Prediction of recurrence after surgery in colorectal cancer patients using radiomics from diagnostic contrast-enhanced computed tomography: a two-center study
Prediction of recurrence after surgery in colorectal cancer patients using radiomics from diagnostic contrast-enhanced computed tomography: a two-center study
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DOI：
10.1007/s00330-021-08104-4
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发表时间：
2021-06-25
影响因子：
5.9
通讯作者：
Visvikis, Dimitris
中科院分区：
文献类型：
--
作者：
Badic, Bogdan;Da-Ano, Ronrick;Visvikis, Dimitris
目的在双中心研究中评估通过放射组学表征的对比增强（CE）诊断CT扫描作为II期和III期结直肠癌患者复发预测因子的价值。材料与方法本研究纳入了2008年7月1日至2017年3月15日在两所不同的法国大学医院诊断为II期和III期结直肠腺癌的193例患者。为了补偿双中心数据的变异性，使用了统计协调方法Bootstrapped ComBat（B-ComBat）。使用3种不同的机器学习（ML）构建预测无病生存期（DFS）的模型：（1）基于最小绝对收缩和选择算子（LASSO）的特征选择后具有10倍交叉验证的多变量回归（MR），（2）随机森林（RF）和（3）支持向量机（SVM），两者都具有嵌入式特征选择。结果与使用原始未转换数据相比，在我们提出的B-ComBat协调后，平衡和95%灵敏度模型的性能系统性更高。通过将临床变量（术后化疗）与两个放射组学形状描述符（紧凑性和最小轴长）相结合的多变量回归模型实现了最具临床相关性的性能，其中BAcc为0.78，MCC为0.6，所需灵敏度为95%。由此产生的DFS分层具有显著性（p = 0.00021），尤其是与使用不协调的原始数据（p = 0.17）相比。结论：来自对比增强CT的放射组学模型可以在双中心队列中进行训练和验证，对II期和III期结直肠癌患者的复发具有良好的预测性能。
Objectives To assess the value of contrast-enhanced (CE) diagnostic CT scans characterized through radiomics as predictors of recurrence for patients with stage II and III colorectal cancer in a two-center context. Materials and methods This study included 193 patients diagnosed with stage II and III colorectal adenocarcinoma from 1 July 2008 to 15 March 2017 in two different French University Hospitals. To compensate for the variability in two-center data, a statistical harmonization method Bootstrapped ComBat (B-ComBat) was used. Models predicting disease-free survival (DFS) were built using 3 different machine learning (ML): (1) multivariate regression (MR) with 10-fold cross-validation after feature selection based on least absolute shrinkage and selection operator (LASSO), (2) random forest (RF), and (3) support vector machine (SVM), both with embedded feature selection. Results The performance for both balanced and 95% sensitivity models was systematically higher after our proposed B-ComBat harmonization compared to the use of the original untransformed data. The most clinically relevant performance was achieved by the multivariate regression model combining a clinical variable (postoperative chemotherapy) with two radiomics shape descriptors (compactness and least axis length) with a BAcc of 0.78 and an MCC of 0.6 associated with a required sensitivity of 95%. The resulting stratification in terms of DFS was significant (p = 0.00021), especially compared to the use of unharmonized original data (p = 0.17). Conclusions Radiomics models derived from contrast-enhanced CT could be trained and validated in a two-center cohort with a good predictive performance of recurrence in stage II et III colorectal cancer patients.