An auxiliary diagnosis model for the pathological classification of cervical cancer based on radiomics biomarkers

Cervical cancer remains a major global health burden, and accurate pathological classification is essential for personalized treatment planning. However, conventional radiomics studies often rely on manual lesion delineation and are limited in extracting meaningful imaging biomarkers from heterogeneous cervical cancer lesions.

We proposed a convolutional recurrent feature extraction (CRFE)-based automatic segmentation framework for cervical cancer MRI images and developed histogram-based imaging features reflecting lesion pixel concentration trends. These features were integrated with conventional radiomics and clinical features. Feature engineering and machine learning classifiers, including random forest (RF), XGBoost, support vector machine, and logistic regression, were evaluated to construct an auxiliary diagnostic model for pathological classification. The dataset included 114 patients with cervical cancer who underwent MRI examinations.

The CRFE segmentation model achieved an Intersection over Union (IoU) of 0.9443, a Dice coefficient of 0.5980, and an F1-score of 0.7085. Feature selection retained 30 key imaging biomarkers, including the median of the histogram, GLSZM large-area low gray-level emphasis (LoG, σ = 2.0mm, 3D), and GLRLM long-run low gray-level emphasis (LoG, σ = 2.0mm, 3D). Among the evaluated classifiers, the RF model achieved the best performance, with an accuracy of 87.27% and an F1-score of 86.91% in pathological classification.

The proposed deep learning-radiomics framework enables accurate lesion segmentation and effective pathological classification of cervical cancer. This auxiliary diagnostic model may reduce unnecessary invasive procedures and improve early screening and clinical decision-making.