{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T12:13:40Z","timestamp":1785413620500,"version":"3.56.0"},"reference-count":103,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"\u201cDunarea de Jos\u201d University of Galati","award":["RO50411550"],"award-info":[{"award-number":["RO50411550"]}]},{"award":["RO50411550"],"award-info":[{"award-number":["RO50411550"]}],"id":[{"id":"https:\/\/ror.org\/052sta926","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Multiparametric ultrasound (MPUS) has emerged as a clinically relevant platform for the non-invasive generation of quantitative imaging biomarkers, transforming conventional abdominal sonography from a morphological screening tool into a functional, data-rich instrument aligned with predictive and personalized medicine. This review aims to synthesize current evidence on MPUS-derived imaging biomarkers across the principal abdominal organ systems and to examine their integration with radiomics and artificial intelligence. A narrative structured review was conducted through searches of PubMed\/MEDLINE, Scopus, and Web of Science (January 2007\u2013May 2026), combining terms related to multiparametric ultrasound, elastography, contrast-enhanced ultrasound (CEUS), quantitative ultrasound, radiomics, and imaging biomarkers. Articles were selected purposively on the basis of relevance, methodological quality, and recency; the review does not follow PRISMA methodology and does not include a formal quantitative synthesis. MPUS integrates B-mode imaging, Doppler, elastography, CEUS, and quantitative acoustic parameters (attenuation coefficient, sound speed, and viscosity) within a single examination. In hepatology, multiparametric protocols enable non-invasive staging of steatosis, fibrosis, and steatohepatitis in metabolic dysfunction-associated steatotic liver disease, portal hypertension assessment, and focal lesion characterization. Emerging applications span pancreatic, renal, vascular, and digestive pathology. The integration of radiomics and artificial intelligence amplifies biomarker potential, enabling molecular subtype prediction, treatment-response assessment, and prognostic stratification. Multiparametric abdominal ultrasound holds genuine promise as a central, non-invasive platform in precision medicine. Standardization of protocols, multicenter validation of quantitative thresholds, and integration with clinical, biochemical, and molecular data remain the principal challenges limiting broader clinical translation.<\/jats:p>","DOI":"10.3390\/diagnostics16132045","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T16:56:55Z","timestamp":1782838615000},"page":"2045","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multiparametric Abdominal Ultrasound as a Source of Imaging Biomarkers in Predictive and Personalized Medicine: A Narrative Review"],"prefix":"10.3390","volume":"16","author":[{"given":"Simona Steliana","family":"Tudor","sequence":"first","affiliation":[{"name":"Faculty of Medicine and Pharmacy, Medical-Pharmaceutical Research Center, \u201cDunarea de Jos\u201d University of Gala\u021bi, 800008 Gala\u021bi, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3855-2056","authenticated-orcid":false,"given":"Ancuta Elena","family":"Tupu","sequence":"additional","affiliation":[{"name":"Faculty of Medicine and Pharmacy, Medical-Pharmaceutical Research Center, \u201cDunarea de Jos\u201d University of Gala\u021bi, 800008 Gala\u021bi, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1514-953X","authenticated-orcid":false,"given":"Ionela Daniela","family":"Fer\u021bu","sequence":"additional","affiliation":[{"name":"Faculty of Medicine and Pharmacy, Medical-Pharmaceutical Research Center, \u201cDunarea de Jos\u201d University of Gala\u021bi, 800008 Gala\u021bi, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caterina Nela","family":"Dumitru","sequence":"additional","affiliation":[{"name":"Faculty of Medicine and Pharmacy, Medical-Pharmaceutical Research Center, \u201cDunarea de Jos\u201d University of Gala\u021bi, 800008 Gala\u021bi, Romania"},{"name":"Clinical Hospital of Infectious Diseases \u201cSt. Venerable Parascheva\u201d, 800179 Gala\u021bi, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claudia Simona","family":"Stefan","sequence":"additional","affiliation":[{"name":"Faculty of Medicine and Pharmacy, Medical-Pharmaceutical Research Center, \u201cDunarea de Jos\u201d University of Gala\u021bi, 800008 Gala\u021bi, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1038\/nrclinonc.2016.162","article-title":"Imaging biomarker roadmap for cancer studies","volume":"14","author":"Aboagye","year":"2017","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1636","DOI":"10.1001\/jamaoncol.2016.2631","article-title":"The Potential of Radiomic-Based Phenotyping in Precision Medicine: A Review","volume":"2","author":"Aerts","year":"2016","journal-title":"JAMA Oncol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","article-title":"Radiomics: Extracting more information from medical images using advanced feature analysis","volume":"48","author":"Lambin","year":"2012","journal-title":"Eur. J. Cancer"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1663","DOI":"10.3748\/wjg.v30.i12.1663","article-title":"Multiparametric ultrasound as a new concept of assessment of liver tissue damage","volume":"30","author":"Peltec","year":"2024","journal-title":"World J. Gastroenterol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.rcl.2024.07.003","article-title":"Multiparametric Ultrasound for Chronic Liver Disease","volume":"63","author":"Barr","year":"2025","journal-title":"Radiol. Clin. N. Am."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"252","DOI":"10.4103\/EUS-D-21-00151","article-title":"Ultrasound elastography","volume":"11","author":"Cui","year":"2022","journal-title":"Endosc. Ultrasound"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.7150\/thno.18650","article-title":"Ultrasound Elastography: Review of Techniques and Clinical Applications","volume":"7","author":"Sigrist","year":"2017","journal-title":"Theranostics"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1136\/gut.2006.111302","article-title":"Reproducibility of transient elastography in the evaluation of liver fibrosis in patients with chronic liver disease","volume":"56","author":"Fraquelli","year":"2007","journal-title":"Gut"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"485","DOI":"10.5152\/dir.2019.19321","article-title":"Radiomics with artificial intelligence: A practical guide for beginners","volume":"25","author":"Durmaz","year":"2019","journal-title":"Diagn. Interv. Radiol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Maniaci, A., Lavalle, S., Gagliano, C., Lentini, M., Masiello, E., Parisi, F., Iannella, G., Cilia, N.D., Salerno, V., and Cusumano, G. (2024). The Integration of Radiomics and Artificial Intelligence in Modern Medicine. Life, 14.","DOI":"10.3390\/life14101248"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"105518","DOI":"10.3748\/wjg.v31.i25.105518","article-title":"Multiparametric ultrasound for non-invasive assessment of liver steatosis, fibrosis, and inflammation in metabolic dysfunction-associated steatotic liver disease","volume":"31","author":"Liguori","year":"2025","journal-title":"World J. Gastroenterol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1053\/j.gastro.2018.12.036","article-title":"Noninvasive Assessment of Liver Disease in Patients with Nonalcoholic Fatty Liver Disease","volume":"156","author":"Castera","year":"2019","journal-title":"Gastroenterology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1055\/s-0042-124369","article-title":"Contrast Enhanced Ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS\u00ae): The official version by the American College of Radiology (ACR)","volume":"38","author":"Kono","year":"2017","journal-title":"Ultraschall Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e232369","DOI":"10.1148\/radiol.232369","article-title":"Liver Imaging Reporting and Data System Contrast-Enhanced US Nonradiation Treatment Response Assessment Version 2024","volume":"311","author":"Lyshchik","year":"2024","journal-title":"Radiology"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e2","DOI":"10.1055\/a-0586-1107","article-title":"The EFSUMB Guidelines and Recommendations for the Clinical Practice of Contrast-Enhanced Ultrasound (CEUS) in Non-Hepatic Applications: Update 2017 (Long Version)","volume":"39","author":"Sidhu","year":"2018","journal-title":"Ultraschall Med."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Puca, P., Del Vecchio, L.E., Ainora, M.E., Gasbarrini, A., Scaldaferri, F., and Zocco, M.A. (2022). Role of Multiparametric Intestinal Ultrasound in the Evaluation of Response to Biologic Therapy in Adults with Crohn\u2019s Disease. Diagnostics, 12.","DOI":"10.3390\/diagnostics12081991"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1038\/s41568-018-0016-5","article-title":"Artificial intelligence in radiology","volume":"18","author":"Hosny","year":"2018","journal-title":"Nat. Rev. Cancer"},{"key":"ref_18","first-page":"127","article-title":"Artificial intelligence in cancer imaging: Clinical challenges and applications","volume":"69","author":"Bi","year":"2019","journal-title":"CA Cancer J. Clin."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1136\/gutjnl-2018-316204","article-title":"Deep learning radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: A prospective multicentre study","volume":"68","author":"Wang","year":"2019","journal-title":"Gut"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1186\/s42492-023-00147-2","article-title":"Application and prospects of AI-based radiomics in ultrasound diagnosis","volume":"6","author":"Zhang","year":"2023","journal-title":"Vis. Comput. Ind. Biomed. Art"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4539","DOI":"10.1007\/s00330-026-12323-y","article-title":"Development of a quantitative multiparametric ultrasound and deep learning classifier for the detection of prostate cancer","volume":"36","author":"Delberghe","year":"2026","journal-title":"Eur. Radiol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1148\/radiol.2018170601","article-title":"Quantitative Elastography Methods in Liver Disease: Current Evidence and Future Directions","volume":"286","author":"Kennedy","year":"2018","journal-title":"Radiology"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"19","DOI":"10.14366\/usg.24163","article-title":"Unraveling distinctions between contrast-enhanced ultrasound and CT\/MRI for liver mass diagnosis","volume":"44","author":"Murad","year":"2025","journal-title":"Ultrasonography"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"137","DOI":"10.3348\/kjr.2025.0966","article-title":"Imaging Evaluation for Steatotic Liver Disease","volume":"27","author":"Chan","year":"2026","journal-title":"Korean J. Radiol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.jhep.2017.02.003","article-title":"Non-invasive evaluation of portal hypertension using ultrasound elastography","volume":"67","author":"Berzigotti","year":"2017","journal-title":"J. Hepatol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"22","DOI":"10.2214\/AJR.15.14552","article-title":"Ultrasound Elastography and MR Elastography for Assessing Liver Fibrosis: Part 1, Principles and Techniques","volume":"205","author":"Tang","year":"2015","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1148\/radiol.2015150619","article-title":"Elastography Assessment of Liver Fibrosis: Society of Radiologists in Ultrasound Consensus Conference Statement","volume":"276","author":"Barr","year":"2015","journal-title":"Radiology"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1148\/radiol.2020192437","article-title":"Update to the Society of Radiologists in Ultrasound Liver Elastography Consensus Statement","volume":"296","author":"Barr","year":"2020","journal-title":"Radiology"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1016\/j.jhep.2008.02.008","article-title":"Non-invasive evaluation of liver fibrosis using transient elastography","volume":"48","author":"Castera","year":"2008","journal-title":"J. Hepatol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1022","DOI":"10.1016\/j.jhep.2016.12.022","article-title":"Individual patient data meta-analysis of controlled attenuation parameter (CAP) technology for assessing steatosis","volume":"66","author":"Karlas","year":"2017","journal-title":"J. Hepatol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1007\/s00261-017-1375-1","article-title":"Shear wave liver elastography","volume":"43","author":"Barr","year":"2018","journal-title":"Abdom. Radiol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"17562848221140657","DOI":"10.1177\/17562848221140657","article-title":"Update on the role of elastography in liver disease","volume":"15","author":"Ferraioli","year":"2022","journal-title":"Ther. Adv. Gastroenterol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"6662760","DOI":"10.1155\/2021\/6662760","article-title":"Controlled Attenuation Parameter for Quantification of Steatosis: Which Cut-Offs to Use?","volume":"2021","author":"Sirli","year":"2021","journal-title":"Can. J. Gastroenterol. Hepatol."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, X.-Y., Wei, Q., Wu, G.-G., Tang, Q., Pan, X.-F., Chen, G.-Q., Zhang, D., Dietrich, C.F., and Cui, X.-W. (2023). Artificial intelligence-based ultrasound elastography for disease evaluation: A narrative review. Front. Oncol., 13.","DOI":"10.3389\/fonc.2023.1197447"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2579","DOI":"10.1016\/j.ultrasmedbio.2020.04.030","article-title":"Guidelines and Good Clinical Practice Recommendations for Contrast-Enhanced Ultrasound (CEUS) in the Liver, Update 2020 WFUMB in Cooperation with EFSUMB, AFSUMB, AIUM, and FLAUS","volume":"46","author":"Dietrich","year":"2020","journal-title":"Ultrasound Med. Biol."},{"key":"ref_36","first-page":"11","article-title":"Guidelines and good clinical practice recommendations for contrast enhanced ultrasound (CEUS) in the liver, Update 2012: A WFUMB-EFSUMB initiative in cooperation with representatives of AFSUMB, AIUM, ASUM, FLAUS and ICUS","volume":"34","author":"Claudon","year":"2013","journal-title":"Ultraschall Med."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"320","DOI":"10.2214\/AJR.17.17843","article-title":"Contrast-Enhanced Ultrasound of the Liver: Optimizing Technique and Clinical Applications","volume":"210","author":"Pang","year":"2018","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1055\/s-0031-1281676","article-title":"The EFSUMB Guidelines and Recommendations on the Clinical Practice of Contrast Enhanced Ultrasound (CEUS): Update 2011 on non-hepatic applications","volume":"33","author":"Piscaglia","year":"2012","journal-title":"Ultraschall Med."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chattopadhyay, T., Chan, H.J., Le, D.C., Wang, C.Y., Tai, D.I., Zhou, Z., and Tsui, P.H. (2025). Multiparametric Quantitative Ultrasound as a Potential Imaging Biomarker for Noninvasive Detection of Nonalcoholic Steatohepatitis: A Clinical Feasibility Study. Diagnostics, 15.","DOI":"10.3390\/diagnostics15172214"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2655","DOI":"10.1038\/s41598-021-82153-z","article-title":"Multiparametric ultrasound imaging for the assessment of normal versus steatotic livers","volume":"11","author":"Basavarajappa","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"117","DOI":"10.11152\/mu-4327","article-title":"Multiparametric ultrasound evaluation of liver fibrosis, steatosis, and viscosity in patients with chronic liver disease","volume":"26","author":"Minciuna","year":"2024","journal-title":"Med. Ultrason."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Popa, A., Sporea, I., Bende, F., Popescu, A., Fofiu, R., Borlea, A., B\u00e2ldea, V., Pascu, A., Foncea, C.G., and Cotr\u0103u, R. (2022). The Non-Invasive Ultrasound-Based Assessment of Liver Viscosity in a Healthy Cohort. Diagnostics, 12.","DOI":"10.3390\/diagnostics12061451"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1002\/jum.15819","article-title":"Artificial Intelligence in Quantitative Ultrasound Imaging: A Survey","volume":"41","author":"Zhou","year":"2022","journal-title":"J. Ultrasound Med."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1148\/rg.2017160130","article-title":"Machine Learning for Medical Imaging","volume":"37","author":"Erickson","year":"2017","journal-title":"Radiographics"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"111475","DOI":"10.1016\/j.ejrad.2024.111475","article-title":"Evaluation of spleen stiffness by 2D shear wave elastography for ruling out high-risk varices in patients with chronic advanced liver disease: A systematic review and meta-analysis","volume":"175","author":"Karagiannakis","year":"2024","journal-title":"Eur. J. Radiol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1111\/hepr.13841","article-title":"Spleen stiffness in patients with chronic liver disease evaluated by 2-D shear wave elastography with ultrasound multiparametric imaging","volume":"53","author":"Hirooka","year":"2023","journal-title":"Hepatol. Res."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Ainora, M.E., Cerrito, L., Liguori, A., Mignini, I., De Luca, A., Galasso, L., Garcovich, M., Riccardi, L., Ponziani, F., and Santopaolo, F. (2023). Multiparametric Dynamic Ultrasound Approach for Differential Diagnosis of Primary Liver Tumors. Int. J. Mol. Sci., 24.","DOI":"10.3390\/ijms24108548"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zou, X., Zhao, D., Shang, Y., Song, Y., Deng, Y., and Lu, L. (2025). Combined imaging and serum biomarkers in CEUS-guided microwave ablation for hepatocellular carcinoma: A meta-analysis. Front. Oncol., 15.","DOI":"10.3389\/fonc.2025.1687044"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1007\/s40477-018-0320-7","article-title":"Multiparametric ultrasound evaluation with CEUS and shear wave elastography for carotid plaque risk stratification","volume":"21","author":"Venturini","year":"2018","journal-title":"J. Ultrasound"},{"key":"ref_50","first-page":"100284","article-title":"Contrast-enhanced ultrasound microbubble uptake and abnormal plasma biomarkers are seen in patients with abdominal aortic aneurysms","volume":"6","author":"Ali","year":"2025","journal-title":"J. Vasc. Surg. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"4520","DOI":"10.21037\/qims-23-1867","article-title":"Research on ultrasound-based radiomics: A bibliometric analysis","volume":"14","author":"Yu","year":"2024","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_53","first-page":"21","article-title":"Noninvasive Methods for Assessing Liver Fibrosis and Steatosis","volume":"20","author":"Kazi","year":"2024","journal-title":"Gastroenterol. Hepatol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1111\/hepr.13764","article-title":"Noninvasive assessment of liver fibrosis and its clinical significance in nonalcoholic fatty liver disease","volume":"52","author":"Tamaki","year":"2022","journal-title":"Hepatol. Res."},{"key":"ref_55","first-page":"218","article-title":"Non-invasive assessment of liver fibrosis","volume":"25","author":"Papastergiou","year":"2012","journal-title":"Ann. Gastroenterol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1097\/HEP.0000000000000351","article-title":"Noninvasive assessment of liver disease severity in patients with nonalcoholic fatty liver disease (NAFLD) and type 2 diabetes","volume":"78","author":"Pennisi","year":"2023","journal-title":"Hepatology"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1006","DOI":"10.1136\/gutjnl-2021-324243","article-title":"Diagnostic accuracy of non-invasive tests for advanced fibrosis in patients with NAFLD: An individual patient data meta-analysis","volume":"71","author":"Selvaraj","year":"2022","journal-title":"Gut"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1817","DOI":"10.1002\/hep.28394","article-title":"Liver stiffness in nonalcoholic fatty liver disease: A comparison of supersonic shear imaging, FibroScan, and ARFI with liver biopsy","volume":"63","author":"Cassinotto","year":"2016","journal-title":"Hepatology"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1002\/jmri.22580","article-title":"Quantitative Assessment of Liver Fat with Magnetic Resonance Imaging and Spectroscopy","volume":"34","author":"Reeder","year":"2011","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"5152","DOI":"10.1007\/s00261-021-03225-2","article-title":"Assessment of chronic liver disease by multiparametric ultrasound: Results from a private practice outpatient facility","volume":"46","author":"Aitharaju","year":"2021","journal-title":"Abdom. Radiol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"4547","DOI":"10.1038\/s41598-025-87974-w","article-title":"Impact of shear wave elastography and attenuation imaging for predicting life-threatening event in patients with metabolic dysfunction-associated steatotic liver disease","volume":"15","author":"Fujiwara","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"4629","DOI":"10.1007\/s00261-021-02960-w","article-title":"Liver shear wave elastography and attenuation imaging coefficient measures: Prospective evaluation in healthy children","volume":"46","author":"Cailloce","year":"2021","journal-title":"Abdom. Radiol."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/s00261-017-1317-y","article-title":"US LI-RADS: Ultrasound liver imaging reporting and data system for screening and surveillance of hepatocellular carcinoma","volume":"43","author":"Morgan","year":"2018","journal-title":"Abdom. Radiol."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Bu, D., Duan, S., Ren, S., Ma, Y., Liu, Y., Li, Y., Cai, X., and Zhang, L. (2025). Machine learning-based ultrasound radiomics for predicting TP53 mutation status in hepatocellular carcinoma. Front. Med., 12.","DOI":"10.3389\/fmed.2025.1565618"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Jiang, D., Liu, Z., Wang, K., Qian, Y., Feng, J., Gong, L., Ren, J., Xiang, Y., Zhang, F., and Liu, L. (2025). Integrated ultrasound radiomics and clinical data to predict PD-1 blockade efficacy in unresectable hepatocellular carcinoma. BMC Gastroenterol., 26.","DOI":"10.1186\/s12876-025-04512-8"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s13089-025-00452-2","article-title":"B-mode ultrasound and contrast-enhanced ultrasound-based radiomics interpretable analysis for the prediction of macrotrabecular-massive subtype of hepatocellular carcinoma","volume":"17","author":"Lu","year":"2025","journal-title":"Ultrasound J."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Pugliesi, R.A., Ben Mansour, K., Apitzsch, J., Papachristodoulou, A., Rafailidis, V., and Katz, D.S. (2025). Meta-Analysis of AI Integration in Abdominal Imaging for Liver Fibrosis and MASLD: Evaluating Diagnostic Accuracy and Clinical Impact. J. Clin. Med., 14.","DOI":"10.3390\/jcm14238466"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1136\/gutjnl-2012-302779","article-title":"Acute Pancreatitis Classification Working Group. Classification of acute pancreatitis,2012: Revision of the Atlanta classification and definitions by international consensus","volume":"62","author":"Banks","year":"2013","journal-title":"Gut"},{"key":"ref_69","first-page":"2267","article-title":"Contrast-enhanced ultrasonography in the staging of acute pancreatitis","volume":"20","author":"Delgado","year":"2010","journal-title":"Eur. Radiol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1644","DOI":"10.1016\/j.ejrad.2015.02.021","article-title":"CEUS of the pancreas: Still research or the standard of care","volume":"84","author":"Canestrini","year":"2015","journal-title":"Eur. J. Radiol."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Yamashita, Y., Yamazaki, H., Nakahata, A., Emori, T., Kawaji, Y., Tamura, T., Itonaga, M., Ashida, R., and Kitano, M. (2025). Advances in Endoscopic Ultrasonography-Based Diagnosis of Pancreatic Lesions: Narrative Review. Cancers, 17.","DOI":"10.3390\/cancers17020172"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Roussel, E., Campi, R., Amparore, D., Bertolo, R., Carbonara, U., Erdem, S., Ingels, A., Kara, \u00d6., Marandino, L., and Marchioni, M. (2022). Expanding the role of ultrasound for the characterization of renal masses. J. Clin. Med., 11.","DOI":"10.3390\/jcm11041112"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.eururo.2022.03.006","article-title":"European Association of Urology Guidelines on Renal Cell Carcinoma: The 2022 Update","volume":"82","author":"Ljungberg","year":"2022","journal-title":"Eur. Urol."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"2551","DOI":"10.1002\/cam4.5101","article-title":"Contrast-enhanced ultrasound of benign and malignant renal tumors: Distinguishing CEUS features differ with tumor size","volume":"12","author":"Zhu","year":"2023","journal-title":"Cancer Med."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"95","DOI":"10.14740\/jocmr4621","article-title":"Shear wave elastography in the evaluation of renal parenchymal stiffness in patients with chronic kidney disease: A meta-analysis","volume":"14","author":"Mo","year":"2022","journal-title":"J. Clin. Med. Res."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1093\/bjr\/tqad030","article-title":"Association of renal elasticity evaluated by real-time shear wave elastography with renal fibrosis in patients with chronic kidney disease","volume":"97","author":"Chen","year":"2024","journal-title":"Br. J. Radiol."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1093\/bjr\/tqaf264","article-title":"Quantitative assessment of renal stiffness with shear wave elastography in heart failure: A prospective study","volume":"99","author":"Burulday","year":"2026","journal-title":"Br. J. Radiol."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1177\/1742271X18762250","article-title":"Vascular ultrasound, the potential of integration of multiparametric ultrasound into routine clinical practice","volume":"26","author":"Rafailidis","year":"2018","journal-title":"Ultrasound"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1080\/17474124.2026.2624595","article-title":"Multiparametric intestinal ultrasound in inflammatory bowel diseases: A narrative review","volume":"20","author":"Cataletti","year":"2026","journal-title":"Expert Rev. Gastroenterol. Hepatol."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Tagliamonte, G., Santagata, F., and Fraquelli, M. (2024). Current Developments and Role of Intestinal Ultrasound including the Advent of AI. Diagnostics, 14.","DOI":"10.3390\/diagnostics14070759"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"M\u00f6ller, K., Jenssen, C., Dirks, K., Hollerweger, A., Gottschall, H., Faiss, S., and Dietrich, C.F. (2025). Small Intestine Tumors: Diagnostic Role of Multiparametric Ultrasound. Healthcare, 13.","DOI":"10.3390\/healthcare13212776"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Termite, F., Galasso, L., Capece, G., Messina, F., Esposto, G., Ainora, M.E., Mignini, I., Borriello, R., Vitiello, R., and Maccauro, G. (2025). Multiparametric Ultrasound in the Differential Diagnosis of Soft Tissue Tumors: A Comprehensive Review. Biomedicines, 13.","DOI":"10.3390\/biomedicines13071786"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1186\/s13089-025-00444-2","article-title":"Advances and innovations in ultrasound-based tumor management: Current applications and emerging directions","volume":"17","author":"Rui","year":"2025","journal-title":"Ultrasound J."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1016\/j.jacr.2017.12.026","article-title":"Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success","volume":"15","author":"Thrall","year":"2018","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Liang, L., Zhi, X., Sun, Y., Li, H., Wang, J., Xu, J., and Guo, J. (2021). A Nomogram Based on a Multiparametric Ultrasound Radiomics Model for Discrimination Between Malignant and Benign Prostate Lesions. Front. Oncol., 11.","DOI":"10.3389\/fonc.2021.610785"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, H., Deng, L., and He, X. (2025). Development and validation of an ultrasound-based AI-radiomics model for diagnosing and risk-stratifying gastrointestinal stromal tumors: A retrospective diagnostic study. BMC Med. Imaging, 25.","DOI":"10.1186\/s12880-025-02050-z"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Xia, Z., Liu, L., Chen, H., Mo, Y., Shen, Y., Xie, X., Qiu, M., Liao, C., Cui, H., and Zhang, S. (2026). Deep learning radiomics models based on contrast-enhanced transrectal ultrasound for predicting distant metastasis in rectal cancer. Front. Oncol., 16.","DOI":"10.3389\/fonc.2026.1671887"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Li, S.Y., Li, Y.M., Fang, Y.Q., Jin, Z.Y., Li, J.K., Zou, X.M., Huang, S.S., Niu, R.L., Fu, N.Q., and Shao, Y.H. (2025). Contrast-enhanced ultrasound radiomics model for predicting axillary lymph node metastasis and prognosis in breast cancer: A multicenter study. BMC Cancer, 25.","DOI":"10.1186\/s12885-025-14632-9"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Putin, R., Stana, L.G., Ilie, A.C., Tanase, E., and Cotoraci, C. (2026). Quantitative Ultrasound Radiomics for Predicting and Monitoring Neoadjuvant Chemotherapy Response in Breast Cancer: A Systematic Review. Diagnostics, 16.","DOI":"10.3390\/diagnostics16030425"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Hu, W., Li, X., Tang, Y., Mao, X., Li, C., Wang, Y., Li, Z., Zuo, M., Yin, L., and Deng, Y. (2025). Performance of artificial intelligence-assisted ultrasound elastography in classifying benign and malignant breast tumors: A systematic review and meta-analysis. BMC Med. Imaging, 25.","DOI":"10.1186\/s12880-025-01982-w"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"e937738","DOI":"10.12659\/MSM.937738","article-title":"A Review of the Role of Ultrasound Radiomics and Its Application and Limitations in the Investigation of Thyroid Disease","volume":"28","author":"Lu","year":"2022","journal-title":"Med. Sci. Monit."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Solomon, C., Petea-Balea, D.R., Dudea, S.M., Bene, I., Silaghi, C.A., and Lenghel, M.L. (2025). Role of Ultrasound Elastography and Contrast-Enhanced Ultrasound (CEUS) in Diagnosis and Management of Malignant Thyroid Nodules, An Update. Diagnostics, 15.","DOI":"10.3390\/diagnostics15050599"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Nechita, L.C., Tupu, A.E., Nechita, A., Voipan, D., Voipan, A.E., Tutunaru, D., and Musat, C.L. (2025). The Impact of Quality of Life on Cardiac Arrhythmias: A Clinical, Demographic, and AI-Assisted Statistical Investigation. Diagnostics, 15.","DOI":"10.3390\/diagnostics15070856"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Nechita, L.C., Gurau, T.V., Musat, C.L., Tupu, A.E., Gurau, G., Voinescu, D.C., and Nechita, A. (2025). Integrated Anthropometric, Physiological and Biological Assessment of Elite Youth Football Players Using Machine Learning. Diagnostics, 15.","DOI":"10.3390\/diagnostics15243238"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Nechita, L.C., Tutunaru, D., Nechita, A., Voipan, A.E., Voipan, D., Ionescu, A.M., Dr\u0103goiu, T.S., and Musat, C.L. (2025). A Resting ECG Screening Protocol Improved with Artificial Intelligence for the Early Detection of Cardiovascular Risk in Athletes. Diagnostics, 15.","DOI":"10.3390\/diagnostics15040477"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.acra.2022.10.026","article-title":"Multiparametric quantitative imaging biomarker as a multivariate descriptor of health: A roadmap","volume":"30","author":"Raunig","year":"2023","journal-title":"Acad. Radiol."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.mric.2015.08.005","article-title":"Multiparametric MR Imaging in Abdominal Malignancies","volume":"24","author":"Luna","year":"2016","journal-title":"Magn. Reson. Imaging Clin. N. Am."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Gagiu, C., Dionisie, V., Manea, M.C., Covaliu, A., Vlad, A.D., Tupu, A.E., and Manea, M. (2025). Quality of Life in Caregivers of Patients with Schizophrenia: A Systematic Review of the Impact of Sociodemographic, Clinical, and Psychological Factors. Behav. Sci., 15.","DOI":"10.3390\/bs15050684"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"886","DOI":"10.12680\/balneo.2025.886","article-title":"Assessment of Quality of Life and Depressive Symptoms in Adolescent and Young Adult Athletes Using SF-36 and PHQ-9: A Comparison Between Team and Individual Sports","volume":"16","author":"Iordan","year":"2025","journal-title":"Balneo PRM Res. J."},{"key":"ref_100","first-page":"922","article-title":"Postural Disorders of the Spine in Children and Adolescents: Risk Factors and the Effectiveness of Corrective Interventions","volume":"16","author":"Iordan","year":"2025","journal-title":"Balneo PRM Res. J."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Dag, N., Berktas, H.B., Uslu, A.G., and Burulday, V. (2025). Sonoelastographic evaluation of diaphragmatic thickness and stiffness in dialysis patients. BMC Med. Imaging, 25.","DOI":"10.1186\/s12880-025-01932-6"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s00431-024-05852-5","article-title":"Ultrasound and shear wave elastography assessment of diaphragm thickness and stiffness in malnourished pediatric patients","volume":"184","author":"Varol","year":"2024","journal-title":"Eur. J. Pediatr."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"2734","DOI":"10.1038\/s41598-025-87303-1","article-title":"Evaluation of the core muscles of children and adolescents with nocturnal enuresis using shear wave elastography: A preliminary study","volume":"15","year":"2025","journal-title":"Sci. Rep."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/13\/2045\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:22:12Z","timestamp":1782840132000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/13\/2045"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,30]]},"references-count":103,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["diagnostics16132045"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16132045","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,30]]}}}