ArticleFrontiers in oncology2022
Prediction of early recurrence of HCC after hepatectomy by contrast-enhanced ultrasound-based deep learning radiomics.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses 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
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 22 citations in OpenAlex.
- Artificial intelligence for the prediction of posthepatectomy recurrence in hepatocellular carcinoma: a systematic review and meta-analysis.Annals of medicine · 2025Pooled it
- Artificial intelligence in predicting recurrence after first-line treatment of liver cancer: a systematic review and meta-analysis.BMC medical imaging · 2024Pooled it
- AI-Driven Predictive Models of Early Recurrence of HCC After Surgical Resection: A Systematic Review.Cancers · 2026Review
- Artificial intelligence in contrast enhanced ultrasound: A new era for liver lesion assessment.World journal of gastroenterology · 2025Review
- Gd-EOB-DTPA-enhanced MRI radiomics and deep learning models to predict microvascular invasion in hepatocellular carcinoma: a multicenter study.BMC medical imaging · 2025Article
- CT-Based 2.5D Deep Learning-Multi-Instance Learning for Predicting Early Recurrence of Hepatocellular Carcinoma and Correlating with Recurrence-Related Pathological Indicators.Journal of hepatocellular carcinoma · 2025Article
- Artificial intelligence-aided ultrasound imaging in hepatopancreatobiliary surgery: where are we now?Surgical endoscopy · 2024Review
- Research on ultrasound-based radiomics: a bibliometric analysis.Quantitative imaging in medicine and surgery · 2024Article
- A clinical-radiomic-pathomic model for prognosis prediction in patients with hepatocellular carcinoma after radical resection.Cancer medicine · 2024Article
- Dual-modal radiomics nomogram based on contrast-enhanced ultrasound to improve differential diagnostic accuracy and reduce unnecessary biopsy rate in ACR TI-RADS 4-5 thyroid nodules.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Machine learning-based ultrasomics for predicting response to tyrosine kinase inhibitor in combination with anti-PD-1 antibody immunotherapy in hepatocellular carcinoma: a two-center study.Frontiers in oncology · 2024Article
- Bridging the Gap Between Imaging and Molecular Characterization: Current Understanding of Radiomics and Radiogenomics in Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2024Review
- Prediction of 3-year recurrence rate of hepatocellular carcinoma after resection based on contrast-enhanced CT: a single-centre study.The British journal of radiology · 2023Article
- Radiological findings in non-surgical recurrent hepatocellular carcinoma: From locoregional treatments to immunotherapy.World journal of gastroenterology · 2023Review
- CNN-based automatic segmentations and radiomics feature reliability on contrast-enhanced ultrasound images for renal tumors.Frontiers in oncology · 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
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This study aims to evaluate the predictive model based on deep learning (DL) and radiomics features from contrast-enhanced ultrasound (CEUS) to predict early recurrence (ER) in patients with hepatocellular carcinoma (HCC). Methods: One hundred seventy-two patients with HCC who underwent hepatectomy and followed up for at least 1 year were included in this retrospective study. The data were divided according to the 7:3 ratios of training and test data. The ResNet-50 architecture, CEUS-based radiomics, and the combined model were used to predict the early recurrence of HCC after hepatectomy. The receiver operating characteristic (ROC) curve and calibration curve were drawn to evaluate its diagnostic efficiency. Results: The CEUS-based radiomics ROCs of the "training set" and "test set" were 0.774 and 0.763, respectively. The DL model showed increased prognostic value, the ROCs of the "training set" and "test set" were 0.885 and 0.834, respectively. The combined model ROCs of the "training set" and "test set" were 0.943 and 0.882, respectively. Conclusion: The deep learning radiomics model integrating DL and radiomics features from CEUS was used to predict ER and achieve satisfactory performance. Its diagnostic efficiency is significantly better than that of the single model.
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.