675458
复制
发表时间：
2021
影响因子：
4.7
通讯作者：
Peng J
中科院分区：
文献类型：
--
作者：
Chen Y;Wei K;Liu D;Xiang J;Wang G;Meng X;Peng J
建立并验证基于机器学习算法的进展期胃癌（AGC）新辅助化疗（NAC）主要病理反应预测模型。本研究共入组了221例在2013年2月至2020年9月期间接受NAC和根治性胃切除术的患者。共有144名患者被分配到训练队列进行模型构建，77名患者被分配到验证队列。主要病理学缓解定义为原发性肿瘤消退至ypT 0或T1。通过机器学习算法选择从静脉相计算机断层扫描（CT）图像中提取的放射组学特征，以计算放射评分。与单因素分析选择的其他临床变量一起，将radscore纳入二元logistic回归分析以构建综合预测模型。验证队列获得的数据用于测试模型的预测准确性。共有27.6%（61/221）的患者实现了重大病理学缓解。从572个辐射组学特征中选取5个特征计算辐射评分。最终建立的模型包括腺癌分化和radscores。该模型显示出令人满意的预测准确度，C指数为0.763，验证数据与校准曲线中的模型拟合良好。结合腺癌分化和radscores的预测模型的开发和验证。该模型有助于根据患者对NAC的潜在敏感性对患者进行分层，并可作为AGC患者的个体化治疗策略制定工具。
To develop and validate a model for predicting major pathological response to neoadjuvant chemotherapy (NAC) in advanced gastric cancer (AGC) based on a machine learning algorithm. A total of 221 patients who underwent NAC and radical gastrectomy between February 2013 and September 2020 were enrolled in this study. A total of 144 patients were assigned to the training cohort for model building, and 77 patients were assigned to the validation cohort. A major pathological response was defined as primary tumor regressing to ypT0 or T1. Radiomic features extracted from venous-phase computed tomography (CT) images were selected by machine learning algorithms to calculate a radscore. Together with other clinical variables selected by univariate analysis, the radscores were included in a binary logistic regression analysis to construct an integrated prediction model. The data obtained for the validation cohort were used to test the predictive accuracy of the model. A total of 27.6% (61/221) patients achieved a major pathological response. Five features of 572 radiomic features were selected to calculate the radscores. The final established model incorporates adenocarcinoma differentiation and radscores. The model showed satisfactory predictive accuracy with a C-index of 0.763 and good fitting between the validation data and the model in the calibration curve. A prediction model incorporating adenocarcinoma differentiation and radscores was developed and validated. The model helps stratify patients according to their potential sensitivity to NAC and could serve as an individualized treatment strategy-making tool for AGC patients.
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DOI：
10.1016/s1470-2045(13)70334-6
发表时间：
2014-01
期刊：
The Lancet. Oncology
影响因子：
--
作者：
Hellmann MD;Chaft JE;William WN Jr;Rusch V;Pisters KM;Kalhor N;Pataer A;Travis WD;Swisher SG;Kris MG;University of Texas MD Anderson Lung Cancer Collaborative Group
通讯作者：
University of Texas MD Anderson Lung Cancer Collaborative Group
影响因子：
3.3
作者：
Xu, Fei;Ma, Xiaohong;Zhao, Xinming
通讯作者：
Zhao, Xinming
影响因子：
8.4
作者：
Eisenhauer, E. A.;Therasse, P.;Verweij, J.
通讯作者：
Verweij, J.
影响因子：
11.2
作者：
van Griethuysen JJM;Fedorov A;Parmar C;Hosny A;Aucoin N;Narayan V;Beets-Tan RGH;Fillion-Robin JC;Pieper S;Aerts HJWL
通讯作者：
Aerts HJWL
CT纹理分析在预测胃癌组织病理学特征中的应用
影响因子：
5.9
作者：
Liu, Shunli;Liu, Song;Zhou, Zhengyang
通讯作者：
Zhou, Zhengyang