{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T10:56:26Z","timestamp":1782989786197,"version":"3.54.5"},"reference-count":70,"publisher":"IOP Publishing","issue":"13","license":[{"start":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T00:00:00Z","timestamp":1782950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T00:00:00Z","timestamp":1782950400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T00:00:00Z","timestamp":1782950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"},{"start":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T00:00:00Z","timestamp":1782950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"}],"funder":[{"name":"Oncotech Consortium","award":["293741"],"award-info":[{"award-number":["293741"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2026,7,14]]},"abstract":"Abstract<\/jats:title>\n \n Objective.<\/jats:italic>\n To compare four attenuation-compensation methods for backscatter coefficient (BSC) estimation, assessment of contrast, and classification of focal liver lesions (FLL).\n Approach<\/jats:italic>\n . Ninety-seven patients with 100 FLL were scanned to collect radiofrequency ultrasound images. BSC methods relied on a reference phantom for system and operator-settings independent estimations. Method #1 employed\n a priori<\/jats:italic>\n tissue layer segmentation and documented attenuation coefficients (AC) of each layer. Method #2 used a fixed total AC (0.85 dB\/cm\/MHz). Method #3 used local AC compensation. Method #4 jointly estimated total AC and BSC with a power law frequency model (\n \n \n \n <\/jats:tex-math>\n \n \n b<\/mml:mi>\n \n f<\/mml:mi>\n \n \n \n \u03b7<\/mml:mi>\n <\/mml:msup>\n <\/mml:mrow>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ). BSC@3 MHz,\n \n \n \n <\/jats:tex-math>\n \n \n b<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ,\n \n \n \n <\/jats:tex-math>\n \n \n \u03b7<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n , total AC slope, and total AC were computed within segmented lesions. Lesion contrast was assessed with the contrast-to-noise ratio (CNR) and classification performances were evaluated with the area under the receiver operating characteristic curve (AUC). The composite clinical reference standard was a combination of MRI and histopathology.\n Main results.<\/jats:italic>\n The study included 30 primary and 26 secondary cancers, and 44 benign nodules. Parameter\n \n \n \n <\/jats:tex-math>\n \n \n b<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n provided the highest CNRs among BSC parameters (\n p<\/jats:italic>\n < 0.0001) and gave higher CNRs than B-mode images (\n p<\/jats:italic>\n < 0.0001). Method #3 was unsuitable with out-of-range values. Methods #1, #2, and #4 showed no significant differences for\n \n \n \n <\/jats:tex-math>\n \n \n b<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ,\n \n \n \n <\/jats:tex-math>\n \n \n \u03b7<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n , total AC slope, and total AC, whereas BSC@3 MHz showed overestimations with Method #4 compared with Methods #1 and #2 (\n p<\/jats:italic>\n < 0.001 and\n p<\/jats:italic>\n < 0.0001, respectively). For differentiating benign and malignant lesions,\n \n \n \n <\/jats:tex-math>\n \n \n \u03b7<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n provided the highest AUC of 0.73 (95% confidence interval (CI): 0.62\u20130.82). For differentiating primary and secondary cancers, BSC@3 MHz provided the highest AUC of 0.71 (95% CI: 0.55\u20130.83). Classification AUCs did not differ between Methods #1, #2 and #4.\n Significance<\/jats:italic>\n . BSC imaging improved lesion contrast compared to B-mode and could classify FLL. Method #4 emerged as the most practical as it does not require any\n a priori<\/jats:italic>\n AC or segmentation.\n <\/jats:p>","DOI":"10.1088\/1361-6560\/ae7cd1","type":"journal-article","created":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T22:54:53Z","timestamp":1781304893000},"page":"135012","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparison of four attenuation-compensation methods for backscatter coefficient estimation and characterization of focal liver lesions"],"prefix":"10.1088","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-9573-160X","authenticated-orcid":true,"given":"Arnaud","family":"H\u00e9roux","sequence":"first","affiliation":[{"name":"Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center"},{"name":"Institute of Biomedical Engineering, University of Montreal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2645-4196","authenticated-orcid":true,"given":"Fran\u00e7ois","family":"Destrempes","sequence":"additional","affiliation":[{"name":"Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9934-8792","authenticated-orcid":true,"given":"Iman","family":"Rafati","sequence":"additional","affiliation":[{"name":"Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Barat","family":"Maxime","sequence":"additional","affiliation":[{"name":"Clinical Laboratory of Image Processing, University of Montreal Hospital Research Center"},{"name":"Department of Radiology, Radiation Oncology and Nuclear Medicine, University of Montreal"},{"name":"University of Montreal Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elige","family":"Karam","sequence":"additional","affiliation":[{"name":"Clinical Laboratory of Image Processing, University of Montreal Hospital Research Center"},{"name":"Department of Radiology, Radiation Oncology and Nuclear Medicine, University of Montreal"},{"name":"University of Montreal Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5598-4033","authenticated-orcid":true,"given":"Audrey","family":"Fohlen","sequence":"additional","affiliation":[{"name":"Clinical Laboratory of Image Processing, University of Montreal Hospital Research Center"},{"name":"Department of Radiology, Radiation Oncology and Nuclear Medicine, University of Montreal"},{"name":"University of Montreal Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bich N","family":"Nguyen","sequence":"additional","affiliation":[{"name":"University of Montreal Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H\u00e9l\u00e8ne","family":"Castel","sequence":"additional","affiliation":[{"name":"University of Montreal Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8967-5503","authenticated-orcid":false,"given":"An","family":"Tang","sequence":"additional","affiliation":[{"name":"Clinical Laboratory of Image Processing, University of Montreal Hospital Research Center"},{"name":"Department of Radiology, Radiation Oncology and Nuclear Medicine, University of Montreal"},{"name":"University of Montreal Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4957-4844","authenticated-orcid":true,"given":"Guy","family":"Cloutier","sequence":"additional","affiliation":[{"name":"Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center"},{"name":"Institute of Biomedical Engineering, University of Montreal"},{"name":"Department of Radiology, Radiation Oncology and Nuclear Medicine, University of Montreal"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,7,2]]},"reference":[{"key":"pmbae7cd1bib1","doi-asserted-by":"publisher","first-page":"1384","DOI":"10.1016\/j.jhep.2011.10.027","type":"journal-article","article-title":"Hepatocellular carcinoma in non-alcoholic fatty liver disease: an emerging menace","volume":"56","author":"Baffy","year":"2012","journal-title":"J. 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