{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T10:03:49Z","timestamp":1761213829220,"version":"build-2065373602"},"reference-count":28,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T00:00:00Z","timestamp":1761177600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"\u201cIuliu Ha\u021bieganu\u201d University of Medicine and Pharmacy"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background and Objectives: Liver fibrosis is the key prognostic factor in patients with chronic liver diseases (CLD). Computed tomography (CT) is widely used in clinical practice, but it has limited value in assessing liver fibrosis in precirrhotic stages. Quantitative CT analysis based on radiomics can provide additional information by extracting hidden image patterns, but the optimal approach remains to be determined. The aims of this study were to evaluate automated CT-based radiomic models for predicting biopsy-proven liver fibrosis, to compare different segmentation strategies and organ inclusions approaches, and to assess its performance against vibration-controlled transient elastography (VCTE). We also examined whether these models could predict liver steatosis. Methods: In this retrospective study, 58 patients with biopsy-proven CLD and 9 controls underwent VCTE and contrast-enhanced abdominal CT within three months of biopsy. Radiomic features were extracted from portal-venous-phase images using both two-dimensional (2D) and three-dimensional (3D) segmentations of the liver, spleen, and combined liver\u2013spleen. Multilayer perceptron neural (MLP) networks were trained to predict fibrosis staging (\u2265F1, \u2265F2, \u2265F3, and F4) and steatosis grading (\u2265S1, \u2265S2, and S3). Model performance was assessed using area under the receiver operating characteristic curve (AUROC) and accuracy. Results: The 3D radiomic models outperformed 2D models in predicting liver fibrosis stages. In the 3D radiomic model category, the combined 3D liver\u2013spleen model achieved very good to excellent performance (AUROCs 0.974, 0.929, 0.928, and 0.898, respectively, for \u2265F1, \u2265F2, \u2265F3, and F4), with comparable results to VCTE (AUROCs 0.921, 0.957, 0.968, and 0.909, respectively, for \u2265F1, \u2265F2, \u2265F3, and F4). Radiomic models showed poor predictive ability for steatosis grades (AUROCs 0.44\u20130.69) compared to controlled attenuation parameter (CAP) (AUROCs 0.798\u20130.917). Conclusions: CT-based radiomic models showed potential for predicting liver fibrosis stage. The 3D model of liver and spleen had the highest performance, comparable to VCTE. This approach could be valuable in clinical settings where elastography is unavailable or inconclusive and for opportunistic screening in patients already undergoing CT for other medical indications. In contrast, portal-venous-phase radiomics lacked predictive value for steatosis assessment. Larger, multicenter studies are required to validate these results.<\/jats:p>","DOI":"10.3390\/diagnostics15212671","type":"journal-article","created":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T09:44:30Z","timestamp":1761212670000},"page":"2671","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CT-Based Radiomic Models in Biopsy-Proven Liver Fibrosis Staging: Direct Comparison of Segmentation Types and Organ Inclusion"],"prefix":"10.3390","volume":"15","author":[{"given":"Andreea Mihaela","family":"Morariu-Barb","sequence":"first","affiliation":[{"name":"Department of Medical Imaging, \u201cIuliu Hatieganu\u201d University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania"},{"name":"Department of Medical Imaging, Regional Institute of Gastroenterology and Hepatology \u201cOctavian Fodor\u201d, 400162 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0097-262X","authenticated-orcid":false,"given":"Tudor","family":"Drugan","sequence":"additional","affiliation":[{"name":"Department of Medical Informatics and Biostatistics, Faculty of Medicine, Iuliu Ha\u021bieganu University of Medicine and Pharmacy, 400349 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mihai Adrian","family":"Socaciu","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, \u201cIuliu Hatieganu\u201d University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania"},{"name":"Department of Medical Imaging, Regional Institute of Gastroenterology and Hepatology \u201cOctavian Fodor\u201d, 400162 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4034-5471","authenticated-orcid":false,"given":"Horia","family":"Stefanescu","sequence":"additional","affiliation":[{"name":"Liver Unit, Regional Institute of Gastroenterology and Hepatology \u201cOctavian Fodor\u201d, 400162 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrei Demirel","family":"Morariu","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, Emergency County Hospital, 400006 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7918-1956","authenticated-orcid":false,"given":"Monica","family":"Lupsor-Platon","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, \u201cIuliu Hatieganu\u201d University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania"},{"name":"Department of Medical Imaging, Regional Institute of Gastroenterology and Hepatology \u201cOctavian Fodor\u201d, 400162 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1038\/s41575-023-00759-2","article-title":"Global epidemiology of cirrhosis\u2014Aetiology, trends and predictions","volume":"20","author":"Huang","year":"2023","journal-title":"Nat. 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