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Imaging Studies I: Kidney, Ureter, and Bladder Studies01:28

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Kidney, Ureter, and Bladder (KUB) StudiesKidney, Ureter, and Bladder (KUB) studies are standard diagnostic imaging procedures used to assess the anatomy of the urinary system. They are commonly utilized for patients experiencing abdominal pain or urinary symptoms. By using a simple X-ray of the abdomen, KUB studies can reveal structural and pathological abnormalities within the kidneys, ureters, and bladder. These studies are particularly valuable in diagnosing kidney stones, urinary...
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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Artificial intelligence-based multi-class histopathologic classification of kidney neoplasms.

Dibson D Gondim1, Khaleel I Al-Obaidy2, Muhammad T Idrees3

  • 1Department of Pathology, University of Louisville School of Medicine, Louisville, KY 40202, USA.

Journal of Pathology Informatics
|March 14, 2023
PubMed
Summary
This summary is machine-generated.

Artificial intelligence shows promise in classifying renal cell carcinoma (RCC) subtypes from histopathology images. While accurate for most types, distinguishing clear cell RCC from clear cell papillary RCC requires further refinement.

Keywords:
Artificial intelligenceDigital pathologyHistopathologyMetanephric adenomaRenal cell carcinomaRenal oncocytoma

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Area of Science:

  • Computational pathology
  • Digital histopathology
  • Oncology research

Background:

  • Artificial intelligence (AI) offers potential for enhancing histopathological diagnosis accuracy and reproducibility.
  • Renal cell carcinoma (RCC) is a heterogeneous cancer requiring precise classification to guide treatment.
  • Current AI models show promise for classifying a few RCC subtypes, but performance in complex, real-world scenarios with multiple subtypes and benign mimics is less understood.

Purpose of the Study:

  • To develop and evaluate an AI-based computational model for classifying multiple subtypes of renal cell carcinoma (RCC) and benign mimickers using whole slide images (WSIs).
  • To assess the model's performance in a scenario reflecting actual pathology practice, including differentiating between various RCC subtypes and potential mimics.

Main Methods:

  • A computational model was developed using 298,071 image patches from 252 whole slide images (WSIs) of clear cell RCC, papillary RCC, chromophobe RCC, clear cell papillary RCC, and metanephric adenoma.
  • The AI-based image classifier was trained on these patches and subsequently applied to a secondary dataset of WSIs for performance evaluation.

Main Results:

  • The AI model correctly classified 47 out of 55 (85%) whole slide images in the secondary dataset.
  • The model demonstrated excellent performance across most classifications but showed limitations in distinguishing between clear cell RCC and clear cell papillary RCC.

Conclusions:

  • The developed AI computational model shows significant potential for assisting in the histopathological classification of renal cell carcinoma (RCC).
  • Further validation with large, multi-institutional datasets and prospective studies are necessary to confirm its clinical utility and translation potential.