Deep learning for oncologic treatment outcomes and endpoints evaluation from CT scans in liver cancer.

ABSTRACT: Accurate treatment response assessment using serial CT scans is essential in oncological clinical trials. However, oncologists' assessment following the Response Evaluation Criteria in Solid Tumors (RECIST) guideline is subjective, time-consuming, and sometimes fallible. Advanced liver cancer often presents multifocal hepatic lesions on CT imaging, making accurate characterization more challenging than with other malignancies. In this work, we developed a tumor volume guided comprehensive objective response evaluation based on deep learning (RECORD) for liver cancer. RECORD performs liver tumor segmentation, followed by sum of the volume (SOV)-based treatment response classification and new lesion assessment. Then, it can provide treatment evaluations of response, stability, and progressio

SUBMITTER: Xia Y 

PROVIDER: S-EPMC11570623 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature