Early Follicular Lymphoma Grading via PET-CT Fusion and Bayesian Deep Learning.
Authors
Affiliations (9)
Affiliations (9)
- Hertfordshire College, Changzhou Institute of Technology, Changzhou, 213032, Jiangsu, China.
- Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361003, Fujian, China.
- Department of Nuclear Medicine, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, China.
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, Jiangsu, China. [email protected].
- Department of Nuclear Medicine, First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210028, Jiangsu, China.
- Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361003, Fujian, China. [email protected].
- Department of Nuclear Medicine, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, China. [email protected].
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China. [email protected].
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.