ArticleScientific reports2023
Noninvasive imaging-based machine learning algorithm to identify progressive disease in advanced hepatocellular carcinoma receiving second-line systemic therapy.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis 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
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Performance of Artificial Intelligence Models in Predicting Responsiveness of Hepatocellular Carcinoma to Transarterial Chemoembolization (TACE): A Systematic Review and Meta-Analysis.Journal of the American College of Radiology : JACR · 2026Pooled it
- Clustering of quantitative CT features identifies HCC subtypes with distinct prognosis and immune signatures.European radiology experimental · 2026Article
- Sorafenib nanomedicine in HCC: nano-bio interactions and combination therapies.Journal of nanobiotechnology · 2026Review
- Review
- Article
- A Metrology Informatics Investigation of Conversion Therapy in Hepatocellular Carcinoma: 2014-2023.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2025Article
- Machine Learning-Based Survival Analysis for Patients Receiving Lenvatinib for Unresectable Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2025Article
- The Immune Landscape of Pheochromocytoma and Paraganglioma: Current Advances and Perspectives.Endocrine reviews · 2024Review
- 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
- Machine Learning Combined with Radiomics Facilitating the Personal Treatment of Malignant Liver Tumors.Biomedicines · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this study was to predict tyrosine kinase inhibitors (TKI) plus anti-PD-1 antibodies (TKI-PD-1) efficacy as second-line treatment in advanced hepatocellular carcinoma (HCC) using radiomics analysis. From November 2018 to November 2019, a total of 55 patients were included. Radiomic features were obtained from the CT images before treatment and filtered using intraclass correlation coefficients (ICCs) and least absolute shrinkage and selection operator (LASSO) methods. Subsequently, ten prediction algorithms were developed and validated based on radiomic characteristics. The accuracy of the constructed model was measured through area under the receiver operating characteristic curve (AUC) analysis; survival analysis was performed via Kaplan-Meier and Cox regression analyses. Overall, 18 (32.7%) out of 55 patients had progressive disease. Through ICCs and LASSO, ten radiomic features were entered into the algorithm construction and validation. Ten machine learning algorithms showed different accuracies, with the support vector machine (SVM) model having the highest AUC value of 0.933 in the training cohort and 0.792 in the testing cohort. The radiomic features were associated with overall survival. In conclsion, the SVM algorithm is a useful method to predict TKI-PD-1 efficacy in patients with advanced HCC using images taken prior to treatment.
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.