Siegel, R. L., Kratzer, T. B., Giaquinto, A. N., Sung, H. & Jemal, A. Cancer statistics, 2025. CA Cancer J. Clin. 75, 10–45 (2025).
Rahib, L., Wehner, M. R., Matrisian, L. M. & Nead, K. T. Estimated projection of US cancer incidence and death to 2040. JAMA Netw. Open. 4, e214708 (2021).
Oettle, H. et al. Adjuvant chemotherapy with gemcitabine and long-term outcomes among patients with resected pancreatic cancer: the CONKO-001 randomized trial. JAMA 310, 1473–1481 (2013).
Neoptolemos, J. P. et al. Comparison of adjuvant gemcitabine and capecitabine with gemcitabine monotherapy in patients with resected pancreatic cancer (ESPAC-4): a multicentre, open-label, randomised, phase 3 trial. Lancet 389, 1011–1024 (2017).
Conroy, T. et al. FOLFIRINOX or gemcitabine as adjuvant therapy for pancreatic cancer. N. Engl. J. Med. 379, 2395–2406 (2018).
Rigiroli, F. et al. CT radiomic features of superior mesenteric artery involvement in pancreatic ductal adenocarcinoma: a pilot study. Radiology 301, 610–622 (2021).
Giovinazzo, F., Turri, G., Katz, M. H., Heaton, N. & Ahmed, I. Meta-analysis of benefits of portal–superior mesenteric vein resection in pancreatic resection for ductal adenocarcinoma. Br. J. Surg. 103, 179–191 (2016).
Wang, X. et al. Venous resection during pancreatectomy for pancreatic cancer: a systematic review. Transl. Gastroenterol. Hepatol. 4, 46 (2019).
Delpero, J. R. & Sauvanet, A. Vascular resection for pancreatic cancer: 2019 French recommendations based on a literature review from 2008 to 6–2019. Front. Oncol. 10, 40 (2020).
Al-Hawary, M. M. et al. Pancreatic ductal adenocarcinoma radiology reporting template: consensus statement of the Society of Abdominal Radiology and the American Pancreatic Association. Radiology 270, 248–260 (2014).
Zamboni, G. A. et al. Pancreatic adenocarcinoma: value of multidetector CT angiography in preoperative evaluation. Radiology 245, 770–778 (2007).
National Comprehensive Cancer Network. NCCN Clinical Practice Guidelines in Oncology: Pancreatic Adenocarcinoma (Version 2.2024) (National Comprehensive Cancer Network, 2024).
Miao, Y., Cai, B. & Lu, Z. Technical options in surgery for artery-involving pancreatic cancer: invasion depth matters. Surg. Open Sci. 12, 55–61 (2023).
Bockhorn, M. et al. Borderline resectable pancreatic cancer: a consensus statement by the International Study Group of Pancreatic Surgery (ISGPS). Surgery. 155, 977–988 (2014).
Brook, O. R. et al. Structured reporting of multiphasic CT for pancreatic cancer: potential effect on staging and surgical planning. Radiology. 274, 464–472 (2015).
Alemi, F., Rocha, F. G., Helton, W. S., Biehl, T. & Alseidi, A. Classification and techniques of en bloc venous reconstruction for pancreaticoduodenectomy. HPB 18, 827–834 (2016).
Hong, S. B. et al. Pancreatic cancer CT: prediction of resectability according to NCCN criteria. Radiology 289, 710–719 (2018).
Ferrone, C. R. et al. Radiological and surgical implications of neoadjuvant treatment with FOLFIRINOX for locally advanced and borderline resectable pancreatic cancer. Ann. Surg. 261, 12–17 (2015).
Michelakos, T. et al. Predictors of resectability and survival in patients with borderline and locally advanced pancreatic cancer who underwent neoadjuvant treatment with FOLFIRINOX. Ann. Surg. 269, 733–740 (2019).
Asbun, H. J. et al. The miami international evidence-based guidelines on minimally invasive pancreas resection. Ann. Surg. 271, 1–14 (2020).
Cao, K. et al. Large-scale pancreatic cancer detection via non-contrast CT and deep learning. Nat. Med. 29, 3033–3043 (2023).
Placido, D. et al. A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories. Nat. Med. 29, 1113–1122 (2023).
Fu, N. et al. A deep-learning radiomics-based lymph node metastasis predictive model for pancreatic cancer: a diagnostic study. Int. J. Surg. 109, 2196–2203 (2023).
Antony, A. et al. AI-driven insights in pancreatic cancer imaging: from pre-diagnostic detection to prognostication. Abdom. Radiol. 50, 3214–3224 (2025).
Murray, K. et al. Biomarkers, omics and artificial intelligence for early detection of pancreatic cancer. Semin. Cancer Biol. 111, 76–88 (2025).
Bereska, J. I. et al. Artificial intelligence for assessment of vascular involvement and tumor resectability on CT in patients with pancreatic cancer. Eur. Radiol. Exp. 8, 18 (2024).
Vivier, C. et al. Segmentation-based assessment of tumor–vessel involvement for surgical resectability prediction of pancreatic ductal adenocarcinoma. In Proc. IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) 2413–2423 (IEEE, 2023).
Bian, Y. et al. Performance of CT-based radiomics in diagnosis of superior mesenteric vein resection margin in patients with pancreatic head cancer. Abdom. Radiol. 45, 759–773 (2020).
Chen, F. et al. Radiomics-assisted presurgical prediction for surgical portal vein–superior mesenteric vein invasion in pancreatic ductal adenocarcinoma. Front. Oncol. 10, 523543 (2020).
Cohen, J. F. et al. STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration. BMJ Open. 6, e012799 (2016).
Collins, G. S., Reitsma, J. B., Altman, D. G. & Moons, K. G. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ 350, g7594 (2015).
Isensee, F. et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods. 18, 203–211 (2021).
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