ArticleNPJ digital medicine2025
Machine learning model for differentiating xanthogranulomatous cholecystitis and gallbladder cancer in multicenter largescale study.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Preoperative differentiation between xanthogranulomatous cholecystitis (XGC) and gallbladder cancer (GBC) remains challenging due to overlapping clinical and imaging features. This multicenter retrospective study developed a machine learning (ML) model, LIDGAX, using preoperative clinical, imaging, and laboratory data from 1246 patients (554 XGC, 692 GBC). Twelve variables were identified as independent predictors via multivariate logistic regression and least absolute shrinkage and selection operator analyses. LIDGAX achieved area under the curve (AUC) values of 0.94 (internal validation) and 0.88 (external testing), outperforming the other five ML models. Calibration and decision curve analyses demonstrated its superior clinical utility. Compared to six radiologists, LIDGAX improved sensitivity (1.2-8.5%), specificity (0.0-4.6%), and balanced accuracy (1.8-6.6%), while reducing average diagnostic time per patient by 30.44-35.76 s. LIDGAX was deployed on an open-source online platform, maintaining high performance (AUC 0.95, accuracy 0.92). This non-invasive tool shows strong potential for clinical translation in preoperative differentiation of XGC and GBC.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.