ArticleQuantitative imaging in medicine and surgery2021
A magnetic resonance imaging (MRI)-based nomogram for predicting lymph node metastasis in rectal cancer: a node-for-node comparative study of MRI and histopathology.
Article in Quantitative imaging in medicine and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 15 citations in OpenAlex.
- A multivariate IVIM-DWI-based model for preoperative prediction of perineural invasion status in rectal cancer: a feasibility study.Magma (New York, N.Y.) · 2026Article
- Multiparametric MRI with pea pod sign and muscularis propria striped enhancement improves T2 vs T3a-b rectal cancer staging accuracy.European radiology · 2026Article
- Analysis of the clinical value of gemstone spectral computed tomography imaging in the preoperative assessment of colorectal cancer.World journal of gastrointestinal surgery · 2025Article
- Novel deep learning algorithm based MRI radiomics for predicting lymph node metastases in rectal cancer.Scientific reports · 2025Article
- A nomogram model for predicting lymph node metastasis of rectal cancer by combining preoperative magnetic resonance imaging signs and tumour markers.Polish journal of radiology · 2025Article
- Predicting axillary lymph node metastasis in breast cancer using the similarity of quantitative dual-energy CT parameters between the primary lesion and axillary lymph node.Japanese journal of radiology · 2022Article
- A nomogram model based on MRI and radiomic features developed and validated for the evaluation of lymph node metastasis in patients with rectal cancer.Abdominal radiology (New York) · 2022Article
- Intra- and Interobserver Variability in Magnetic Resonance Imaging Measurements in Rectal Cancer Patients.Cancers · 2021Article
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10 authors at 1 institution in 1 country.
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Abstract
backgroundThe aim of the present study was to investigate the potential risk factors for lymph node metastasis (LNM) in rectal cancer using magnetic resonance imaging (MRI), and to construct and validate a nomogram to predict its occurrence with node-for-node histopathological validation.
methodsOur prediction model was developed between March 2015 and August 2016 using a prospective primary cohort (32 patients, mean age: 57.3 years) that included 324 lymph nodes (LNs) from MR images with node-for-node histopathological validation. We evaluated multiple MRI variables, and a multivariable logistic regression analysis was used to develop the predictive nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. The performance of the nomogram in predicting LNM was validated in an independent clinical validation cohort comprising 182 consecutive patients.
resultsThe predictors included in the individualized prediction nomogram were chemical shift effect (CSE), nodal border, short-axis diameter of nodes, and minimum distance to rectal cancer or rectal wall. The nomogram showed good discrimination (C-index: 0.947; 95% confidence interval: 0.920-0.974) and good calibration in the primary cohort. Decision curve analysis confirmed the clinical usefulness of the nomogram in predicting the status of each LN. For the prediction of LN status in the clinical validation cohort by readers 1 and 2, the areas under the curves using the nomogram were 0.890 and 0.841, and the areas under the curves of readers using their experience were 0.754 and 0.704, respectively. Diagnostic efficiency was significantly improved by using the nomogram (P<0.001).
conclusionsThe nomogram, which incorporates CSE, nodal location, short-axis diameter, and minimum distance to rectal cancer or rectal wall, can be conveniently applied in clinical practice to facilitate the prediction of LNM in patients with rectal cancer.
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