Computed Tomography Scans in the Prediction of Margin‐Negative Resection in Pancreatic Cancer Following Neoadjuvant Treatment: An Updated Systematic Review and Meta‐Analysis
Year: 2026
Type: review
Study designs: Systematic Review, Meta-Analysis
Abstract: ABSTRACT Pancreatic cancer is a highly lethal malignancy with a rising incidence. An early diagnosis of pancreatic cancer is challenging, and although computed tomography (CT) scans are the primary imaging modality for assessing resectability after neoadjuvant treatment, their accuracy in predicting margin‐negative (R0) resection remains a concern. This systematic review and meta‐analysis evaluated the role of CT scans in predicting R0 resection in patients with pancreatic cancer post‐neoadjuvant therapy. A search of PubMed, Scopus, and Google Scholar was conducted up to October 4, 2025, with adherence to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines. Study selection, data extraction, and quality assessment were performed independently by two reviewers. Pooled sensitivity, specificity, and the area under the curve were assessed using the bivariate random‐effects model. Nine unique articles providing 10 study estimates met the inclusion criteria. A meta‐analysis showed significant heterogeneity for specificity ( I 2 = 87%) and sensitivity ( I 2 = 90%). Pooled sensitivity was 0.50 (95% confidence interval [CI]: 0.34–0.66) and pooled specificity was 0.75 (95% CI: 0.61–0.85). The area under the curve for overall diagnostic performance was 0.69 (95% CI: 0.65–0.73). The pooled diagnostic odds ratio was 3.00 (95% CI: 1.85–4.86). Deeks' funnel plot analysis showed no significant publication bias. Although CT scans play a crucial role in pancreatic cancer staging, their ability to predict R0 resection after neoadjuvant therapy is limited by low sensitivity and moderate specificity, as shown by the low area under the curve. These findings indicate the requirement for multimodal preoperative assessment strategies incorporating advanced imaging techniques and other diagnostic modalities to improve surgical planning and patients' outcomes.
Source: iRadiology
DOI: 10.1002/ird3.70064
Institutions: Tehran University of Medical Sciences, Mazandaran University of Medical Sciences, Shiraz University of Medical Sciences, Islamic Azad University of Tabriz, Ondokuz Mayıs University, Islamic Azad University of Najafabad, Iran University of Medical Sciences, Dezful University of Medical Sciences, Islamic Azad University, Mashhad, Shahid Beheshti University, Shahid Beheshti University of Medical Sciences, Tabriz University of Medical Sciences, University of Isfahan
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7153876949