Trial reportScientific reports2024
Comparative analysis of radiomics and deep-learning algorithms for survival prediction in hepatocellular carcinoma.
Trial report in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed.
- Development and Validation of a Machine Learning Model to Prognosticate Hepatocellular Carcinoma.Alimentary pharmacology & therapeutics · 2026Article
- Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence.NPJ digital medicine · 2026Article
- Radiomics-based differentiation between glioblastoma and primary central nervous system lymphoma: CT vs MRI.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Early prediction of adverse outcomes in liver cirrhosis using a CT-based multimodal deep learning model.Abdominal radiology (New York) · 2026Article
- Evaluation of therapeutic efficacy in hepatocellular carcinoma ablation based on CT radiomics and deep learning multimodal models.Frontiers in oncology · 2026Article
- The value of dual-energy CT radiomics in evaluating vascular maturity and prognosis in clear cell renal cell carcinoma.Discover oncology · 2025Article
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- Clinical utility of artificial intelligence models in radiology: a systemic scoping review of diagnostic and endovascular applications.CVIR endovascular · 2025Review
- RadGLO: an interactive platform for radiomic feature analysis and prognostic modeling in glioma.NPJ precision oncology · 2025Article
- Development of a predictive model for distant metastasis in HCC patients post-TACE using clinical data, radiomics, and deep learning.Journal of cancer research and clinical oncology · 2025Article
- Review
- Reproducibility of methodological radiomics score (METRICS): an intra- and inter-rater reliability study endorsed by EuSoMII.European radiology · 2025Article
- AutoFRS: an externally validated, annotation-free approach to computational preoperative complication risk stratification in pancreatic surgery - an experimental study.International journal of surgery (London, England) · 2025Article
- Deep Learning-Based Prediction of Post-treatment Survival in Hepatocellular Carcinoma Patients Using Pre-treatment CT Images and Clinical Data.Journal of imaging informatics in medicine · 2025Article
- Integrating CT Radiomics and Clinical Features to Optimize TACE Technique Decision-Making in Hepatocellular Carcinoma.Cancers · 2025Article
- Integration of intratumoral/peritumoral radiomics and deep learning for predicting overall survival in non-small cell lung cancer patients: a multicenter study.Frontiers in oncology · 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
- Radiomics in radiology: What the radiologist needs to know about technical aspects and clinical impact.La Radiologia medica · 2024Review
- Article
- Machine Learning-Based Integration of Single-Cell and Bulk Transcriptome Reveals Coagulation Signature and Phenotypic Heterogeneity in Hepatocellular Carcinoma.IET systems biologyArticle
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
No grant is acknowledged in the PubMed record.
Abstract
To examine the comparative robustness of computed tomography (CT)-based conventional radiomics and deep-learning convolutional neural networks (CNN) to predict overall survival (OS) in HCC patients. Retrospectively, 114 HCC patients with pretherapeutic CT of the liver were randomized into a development (n = 85) and a validation (n = 29) cohort, including patients of all tumor stages and several applied therapies. In addition to clinical parameters, image annotations of the liver parenchyma and of tumor findings on CT were available. Cox-regression based on radiomics features and CNN models were established and combined with clinical parameters to predict OS. Model performance was assessed using the concordance index (C-index). Log-rank tests were used to test model-based patient stratification into high/low-risk groups. The clinical Cox-regression model achieved the best validation performance for OS (C-index [95% confidence interval (CI)] 0.74 [0.57-0.86]) with a significant difference between the risk groups (p = 0.03). In image analysis, the CNN models (lowest C-index [CI] 0.63 [0.39-0.83]; highest C-index [CI] 0.71 [0.49-0.88]) were superior to the corresponding radiomics models (lowest C-index [CI] 0.51 [0.30-0.73]; highest C-index [CI] 0.66 [0.48-0.79]). A significant risk stratification was not possible (p > 0.05). Under clinical conditions, CNN-algorithms demonstrate superior prognostic potential to predict OS in HCC patients compared to conventional radiomics approaches and could therefore provide important information in the clinical setting, especially when clinical data is limited.
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.