Early Follicular Lymphoma Grading via PET-CT Fusion and Bayesian Deep Learning.

Authors

Chunjun Qian, Lulu He, Qiuhui Jiang, Hang Zhou, Zekun Jiang, Yue Teng, Chongyang Ding, Bing Xu, Xin Li, Chong Jiang

Published in

Journal of imaging informatics in medicine. Aug 04, 2026. Epub Aug 04, 2026.

Abstract

Accurate grading of follicular lymphoma (FL) is crucial for personalized treatment, but biopsy-based histopathology is invasive and limited by observer variability. To address these limits, we present an artificial intelligence framework for physician-guided assisted FL grading using PET-CT imaging. Our approach integrates an enhanced dual-discriminator conditional GAN (DDCGAN) featuring similarity and chrominance constraints to generate task-specific fused images with preserved metabolic-structural cues. Furthermore, a Bayesian ResNet is introduced to explicitly model predictive uncertainty, effectively resolving classification ambiguity between adjacent FL Grades I and II. Rigorous evaluation on a multi-center dataset of 837 patients, including FL and diffuse large B-cell lymphoma (DLBCL), proves that our framework delivers superior generalizability. It achieves an accuracy of 0.871, precision of 0.875, and macro-F1 of 0.816, outperforming single-modality and existing state-of-the-art fusion models. Ultimately, this task-oriented image fusion and uncertainty-aware framework offers a highly practical, non-invasive decision-support tool to support scalable clinical decision-making in hospital workflows.

PMID:
42552270
Bibliographic data and abstract were imported from PubMed on 05 Aug 2026.

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