ArticleEuropean radiology2022
Pre-treatment CT-based radiomics nomogram for predicting microsatellite instability status in colorectal cancer.
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 4 of them syntheses that pooled it.
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Who cites it
42 citing papers in PubMed, 4 syntheses or guidelines pooled it, 107 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
- 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
- Radiomics in precision medicine for colorectal cancer: a bibliometric analysis (2013-2023).Frontiers in oncology · 2024Pooled it
- Systematic review of machine learning-based radiomics approach for predicting microsatellite instability status in colorectal cancer.La Radiologia medica · 2023Pooled it
- Multitask radioclinical decision stratification in non-metastatic colon cancer: integrating MMR status, pT staging, and high-risk pathological factors.Abdominal radiology (New York) · 2026Article
- Imaging of young-onset colorectal cancer: what the radiologist needs to know.Abdominal radiology (New York) · 2025Review
- Improving radiologists' diagnostic accuracy for lymphovascular invasion in colorectal cancer: insights from a multicenter CT-based study.Abdominal radiology (New York) · 2025Article
- Integrative intratumoral and peritumoral radiomic model for predicting endometrial cancer grade.Oncology letters · 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
- Intratumoral and peritumoral radiomics for forecasting microsatellite status in gastric cancer: a multicenter study.BMC cancer · 2025Article
- A two-stage deep learning prediction system for colon cancer microsatellite instability status using CT images.Frontiers in oncology · 2025Article
- Ensemble learning for predicting microsatellite instability in colorectal cancer using pretreatment colonoscopy images and clinical data.Frontiers in oncology · 2025Article
- Establishment and Validation of a Prognostic Nomogram for Predicting Postoperative Overall Survival in Advanced Stage III-IV Colorectal Cancer Patients.Cancer medicine · 2024Article
- Exploring the value of multiple preprocessors and classifiers in constructing models for predicting microsatellite instability status in colorectal cancer.Scientific reports · 2024Article
- Non-invasive CT radiomic biomarkers predict microsatellite stability status in colorectal cancer: a multicenter validation study.European radiology experimental · 2024Article
- Prediction of non-muscle invasive bladder cancer recurrence using deep learning of pathology image.Scientific reports · 2024Article
- A radiomics-boosted deep-learning for risk assessment of synchronous peritoneal metastasis in colorectal cancer.Insights into imaging · 2024Article
- A nomogram based on MRI radiomics features of mesorectal fat for diagnosing T2- and T3-stage rectal cancer.Abdominal radiology (New York) · 2024Article
- A multicenter study: predicting KRAS mutation and prognosis in colorectal cancer through a CT-based radiomics nomogram.Abdominal radiology (New York) · 2024Article
- Preoperative prediction of microsatellite instability status in colorectal cancer based on a multiphasic enhanced CT radiomics nomogram model.BMC medical imaging · 2024Article
Corrections and comments
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Authors and funding
12 authors at 2 institutions in 2 countries.
Funding
Abstract
objectivesStratification of microsatellite instability (MSI) status in patients with colorectal cancer (CRC) improves clinical decision-making for cancer treatment. The present study aimed to develop a radiomics nomogram to predict the pre-treatment MSI status in patients with CRC.
methodsA total of 762 patients with CRC confirmed by surgical pathology and MSI status determined with polymerase chain reaction (PCR) method were retrospectively recruited between January 2013 and May 2019. Radiomics features were extracted from routine pre-treatment abdominal pelvic computed tomography (CT) scans acquired as part of the patients' clinical care. A radiomics nomogram was constructed using multivariate logistic regression. The performance of the nomogram was evaluated using discrimination, calibration, and decision curves.
resultsThe radiomics nomogram incorporating radiomics signatures, tumor location, patient age, high-density lipoprotein expression, and platelet counts showed good discrimination between patients with non-MSI-H and MSI-H, with an area under the curve (AUC) of 0.74 [95% CI, 0.68-0.80] in the training cohort and 0.77 [95% CI, 0.68-0.85] in the validation cohort. Favorable clinical application was observed using decision curve analysis. The addition of pathological characteristics to the nomogram failed to show incremental prognostic value.
conclusionsWe developed a radiomics nomogram incorporating radiomics signatures and clinical indicators, which could potentially be used to facilitate the individualized prediction of MSI status in patients with CRC. KEY POINTS: • There is an unmet need to non-invasively determine MSI status prior to treatment. However, the traditional radiological evaluation of CT is limited for evaluating MSI status. • Our non-invasive CT imaging-based radiomics method could efficiently distinguish patients with high MSI disease from those with low MSI disease. • Our radiomics approach demonstrated promising diagnostic efficiency for MSI status, similar to the commonly used IHC method.
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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.