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Background <p>Preoperative 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.</p> Methods <p>This 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.</p> Results <p>Data 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.</p> Conclusions <p>The 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.</p>

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Nomogram based on MRI images and clinical data for differentiating mucinous from non-mucinous rectal adenocarcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月25日

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  • Shuzhen Wu,
  • Zhipeng Wang,
  • Chenyang Qiu,
  • Yinchao Ma,
  • Jiahao Liu,
  • Kun Han,
  • Ming Li,
  • Mengjun Xiao,
  • Wenting Fu,
  • Haiyan Wang

摘要

Background

Preoperative 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.

Methods

This 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.

Results

Data 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.

Conclusions

The 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.