Wang Mingxiao, Liu Guoli, Zhang Nan, Li Yanhua, Sun Shuo, Tan Yahong, Ma Lin
Medical School of Chinese PLA, No.28 Fuxing Road, Haidian District, Beijing, 100853, China.
Department of Radiology, First Medical Center, Chinese PLA General Hospital, No.28 Fuxing Road, Haidian District, Beijing, 100853, China.
Neuroradiology. 2025 Mar;67(3):563-573. doi: 10.1007/s00234-025-03551-y. Epub 2025 Jan 24.
In primary central nervous system lymphoma (PCNSL), B-cell lymphoma-6 (BCL-6) is an unfavorable prognostic biomarker. We aim to non-invasively detect BCL-6 overexpression in PCNSL patients using multiparametric MRI and machine learning techniques.
65 patients (101 lesions) with primary central nervous system lymphoma (PCNSL) diagnosed from January 2013 to July 2023, and all patients were randomly divided into a training set and a validation set according to a ratio of 8 to 2. ADC map derived from DWI (b = 0/1000 s/mm2), fast spin echo T2WI, T2FLAIR, were collected at 3.0 T. A total of 2234 radiomics features from the tumor segmentation area were extracted and LASSO were used to select features. Logistic regression (LR), Naive bayes (NB), Support vector machine (SVM), K-nearest Neighbor, (KNN) and Multilayer Perceptron (MLP), were used for machine learning, and sensitivity, specificity, accuracy F1-score, and area under the curve (AUC) was used to evaluate the detection performance of five classifiers, 6 groups with combinations of different sequences were fitted to 5 classifiers, and optimal classifier was obtained.
BCL-6 status could be identified to varying degrees with 30 models based on radiomics, and model performance could be improved by combining different sequences and classifiers. Support vector machine (SVM) combined with three sequence group had the largest AUC (0.95) in training set and satisfactory AUC (0.87) in validation set.
Multiparametric MRI based machine learning is promising in detecting BCL-6 overexpression.
在原发性中枢神经系统淋巴瘤（PCNSL）中，B细胞淋巴瘤-6（BCL-6）是一种不良预后生物标志物。我们旨在使用多参数MRI和机器学习技术对PCNSL患者进行BCL-6过表达的无创检测。
2013年1月至2023年7月诊断的65例原发性中枢神经系统淋巴瘤（PCNSL）患者（101个病灶），所有患者按8:2的比例随机分为训练集和验证集。在3.0T条件下采集源自扩散加权成像（DWI，b=0/1000s/mm²）的表观扩散系数（ADC）图、快速自旋回波T2加权成像（T2WI）、液体衰减反转恢复序列（T2FLAIR）。从肿瘤分割区域提取总共2234个放射组学特征，并使用套索回归（LASSO）进行特征选择。采用逻辑回归（LR）、朴素贝叶斯（NB）、支持向量机（SVM）、K近邻（KNN）和多层感知器（MLP）进行机器学习，使用灵敏度、特异性、准确率、F1分数和曲线下面积（AUC）评估五个分类器的检测性能，将6组不同序列组合拟合到5个分类器中，获得最优分类器。
基于放射组学的30个模型能够不同程度地识别BCL-6状态，通过组合不同序列和分类器可提高模型性能。支持向量机（SVM）与三个序列组组合在训练集中AUC最大（0.95），在验证集中AUC也令人满意（0.87）。
基于多参数MRI的机器学习在检测BCL-6过表达方面具有前景。
