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Follicular lymphoma (FL) is a non-Hodgkin lymphoma and an indolent B-cell lymphoproliferative disorder of transformed follicular center B cells. In the diagnosis, FL should be graded by counting the number of centroblasts in the pathological image, which is time-consuming. In this study, we try to propose a FL grading method based on the PET and CT images. We propose a 3D-DDcGAN to fuse the simultaneously collected PET and CT images. Then, the BayesianResNet18 (ResNet18 is improved by introducing Bayes’ theorem) is adopted for the FL grading. Our method is trained and tested on mixed data consisting of FL grades I-III and DLBCL. Finally, the evaluation metrics for our method are accuracy 0.814, precision 0.782, recall 0.699, macro-averaged F1-score 0.731, and micro-averaged F1-score 0.817. The method based on deep learning and medical imaging will help assist in disease grading and developing personalized treatment plans.

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Follicular Lymphoma Grading Based on 3D-DDcGAN and Bayesian CNN Using PET-CT Images

  • 会议论文

  • 发表日期：

    2025年

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  • Lulu He,
  • Chunjun Qian,
  • Yue Teng,
  • Chongyang Ding,
  • Chong Jiang

摘要

Follicular lymphoma (FL) is a non-Hodgkin lymphoma and an indolent B-cell lymphoproliferative disorder of transformed follicular center B cells. In the diagnosis, FL should be graded by counting the number of centroblasts in the pathological image, which is time-consuming. In this study, we try to propose a FL grading method based on the PET and CT images. We propose a 3D-DDcGAN to fuse the simultaneously collected PET and CT images. Then, the BayesianResNet18 (ResNet18 is improved by introducing Bayes’ theorem) is adopted for the FL grading. Our method is trained and tested on mixed data consisting of FL grades I-III and DLBCL. Finally, the evaluation metrics for our method are accuracy 0.814, precision 0.782, recall 0.699, macro-averaged F1-score 0.731, and micro-averaged F1-score 0.817. The method based on deep learning and medical imaging will help assist in disease grading and developing personalized treatment plans.