ArticleJournal of translational medicine2024
CT-based radiomics nomogram to predict proliferative hepatocellular carcinoma and explore the tumor microenvironment.
Article in Journal of translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Review
- Chinese expert concern and consensus on applications of artificial intelligence in clinical cancer imaging.Insights into imaging · 2026Article
- Article
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- Ki-67 expression correlates with hepatocellular carcinoma recurrence and is predictable using radiomics features.Abdominal radiology (New York) · 2026Article
- Prediction of colorectal cancer liver metastasis through an MRI radiomic model.Scientific reports · 2026Article
- Unlocking the potential of radiomics in predicting the response of neoadjuvant immunochemotherapy for operable locally advanced esophageal squamous cell carcinoma: a narrative review.Journal of thoracic disease · 2026Review
- A deep learning radiopathomic signature predicts recurrence risk of hepatocellular carcinoma after hepatectomy.Communications biology · 2026Article
- A CT-based radiomics model for preoperative prediction of lymphovascular invasion in colorectal cancer.Frontiers in oncology · 2026Article
- Phantom-based evaluation of radiomics feature stability for low-dose CT lung cancer screening.Frontiers in endocrinology · 2026Article
- Dynamic Vascular Spatiotemporal Heterogeneity on Multiphase CT for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2026Article
- Optimized digital polymerase chain reaction enables detection of telomerase reverse transcriptase C228T mutation for prognostic assessment in hepatocellular carcinoma.World journal of gastrointestinal oncology · 2025Article
- A nomogram for predicting overall survival in advanced hepatocellular carcinoma patients receiving radiotherapy combined with targeted therapy: a multicenter retrospective study.Translational cancer research · 2025Article
- Decision-Making Biomarkers Guiding Therapeutic Strategies in Hepatocellular Carcinoma: From Prediction to Personalized Care.Cancers · 2025Review
- From texture analysis to artificial intelligence: global research landscape and evolutionary trajectory of radiomics in hepatocellular carcinoma.Discover oncology · 2025Article
- Augmenting conventional criteria: a CT-based deep learning radiomics nomogram for early recurrence risk stratification in hepatocellular carcinoma after liver transplantation.Insights into imaging · 2025Article
- CT-based deep learning radiomics model for predicting proliferative hepatocellular carcinoma: application in transarterial chemoembolization and radiofrequency ablation.BMC medical imaging · 2025Article
- Computed tomography 3D reconstruction and texture analysis for evaluating the efficacy of neoadjuvant chemotherapy in advanced gastric cancer.World journal of gastrointestinal surgery · 2025Article
- A CECT-Based Radiomics Nomogram Predicts the Overall Survival of Patients with Hepatocellular Carcinoma After Surgical Resection.Biomedicines · 2025Article
- Development of a radiomic model to predict CEACAM1 expression and prognosis in ovarian cancer.Scientific reports · 2025Article
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14 authors.
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Abstract
backgroundProliferative hepatocellular carcinomas (HCCs) is a class of aggressive tumors with poor prognosis. We aimed to construct a computed tomography (CT)-based radiomics nomogram to predict proliferative HCC, stratify clinical outcomes and explore the tumor microenvironment.
methodsPatients with pathologically diagnosed HCC following a hepatectomy were retrospectively collected from two medical centers. A CT-based radiomics nomogram incorporating radiomics model and clinicoradiological features to predict proliferative HCC was constructed using the training cohort (n = 184), and validated using an internal test cohort (n = 80) and an external test cohort (n = 89). The predictive performance of the nomogram for clinical outcomes was evaluated for HCC patients who underwent surgery (n = 201) or received transarterial chemoembolization (TACE, n = 104). RNA sequencing data and histological tissue slides from The Cancer Imaging Archive database were used to perform transcriptomics and pathomics analysis.
resultsThe areas under the receiver operating characteristic curve of the radiomics nomogram to predict proliferative HCC were 0.84, 0.87, and 0.85 in the training, internal test, and external test cohorts, respectively. The radiomics nomogram could stratify early recurrence-free survivals in the surgery outcome cohort (hazard ratio [HR] = 2.25; P < 0.001) and progression-free survivals in the TACE outcome cohort (HR = 2.21; P = 0.03). Transcriptomics and pathomics analysis indicated that the radiomics nomogram was associated with carbon metabolism, immune cells infiltration, TP53 mutation, and heterogeneity of tumor cells.
conclusionThe CT-based radiomics nomogram could predict proliferative HCC, stratify clinical outcomes, and measure a pro-tumor microenvironment.
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