Deep Learning-Based Prediction of Myelosuppression in Lymphoma Patients During Chemotherapy Using Multimodal Radiological Images with Subcutaneous Adipose Tissue

Lymphoma is a malignant tumor, and diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin's lymphoma. Due to its biological characteristics, surgical treatment is difficult. The main treatment for DLBCL is chemotherapy, with the R-CHOP regimen being the most common. The vast majority of patients require lifelong treatment. Myelosuppression during chemotherapy is the most common adverse reaction in DLBCL patients and directly affects the progress of chemotherapy. Accurately predicting whether patients need early intervention before chemotherapy can greatly improve their prognosis. In this paper, we propose a neural network that uses PET/CT images of subcutaneous adipose tissue before treatment to predict myelosuppression in DLBCL patients. The model achieves a classification accuracy of 93.57%. This indicates that the growth distribution pattern and metabolic characteristics of adipose tissue are important for DLBCL patients.