SynthesisLa Radiologia medica2023
Systematic review of machine learning-based radiomics approach for predicting microsatellite instability status in colorectal cancer.
Synthesis in La Radiologia medica, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 3 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
31 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Predictive Performance of Radiomics-Based Machine Learning for Colorectal Cancer Recurrence Risk: Systematic Review and Meta-Analysis.JMIR medical informatics · 2025Pooled it
- Preoperative radiomics models using CT and MRI for microsatellite instability in colorectal cancer: a systematic review and meta-analysis.Abdominal radiology (New York) · 2025Pooled it
- Diagnostic performance of radiomics models for preoperative prediction of microsatellite instability status in endometrial cancer: a systematic review and meta-analysis.Abdominal radiology (New York) · 2025Pooled it
- Molecular subtyping and prognostic model construction based on endosome-related genes in colorectal cancer.Journal of gastrointestinal oncology · 2026Article
- Multi-DECT image-based radiomics with interpretable machine learning for preoperative prediction of tumor budding grade and prognosis in colorectal cancer: a dual-center study.Abdominal radiology (New York) · 2026Article
- Multiparametric MRI radiomics nomogram predicts synchronous distant metastasis in rectal cancer.Scientific reports · 2026Article
- Predictive value of 18F-FDG PET/CT delta radiomics model for prognosis after neoadjuvant therapy for locally advanced pancreatic cancer.Frontiers in oncology · 2026Article
- Predictive value of [European radiology · 2025Article
- Review
- How aging may impact the failure to rescue after colorectal laparoscopic surgery. Analysis of 1000 patients in a single high-volume center.Updates in surgery · 2025Article
- Computed tomography radiomics to predict microsatellite instability status and immunotherapy response in gastric cancer.Insights into imaging · 2025Article
- Systematic review and meta-analysis of deep learning for MSI-H in colorectal cancer whole slide images.NPJ digital medicine · 2025Article
- Image Analysis as tool for Predicting Colorectal Cancer Molecular Alterations: A Scoping Review.Molecular imaging and radionuclide therapy · 2025Article
- A CT-based deep learning for segmenting tumors and predicting microsatellite instability in patients with colorectal cancers: a multicenter cohort study.La Radiologia medica · 2025Article
- A Narrative Review on the Role of Artificial Intelligence (AI) in Colorectal Cancer Management.Cureus · 2025Review
- MRI management of focal liver lesions: what a beginner cannot fail to know.Frontiers in oncology · 2025Review
- Comprehensive pan-cancer analysis identifies PLAG1 as a key regulator of tumor immune microenvironment and prognostic biomarker.Frontiers in immunology · 2025Article
- Robust radiomics: a review of guidelines for radiomics in medical imaging.Frontiers in radiology · 2025Review
- Machine learning-based radiomics approach assessing preoperative non-contrast CT for microsatellite instability prediction in colon cancer.Frontiers in physiology · 2025Article
- Radiomics in radiology: What the radiologist needs to know about technical aspects and clinical impact.La Radiologia medica · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
This study aimed to systematically summarize the performance of the machine learning-based radiomics models in the prediction of microsatellite instability (MSI) in patients with colorectal cancer (CRC). It was conducted according to the preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA) guideline and was registered at the PROSPERO website with an identifier CRD42022295787. Systematic literature searching was conducted in databases of PubMed, Embase, Web of Science, and Cochrane Library up to November 10, 2022. Research which applied radiomics analysis on preoperative CT/MRI/PET-CT images for predicting the MSI status in CRC patients with no history of anti-tumor therapies was eligible. The radiomics quality score (RQS) and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) were applied to evaluate the research quality (full score 100%). Twelve studies with 4,320 patients were included. All studies were retrospective, and only four had an external validation cohort. The median incidence of MSI was 19% (range 8-34%). The area under the receiver operator curve of the models ranged from 0.78 to 0.96 (median 0.83) in the external validation cohort. The median sensitivity was 0.76 (range 0.32-1.00), and the median specificity was 0.87 (range 0.69-1.00). The median RQS score was 38% (range 14-50%), and half of the studies showed high risk in patient selection as evaluated by QUADAS-2. In conclusion, while radiomics based on pretreatment imaging modalities had a high performance in the prediction of MSI status in CRC, so far it does not appear to be ready for clinical use due to insufficient methodological quality.
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.