Predicting Short-term and Long-term Efficacy of HIFU Treatment for Uterine Fibroids Based on Clinical Information and MRI: A Retrospective Study.

Rationale And Objectives: This study aimed to address the challenge of predicting treatment outcomes for patients with uterine fibroids undergoing high-intensity focused ultrasound (HIFU) ablation. We developed medical-assisted diagnostic models to accurately predict the ablation rates and volume reduction rates, thus assessing both short-term and long-term treatment effects of fibroids.

Materials And Methods: For the ablation rate prediction, our study included 348 fibroids, categorized into 181 fully ablated and 167 inadequately ablated fibroids.

DMGM: deformable-mechanism based cervical cancer staging via MRI multi-sequence.

This study aims to leverage a deep learning approach, specifically a deformable convolutional layer, for staging cervical cancer using multi-sequence MRI images. This is in response to the challenges doctors face in simultaneously identifying multiple sequences, a task that computer-aided diagnosis systems can potentially improve due to their vast information storage capabilities.To address the challenge of limited sample sizes, we introduce a sequence enhancement strategy to diversify samples and mitigate overfitting.