Hepatocellular carcinoma (HCC) is a common malignancy and a leading cause of cancer-related mortality. Current guidelines and staging systems provide coarse categories, but often miss within-stage heterogeneity and the clinical context in electronic medical records (EMRs). We present HCC-STAR (Hepatocellular Carcinoma Staging, Treatment And pRognosis), a clinically aligned large language model that reads routine EMR narratives and jointly outputs risk score-based staging, ranked guideline-consistent treatments with evidence-based rationales, and individualized survival estimates. We curated about 30,000 HCC cases from SEER and expanded them into EMR-style narrative training data using a clinician-validated, prompt-based augmentation workflow. On this corpus, we developed a knowledge-aligned reasoning framework optimized with a step-verifiable composite reward, moving beyond text-level memorization of clinical guidelines. In a multi-center cohort of 6,668 patients from 12 hospitals in China, HCC-STAR achieved state-of-the-art performance in treatment recommendation and risk stratification compared with clinical guidelines and competitive models, including GPT-5 and Gemini-2.5 Pro. Hypothetical overall-survival analysis showed a median survival of 51 months under adherence to HCC-STAR recommendations, compared with 29 and 32 months under BCLC and CNLC. In clinician-centric evaluations, blinded hepatobiliary specialists rated HCC-STAR's reasoning and evidence-based justifications as trustworthy. The model surpassed resident and attending physicians in treatment accuracy and helped physicians make more accurate decisions faster when used as an assistant. These findings support HCC-STAR as a reliable and verifiable decision-support system for risk stratification and precision therapy in HCC.
Peng Cui, Ji-tao Wang, Siyan Xue et al.· 0 citations
Acute pancreatitis (AP) incidence is rising globally. Current scoring systems lack sensitivity for early organ failure (OF) prediction and suffer from interobserver variability. This study aimed to develop and validate an artificial intelligence (AI)-driven model for fully automated early prediction of OF in AP using multiphase computed tomography (CT) imaging.
This multicenter study included 2746 AP patients from two tertiary hospitals (2011–2024). Patients were split into training (
n
=1820), validation (
n
=456), and test cohorts (
n
=470). An nnMamba-based segmentation model delineated pancreatic/peripancreatic regions on CT. An organ failure risk assessment with CT and learning engine (ORACLE) model integrated deep learning radiomics (severe organ failure–deep learning radiomics [SOF-DLR] score from 57 optimal features) with clinical variables. The primary outcome was OF (Modified Marshall Score≥2).
OF occurred in 8.7% (
n
=240). The ORACLE model achieved the receiver operating characteristic curves (area under the curve [AUCs]) of 0.85 (training), 0.89 (validation), and 0.81 (test), outperforming Modified CT Severity Index (M-CTSI) (AUC 0.68–0.74) and clinical models (AUC 0.67–0.71; DeLong’s
P
<0.001). The overall negative predictive value for the entire cohort (
n
=2746) was 97.2%. High-risk patients (
P
>0.700; 1.4% of cohort) had 92.1% OF incidence. The model provided a median early warning time of 3.5 hours (mean 9.17 h) before clinical OF onset, with 55% of cases predicted ≥3 h in advance.
This AI-based tool enables accurate, automated OF prediction 3.5 h before clinical manifestation, facilitating risk-stratified management. Its generalizability is confirmed in multicenter validation.
Yifei Guo, Chengwei Chen, Tiegong Wang et al.· Journal of Pancreatology· 0 citations
Social media has become an integral part of college students’ daily lives, yet the issues of information overload and emotional distress it brings are increasingly prominent. Previous studies have primarily focused on the associations between social media use and psychological outcomes such as well-being, anxiety, or depression, while paying insufficient attention to the underlying mechanisms through which it influences individuals’ core self-evaluation. This study aims to explore the mediating roles of information overload and fear of missing out (FoMO) in the relationship between social media use and core self-evaluation. A cross-sectional survey was conducted in a prefecture-level city in Henan Province, China, from November to December 2023, involving two universities. The questionnaire was administered online via the Wenjuanxing platform. A total of 1519 college students participated in the cross-sectional survey. Results from the chained mediation model indicate that social media use does not directly and positively predict college students’ core self-evaluation; rather, its influence is primarily exerted indirectly through information overload and fear of missing out. Both variables serve as significant mediators and form a sequential mediating pathway. Information overload and FoMO were identified as partial mediators in the correlation between social media usage and core self-evaluation among college students, highlighting the existence of a chain mediation mechanism linking them. This study elucidates the mechanism through which social media influences college students’ self-cognition. Theoretically, it advances the understanding of the relationship between social media use and core self-evaluation; practically, it offers insights for universities to develop interventions targeting information management and emotion regulation, thereby supporting students’ mental health.