ArticleBMC medical imaging2025
Nomogram based on MRI images and clinical data for differentiating mucinous from non-mucinous rectal adenocarcinoma.
Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundPreoperative differentiation between rectal mucinous adenocarcinoma (MAC) and non-mucinous adenocarcinoma (NMAC) remains a clinical challenge. This study aimed to develop and validate a nomogram incorporating baseline clinical characteristics and magnetic resonance imaging (MRI) features to distinguish MAC from NMAC.
methodsThis retrospective study included clinical baseline characteristics, laboratory parameters, and MRI features of patients with MAC and NMAC from two medical centers. Relevant variables were identified using univariate logistic regression analysis. Separate models based on clinical and imaging features were developed and subsequently integrated into a nomogram. Model performance was evaluated using receiver operating characteristic (ROC) curves and the area under the curve (AUC), and decision curve analysis (DCA) was conducted to assess clinical utility.
resultsData from 221 patients (NMAC = 160, MAC = 61) from Center 1 were collected for this study. Data from 76 patients (NMAC = 54, MAC = 22) from Center 2 were used as an external validation cohort to verify the robustness of the models. We developed three models: a clinical model, an imaging feature model, and a nomogram. The nomogram integrating both clinical and imaging features demonstrated the best performance, with an AUC of 0.937 (95% CI, 0.894-0.979) in the training cohort and 0.882 (95% CI, 0.793-0.971) in the validation cohort. In the validation cohort, the nomogram achieved a sensitivity of 0.869, specificity of 0.925, and accuracy of 0.909. Furthermore, calibration curves confirmed good agreement between the predicted and observed outcomes.
conclusionsThe nomogram integrating clinical characteristics with MRI features enables efficient and practical differentiation between rectal MAC and NMAC, providing a valuable reference for individualized treatment decisions.
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