Diagnosis of moderate-to-severe hepatic steatosis using deep learning-based automated attenuation measurements on contrast-enhanced CT.
Purpose: To evaluate the utility of deep learning-based automated attenuation measurements on contrast-enhanced CT (CECT) for diagnosing moderate-to-severe hepatic steatosis (HS), using histology as reference standard. Methods: This retrospective study included 3,620 liver donors (2,393 men and 1,22...
| Publicado en: | Abdominal Radiology Vol. 50; no. 9; pp. 4139 - 4148 |
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| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187189764&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187189764 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Sep2025 vid: 50 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187189764 183747330 10.1007/s00261-025-04872-5 187189764 ppf: 4139 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Diagnosis of moderate-to-severe hepatic steatosis using deep learning-based automated attenuation measurements on contrast-enhanced CT. aug: au: Kim, Hae Young Lee, Kyung Jin Lee, Seung Soo Choi, Se Jin Kim, Dong Hwan Heo, Subin Jang, Hyeon Ji Choi, Sang Hyun affil: https://ror.org/03s5q0090 Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea sug: ab: Purpose: To evaluate the utility of deep learning-based automated attenuation measurements on contrast-enhanced CT (CECT) for diagnosing moderate-to-severe hepatic steatosis (HS), using histology as reference standard. Methods: This retrospective study included 3,620 liver donors (2,393 men and 1,227 women; mean age, 31.7 ± 9.4 years), divided into the development (n = 2,714) and test (n = 906) cohorts. Attenuation values of the liver and spleen on CECT were measured both manually and using a deep learning algorithm (before and after radiologists' correction of segmentation errors). Performance of: (1) liver attenuation and (2) liver–spleen attenuation difference for diagnosing moderate-to-severe HS (> 33%) was assessed using the area under the receiver operating characteristic curve (AUC). Three different criteria targeting 95% sensitivity, 95% specificity, and the maximum Youden's index, respectively, for diagnosing moderate-to-severe HS, were developed and validated. Results: The performance of deep learning-based measurements did not differ significantly, with or without radiologists' corrections (p = 0.13). Liver–spleen attenuation difference outperformed liver attenuation alone in diagnosing moderate-to-severe HS in both deep learning-based (AUC, 0.868 vs. 0.821; p = 0.001) and manual (AUC, 0.871 vs. 0.823; p = 0.001) measurements. In the test cohort, the criterion targeting 95% sensitivity for diagnosing moderate-to-severe HS (liver–spleen attenuation difference ≤ 2.8 HU) yielded 92.0% (69/75) sensitivity and 48.5% (403/831) specificity. The criterion targeting 95% specificity (liver–spleen attenuation difference ≤ -18.8 HU) yielded 53.3% (40/75) sensitivity and 95.7% (795/831) specificity. The criterion targeting the maximum Youden's index (liver–spleen attenuation difference ≤ -8.2 HU) yielded 82.7% (62/75) sensitivity and 80.7% (671/831) specificity. Conclusion: Deep learning-based automated measurements of liver and spleen attenuation on CECT can be used reliably to detect moderate-to-severe HS. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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