ArticleEBioMedicine2020
Development and validation of MMR prediction model based on simplified clinicopathological features and serum tumour markers.
Article in EBioMedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Development and Interpretation of a Clinicopathological-Based Model for the Identification of Microsatellite Instability in Colorectal Cancer.Disease markers · 2023Trial
- Certain pMMR colorectal cancer patients should undergo additional MSI-PCR testing to reduce the risk of misdiagnosing MSI-H and Lynch syndrome.BMC cancer · 2025Article
- Apolipoprotein A-I levels in the survival of patients with colorectal cancer: a retrospective study.Frontiers in endocrinology · 2024Article
- Clinicopathological characteristics of high microsatellite instability/mismatch repair-deficient colorectal cancer: A narrative review.Frontiers in immunology · 2022Review
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11 authors.
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Abstract
backgroundAlthough simplified clinicopathological features and serum tumour markers (STMs) were reported to be associated with the status of mismatch repair (MMR) in colorectal cancer (CRC) patients, their predictive value alone or in combination for MMR status remains unknown.
methodsA retrospective analysis of 3274 participants with MMR testing and STMs measurements from two institutions was conducted. The prediction model was developed in the primary cohort that consisted of 1964 participants. Best subset regression was applied to select the most useful predictors from the primary dataset. The performance of the nomogram was evaluated with respect to its calibration, discrimination, and clinical usefulness. External validation was performed in an independent validation cohort of 1310 consecutive CRC patients.
findingsAmong the ten simplified clinicopathological features, seven variables were selected as the best subset of risk factors to develop pathology-based model, including age, tumour diameters, histology, tumour location, perineural invasion, the number of sampled lymph nodes (LNs) and positive LNs. The model showed good calibration and discrimination, with an AUC of 0.756 (95% CI, 0.722 to 0.789) in the primary cohort and 0.754 (95% CI, 0.715 to 0.793) in the validation cohort. After the addition of CEA and CA 72-4, the performance of pathology-based model was significantly improved in in both the primary cohort (AUC: 0.805 (0.774-0.835) vs. 0.756 (0.722-0.789), P < 0.001) and validation cohort (AUC: 0.796 (0.758-0.835) vs. 0.754 (0.715-0.793), P < 0.001). The results of decision curve analysis revealed that using our models to predict the status of MMR would add more benefit than either the detect-all-patients scheme or the detect-none scheme.
interpretationThe models based on simplified clinicopathological features alone or in combination with STMs can be conveniently used to facilitate the postoperative individualized prediction of MMR status in CRC patients.
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