Nie Tingting, Yuan Zilong, He Yaoyao, Xu Haibo, Guo Xiaofang, Liu Yulin
Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China.
Technol Cancer Res Treat. 2024 Jan-Dec;23:15330338241305463. doi: 10.1177/15330338241305463.
Since the response of patients with rectal cancer (RC) to neoadjuvant therapy is highly variable, there is an urgent need to develop accurate methods to predict the post-treatment T (pT) stage. The purpose of this study was to evaluate the utility of multi-parametric MRI radiomics models and identify the most accurate machine learning (ML) algorithms for predicting pT stage of RC.
This retrospective study analyzed pretreatment clinical features of 171 RC patients who underwent 3 T MRI prior to neoadjuvant therapy and subsequent total mesorectal excision. Tumors were manually drawn as regions of interest (ROI) layer by layer on high-resolution T2-weighted image (T2WI) and contrast-enhanced T1-weighted image (CE-T1WI) using ITK-SNAP software. The most relevant features of pT stage from CE-T1WI, T2WI, and fusion features (combination of clinical features, CE-T1WI, and T2WI radiomics features) were extracted by the Least Absolute Shrinkage and Selection Operator method. Clinical, CE-T1WI radiomics, T2WI radiomics, and fusion models were established by ML multiple classifiers.
In the clinical model, the LightGBM algorithm demonstrated the highest efficiency, with AUC values of 0.857 and 0.702 for the training and test cohorts, respectively. For the T2WI and CE-T1WI models, the SVM algorithm was the most efficient; AUC = 0.969 and 0.868 in the training cohort, and 0.839 and 0.760 in the test cohort, respectively. The fusion model yielded the highest predictive performance using the LR algorithm; AUC = 0.967 and 0.932 in the training and test cohorts, respectively.
Radiomics features extracted from CE-T1WI and T2WI images and clinical features were effective predictors of pT stage in patients with rectal cancer who underwent neoadjuvant therapy. ML-based multi-parameter MRI radiomics model incorporating relevant clinical features can improve the pT stage prediction accuracy of RC.
由于直肠癌（RC）患者对新辅助治疗的反应高度可变，因此迫切需要开发准确的方法来预测治疗后的T（pT）分期。本研究的目的是评估多参数MRI放射组学模型的效用，并确定用于预测RC患者pT分期的最准确机器学习（ML）算法。
本回顾性研究分析了171例RC患者在新辅助治疗前接受3T MRI检查及随后全直肠系膜切除的治疗前临床特征。使用ITK-SNAP软件在高分辨率T2加权图像（T2WI）和对比增强T1加权图像（CE-T1WI）上逐层手动绘制肿瘤作为感兴趣区域（ROI）。通过最小绝对收缩和选择算子方法从CE-T1WI、T2WI和融合特征（临床特征、CE-T1WI和T2WI放射组学特征的组合）中提取与pT分期最相关的特征。通过ML多分类器建立临床、CE-T1WI放射组学、T2WI放射组学和融合模型。
在临床模型中，LightGBM算法效率最高，训练队列和测试队列的AUC值分别为0.857和0.702。对于T2WI和CE-T1WI模型，SVM算法效率最高；训练队列中的AUC分别为0.969和0.868，测试队列中的AUC分别为0.839和0.760。融合模型使用LR算法产生了最高的预测性能；训练队列和测试队列中的AUC分别为0.967和0.932。
从CE-T1WI和T2WI图像中提取的放射组学特征以及临床特征是接受新辅助治疗的直肠癌患者pT分期的有效预测指标。结合相关临床特征的基于ML的多参数MRI放射组学模型可以提高RC患者pT分期的预测准确性。
