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Background <p>Rectocele (RC) is a common pelvic organ prolapse (POP) that can cause obstructed defecation and reduced quality of life. Magnetic resonance defecography (MRD) offers high-resolution, radiation-free visualization of pelvic floor anatomy but relies on time-consuming, observer-dependent manual measurements. Our research constructs a nomogram model incorporating intra-ROI and habitat radiomics features to improve MRD-based RC diagnosis.</p> Methods <p>We retrospectively analyzed 222 MRD patients (155 training, 67 testing). Clinical features were selected via univariate and multivariate logistic regression. The least absolute shrinkage and selection operator (LASSO) algorithm was applied, and features with non-zero coefficients were retained to construct the radiomics signatures. A support vector machine (SVM) learning algorithm was used to construct the intra-ROI combined with the habitat radiomics model. Clinical features were then combined with radiomics features using a multivariable logistic regression algorithm to generate a clinical-radiomics nomogram. Model performance was assessed using receiver operating characteristic curve (ROC) and decision curve analysis (DCA).</p> Results <p>The combined intra-ROI and habitat radiomics model outperformed intra-ROI or habitat radiomics models alone, achieving areas under the curve (AUCs) of 0.913 (training) and 0.805 (testing). The nomogram integrating radiomics features and gender showed strong calibration and discrimination, with AUCs of 0.930 and 0.852 in the training and testing cohorts, respectively.</p> Conclusions <p>Our findings suggest that integrating intra-ROI with habitat radiomics features can aid RC assessment. While the clinical–radiomics nomogram showed the highest internal performance, this single-center retrospective study lacks external validation and includes a relatively small test cohort. Therefore, risk of model overfitting cannot be excluded. Prospective, multi-center validation and larger cohorts are warranted before routine clinical deployment.</p>

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A radiomics nomogram utilizing T2-weighted MRI for accurate diagnosis of rectocele

  • 期刊论文

  • 发表日期：

    2025年9月25日

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  • Weiwei Lai,
  • Guanghong Wang,
  • Zeyun Zhao

摘要

Background

Rectocele (RC) is a common pelvic organ prolapse (POP) that can cause obstructed defecation and reduced quality of life. Magnetic resonance defecography (MRD) offers high-resolution, radiation-free visualization of pelvic floor anatomy but relies on time-consuming, observer-dependent manual measurements. Our research constructs a nomogram model incorporating intra-ROI and habitat radiomics features to improve MRD-based RC diagnosis.

Methods

We retrospectively analyzed 222 MRD patients (155 training, 67 testing). Clinical features were selected via univariate and multivariate logistic regression. The least absolute shrinkage and selection operator (LASSO) algorithm was applied, and features with non-zero coefficients were retained to construct the radiomics signatures. A support vector machine (SVM) learning algorithm was used to construct the intra-ROI combined with the habitat radiomics model. Clinical features were then combined with radiomics features using a multivariable logistic regression algorithm to generate a clinical-radiomics nomogram. Model performance was assessed using receiver operating characteristic curve (ROC) and decision curve analysis (DCA).

Results

The combined intra-ROI and habitat radiomics model outperformed intra-ROI or habitat radiomics models alone, achieving areas under the curve (AUCs) of 0.913 (training) and 0.805 (testing). The nomogram integrating radiomics features and gender showed strong calibration and discrimination, with AUCs of 0.930 and 0.852 in the training and testing cohorts, respectively.

Conclusions

Our findings suggest that integrating intra-ROI with habitat radiomics features can aid RC assessment. While the clinical–radiomics nomogram showed the highest internal performance, this single-center retrospective study lacks external validation and includes a relatively small test cohort. Therefore, risk of model overfitting cannot be excluded. Prospective, multi-center validation and larger cohorts are warranted before routine clinical deployment.