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. 2024 May 12;5(7):665-667.
doi: 10.1002/bco2.373. eCollection 2024 Jul.

Evaluating a deep learning AI algorithm for detecting residual prostate cancer on MRI after focal therapy

David G Gelikman  1 , Stephanie A Harmon  1 , Alexander P Kenigsberg  2 , Yan Mee Law  3 , Enis C Yilmaz  1 , Maria J Merino  4 , Bradford J Wood  5   6 , Peter L Choyke  1 , Peter A Pinto  2 , Baris Turkbey  1
Affiliations

Evaluating a deep learning AI algorithm for detecting residual prostate cancer on MRI after focal therapy

David G Gelikman et al. BJUI Compass. .

Erratum in

  • Erratum.
    [No authors listed] [No authors listed] BJUI Compass. 2024 Dec 30;5(12):1324-1329. doi: 10.1002/bco2.482. eCollection 2024 Dec. BJUI Compass. 2024. PMID: 39744071 Free PMC article.
No abstract available

Keywords: artificial intelligence; focal therapy; prostate cancer.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

FIGURE 1
FIGURE 1
Biparametric MRI (bpMRI) of a 75‐year‐old patient post‐cryoablation with a serum PSA of 3.68 ng/ml. Axial MRI shows a homogenously hypointense lesion in the right mid anterior transition zone (arrow) on T2‐weighted MRI (A), hyperintensity on high b‐value (b = 1500 s/mm2) diffusion‐weighted MRI (B), hypointensity on apparent diffusion coefficient map (C), and binary lesion prediction model overlay on T2‐weighted image, with suspicious lesion predicted in red (D). MRI/TRUS fusion guided biopsy of this lesion demonstrated Gleason Grade Group 2 prostate cancer.
See this image and copyright information in PMC

References

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