ArticleBMC cancer2026
Identification and validation of an ultrasound-based interpretable machine learning model for the preoperative evaluation of microvascular invasion in patients with hepatocellular carcinoma.
Article in BMC cancer, 2026. 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
8 authors.
Funding
Abstract
objectiveThe aim of our study was to develop and validate a machine learning model for the preoperative identification of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC).
methodsThis retrospective multicenter study was conducted in China. Patients with HCC from June 2017 to December 2024 were enrolled. Database was divided into training and internal validation sets randomly. Least absolute shrinkage and selection operator (LASSO) regression was employed for feature selection. Four machine learning algorithms were compared for MVI prediction. Model performance was evaluated using the area under the receiver operating characteristic (AUC), accuracy, sensitivity, specificity, precision, Youden's index, and F1 score. Finally, the machine learning model with the best performance was selected as our final model while using it for an independent external validation set. The SHapley Additive exPlanations (SHAP) diagram was utilized to elucidate the variable importance within the model, culminating in the amalgamation of the above metrics to discern the most succinct features.
resultsThe study finally enrolled 496 patients, comprising 229 MVI-positive and 267 MVI-negative cases. A total of 42 patients with HCC were collected in the independent external validation center, of which 18 were MVI-positive. LASSO regression showed that AFP, tumor size, peripheral enhancement, mosaic architecture and washout start time were the significant predictors. Among the four models, the Gradient Boosting Machine (GBM) model showed the best performance in the internal validation set, with an AUC of 0.829. In the independent external validation set, the GBM model demonstrated an AUC of 0.812.
conclusionThe machine learning model shows promising efficacy in preoperative MVI identification for HCC patients. This method has potential clinical applications and may help identify MVI preoperatively, potentially improving clinical outcomes.
Indexed as
Identifiers
What Socratic 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 Socratic graph.