Trial reportCancer medicine2021
Development and validation of MRI-based deep learning models for prediction of microsatellite instability in rectal cancer.
Trial report in Cancer medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed, 2 syntheses or guidelines pooled it, 64 citations in OpenAlex.
- Radiomics for predicting microsatellite instability-high status in colorectal cancer: a systematic review and meta-analysis.World journal of surgical oncology · 2026Pooled it
- Microsatellite instability in rectal cancer: what does it mean? A study of two randomized trials and a systematic review of the literature.Histopathology · 2022Pooled it
- Predicting Mismatch-Repair Status in Rectal Cancer Using Multiparametric MRI-Based Radiomics Models: A Preliminary Study.BioMed research international · 2022Trial
- Artificial intelligence for optimization of immunotherapy: current applications and transformative potential.Frontiers in immunology · 2026Review
- Transparency and Representation in Clinical Research Utilizing Artificial Intelligence in Oncology: A Scoping Review.Cancer medicine · 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
- Amide proton transfer-weighted imaging and T1 mapping as diagnostic tools to evaluate high-risk histopathologic phenotypes before surgery in rectal adenocarcinoma and their correlation with Ki-67 expression:a two-center study.Frontiers in oncology · 2025Article
- Gastrointestinal tumor personalized immunotherapy: an integrated analysis from molecular genetics to imaging biomarkers.Therapeutic advances in gastroenterology · 2025Review
- A pelvis MR transformer-based deep learning model for predicting lung metastases risk in patients with rectal cancer.Frontiers in oncology · 2025Article
- The Role of Predictive and Prognostic MRI-Based Biomarkers in the Era of Total Neoadjuvant Treatment in Rectal Cancer.Cancers · 2024Review
- Radiology and multi-scale data integration for precision oncology.NPJ precision oncology · 2024Review
- A combinatorial MRI sequence-based radiomics model for preoperative prediction of microsatellite instability status in rectal cancer.Scientific reports · 2024Article
- An integrative clinical and CT-based tumoral/peritumoral radiomics nomogram to predict the microsatellite instability in rectal carcinoma.Abdominal radiology (New York) · 2024Article
- Performance reporting design in artificial intelligence studies using image-based TNM staging and prognostic parameters in rectal cancer: a systematic review.Annals of coloproctology · 2024Review
- Artificial Intelligence Applications in the Treatment of Colorectal Cancer: A Narrative Review.Clinical Medicine Insights. Oncology · 2024Review
- Cancer Biomarkers and Precision Oncology: A Review of Recent Trends and Innovations.Clinical Medicine Insights. Oncology · 2024Review
- A novel radiomics based on multi-parametric magnetic resonance imaging for predicting Ki-67 expression in rectal cancer: a multicenter study.BMC medical imaging · 2023Article
- Peripheral and tumor-infiltrating immune cells are correlated with patient outcomes in ovarian cancer.Cancer medicine · 2023Article
- CT-based deep learning model for the prediction of DNA mismatch repair deficient colorectal cancer: a diagnostic study.Journal of translational medicine · 2023Article
- Development and external validation of a multiparametric MRI-based radiomics model for preoperative prediction of microsatellite instability status in rectal cancer: a retrospective multicenter study.European radiology · 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 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMicrosatellite instability (MSI) predetermines responses to adjuvant 5-fluorouracil and immunotherapy in rectal cancer and serves as a prognostic biomarker for clinical outcomes. Our objective was to develop and validate a deep learning model that could preoperatively predict the MSI status of rectal cancer based on magnetic resonance images.
methodsThis single-center retrospective study included 491 rectal cancer patients with pathologically proven microsatellite status. Patients were randomly divided into the training/validation cohort (n = 395) and the testing cohort (n = 96). A clinical model using logistic regression was constructed to discriminate MSI status using only clinical factors. Based on a modified MobileNetV2 architecture, deep learning models were tested for the predictive ability of MSI status from magnetic resonance images, with or without integrating clinical factors.
resultsThe clinical model correctly classified 37.5% of MSI status in the testing cohort, with an AUC value of 0.573 (95% confidence interval [CI], 0.468 ~ 0.674). The pure imaging-based model and the combined model correctly classified 75.0% and 85.4% of MSI status in the testing cohort, with AUC values of 0.820 (95% CI, 0.718 ~ 0.884) and 0.868 (95% CI, 0.784 ~ 0.929), respectively. Both deep learning models performed better than the clinical model (p < 0.05). There was no statistically significant difference between the deep learning models with or without integrating clinical factors.
conclusionsDeep learning based on high-resolution T2-weighted magnetic resonance images showed a good predictive performance for MSI status in rectal cancer patients. The proposed model may help to identify patients who would benefit from chemotherapy or immunotherapy and determine individualized therapeutic strategies for these patients.
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.