ArticleBiomarker research2020
A radiomics-based model on non-contrast CT for predicting cirrhosis: make the most of image data.
Article in Biomarker research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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
33 citing papers in PubMed, 51 citations in OpenAlex.
- Radiomics nomograms based on R2* mapping and clinical biomarkers for staging of liver fibrosis in patients with chronic hepatitis B: a single-center retrospective study.European radiology · 2023Trial
- Radiomics analysis of contrast-enhanced CT for staging liver fibrosis: an update for image biomarker.Hepatology international · 2022Trial
- Multimodal artificial intelligence models for liver fibrosis staging: a scoping review.Abdominal radiology (New York) · 2026Review
- CT-based radiomics-clinical model for risk assessment of parenteral nutrition-associated hepatic steatosis in chronic intestinal failure and its metabolomic interpretation.Frontiers in nutrition · 2026Article
- Molecular Imaging Probes for Early Detection and Staging of Liver Fibrosis.Chemical & biomedical imaging · 2025Review
- CT-Based Radiomic Models in Biopsy-Proven Liver Fibrosis Staging: Direct Comparison of Segmentation Types and Organ Inclusion.Diagnostics (Basel, Switzerland) · 2025Article
- Non-invasive liver fibrosis screening on CT images using radiomics.BMC medical imaging · 2025Article
- The application performance of MRI-PDFF detection values of different hepatic lobes in diagnosing liver steatosis.Clinical and experimental medicine · 2025Article
- Precision medicine in the prediction of metachronous liver metastasis in rectal cancer: Applications and challenges.World journal of gastrointestinal oncology · 2025Article
- Intratumoral and peritumoral radiomics using multi-phase contrast-enhanced CT for diagnosis of renal oncocytoma and chromophobe renal cell carcinoma: a multicenter retrospective study.Frontiers in oncology · 2025Article
- A combination model of CT-based radiomics and clinical biomarkers for staging liver fibrosis in the patients with chronic liver disease.Scientific reports · 2024Article
- Deep-learning radiomics based on ultrasound can objectively evaluate thyroid nodules and assist in improving the diagnostic level of ultrasound physicians.Quantitative imaging in medicine and surgery · 2024Article
- Predicting Survival Using Whole-Liver MRI Radiomics in Patients with Hepatocellular Carcinoma After TACE Refractoriness.Cardiovascular and interventional radiology · 2024Article
- Noninvasive diagnosis of liver cirrhosis: qualitative and quantitative imaging biomarkers.Abdominal radiology (New York) · 2024Review
- Radiomics in colorectal cancer patients.World journal of gastroenterology · 2023Review
- Assessing Liver Fibrosis Using 2D-SWE Liver Ultrasound Elastography and Dynamic Liver Scintigraphy with 99mTc-mebrofenin: A Comparative Prospective Single-Center Study.Medicina (Kaunas, Lithuania) · 2023Article
- An integrated radiomics nomogram based on conventional ultrasound improves discriminability between fibroadenoma and pure mucinous carcinoma in breast.Frontiers in oncology · 2023Article
- Clinical-radiomic analysis for non-invasive prediction of liver steatosis on non-contrast CT: A pilot study.Frontiers in genetics · 2023Article
- Virtual biopsy in abdominal pathology: where do we stand?BJR open · 2023Review
- Editorial: The use of data mining in radiological-pathological images for personal medicine.Frontiers in genetics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo establish and validate a radiomics-based model for predicting liver cirrhosis in patients with hepatitis B virus (HBV) by using non-contrast computed tomography (CT).
methodsThis retrospective study developed a radiomics-based model in a training cohort of 144 HBV-infected patients. Radiomic features were extracted from abdominal non-contrast CT scans. Features selection was performed with the least absolute shrinkage and operator (LASSO) method based on highly reproducible features. Support vector machine (SVM) was adopted to build a radiomics signature. Multivariate logistic regression analysis was used to establish a radiomics-based nomogram that integrated radiomics signature and other independent clinical predictors. Performance of models was evaluated through discrimination ability, calibration and clinical benefits. An internal validation was conducted in 150 consecutive patients.
resultsThe radiomics signature comprised 25 cirrhosis-related features and showed significant differences between cirrhosis and non-cirrhosis cohorts (
conclusionsOur developed radiomics-based nomogram can successfully diagnose the status of cirrhosis in HBV-infected patients, that may help clinical decision-making.
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.