Application of Radiomics model Based on FDG-PET/CT for the Assessment of Therapeutic Effect in Patients with Newly-diagnosed Multiple Myeloma

Fukai Li¹Fei Li²Xiaodan Xu¹Qingyang Yu¹Guangwen Duan¹Jiayang Yan¹Baiyang Jiang¹Hongbiao Sun¹Shaochun Xu¹Xiang Wang^1*Yi Xiao^1*Shiyuan LIU^1*

• ¹Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China
• ²Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China

Objectives To evaluate the prediction value of radiomics models based on FDG-PET/CT for the therapeutic effect in patients with newly-diagnosed multiple myeloma (MM). Materials and Methods We retrospectively reviewed the clinical characteristics and 18F-FDG-PET/CT imaging data of 165 MM patients. Randomly divided into a training set (n=133) and a test set (n=32) at a ratio of 8:2. All patients underwent whole-body PET-CT scans within one month prior to the commencement of treatment. Overall response rate was the principal efficacy endpoint, including stringent complete response (sCR), complete response (CR), very good partial response (VGPR), partial response (PR), minimal response (MR), disease stabilization (SD), and disease progression (PD). Deep response (DR) was defined as sCR, CR, and VGPR, while non-deep response included PR, MR, SD and PD, 74 patients attained DR. Different models involving clinical, radiomics extracted from PET/CT, and their combination were constructed based on multiple logistic regression and logistic regression machine learning classifier after features selection, respectively. The models predicting performance were evaluated by the area under the ROC curve (AUC), sensitivity, specificity, accuracy, precision, and F1 score. Receiver Operating Characteristic (ROC) curves, decision curves, calibration curves, and DeLong's test were applied to compare their ability. Results Gender was the only one of clinical characteristics found to be independent prognosis factor for treatment evaluation, with a p-value of 0.041. The radiomics models outperformed the Clinical model significantly, among which the PET-CT model yielded the best results with the AUC of 0.809. The PET + CT + Clinical model achieved the optimal performance after integrating clinical and radiomic features, with the AUC of 0.813. Conclusions The FDG-PET/CT-based radiomics model, particularly when integrated with clinical features, can more effectively predict deep treatment response in newly diagnosed MM patients, offering significant clinical utility for early treatment stratification and personalized therapeutic guidance.