ArticleRevista da Associacao Medica Brasileira (1992)2024
Predicting treatment responses using magnetic resonance imaging-based radiomics in hepatocellular carcinoma patients undergoing transarterial radioembolization.
Article in Revista da Associacao Medica Brasileira (1992), 2024. 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.
- Emerging Role of MRI-Based Artificial Intelligence in Individualized Treatment Strategies for Hepatocellular Carcinoma: A Narrative Review.Journal of magnetic resonance imaging : JMRI · 2026Review
- From texture analysis to artificial intelligence: global research landscape and evolutionary trajectory of radiomics in hepatocellular carcinoma.Discover oncology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study evaluates the efficacy of magnetic resonance imaging-based radiomics in predicting treatment responses in hepatocellular carcinoma patients undergoing transarterial radioembolization.
methodsPre-treatment magnetic resonance imaging scans from 65 hepatocellular carcinoma patients were analyzed. Radiomic features were extracted from axial T1-weighted and T2-weighted sequences using a standardized workflow involving image preprocessing, segmentation, and feature extraction. Multivariate logistic regression models combining radiomic and clinical features were developed to predict treatment outcomes. The performance of the models was evaluated using the area under the curve metric.
resultsThe study included 65 patients with a median age of 64 years; 44.6% showed a complete response, while 55.4% showed a non-complete response. The median radiomics score in the T1-weighted portal phase was -0.49 for non-complete responders and -0.07 for complete responders (p<0.001). In the T2-weighted sequence, the median radiomics score was -0.76 for non-complete responders and 1.1 for complete responders (p<0.001). Tumor size ≥5 cm was a significant predictor of non-complete response in univariate analysis (p=0.027) but not in multivariate analysis after adding radiomics scores. The area under the curve for the radiomics signature in predicting non-complete response was 0.754 for T1-weighted and 0.850 for T2-weighted sequences.
conclusionMagnetic resonance imaging-based radiomics enhances the prediction of treatment responses in hepatocellular carcinoma patients undergoing transarterial radioembolization. Integrating radiomic features with clinical parameters significantly improves predictive accuracy.
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.