990P Prognosis prediction for unresectable hepatocellular carcinoma undergoing hepatic arterial infusion chemotherapy using convolutional neural network model

Hepatic arterial infusion chemotherapy (HAIC) is known to be more effective than conventional systemic chemotherapy, showing great potential in treating unresectable hepatocellular carcinoma (HCC) patients. However, there is still unclear which group can benefit more from HAIC. 191 patients with unresectable HCC undergoing HAIC from Zhongshan Hospital between May 2019 and March 2022 were retrospectively recruited. Radiomics scores were calculated based on enhanced-T1-weighted, enhanced-T2-weighted, arterial phase and delayed phase images. Clinical factors related to OS and PFS were identified by Cox regression analysis. Three different CNNs of AlexNet, ResNet, and Inception architectures were constructed on 70% of the data set and tested on the remaining 30%. Radiomics scores and clinical factors were reflected to a model eventually. Mean squared error (MSE) and time-dependent receiver operating characteristic curve were calculated for the models. The OS model included radiomics score with No. of HAIC cycles, tumor thrombus, PIVKA-II, neutrophil-lymphocyte ratio (NLR), aspartate aminotransferase (AST), gamma-glutamyltranspeptidase (γ-GT) and C-reactive protein. And the PFS model included radiomics score with No. of HAIC cycles, tumor thrombus, NLR and γ-GT. The AlexNet-OS model, ResNet-OS model, and Inception-OS model achieved the best MSE of 1.0068, 0.9023 and 0.8506, respectively. The AlexNet-PFS model, ResNet- PFS model, and Inception- PFS model achieved the best MSE of 0.6658, 0.6819 and 1.1012, respectively. The present models which integrated radiomics information and clinical factors helped predict OS and PFS of unresectable HCC patients undergoing HAIC treatment.