开发和验证一种放射组学时空模型，以预测接受新辅助治疗的直肠癌患者的病理完全缓解：基于机器学习的人工智能模型研究。

Develop and validate a radiomics space-time model to predict the pathological complete response in patients undergoing neoadjuvant treatment of rectal cancer: an artificial intelligence model study based on machine learning.

机构信息

Jinzhou medical university, Jinzhou, Liaoning Province, China.

Department of Radiology, The First Affiliated Hospital of Chongqing Medical and Pharmaceutical College, Chongqing, China.

出版信息

BMC Cancer. 2023 Apr 21;23(1):365. doi: 10.1186/s12885-023-10855-w.

DOI:10.1186/s12885-023-10855-w

PMID:37085830

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10120125/

Abstract

OBJECTIVE

In this study, we aimed to investigate the predictive efficacy of magnetic resonance imaging (MRI) radiomics features at different time points of neoadjuvant therapy for rectal cancer in patients with pathological complete response (pCR). Furthermore, we aimed to develop and validate a radiomics space-time model (RSTM) using machine learning for artificial intelligence interventions in predicting pCR in patients.

METHODS

Clinical and imaging data of 83 rectal cancer patients were retrospectively analyzed, and the patients were classified as pCR and non-pCR patients according to their postoperative pathological results. All patients received one MRI examination before and after neoadjuvant therapy to extract radiomics features, including pre-treatment, post-treatment, and delta features. Delta features were defined by the ratio of the difference between the pre- and the post-treatment features to the pre-treatment feature. After feature dimensionality reduction based on the above three feature types, the RSTM was constructed using machine learning methods, and its performance was evaluated using the area under the curve (AUC).

RESULTS

The AUC values of the individual basic models constructed by pre-treatment, post-treatment, and delta features were 0.771, 0.681, and 0.871, respectively. Their sensitivity values were 0.727, 0.864, and 0.909, respectively, and their specificity values were 0.803, 0.492, and 0.656, respectively. The AUC, sensitivity, and specificity values of the combined basic model constructed by combining pre-treatment, post-treatment, and delta features were 0.901, 0.909, and 0.803, respectively. The AUC, sensitivity, and specificity values of the RSTM constructed using the K-Nearest Neighbor (KNN) classifier on the basis of the combined basic model were 0.944, 0.871, and 0.983, respectively. The Delong test showed that the performance of RSTM was significantly different from that of pre-treatment, post-treatment, and delta models (P < 0.05) but not significantly different from the combined basic model of the three (P > 0.05).

CONCLUSIONS

The RSTM constructed using the KNN classifier based on the combined features of before and after neoadjuvant therapy and delta features had the best predictive efficacy for pCR of neoadjuvant therapy. It may emerge as a new clinical tool to assist with individualized management of rectal cancer patients.

摘要

目的

本研究旨在探讨磁共振成像（MRI）放射组学特征在接受新辅助治疗的直肠癌患者病理完全缓解（pCR）中的预测效能。此外，我们旨在开发和验证一种基于机器学习的放射组学时空模型（RSTM），用于人工智能干预预测患者的 pCR。

方法

回顾性分析 83 例直肠癌患者的临床和影像学资料，根据术后病理结果将患者分为 pCR 组和非 pCR 组。所有患者均在新辅助治疗前后接受一次 MRI 检查，以提取放射组学特征，包括治疗前、治疗后和差值特征。差值特征定义为治疗前后特征与治疗前特征的比值。在基于上述三种特征类型进行特征降维后，使用机器学习方法构建 RSTM，并使用曲线下面积（AUC）评估其性能。

结果

基于治疗前、治疗后和差值特征分别构建的个体基本模型的 AUC 值分别为 0.771、0.681 和 0.871，其敏感性值分别为 0.727、0.864 和 0.909，特异性值分别为 0.803、0.492 和 0.656。基于治疗前、治疗后和差值特征联合构建的组合基本模型的 AUC 值、敏感性值和特异性值分别为 0.901、0.909 和 0.803。基于组合基本模型，使用 K 最近邻（KNN）分类器构建的 RSTM 的 AUC 值、敏感性值和特异性值分别为 0.944、0.871 和 0.983。Delong 检验显示，RSTM 的性能与治疗前、治疗后和差值模型有显著差异（P<0.05），但与三种基本模型的联合模型无显著差异（P>0.05）。