Work

CT-based intratumoral, peritumoral radiomics and clinical features: a combined model for perineural invasion prediction in PDAC

Year: 2026

Type: article

Abstract: PURPOSE: To develop and validate a general radiomics nomogram capable of identifying perineural invasion (PNI) status in pancreatic ductal adenocarcinoma (PDAC) patients. METHODS: A total of 175 pancreatic cancer patients were retrospectively enrolled in this study. Patients were randomly divided into a training cohort (n = 123) and a test cohort (n = 52) at a ratio of 7:3. Senior physicians manually delineated the intratumoral region of interest (ROI), and the peritumoral ROI was obtained by expanding 3 mm outward from the intratumoral ROI. After extracting and selecting radiomics features, the ExtraTrees algorithm was used to construct intratumoral, peritumoral, and intratumoral + peritumoral radiomics models, respectively. The optimal radiomics model was selected to construct a combined model with clinical characteristics. Receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were used to evaluate the predictive performance of the models. RESULTS: Multivariate analysis showed that carbohydrate antigen 199 (CA199) (p < 0.05) and vascular invasion (p < 0.05) were associated with an increased risk of PNI. In the training cohort, the area under the curve (AUC), sensitivity, and specificity of the combined model were 0.855, 76.8%, and 80.5%, respectively; in the test cohort, they were 0.844, 76.5%, and 77.8%, respectively. The performance of the combined model was superior to that of the clinical model or radiomics model alone. CONCLUSIONS: The combined predictive model integrating intratumoral + peritumoral radiomics features based on contrast-enhanced computed tomography (CE-CT) with clinical characteristics can effectively predict PNI in pancreatic cancer.

Source: Journal of Cancer Research and Clinical Oncology

DOI: 10.1007/s00432-026-06462-4

Authors: Qi Lan, Yuying Wang, Fan Xia, Yalin Sun, Yingying Cao, Zhongqiu Wang

Institution: Nanjing University of Chinese Medicine

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $4,390