MRI-based radiomics and deep learning model construction:non-invasive differentiation of molecular subtypes in primary intracranial diffuse large B-cell lymphoma
10.19401/j.cnki.1007-3639.2025.08.001
- VernacularTitle:基于MRI的影像组学和深度学习模型构建:无创鉴别原发颅内弥漫大B细胞淋巴瘤分子亚型
- Author:
Yanwei ZENG
1
;
Zhijian XU
;
Xin CAO
;
Kun LÜ
;
Huiming LI
;
Min GAO
;
Shenghong JU
;
Jun LIU
;
Daoying GENG
Author Information
1. 复旦大学附属华山医院放射科,上海 200040;上海脑重大疾病智能影像工程技术研究中心,上海 200040;复旦大学医学功能与分子影像研究所,上海 200040
- Publication Type:Journal Article
- Keywords:
Diffuse large B-cell lymphoma;
Germinal center B-cell-like;
Non-GCB;
Radiomics;
Deep learning
- From:
China Oncology
2025;35(8):735-742
- CountryChina
- Language:Chinese
-
Abstract:
Background and purpose:Diffuse large B-cell lymphoma(DLBCL)is subclassified into germinal center B-cell-like(GCB)and non-GCB subtypes,which differ in prognosis and treatment response.However,current distinction still relies on invasive pathological assays.This study developed radiomics and deep-learning models based on multiparametric magnetic resonance imaging(MRI)to non-invasively differentiate the two subtypes preoperatively,thereby reducing dependence on histopathological examination.Methods:This study retrospectively included patients with pathologically confirmed DLBCL diagnosed at Huashan Hospital,Fudan University,and other institutions between March 2013 and December 2024.Using multiparametric MRI data,we developed DLBCL-subtype classification models that combined 4 radiomics-based machine-learning algorithms:support vector machine(SVM),logistic regression(LR),Gaussian process(GP)and Naive Bayes(NB),with 3 deep-learning architectures[densely-connected convolutional networks 121(DenseNet121),residual network 101(ResNet101)and EfficientNet-b5].Additionally,two radiologists with different experience levels independently classified DLBCL on MRI in a blinded fashion.Model and radiologist performance were quantified using the area under the receiver operating characteristic curve(AUC),accuracy(ACC),and F1-score to evaluate their ability to distinguish GCB from non-GCB subtypes.This study was approved by the Ethics Committee of Huashan Hospital of Fudan University(No.KY2024-663),and all patients signed informed consents.Results:A total of 173 patients were enrolled(55 with GCB subtype and 118 with non-GCB subtype).Radiomics and deep learning methods effectively distinguished DLBCL subtypes.Among these,the GP radiomics model(based on T1-CE+T2-FLAIR+ADC sequences)and DenseNet121 deep learning model(based on T1-CE+T2-FLAIR+ADC sequences)demonstrated optimal performance.Both achieved excellent results on the internal validation set(GP:AUC=0.900,ACC=0.896,F1=0.840;DenseNet121:AUC=0.846,ACC=0.854,F1=0.774)and maintained robustness on the external validation set.Furthermore,the classification efficacy of the optimal AI model surpassed that of experienced radiologists(highest physician AUC=0.678).Conclusion:Radiomics and deep-learning models based on multiparametric MRI features can effectively differentiate GCB from non-GCB subtypes of DLBCL.Among them,GP and DenseNet121 exhibit outstanding performance,especially when integrating multi-sequence feature sets for classifying DLBCL subtypes on complex imaging data.
