Trial reportWorld journal of gastroenterology2022
Preoperative contrast-enhanced computed tomography-based radiomics model for overall survival prediction in hepatocellular carcinoma.
Trial report in World journal of gastroenterology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive value of contrast-enhanced carotid ultrasound features for stroke risk: a systematic review and meta-analysis.Frontiers in neurology · 2025Pooled it
- Artificial intelligence in imaging diagnosis of liver tumors: current status and future prospects.Abdominal radiology (New York) · 2026Review
- Establishment and validation of a CT-based clinical deep learning radiomics nomogram for predicting the response to transcatheter arterial chemoembolization in patients with hepatocellular carcinoma.Frontiers in oncology · 2026Article
- A CECT-Based Radiomics Nomogram Predicts the Overall Survival of Patients with Hepatocellular Carcinoma After Surgical Resection.Biomedicines · 2025Article
- A Refined Prognostic Model for Postoperative Overall Survival in Hepatocellular Carcinoma Based on CODEX-Based Multiproteomics and Radiomics.Journal of hepatocellular carcinoma · 2025Article
- Neutrophil-to-lymphocyte ratio in females as a predictor of embryo acquisition outcomes after IVF/ICSI treatment: a retrospective study.Frontiers in endocrinology · 2025Article
- A predictive model based on radiomics, clinical features, and pathologic indicators for disease-free survival after liver transplantation for hepatocellular carcinoma: a 7-year retrospective study.Journal of gastrointestinal oncology · 2024Article
- A Review of the Prognostic Significance of Neutrophil-to-Lymphocyte Ratio in Nonhematologic Malignancies.Diagnostics (Basel, Switzerland) · 2024Review
- Article
- Preoperative Noninvasive Prediction of Recurrence-Free Survival in Hepatocellular Carcinoma Using CT-Based Radiomics Model.Journal of hepatocellular carcinoma · 2024Article
- Machine Learning Combined with Radiomics Facilitating the Personal Treatment of Malignant Liver Tumors.Biomedicines · 2023Review
- Computed tomography radiomics signature via machine learning predictsJournal of gastrointestinal oncology · 2023Article
- Editorial: Using physical & genomics markers for smart therapy via expert systems with computer learning.Frontiers in genetics · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHepatocellular carcinoma (HCC) is the most common primary liver malignancy with a rising incidence worldwide. The prognosis of HCC patients after radical resection remains poor. Radiomics is a novel machine learning method that extracts quantitative features from medical images and provides predictive information of cancer, which can assist with cancer diagnosis, therapeutic decision-making and prognosis improvement.
aimTo develop and validate a contrast-enhanced computed tomography-based radiomics model for predicting the overall survival (OS) of HCC patients after radical hepatectomy.
methodsA total of 150 HCC patients were randomly divided into a training cohort (
resultsIn total, seven radiomics features were selected to construct the radiomics signature. According to the results of univariate and multivariate Cox regression analyses, alpha-fetoprotein (AFP), neutrophil-to-lymphocyte ratio (NLR) and radiomics signature were included to build the nomogram. The C-indices of the nomogram in the training and validation cohorts were 0.736 and 0.774, respectively. ROC curve analysis for predicting 1-, 3-, and 5-year OS confirmed satisfactory accuracy [training cohort, area under the curve (AUC) = 0.850, 0.791 and 0.823, respectively; validation cohort, AUC = 0.905, 0.884 and 0.911, respectively]. The calibration curve analysis indicated a good agreement between the nomogram-prediction and actual survival. DCA curves suggested that the nomogram had more benefit than traditional staging system models. Kaplan-Meier survival analysis indicated that patients in the low-risk group had longer OS and disease-free survival (all
conclusionThe nomogram containing the radiomics signature, NLR and AFP is a reliable tool for predicting the OS of HCC patients.
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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.