Radiomics analysis using machine learning to predict perineural invasion in pancreatic cancer.

ABSTRACT:

Background

Pancreatic cancer is one of the most aggressive and lethal malignancies of the digestive system and is characterized by an extremely low five-year survival rate. The perineural invasion (PNI) status in patients with pancreatic cancer is positively correlated with adverse prognoses, including overall survival and recurrence-free survival. Emerging radiomic methods can reveal subtle variations in tumor structure by analyzing preoperative contrast-enhanced computed tomography (CECT) imaging data. Therefore, we propose the development of a preoperative CECT-based radiomic model to predict the risk of PNI in patients with pancreatic cancer.

Patients and methods

This study enrolled patients with pancreatic malignancies who underwent radical resection. Computerized tools

SUBMITTER: Sun Y 

PROVIDER: S-EPMC12487371 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature