External Validation of a Previously Developed Deep Learning–based Prostate Lesion Detection Algorithm on Paired External and In-House Biparametric MRI Scans Article Swipe
Enis C. Yılmaz
,
Stephanie A. Harmon
,
Yan Mee Law
,
Erich P. Huang
,
Mason J. Belue
,
Yue Lin
,
David G. Gelikman
,
Kutsev B Özyörük
,
Dong Yang
,
Ziyue Xu
,
Jesse Tetreault
,
Daguang Xu
,
Lindsey Hazen
,
Charisse Garcia
,
Nathan Lay
,
Philip Eclarinal
,
Antoun Toubaji
,
Maria J. Merino
,
Bradford J. Wood
,
Sandeep Gurram
,
Peter L. Choyke
,
Peter A. Pinto
,
Barış Türkbey
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1148/rycan.240050
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1148/rycan.240050
Purpose To evaluate the performance of an artificial intelligence (AI) model in detecting overall and clinically significant prostate cancer (csPCa)-positive lesions on paired external and in-house biparametric MRI (bpMRI) scans and assess performance differences between each dataset. Materials and Methods This single-center retrospective study included patients who underwent prostate MRI at an external institution and were rescanned at the authors' institution between May 2015 and May 2022. A genitourinary radiologist performed prospective readouts on in-house MRI scans following the Prostate Imaging Reporting and Data System (PI-RADS) version 2.0 or 2.1 and retrospective image quality assessments for all scans. A subgroup of patients underwent an MRI/US fusion-guided biopsy. A bpMRI-based lesion detection AI model previously developed using a completely separate dataset was tested on both MRI datasets. Detection rates were compared between external and in-house datasets with use of the paired comparison permutation tests. Factors associated with AI detection performance were assessed using multivariable generalized mixed-effects models, incorporating features selected through forward stepwise regression based on the Akaike information criterion. Results The study included 201 male patients (median age, 66 years [IQR, 62-70 years]; prostate-specific antigen density, 0.14 ng/mL2 [IQR, 0.10-0.22 ng/mL2]) with a median interval between external and in-house MRI scans of 182 days (IQR, 97-383 days). For intraprostatic lesions, AI detected 39.7% (149 of 375) on external and 56.0% (210 of 375) on in-house MRI scans (P < .001). For csPCa-positive lesions, AI detected 61% (54 of 89) on external and 79% (70 of 89) on in-house MRI scans (P < .001). On external MRI scans, better overall lesion detection was associated with a higher PI-RADS score (odds ratio [OR] = 1.57; P = .005), larger lesion diameter (OR = 3.96; P < .001), better diffusion-weighted MRI quality (OR = 1.53; P = .02), and fewer lesions at MRI (OR = 0.78; P = .045). Better csPCa detection was associated with a shorter MRI interval between external and in-house scans (OR = 0.58; P = .03) and larger lesion size (OR = 10.19; P < .001). Conclusion The AI model exhibited modest performance in identifying both overall and csPCa-positive lesions on external bpMRI scans. Keywords: MR Imaging, Urinary, Prostate Supplemental material is available for this article. © RSNA, 2024.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1148/rycan.240050
- OA Status
- diamond
- Cited By
- 5
- References
- 37
- Related Works
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- OpenAlex ID
- https://openalex.org/W4403335641
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403335641Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1148/rycan.240050Digital Object Identifier
- Title
-
External Validation of a Previously Developed Deep Learning–based Prostate Lesion Detection Algorithm on Paired External and In-House Biparametric MRI ScansWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-10-11Full publication date if available
- Authors
-
Enis C. Yılmaz, Stephanie A. Harmon, Yan Mee Law, Erich P. Huang, Mason J. Belue, Yue Lin, David G. Gelikman, Kutsev B Özyörük, Dong Yang, Ziyue Xu, Jesse Tetreault, Daguang Xu, Lindsey Hazen, Charisse Garcia, Nathan Lay, Philip Eclarinal, Antoun Toubaji, Maria J. Merino, Bradford J. Wood, Sandeep Gurram, Peter L. Choyke, Peter A. Pinto, Barış TürkbeyList of authors in order
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https://doi.org/10.1148/rycan.240050Publisher landing page
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://doi.org/10.1148/rycan.240050Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Algorithm, Lesion, Prostate, Nuclear medicine, Medicine, Pathology, Cancer, Internal medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
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2025: 5Per-year citation counts (last 5 years)
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37Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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