Automated grading of rectocele with an MRI radiomics model.
Authors
Affiliations (3)
Affiliations (3)
- Department of General Surgery, The Second Hospital of Jilin University, No. 4026 Yatai Street, Nanguan District, Changchun, 130000, Jilin, China.
- Department of Dermatology, The Second Hospital of Jilin University, Changchun, China.
- Department of General Surgery, The Second Hospital of Jilin University, No. 4026 Yatai Street, Nanguan District, Changchun, 130000, Jilin, China. [email protected].
Abstract
To develop an automated grading model for rectocele (RC) based on radiomics and evaluate its efficacy. This study retrospectively analyzed a total of 9,392 magnetic resonance imaging (MRI) images obtained from 222 patients who underwent dynamic magnetic resonance defecography (DMRD) over the period from August 2021 to June 2023. The focus was specifically on the defecation phase images of the DMRD, as this phase provides critical information for assessing RC. To develop and evaluate the model, the MRI images from all patients were randomly divided into two groups. 70% of the data were allocated to the training cohort to build the model, and the remaining 30% was reserved as a test cohort to evaluate its performance. First, the severity of RC was assessed using the RC MRI grading criteria by two independent radiologists. To extract and select radiomic features, two additional radiologists independently delineated the regions of interest (ROIs). These features were then dimensionality reduced to retain only the most relevant data for the analysis. The radiomics features were reduced in dimension, and a machine learning model was developed using a Support Vector Machine (SVM). Finally, receiver operating characteristic curve (ROC) and area under the curve (AUC) were used to evaluate the classification efficiency of the model. The AUC (macro/micro) of the model using defecation phase images was 0.794/0.824, and the overall accuracy was 0.754. The radiomics model built using the combination of DMRD defecation phase images is well suited for grading RC and helping clinicians diagnose and treat the disease.