ArticleAbdominal radiology (New York)2024
T2WI-based texture analysis predicts preoperative lymph node metastasis of rectal cancer.
Article in Abdominal radiology (New York), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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2 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.
- Applications and challenges of biomarker-based predictive models in proactive health management.Frontiers in public health · 2025Pooled it
- Factors associated with lymph node metastasis and survival in T2 colon cancer.BMC gastroenterology · 2025Article
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5 authors at 1 institution in 1 country.
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Abstract
backgroundTo prospectively develop and validate the T2WI texture analysis model based on a node-by-node comparison for improving the diagnostic accuracy of lymph node metastasis (LNM) in rectal cancer.
methodsA total of 381 histopathologically confirmed lymph nodes (LNs) were collected. LNs texture features were extracted from MRI-T2WI. Spearman's rank correlation coefficient and the least absolute shrinkage and selection operator were used for feature selection to construct the LN rad-score. Then the clinical risk factors and LN texture features were combined to establish combined predictive model. Model performance was assessed by the area under the receiver operating characteristic (ROC) curve (AUC). Decision curve analysis (DCA) and nomogram were used to evaluate the clinical application of the model.
resultsA total of 107 texture features were extracted from LN-MRI images. After selection and dimensionality reduction, the radiomics prediction model consisting of 8 texture features showed well-predictive performance in the training and validation cohorts (AUC, 0.676; 95% CI 0.582-0.771) (AUC, 0.774; 95% CI 0.648-0.899). A clinical-radiomics prediction model with the best performance was created by combining clinical and radiomics features, 0.818 (95% CI 0.742-0.893) for the training and 0.922 (95% CI 0.863-0.980) for the validation cohort. The LN Rad-score in clinical-radiomics nomogram obtained the highest classification contribution and was well calibrated. DCA demonstrated the superiority of the clinical-radiomics model.
conclusionThe lymph node T2WI-based texture features can help to improve the preoperative prediction of LNM.
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