文章：

基于支持向量机的放射组学特征预测局部晚期直肠癌新辅助放化疗后病理完全缓解

Radiomics Signature Based on Support Vector Machines for the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

原文发布日期：25 October 2023

DOI: 10.3390/cancers15215134

类型: Article

开放获取: 是

英文摘要：

The objective of this study was to evaluate the discriminative capabilities of radiomics signatures derived from three distinct machine learning algorithms and to identify a robust radiomics signature capable of predicting pathological complete response (pCR) after neoadjuvant chemoradiotherapy in patients diagnosed with locally advanced rectal cancer (LARC). In a retrospective study, 211 LARC patients were consecutively enrolled and divided into a training cohort (n= 148) and a validation cohort (n= 63). From pretreatment contrast-enhanced planning CT images, a total of 851 radiomics features were extracted. Feature selection and radiomics score (Radscore) construction were performed using three different machine learning methods: least absolute shrinkage and selection operator (LASSO), random forest (RF) and support vector machine (SVM). The SVM-derived Radscore demonstrated a strong correlation with the pCR status, yielding area under the receiver operating characteristic curves (AUCs) of 0.880 and 0.830 in the training and validation cohorts, respectively, outperforming the RF and LASSO methods. Based on this, a nomogram was developed by combining the SVM-based Radscore with clinical indicators to predict pCR after neoadjuvant chemoradiotherapy. The nomogram exhibited superior predictive power, achieving AUCs of 0.910 and 0.866 in the training and validation cohorts, respectively. Calibration curves and decision curve analyses confirmed its appropriateness. The SVM-based Radscore demonstrated promising performance in predicting pCR for LARC patients. The machine learning-driven nomogram, which integrates the Radscore and clinical indicators, represents a valuable tool for predicting pCR in LARC patients.

摘要翻译： 

本研究旨在评估基于三种不同机器学习算法构建的影像组学特征的判别能力，并确定一个能够预测局部进展期直肠癌患者新辅助放化疗后病理完全缓解的稳健影像组学特征。在一项回顾性研究中，连续纳入211例局部进展期直肠癌患者，并将其分为训练队列（148例）和验证队列（63例）。从治疗前增强计划CT图像中提取了851个影像组学特征。采用三种不同的机器学习方法进行特征筛选和影像组学评分构建：最小绝对收缩与选择算子、随机森林以及支持向量机。基于支持向量机构建的影像组学评分与病理完全缓解状态呈强相关性，在训练队列和验证队列中受试者工作特征曲线下面积分别为0.880和0.830，其性能优于随机森林和最小绝对收缩与选择算子方法。在此基础上，结合基于支持向量机的影像组学评分与临床指标构建了列线图，用于预测新辅助放化疗后的病理完全缓解。该列线图展现出优异的预测能力，在训练队列和验证队列中的曲线下面积分别达到0.910和0.866。校准曲线和决策曲线分析证实了其适用性。基于支持向量机的影像组学评分在预测局部进展期直肠癌患者病理完全缓解方面表现出良好性能。整合影像组学评分与临床指标的机器学习驱动列线图，为预测局部进展期直肠癌患者病理完全缓解提供了有价值的工具。

原文链接：

Radiomics Signature Based on Support Vector Machines for the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer