Detection and PI-RADS classification of focal lesions in prostate MRI: Performance comparison between a deep learning-based algorithm (DLA) and radiologists with various levels of experience.
Purpose: To compare the performance of lesion detection and Prostate Imaging-Reporting and Data System (PI-RADS) classification between a deep learning-based algorithm (DLA), clinical reports and radiologists with different levels of experience in prostate MRI.Methods: This retrospective study inclu...
| Publicado en: | European Journal of Radiology Vol. 142 |
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| Autores principales: | , , , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Sep2021
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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=152003356&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152003356 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 0720048X 3S7 jtl: European Journal of Radiology issn: 0720048X maglogo: N pubinfo: dt: Sep2021 vid: 142 pid: 1004 pub: Elsevier B.V. artinfo: ui: 152003356 152003356 NLM34388625 152003356 10.1016/j.ejrad.2021.109894 NLM34388625 152003356 ppct: 1 formats: tig: atl: Detection and PI-RADS classification of focal lesions in prostate MRI: Performance comparison between a deep learning-based algorithm (DLA) and radiologists with various levels of experience. aug: au: Youn, Seo Yeon Choi, Moon Hyung Kim, Dong Hwan Lee, Young Joon Huisman, Henkjan Johnson, Evan Penzkofer, Tobias Shabunin, Ivan Winkel, David Jean Xing, Pengyi Szolar, Dieter Grimm, Robert von Busch, Heinrich Son, Yohan Lou, Bin Kamen, Ali affil: Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea sug: subj: Prostatic Neoplasms Male Magnetic Resonance Imaging Retrospective Design Scales Male ab: Purpose: To compare the performance of lesion detection and Prostate Imaging-Reporting and Data System (PI-RADS) classification between a deep learning-based algorithm (DLA), clinical reports and radiologists with different levels of experience in prostate MRI.Methods: This retrospective study included 121 patients who underwent prebiopsy MRI and prostate biopsy. More than five radiologists (Reader groups 1, 2: residents; Readers 3, 4: less-experienced radiologists; Reader 5: expert) independently reviewed biparametric MRI (bpMRI). The DLA results were obtained using bpMRI. The reference standard was based on pathologic reports. The diagnostic performance of the PI-RADS classification of DLA, clinical reports, and radiologists was analyzed using AUROC. Dichotomous analysis (PI-RADS cutoff value ≥ 3 or 4) was performed, and the sensitivities and specificities were compared using McNemar's test.Results: Clinically significant cancer [CSC, Gleason score ≥ 7] was confirmed in 43 patients (35.5%). The AUROC of the DLA (0.828) for diagnosing CSC was significantly higher than that of Reader 1 (AUROC, 0.706; p = 0.011), significantly lower than that of Reader 5 (AUROC, 0.914; p = 0.013), and similar to clinical reports and other readers (p = 0.060-0.661). The sensitivity of DLA (76.7%) was comparable to those of all readers and the clinical reports at a PI-RADS cutoff value ≥ 4. The specificity of the DLA (85.9%) was significantly higher than those of clinical reports and Readers 2-3 and comparable to all others at a PI-RADS cutoff value ≥ 4.Conclusions: The DLA showed moderate diagnostic performance at a level between those of residents and an expert in detecting and classifying according to PI-RADS. The performance of DLA was similar to that of clinical reports from various radiologists in clinical practice. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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