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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Artificial Intelligence-assisted Prostate Cancer Diagnosis: Radiologic-Pathologic Correlation.

Lidia Alcalá Mata1, Juan Antonio Retamero1, Rajan T Gupta1

  • 1From the Department of Radiology, Clínica Las Nieves, HT Médica, Calle Carmelo Torres Núm 2, 23007 Jaén, Spain (L.A.M., A.L.); Paige.AI, New York, NY (J.A.R.); Department of Radiology, Duke University Medical Center, Durham, NC (R.T.G.); and Department of Radiology, Complexo Hospitalario Universitario de Santiago de Compostela, Santiago de Compostela, Spain (R.G.F.).

Radiographics : a Review Publication of the Radiological Society of North America, Inc
|October 1, 2021
PubMed
Summary
This summary is machine-generated.

The traditional prostate cancer (PCa) diagnosis has limitations. New pathways using multiparametric MRI (mpMRI) and AI show promise for more accurate PCa detection and grading.

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Area of Science:

  • Radiology and Pathology
  • Oncology
  • Medical Imaging Analysis

Background:

  • The conventional prostate cancer (PCa) diagnostic pathway, relying on prostate-specific antigen (PSA) levels and digital rectal exams followed by systematic biopsies, has significant limitations.
  • Multiparametric MRI (mpMRI) is now standard for men with suspected PCa, alongside PSA density, risk calculators, and omics biomarkers for improved risk stratification.
  • Despite advancements like MRI-targeted biopsies (MRI-TBx), current diagnostic pathways still face challenges, including subjective Gleason grading and poor reproducibility, leading to discrepancies between biopsy and whole-organ pathology.

Purpose of the Study:

  • To review current prostate cancer diagnostic pathways, emphasizing the roles of mpMRI, MRI-TBx, and pathology.
  • To explore the potential of artificial intelligence (AI) in enhancing radiologic and pathologic image analysis for PCa diagnosis.
  • To provide an outlook on future improvements in PCa diagnosis through integrated radiologic-pathologic systems and AI-driven decision support.

Main Methods:

  • Review of current literature on prostate cancer diagnostic modalities.
  • Focus on the integration of multiparametric MRI (mpMRI) and MRI-targeted biopsies (MRI-TBx).
  • Discussion of the application of artificial intelligence (AI) in analyzing radiologic and pathologic images for PCa.

Main Results:

  • Established diagnostic pathways incorporating mpMRI and MRI-TBx aim to overcome limitations of traditional methods.
  • Gleason grading, while correlated with outcomes, suffers from subjectivity and inter-observer variability.
  • AI presents a significant opportunity to improve accuracy and efficiency in PCa diagnosis by analyzing imaging data.

Conclusions:

  • Prostate cancer diagnosis is evolving beyond traditional methods, with mpMRI and MRI-TBx offering improved risk stratification.
  • Artificial intelligence holds substantial promise for enhancing the accuracy and efficiency of PCa diagnosis through advanced image analysis.
  • Future directions involve integrated radiologic-pathologic systems and AI-powered decision support to optimize multidisciplinary team approaches for better patient outcomes.