ArticleBMC medical imaging2022
A comparative study between deep learning and radiomics models in grading liver tumors using hepatobiliary phase contrast-enhanced MR images.
Article in BMC medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomic Analysis of MRI for Assessing Response to Neoadjuvant Chemoradiotherapy in Rectal Adenocarcinoma: A Systematic Review and Metaanalysis.Molecular imagingPooled it
- Hybrid Fusion of Time-Intensity Curve, Deep Learning, and Fractional Zernike-Caputo Features for Accurate Liver Lesion Classification in DCE-MRI.Journal of imaging · 2026Article
- Preoperative prediction of Ki-67 expression and prognosis in breast cancer based on contrast-enhanced cone-beam breast CT: a two-centre study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Can 3D T1 Post-Contrast MRI in A Radiomics-Machine Learning Model Distinguish Infective from Neoplastic Ring-Enhancing Brain Lesions? An Exploratory Study.Diagnostics (Basel, Switzerland) · 2026Article
- Multiparametric MRI-based habitat analysis integrating deep learning and radiomics for predicting preoperative Ki-67 expression level in breast cancer.BMC medical imaging · 2026Article
- Traditional machine learning in biomedical image analysis: before you go too deep.Frontiers in artificial intelligence · 2026Review
- Advancements in artificial intelligence for cancer diagnosis and prognosis prediction: current applications and emerging opportunities.Frontiers in cell and developmental biology · 2026Review
- Targeting tumoral heterogeneity in lung cancer: a novel, CT-texture-guided targeted biopsy approach with exome sequencing.NPJ precision oncology · 2025Article
- Interpretable deep learning model and nomogram for predicting pathological grading of PNETs based on endoscopic ultrasound.BMC medical informatics and decision making · 2025Article
- The value of using deep learning to predict proliferative hepatocellular carcinoma based on multiphasic magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2025Article
- Current State of Evidence for Use of MRI in LI-RADS.Journal of magnetic resonance imaging : JMRI · 2025Review
- Multivariate Framework of Metabolism in Advanced Prostate Cancer Using Whole Abdominal and Pelvic HyperpolarizedCancers · 2025Article
- Comparative analysis of deep learning and radiomics models in predicting hepatocellular carcinoma differentiation via ultrasound.Frontiers in medicine · 2025Article
- Predicting the risk of type 2 diabetes mellitus (T2DM) emergence in 5 years using mammography images: a comparison study between radiomics and deep learning algorithm.Journal of medical imaging (Bellingham, Wash.) · 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
7 authors.
Funding
Abstract
purposeTo compare a deep learning model with a radiomics model in differentiating high-grade (LR-3, LR-4, LR-5) liver imaging reporting and data system (LI-RADS) liver tumors from low-grade (LR-1, LR-2) LI-RADS tumors based on the contrast-enhanced magnetic resonance images.
methodsMagnetic resonance imaging scans of 361 suspected hepatocellular carcinoma patients were retrospectively reviewed. Lesion volume segmentation was manually performed by two radiologists, resulting in 426 lesions from the training set and 83 lesions from the test set. The radiomics model was constructed using a support vector machine (SVM) with pre-defined features, which was first selected using Chi-square test, followed by refining using binary least absolute shrinkage and selection operator (LASSO) regression. The deep learning model was established based on the DenseNet. Performance of the models was quantified by area under the receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity and F1-score.
resultsA set of 8 most informative features was selected from 1049 features to train the SVM classifier. The AUCs of the radiomics model were 0.857 (95% confidence interval [CI] 0.816-0.888) for the training set and 0.879 (95% CI 0.779-0.935) for the test set. The deep learning method achieved AUCs of 0.838 (95% CI 0.799-0.871) for the training set and 0.717 (95% CI 0.601-0.814) for the test set. The performance difference between these two models was assessed by t-test, which showed the results in both training and test sets were statistically significant.
conclusionThe deep learning based model can be trained end-to-end with little extra domain knowledge, while the radiomics model requires complex feature selection. However, this process makes the radiomics model achieve better performance in this study with smaller computational cost and more potential on model interpretability.
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.