ArticleEuropean radiology2021
The development and validation of a predictive model for recurrence in rectal cancer based on radiological and clinicopathological data.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Prognostic value of quantitative multiparametric magnetic resonance imaging parameters and histopathologic features for disease-free survival in locally advanced non-mucinous rectal adenocarcinoma.Abdominal radiology (New York) · 2026Article
- From regression to machine learning: improving prediction of rectal cancer recurrence.Annals of coloproctology · 2026Article
- Integrating perioperative inflammation and pathological invasiveness to predict recurrence after curative colorectal cancer surgery: a multicenter cohort study.Frontiers in oncology · 2026Article
- Whole-tumor amide proton transfer-weighted imaging histogram analysis to predict pathological extramural venous invasion in rectal adenocarcinoma: a preliminary study.European radiology · 2023Article
- Article
- Development and Validation of an MRI-Based Nomogram Model for Predicting Disease-Free Survival in Locally Advanced Rectal Cancer Treated With Neoadjuvant Radiotherapy.Frontiers in oncology · 2021Article
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9 authors.
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Abstract
objectiveTo develop a prediction model for recurrence by incorporating radiological and clinicopathological prognostic factors in rectal cancer patients.
methodsAll radiologic and clinicopathologic data of 489 patients with rectal cancer, retrospectively collected from a single institution between 2009 and 2013, were used to develop a predictive model for recurrence using the Cox regression. The model performance was validated on an independent cohort between 2015 and 2017 (N = 168).
resultsOut of 489 derivative patients, 103 showed recurrence after surgery. The prediction model was constructed with the following four significant predictors: distance from anal verge, MR-based extramural venous invasion, pathologic nodal stage, and perineural invasion (HR: 1.69, 2.09, 2.59, 2.29, respectively). Each factor was assigned a risk score corresponding to HR. The derivation and validation cohort were classified by sum of risk scores into 3 groups: low, intermediate, and high risk. Each of these groups showed significantly different recurrence rates (derivation cohort: 13.4%, 35.3%, 61.5 %; validation cohort: 6.2%, 23.7%, 64.7%). Our new model showed better performance in risk stratification, compared to recurrence rates of tumor node metastasis (TNM) staging in the validation cohort (stage I: 3.6%, II: 12%, III: 30.2%). The area under the receiver operating characteristic curve of the new prediction model was higher than TNM staging at 3-year recurrence in the validation cohort (0.853 vs. 0.731; p = .009).
conclusionsThe new risk prediction model was strongly correlated with a recurrence rate after rectal cancer surgery and excellent for selection of high-risk group, who needs more active surveillance. KEY POINTS: • Multivariate analysis revealed four significant risk factors to be MR-based extramural venous invasion, perineural invasion, nodal metastasis, and the short distance from anal verge among the radiologic and clinicopathologic data. • Our new recurrence prediction model including radiologic data as well as clinicopathologic data showed high predictive performance of disease recurrence. • This model can be used as a comprehensive approach to evaluate individual prognosis and helpful for the selection of highly recurrent group who needs more active surveillance.
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