ArticleJournal of gastrointestinal oncology2023
Computed tomography radiomics signature via machine learning predicts
Article in Journal of gastrointestinal oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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
4 citing papers in PubMed, 6 citations in OpenAlex.
- Current applications of radiomics in the assessment of tumor microenvironment of hepatocellular carcinoma.Abdominal radiology (New York) · 2025Review
- Article
- Computed tomography-based radiomics predicts the fibroblast-related geneWorld journal of clinical cases · 2024Article
- A Transcriptomic Biomarker for Predicting the Response to TACE Correlates with the Tumor Microenvironment and Radiomics Features in Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Radiomics can be used to noninvasively predict molecular markers to address the clinical dilemma that some patients cannot accept invasive procedures. This research evaluated the prognostic significance of the expression level of ribonucleotide reductase regulatory subunit M2 ( Methods: Genomic data for HCC patients and corresponding computed tomography (CT) images were accessed at The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA), which were utilized for prognosis analysis, radiomic feature extraction and model construction, respectively. The maximum relevance minimum redundancy algorithm (mRMR) and recursive feature elimination (RFE) were used for feature selection. Following feature extraction, a logistic regression algorithm was fitted to establish a dichotomous model that predicts Results: High Conclusions: The
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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.