Abstract
Targeted therapy for intrahepatic cholangiocarcinoma (ICC) shows superior survival outcomes but patients with certain targetable alterations are no more than 20%. Genetic alteration screening for all ICC patients is of high cost and not routinely performed. This study intends to develop a histopathology-based artificial intelligence (AI)-assisted system for predicting genetic alteration of ICC. We constructed a Genetic Alteration Prediction (GAP) system based on multi-instance learning and self-supervised learning to predict genetic alterations using whole-slide images (WSIs) of H&E-stained slides. A total of 2069 WSIs from 232 ICC patients underwent surgery of the FAH-SYSU dataset were used for model construction and adjustment by five-fold cross-validation. Another 150 patients from three medical centres were used as independent external validations. We also compared the cost-effectiveness of GAP-assisted precise treatment and all-sequencing strategy to non-sequencing strategy. The GAP was able to predict actionable genetic alterations of ICC, including FGFR2 and IDH. The area under the receiver operating characteristic curves (AUC) for FGFR2 and IDH were 0.754 and 0.713 in the internal dataset, and 0.724 and 0.656 in the external dataset, respectively. Furthermore, compared to giving chemotherapy without sequencing for every patient, GAP-assisted precise treatment could increase 1 progression-free quality-adjusted life month with a cost of $13871.72, the co-responding figure for all-sequencing strategy is $44538.93. Decision curve analysis showed that AI-assisted strategy provides better clinical benefits. We constructed an AI-assisted genetic alteration screening system which is predictable to ICC actionable targets and has potential to assist precise targeted treatment of advanced ICC.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.