Computer-aided detection improves brain metastasis identification on non-enhanced CT in less experienced radiologists.
Background: Brain metastases (BMs) are the most common intracranial tumors causing neurological complications associated with significant morbidity and mortality. Purpose: To evaluate the effect of computer-aided detection (CAD) on the performance of observers in detecting BMs on non-enhanced comput...
| Publicado en: | Acta Radiologica Vol. 64; no. 5; pp. 1958 - 1966 |
|---|---|
| Autores principales: | , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Sage Publications Inc.
May2023
|
| 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=163578960&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 163578960 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02841851 1XG jtl: Acta Radiologica issn: 02841851 maglogo: Y pubinfo: dt: May2023 vid: 64 iid: 5 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 163578960 160392001 10.1177/02841851221139124 163578960 ppf: 1958 ppct: 8 formats: tig: atl: Computer-aided detection improves brain metastasis identification on non-enhanced CT in less experienced radiologists. aug: au: Kato, Shimpei Amemiya, Shiori Takao, Hidemasa Yamashita, Hiroshi Sakamoto, Naoya Miki, Soichiro Watanabe, Yusuke Suzuki, Fumio Fujimoto, Kotaro Mizuki, Masumi Abe, Osamu affil: Department of Radiology, Graduate School of Medicine, 13143University of Tokyo, Tokyo, Japan sug: ab: Background: Brain metastases (BMs) are the most common intracranial tumors causing neurological complications associated with significant morbidity and mortality. Purpose: To evaluate the effect of computer-aided detection (CAD) on the performance of observers in detecting BMs on non-enhanced computed tomography (NECT). Material and Methods: Three less experienced and three experienced radiologists interpreted 30 NECT scans with 89 BMs in 25 cases to detect BMs with and without the assistance of CAD. The observers' sensitivity, number of false positives (FPs), positive predictive value (PPV), and reading time with and without CAD were compared using paired t-tests. The sensitivity of CAD and the observers were compared using a one-sample t-test Results: With CAD, less experienced radiologists' sensitivity significantly increased from 27.7% ± 4.6% to 32.6% ± 4.8% (P = 0.007), while the experienced radiologists' sensitivity did not show a significant difference (from 33.3% ± 3.5% to 31.9% ± 3.7%; P = 0.54). There was no significant difference between conditions with CAD and without CAD for FPs (less experienced radiologists: 23.0 ± 10.4 and 25.0 ± 9.3; P = 0.32; experienced radiologists: 18.3 ± 7.4 and 17.3 ± 6.7; P = 0.76) and PPVs (less experienced radiologists: 57.9% ± 8.3% and 50.9% ± 7.0%; P = 0.14; experienced radiologists: 61.8% ± 12.7% and 64.0% ± 12.1%; P = 0.69). There were no significant differences in reading time with and without CAD (85.0 ± 45.6 s and 73.7 ± 36.7 s; P = 0.09). The sensitivity of CAD was 47.2% (with a PPV of 8.9%), which was significantly higher than that of any radiologist (P < 0.001). Conclusion: CAD improved BM detection sensitivity on NECT without increasing FPs or reading time among less experienced radiologists, but this was not the case among experienced radiologists. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|