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Xinyi Ke

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Open access Jul 2026

AI-driven tumor heterogeneity quantification and survival prediction in pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies worldwide, and accurate prognostic prediction remains highly challenging due to its marked biological heterogeneity and complex tumor microenvironment. To address this challenge, a histopathomics-based survival prediction system (HPSurv) was developed using histopathological whole-slide images (WSIs) for individualized overall survival (OS) prediction. Within this framework, pathological tissue classification, quantitative characterization of tumor spatial heterogeneity, and a survival Transformer were integrated to enable multi-level representation learning from histopathological data. The system was developed and evaluated in 1020 patients across five independent cohorts. Compared with conventional clinicopathological indicators, significantly improved prognostic performance was achieved across multicenter cohorts (p < 0.05), with a mean C-index of 0.761 and time-dependent AUCs of 0.936, 0.877, and 0.772 for predicting 6-month, 2-year, and 3-year survival, respectively. Subgroup analyses further supported its role as an independent prognostic factor and suggested its potential utility in stratifying patients with respect to ACT-related outcomes. In addition, significant associations with key PDAC molecular pathways were observed, providing biological insights into the model predictions and supporting interpretability. In the study, an interpretable and high-performing artificial intelligence (AI) framework for quantitative modeling of PDAC was established. Objective characterization of tumor heterogeneity and accurate postoperative survival prediction are enabled, with potential value for personalized management in PDAC.

Lizhi Shao, Xinyi Ke, Yun Wang et al. · npj Digital Medicine · 0 citations

Jul 2026

AutoPref: Automatic Discovery of Task-Specific Preference Objectives for Neural Combinatorial Optimization

Combinatorial optimization problems (COPs) underpin many real-world decisions, but their exponentially large search spaces make high-quality solutions costly to obtain. Neural combinatorial optimization (NCO) learns fast construction policies, typically with reinforcement learning (RL), while preference-based NCO improves sample efficiency by learning from relative solution quality. However, existing preference objectives combine two distinct design choices in manually specified, one-size-fits-all formulations: what learning signal to extract from each solution pair and how to weight each pair relative to the sampled set. We present AutoPref, the first LLM-guided framework for automated preference-objective discovery in NCO. AutoPref factorizes the objective into a pairwise loss program, which defines the learning signal, and a set-aware weighting program, which determines each pair's relative contribution. Their composition forms a unified programmatic objective space containing existing preference objectives as special cases. To make its search tractable, we introduce a staged conditional search strategy with behavioral gates that filter inadmissible programs before short-horizon training and evaluation. Across TSP, CVRP, FFSP, and JSSP, AutoPref consistently outperforms strong hand-designed baselines across problem scales, demonstrating the benefits and scalability of automated objective discovery for NCO.

Shengda Gu, Kai Li, Xinyi Ke et al. · arXiv.org · 0 citations