Cervical cancer diagnosis from diffusion weighted imaging using deep convolutional neural networks

Abstract

Motivation: Conventional grading of cervix cancer (CC) requires biopsy that can lead to potential side-effects. Radiologists typically rely on multi-imaging for CC staging, which can result in patient discomfort, higher costs, and increased workload. Goal(s): This study aims to introduce noninvasive methods that leverage a single MRI for both grading and staging prediction. Approach: EfficientNetB0 and EfficientNetB3 were applied for tumor classification (binary and four-class) based on apparent diffusion coefficient maps of 85 patients. They were evaluated using the area under the receiver operating characteristic curve (AUC). Results: High AUC=0.924 and AUC=0.931 were obtained for grade and stage predictions respectively. Impact: The results demonstrated the feasibility of noninvasive prediction of cervical cancer grade and stage from diffusion weighted images. This could significantly impact the diagnosis and management of cervical cancer, as it can provide valuable information without biopsy or extensive imaging.