{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T10:37:59Z","timestamp":1784371079471,"version":"3.55.0"},"reference-count":29,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:00:00Z","timestamp":1769731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Next Generation EU and Ministry of University and Research","award":["2022H8F797_002"],"award-info":[{"award-number":["2022H8F797_002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Objective: To analyze radiomic features extracted from CT images of small bowel neuroendocrine tumors and evaluate their association with Ki-67 expression. Methods: 128 small bowel NET primary and secondary lesions from 34 patients were analyzed. Manual segmentation of the lesions was conducted on portal-phase CT images using ITK-SNAP v. 4.0\u00ae, and 107 radiomic features were extracted using the PyRadiomics library. The lesions were categorized into two groups based on their Ki-67 index expression (\u22641% and &gt;1%). Correlation filtering reduced the set of 107 to 41 radiomic features. Inferential statistical analyses (t-test and Mann\u2013Whitney U, following Shapiro\u2013Wilk and Levene\u2019s tests) identified 19 significant features (p &lt; 0.05) that were predominantly texture related. A ranking procedure further reduced these to eight top-performing variables across multiple selection methods (Information Gain, Gini, ANOVA, \u03c72). Five supervised Machine Learning models (Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), XGBoost, and Random Forest) were trained and validated using 5-fold cross-validation. The evaluation metrics employed included AUC, accuracy, precision, recall, F1 score, and a confusion matrix. Results: Random Forest exhibited the best overall performance (AUC = 0.80; F1 score = 0.813; Recall = 0.847). The model\u2019s low false negative rate (15.3%) suggests potential clinical utility in minimizing the risk of underestimating more aggressive lesions. Conclusions: Radiomics represents a promising frontier to identify patterns associated with histopathological markers. This study highlights its potential for non-invasive assessment of proliferative rate in small bowel neuroendocrine tumors, confirming the performance in the literature, and posing an interesting prospect for future research.<\/jats:p>","DOI":"10.3390\/cancers18030463","type":"journal-article","created":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T13:04:46Z","timestamp":1769778286000},"page":"463","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Radiomic Analysis for Ki-67 Classification in Small Bowel Neuroendocrine Tumors"],"prefix":"10.3390","volume":"18","author":[{"given":"Filippo","family":"Checchin","sequence":"first","affiliation":[{"name":"Diagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Davide","family":"Malerba","sequence":"additional","affiliation":[{"name":"Diagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7826-002X","authenticated-orcid":false,"given":"Alessandro","family":"Gambella","sequence":"additional","affiliation":[{"name":"Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, 16132 Genoa, Italy"},{"name":"Pathology Unit, IRCCS Azienda Ospedaliera Metropolitana, 16132 Genoa, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aurora Rita","family":"Puleri","sequence":"additional","affiliation":[{"name":"Diagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Virginia","family":"Sambuceti","sequence":"additional","affiliation":[{"name":"Department of Radiology, IRCCS Azienda Ospedaliera Metropolitana, 16132 Genoa, Italy"},{"name":"Department of Internal Medicine, University of Genoa, 16132 Genoa, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2976-7032","authenticated-orcid":false,"given":"Alessandro","family":"Vanoli","sequence":"additional","affiliation":[{"name":"Unit of Anatomic Pathology, Department of Molecular Medicine, University of Pavia, 27100 Pavia, Italy"},{"name":"Unit of Anatomic Pathology, IRCCS San Matteo Hospital Foundation, 27100 Pavia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6477-3182","authenticated-orcid":false,"given":"Federica","family":"Grillo","sequence":"additional","affiliation":[{"name":"Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, 16132 Genoa, Italy"},{"name":"Pathology Unit, IRCCS Azienda Ospedaliera Metropolitana, 16132 Genoa, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5479-2766","authenticated-orcid":false,"given":"Lorenzo","family":"Preda","sequence":"additional","affiliation":[{"name":"Diagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy"},{"name":"Radiology Institute, Fondazione IRCCS Policlinico San Matteo, 27100 Pavia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9193-9309","authenticated-orcid":false,"given":"Chandra","family":"Bortolotto","sequence":"additional","affiliation":[{"name":"Diagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy"},{"name":"Radiology Institute, Fondazione IRCCS Policlinico San Matteo, 27100 Pavia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100823","DOI":"10.1016\/j.cpsurg.2020.100823","article-title":"Small Bowel Neuroendocrine Tumors","volume":"57","author":"Tran","year":"2020","journal-title":"Curr. Probl. Surg."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"791","DOI":"10.4251\/wjgo.v12.i8.791","article-title":"Gastrointestinal neuroendocrine tumors in 2020","volume":"12","author":"Ahmed","year":"2020","journal-title":"World J. Gastrointest. Oncol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1159\/000503721","article-title":"Neuroendocrine Neoplasms of the Small Bowel and Pancreas","volume":"110","author":"Clift","year":"2019","journal-title":"Neuroendocrinology"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Folkestad, O., Hauso, \u00d8., Mj\u00f8nes, P., Fougner, R., Wasmuth, H.H., and Fossmark, R. (2023). Survival Trends in Patients with Small Intestinal Neuroendocrine Tumours-A Cohort Study in Central Norway. Cancers, 15.","DOI":"10.3390\/cancers15133272"},{"key":"ref_5","first-page":"93","article-title":"The Eighth Edition AJCC Cancer Staging Manual: Continuing to build a bridge from a population-based to a more \u201cpersonalized\u201d approach to cancer staging","volume":"67","author":"Amin","year":"2017","journal-title":"CA Cancer J. Clin."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"509","DOI":"10.5152\/tjg.2016.16391","article-title":"Best prognostic factor of neuroendocrine tumors: Grade or Stage? A multidisciplinary single-center study","volume":"27","author":"Ozaslan","year":"2016","journal-title":"Turk. J. Gastroenterol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1080\/02841860701218659","article-title":"Malignant ileocaecal serotonin-producing carcinoid tumours: The presence of a solid growth pattern and\/or Ki67 index above 1% identifies patients with a poorer prognosis","volume":"46","author":"Cunningham","year":"2007","journal-title":"Acta Oncol."},{"key":"ref_8","first-page":"515","article-title":"Correction to: Ki67 labeling index: Assessment and prognostic role in gastroenteropancreatic neuroendocrine neoplasms","volume":"472","year":"2017","journal-title":"Virchows Arch."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"110055","DOI":"10.1016\/j.ejrad.2021.110055","article-title":"Texture analysis imaging \u201cwhat a clinical radiologist needs to know\u201d","volume":"146","author":"Corrias","year":"2022","journal-title":"Eur. J. Radiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1007\/s11604-022-01271-4","article-title":"Radiomics in medical imaging: Pitfalls and challenges in clinical management","volume":"40","author":"Fusco","year":"2022","journal-title":"Jpn. J. Radiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1186\/s40644-024-00682-y","article-title":"Artificial intelligence-based MRI radiomics and radiogenomics in glioma","volume":"24","author":"Fan","year":"2024","journal-title":"Cancer Imaging"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1016\/j.mri.2012.06.010","article-title":"Radiomics: The process and the challenges","volume":"30","author":"Kumar","year":"2012","journal-title":"Magn. Reson. Imaging"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"488","DOI":"10.2967\/jnumed.118.222893","article-title":"Introduction to Radiomics","volume":"61","author":"Mayerhoefer","year":"2020","journal-title":"J. Nucl. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1717","DOI":"10.1148\/rg.2021210037","article-title":"Radiomics in Oncology: A Practical Guide","volume":"41","author":"Shur","year":"2021","journal-title":"RadioGraphics"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e13423","DOI":"10.1111\/jne.13423","article-title":"European Neuroendocrine Tumor Society (ENETS) 2024 guidance paper for the management of well-differentiated small intestine neuroendocrine tumours","volume":"36","author":"Lamarca","year":"2024","journal-title":"J. Neuroendocr."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1530\/ERC-21-0064","article-title":"Predicting symptomatic mesenteric mass in small intestinal neuroendocrine tumors using radiomics","volume":"28","author":"Blazevic","year":"2021","journal-title":"Endocr.-Relat. Cancer"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1007\/s11547-021-01333-z","article-title":"CT-derived radiomic features to discriminate histologic characteristics of pancreatic neuroendocrine tumors","volume":"126","author":"Benedetti","year":"2021","journal-title":"Radiol. Med."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1111\/his.13975","article-title":"The 2019 WHO classification of tumours of the digestive system","volume":"76","author":"Nagtegaal","year":"2020","journal-title":"Histopathology"},{"key":"ref_19","first-page":"3342","article-title":"ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images","volume":"2016","author":"Yushkevich","year":"2016","journal-title":"Annu. Int. Conf. IEEE Eng. Med. Biol. Soc."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1186\/s13244-020-00887-2","article-title":"Radiomics in medical imaging-\u201chow-to\u201d guide and critical reflection","volume":"11","author":"Cester","year":"2020","journal-title":"Insights Imaging"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"7278","DOI":"10.1007\/s00330-022-08996-w","article-title":"GEP-NET radiomics: A systematic review and radiomics quality score assessment","volume":"32","author":"Staal","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","article-title":"Computational Radiomics System to Decode the Radiographic Phenotype","volume":"77","author":"Fedorov","year":"2017","journal-title":"Cancer Res."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kuhn, M., and Johnson, K. (2013). Data pre-processing. Applied Predictive Modeling, Springer Science Business Media.","DOI":"10.1007\/978-1-4614-6849-3"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"De Muzio, F., Pellegrino, F., Fusco, R., Tafuto, S., Scaglione, M., Ottaiano, A., Petrillo, A., Izzo, F., and Granata, V. (2023). Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and Limits of Radiomics. Diagnostics, 13.","DOI":"10.3390\/diagnostics13182877"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1016\/j.diii.2020.06.006","article-title":"Morphological imaging and CT histogram analysis to differentiate pancreatic neuroendocrine tumor grade 3 from neuroendocrine carcinoma","volume":"101","author":"Azoulay","year":"2020","journal-title":"Diagn. Interv. Imaging"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"100651","DOI":"10.1016\/j.ejro.2025.100651","article-title":"Radiomics in differential diagnosis of pancreatic tumors","volume":"14","author":"Mascarin","year":"2025","journal-title":"Eur. J. Radiol. Open"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1002\/cam4.3628","article-title":"Prediction of the World Health Organization Grade of rectal neuroendocrine tumors based on CT histogram analysis","volume":"10","author":"Liang","year":"2020","journal-title":"Cancer Med."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1159\/000503291","article-title":"Preoperative Prediction of Pancreatic Neuroendocrine Neoplasms Grading Based on Enhanced Computed Tomography Imaging: Validation of Deep Learning with a Convolutional Neural Network","volume":"110","author":"Luo","year":"2019","journal-title":"Neuroendocrinology"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"852","DOI":"10.2214\/AJR.19.22123","article-title":"CT-Based radiomics score for distinguishing between grade 1 and grade 2 nonfunctioning pancreatic neuroendocrine tumors","volume":"215","author":"Bian","year":"2020","journal-title":"AJR Am. J. Roentgenol."}],"container-title":["Cancers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6694\/18\/3\/463\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T13:11:06Z","timestamp":1769778666000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6694\/18\/3\/463"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,30]]},"references-count":29,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["cancers18030463"],"URL":"https:\/\/doi.org\/10.3390\/cancers18030463","relation":{},"ISSN":["2072-6694"],"issn-type":[{"value":"2072-6694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,30]]}}}