Stacking Ensemble Learning–Based [ 18 F]FDG PET Radiomics for Outcome Prediction in Diffuse Large B-Cell Lymphoma

作者：Shuilin Zhao, Jing Wang, Chentao Jin, Xiang Zhang, Chenxi Xue, Rui Zhou, Yan Zhong, Yuwei Liu, Xuexin He, Youyou Zhou, Caiyun Xu, Lixia Zhang, Wenbin Qian, Hong Zhang, Xiaohui Zhang, Mei Tian · 发表于：Journal of Nuclear Medicine · 年份：2023 · DOI：10.2967/jnumed.122.265244 · 被引用次数：39 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Lymphoma Diagnosis and Treatment、Medical Imaging Techniques and Applications

This study aimed to develop an analytic approach based on [ 18 F]FDG PET radiomics using stacking ensemble learning to improve the outcome prediction in diffuse large B-cell lymphoma (DLBCL). Methods: In total, 240 DLBCL patients from 2 medical centers were divided into the training set ( n = 141), internal testing set ( n = 61), and external testing set ( n = 38). Radiomics features were extracted from pretreatment [ 18 F]FDG PET scans at the patient level using 4 semiautomatic segmentation methods (SUV threshold of 2.5, SUV threshold of 4.0 [SUV4.0], 41% of SUV max , and SUV threshold of mean liver uptake [PERCIST]). All extracted features were harmonized with the ComBat method. The intraclass correlation coefficient was used to evaluate the reliability of radiomics features extracted by different segmentation methods. Features from the most reliable segmentation method were selected by Pearson correlation coefficient analysis and the LASSO (least absolute shrinkage and selection operator) algorithm. A stacking ensemble learning approach was applied to build radiomics-only and combined clinical–radiomics models for prediction of 2-y progression-free survival and overall survival based on 4 machine learning classifiers (support vector machine, random forests, gradient boosting decision tree, and adaptive boosting). Confusion matrix, receiver-operating-characteristic curve analysis, and survival analysis were used to evaluate the model performance. Results: Among 4 sem...