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Artificial Intelligence-assisted Prostate Cancer Diagnosis: Radiologic-Pathologic Correlation.

Publication ,  Journal Article
Mata, LA; Retamero, JA; Gupta, RT; García Figueras, R; Luna, A
Published in: Radiographics
October 2021

The classic prostate cancer (PCa) diagnostic pathway that is based on prostate-specific antigen (PSA) levels and the findings of digital rectal examination followed by systematic biopsy has shown multiple limitations. The use of multiparametric MRI (mpMRI) is now widely accepted in men with clinical suspicion for PCa. In addition, clinical information, PSA density, risk calculators, and genomic and other "omics" biomarkers are being used to improve risk stratification. On the basis of mpMRI and MRI-targeted biopsies (MRI-TBx), new diagnostic pathways have been established, aiming to improve the limitations of the classic diagnostic approach. However, these pathways still show limitations associated with mpMRI and MRI-TBx. Definitive PCa diagnosis is made on the basis of histopathologic Gleason grading, which has demonstrated an excellent correlation with clinical outcomes. However, Gleason grading is done subjectively by pathologists and involves poor reproducibility, and PCa may have a heterogeneous distribution of histologic patterns. Thus, important discrepancies persist between biopsy tumor grading and final whole-organ pathologic assessment after radical prostatectomy. PCa offers a unique opportunity to establish a real radiologic-pathologic correlation, as whole-mount radical prostatectomy specimens permit a complete spatial relationship with mpMRI. Artificial intelligence is increasingly being applied to radiologic and pathologic images to improve clinical accuracy and efficiency in PCa diagnosis. This review delineates current PCa diagnostic pathways, with a focus on the role of mpMRI, MRI-TBx, and pathologic analysis. An overview of the expected improvements in PCa diagnosis derived from the use of artificial intelligence, integrated radiologic-pathologic systems, and decision support tools for multidisciplinary teams is provided. An invited commentary by Purysko is available online. Online supplemental material is available for this article. ©RSNA, 2021.

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Published In

Radiographics

DOI

EISSN

1527-1323

Publication Date

October 2021

Volume

41

Issue

6

Start / End Page

1676 / 1697

Location

United States

Related Subject Headings

  • Reproducibility of Results
  • Prostatic Neoplasms
  • Nuclear Medicine & Medical Imaging
  • Neoplasm Grading
  • Male
  • Magnetic Resonance Imaging
  • Image-Guided Biopsy
  • Humans
  • Artificial Intelligence
  • 3202 Clinical sciences
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Mata, L. A., Retamero, J. A., Gupta, R. T., García Figueras, R., & Luna, A. (2021). Artificial Intelligence-assisted Prostate Cancer Diagnosis: Radiologic-Pathologic Correlation. Radiographics, 41(6), 1676–1697. https://doi.org/10.1148/rg.2021210020
Mata, Lidia Alcalá, Juan Antonio Retamero, Rajan T. Gupta, Roberto García Figueras, and Antonio Luna. “Artificial Intelligence-assisted Prostate Cancer Diagnosis: Radiologic-Pathologic Correlation.Radiographics 41, no. 6 (October 2021): 1676–97. https://doi.org/10.1148/rg.2021210020.
Mata LA, Retamero JA, Gupta RT, García Figueras R, Luna A. Artificial Intelligence-assisted Prostate Cancer Diagnosis: Radiologic-Pathologic Correlation. Radiographics. 2021 Oct;41(6):1676–97.
Mata, Lidia Alcalá, et al. “Artificial Intelligence-assisted Prostate Cancer Diagnosis: Radiologic-Pathologic Correlation.Radiographics, vol. 41, no. 6, Oct. 2021, pp. 1676–97. Pubmed, doi:10.1148/rg.2021210020.
Mata LA, Retamero JA, Gupta RT, García Figueras R, Luna A. Artificial Intelligence-assisted Prostate Cancer Diagnosis: Radiologic-Pathologic Correlation. Radiographics. 2021 Oct;41(6):1676–1697.

Published In

Radiographics

DOI

EISSN

1527-1323

Publication Date

October 2021

Volume

41

Issue

6

Start / End Page

1676 / 1697

Location

United States

Related Subject Headings

  • Reproducibility of Results
  • Prostatic Neoplasms
  • Nuclear Medicine & Medical Imaging
  • Neoplasm Grading
  • Male
  • Magnetic Resonance Imaging
  • Image-Guided Biopsy
  • Humans
  • Artificial Intelligence
  • 3202 Clinical sciences