1. Survival prediction in diffuse large B-cell lymphoma patients: multimodal PET/CT deep features radiomic model utilizing automated machine learning.
- Author
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Chen J, Lin F, Dai Z, Chen Y, Fan Y, Li A, and Zhao C
- Subjects
- Humans, Female, Male, Middle Aged, Aged, Adult, Prognosis, Deep Learning, Retrospective Studies, Young Adult, Aged, 80 and over, Radiomics, Lymphoma, Large B-Cell, Diffuse diagnostic imaging, Lymphoma, Large B-Cell, Diffuse pathology, Lymphoma, Large B-Cell, Diffuse mortality, Positron Emission Tomography Computed Tomography methods, Machine Learning
- Abstract
Purpose: We sought to develop an effective combined model for predicting the survival of patients with diffuse large B-cell lymphoma (DLBCL) based on the multimodal PET-CT deep features radiomics signature (DFR-signature)., Methods: 369 DLBCL patients from two medical centers were included in this study. Their PET and CT images were fused to construct the multimodal PET-CT images using a deep learning fusion network. Then the deep features were extracted from those fused PET-CT images, and the DFR-signature was constructed through an Automated machine learning (AutoML) model. Combined with clinical indexes from the Cox regression analysis, we constructed a combined model to predict the progression-free survival (PFS) and the overall survival (OS) of patients. In addition, the combined model was evaluated in the concordance index (C-index) and the time-dependent area under the ROC curve (tdAUC)., Results: A total of 1000 deep features were extracted to build a DFR-signature. Besides the DFR-signature, the combined model integrating metabolic and clinical factors performed best in terms of PFS and OS. For PFS, the C-indices are 0.784 and 0.739 in the training cohort and internal validation cohort, respectively. For OS, the C-indices are 0.831 and 0.782 in the training cohort and internal validation cohort., Conclusions: DFR-signature constructed from multimodal images improved the classification accuracy of prognosis for DLBCL patients. Moreover, the constructed DFR-signature combined with NCCN-IPI exhibited excellent potential for risk stratification of DLBCL patients., (© 2024. The Author(s).)
- Published
- 2024
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