Artificial intelligence-based personalized treatment strategies for unresectable hepatocellular carcinoma: integrating HSP90α for prognosis and survival prediction
Ke Su, X. Johné Liu, Xuelian Wang, Yunwei Han, H. Luo, Lianbin Wen, Jian Chen, Han Li, Susu Xiao, Jianwen Zhang, Chenjie Wang, Yuhang Zhou, Zunyuan Tan, Lexin Wang, P. Wang, Haiqing Chen, Guixu Zhang, Kun He, Xiaosong Li, Hao Chi, Zhenjiang Li, Ke Xu
Abstract
This study aimed to integrate artificial intelligence (AI)with heat shock protein 90 alpha (HSP90α)expression to improve patient selection and prognostic assessment in unresectable hepatocellular carcinoma (HCC)treated with transarterial chemoembolization (TACE). We retrospectively enrolled 2555 unresectable HCC patients treated between 2016 and 2021 at seven Chinese tertiary hospitals. Residual-based methods were used to define TACE benefit. Eight AI models revealed that HSP90α expression, Barcelona Clinic Liver Cancer (BCLC)stage, and tumor size were key predictive factors for TACE benefit. A nomogram based on these three variables achieved an area under the receiver operating characteristic curve (AUC)of 0.901 in the validation cohort. For overall survival (OS), we developed 101 machine learning models. The StepCox[forward] plus random survival forest model showed the best performance. Its C-indices were 0.84, 0.70, and 0.78 in the training, internal validation, and external validation sets, respectively. In the internal validation set, the time-dependent AUCs for 1-, 2-, and 3 year OS were 0.835, 0.821, and 0.776; in the external validation set, they were 0.854, 0.790, and 0.804. Integrating AI with HSP90α enables robust identification of TACE-benefit candidates and accurate prognostic stratification in unresectable HCC.