{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T23:51:08Z","timestamp":1792108268399,"version":"4.4.0"},"reference-count":38,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,3,20]],"date-time":"2025-03-20T00:00:00Z","timestamp":1742428800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Lustgarten Foundation for Pancreatic Cancer Research"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Background\/Objectives: Accurate identification of grade 1 (G1) pancreatic neuroendocrine tumors (PanNETs) is crucial due to their rising incidence and emerging nonsurgical management strategies. This study evaluated whether combining conventional CT imaging features, CT radiomics features, and clinical data improves differentiation of G1 PanNETs from higher-grade tumors (G2\/G3 PanNETs and pancreatic neuroendocrine carcinomas [PanNECs]) compared to using these features individually. Methods: A retrospective analysis included 133 patients with pathologically confirmed PanNETs or PanNECs (70 males, 63 females; mean age, 58.5 years) who underwent pancreas protocol CT. A total of 28 conventional imaging features, 4892 radiomics features, and clinical data (age, gender, and tumor location) were analyzed using a support vector machine (SVM) model. Data were divided into 70% training and 30% testing sets. Results: The SVM model using the top 10 conventional imaging features (e.g., suspicious lymph nodes and hypoattenuating tumors) achieved 75% sensitivity, 81% specificity, and 79% accuracy for identifying higher-grade tumors (G2\/G3 PanNETs and PanNECs). The top 10 radiomics features yielded 94% sensitivity, 46% specificity, and 69% accuracy. Combining all features (imaging, radiomics, and clinical data) improved performance, with 94% sensitivity, 69% specificity, 79% accuracy, and an F1-score of 0.77. The radiomics score demonstrated an AUC of 0.85 in the training and 0.83 in the testing set. Conclusions: Conventional imaging features provided higher specificity, while radiomics offered greater sensitivity for identifying higher-grade tumors. Integrating all three features improved diagnostic accuracy, highlighting their complementary roles. This combined model may serve as a valuable tool for distinguishing higher-grade tumors from G1 PanNETs and potentially guiding patient management.<\/jats:p>","DOI":"10.3390\/cancers17061047","type":"journal-article","created":{"date-parts":[[2025,3,20]],"date-time":"2025-03-20T12:23:11Z","timestamp":1742473391000},"page":"1047","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Diagnostic Performance of Combined Conventional CT Imaging Features and Radiomics Signature in Differentiating Grade 1 Tumors from Higher-Grade Pancreatic Neuroendocrine Neoplasms"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9668-6574","authenticated-orcid":false,"given":"Florent","family":"Tixier","sequence":"first","affiliation":[{"name":"The Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1560-9172","authenticated-orcid":false,"given":"Felipe","family":"Lopez-Ramirez","sequence":"additional","affiliation":[{"name":"The Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1856-5184","authenticated-orcid":false,"given":"Alejandra","family":"Blanco","sequence":"additional","affiliation":[{"name":"The Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5463-5250","authenticated-orcid":false,"given":"Ammar A.","family":"Javed","sequence":"additional","affiliation":[{"name":"Department of Surgery, The NYU Grossman School of Medicine and NYU Langone Health, New York, NY 10016, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9729-2756","authenticated-orcid":false,"given":"Linda C.","family":"Chu","sequence":"additional","affiliation":[{"name":"The Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ralph H.","family":"Hruban","sequence":"additional","affiliation":[{"name":"The Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, School of Medicine, Hopkins University, Baltimore, MD 21205, USA"},{"name":"Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8177-097X","authenticated-orcid":false,"given":"Mohammad","family":"Yasrab","sequence":"additional","affiliation":[{"name":"The Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8582-8485","authenticated-orcid":false,"given":"Daniel Fadaei","family":"Fouladi","sequence":"additional","affiliation":[{"name":"The Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shahab","family":"Shayesteh","sequence":"additional","affiliation":[{"name":"The Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6292-3212","authenticated-orcid":false,"given":"Saeed","family":"Ghandili","sequence":"additional","affiliation":[{"name":"The Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2567-1658","authenticated-orcid":false,"given":"Elliot K.","family":"Fishman","sequence":"additional","affiliation":[{"name":"The Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3577-1388","authenticated-orcid":false,"given":"Satomi","family":"Kawamoto","sequence":"additional","affiliation":[{"name":"The Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1001\/jamaoncol.2017.0589","article-title":"Trends in the Incidence, Prevalence, and Survival Outcomes in Patients with Neuroendocrine Tumors in the United States","volume":"3","author":"Dasari","year":"2017","journal-title":"JAMA Oncol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/s11912-021-01029-7","article-title":"Epidemiology, Incidence, and Prevalence of Neuroendocrine Neoplasms: Are There Global Differences?","volume":"23","author":"Das","year":"2021","journal-title":"Curr. Oncol. Rep."},{"key":"ref_3","unstructured":"Osamura, R.Y., Washington, M.K., and Mete, O. (2022). WHO Classification of Tumours. 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