Histopathologic basis of a deep learning pelvic computed tomography model for prognostic prediction among patients with advanced high-grade serous ovarian carcinoma
Key Points
The deep learning model effectively extracts histopathological features from CT images, making prognostic predictions possible.
With accurate extraction from imaging, the model enhances the ability to predict outcomes in patients with high-grade serous ovarian carcinoma.
Analysis focuses on the deep learning approach, assessing its performance using advanced image processing techniques.
These findings highlight the potential for integrating advanced imaging with machine learning to improve prognostic models in cancer care.
Abstract
The DL model could effectively extract histopathological features of high-grade serous ovarian cancer from CT images.
Histopathologic basis of a deep learning pelvic computed tomography model for prognostic prediction among patients with advanced high-grade serous ovarian carcinoma | Synapse