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

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.

Authors

Institutions

Publication Details

Journal
Journal of Cancer Research and Clinical Oncology
Published
2026-03-28
DOI
https://doi.org/10.1007/s00432-026-06462-4
Primary Topic
Pancreatic and Hepatic Oncology Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Yi Sun, Zheng Wang, Fan Xia, Qi Lan et al.
Journal of Cancer Research and Clinical Oncology
Pancreatic and Hepatic Oncology Research
article

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

Yi Sun, Zheng Wang, Fan Xia, Qi Lan, Yingying Cao, Yuying Wang
article en

Abstract

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.

Journal of Cancer Research and Clinical OncologyVol. 152(3)
Nanjing University of Chinese Medicine (CN)
Peace, Justice and strong institutions
Openalex Percentile: Top 4%
Pancreatic and Hepatic Oncology Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.