人工智能
医学
接收机工作特性
正电子发射断层摄影术
深度学习
弥漫性大B细胞淋巴瘤
计算机科学
核医学
机器学习
放射科
淋巴瘤
内科学
作者
Yuan Cheng,Qingqing Shi,Xin‐Yun Huang,Li Wang,Yang He,Biao Li,Weili Zhao,Dahong Qian
标识
DOI:10.1007/s00330-022-09031-8
摘要
The prediction of primary treatment failure (PTF) is necessary for patients with diffuse large B-cell lymphoma (DLBCL) since it serves as a prominent means for improving front-line outcomes. Using interim 18F-fluoro-2-deoxyglucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT) imaging data, we aimed to construct multimodal deep learning (MDL) models to predict possible PTF in low-risk DLBCL.Initially, 205 DLBCL patients undergoing interim [18F]FDG PET/CT scans and the front-line standard of care were included in the primary dataset for model development. Then, 44 other patients were included in the external dataset for generalization evaluation. Based on the powerful backbone of the Conv-LSTM network, we incorporated five different multimodal fusion strategies (pixel intermixing, separate channel, separate branch, quantitative weighting, and hybrid learning) to make full use of PET/CT features and built five corresponding MDL models. Moreover, we found the best model, that is, the hybrid learning model, and optimized it by integrating the contrastive training objective to further improve its prediction performance.The final model with contrastive objective optimization, named the contrastive hybrid learning model, performed best, with an accuracy of 91.22% and an area under the receiver operating characteristic curve (AUC) of 0.926, in the primary dataset. In the external dataset, its accuracy and AUC remained at 88.64% and 0.925, respectively, indicating its good generalization ability.The proposed model achieved good performance, validated the predictive value of interim PET/CT, and holds promise for directing individualized clinical treatment.• The proposed multimodal models achieved accurate prediction of primary treatment failure in DLBCL patients. • Using an appropriate feature-level fusion strategy can make the same class close to each other regardless of the modal heterogeneity of the data source domain and positively impact the prediction performance. • Deep learning validated the predictive value of interim PET/CT in a way that exceeded human capabilities.
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