ArticleHepatology (Baltimore, Md.)2026
Deep learning-assisted tumor radiomic dynamics on MRI predict pathological complete response in HCC undergoing immune-based therapy followed by hepatectomy.
Article in Hepatology (Baltimore, Md.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
23 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimsPathological complete response (pCR) following conversion therapy for initially unresectable hepatocellular carcinoma (uHCC) remains challenging to predict preoperatively. This study developed and validated a model integrating clinicopathological and radiomic features of the tumor to predict pCR.
methodsIn this multicenter retrospective study, temporal radiomics features were extracted from baseline, post-treatment, and delta (change) MRIs. Serum AFP response was calculated as log₁₀(preoperative AFP)/log₁₀(baseline AFP). Univariate analysis, collinearity assessment, LASSO, and random forest were employed to perform feature selection. Fourteen machine learning models were benchmarked, with performance evaluated by using comprehensive metrics AUC, NPV, PPV, sensitivity, specificity, calibration, and decision curve analysis.
resultsThe model was developed and validated in a training (n=78), an internal test (n=32), and an independent validation cohort (n=44). The delta radiomic model significantly outperformed both baseline (test AUC: 0.835 vs. 0.483, p <0.05; validation AUC: 0.783 vs. 0.434, p <0.05) and preoperative models (test AUC: 0.685, p <0.05; validation AUC: 0.506, p <0.05), demonstrating superior predictive performance and generalization capability in predicting lesion-level pCR. Notably, when predicting patient-level pCR, the radiomic model also showed robust discrimination, with AUCs of 0.819 in the test set and 0.781 in the validation set. The combined radiomics-AFP model achieved even higher AUCs of 0.920 (test) and 0.857 (validation) in predicting lesion-level pCR.
conclusionsDynamic radiomic changes effectively predict pCR in uHCC after conversion therapy. Combining delta radiomics with AFP response significantly improves predictive performance, offering a non-invasive method for assessing pCR and potentially guiding personalized treatment decisions.
Indexed as
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.