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The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
There are several types of targeted therapies against...
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  11. The Current Application And Future Potential Of Artificial Intelligence In Renal Cancer

The Current Application and Future Potential of Artificial Intelligence in Renal Cancer

Adri M Durant1, Ramon Correa Medero2, Logan G Briggs1

  • 1Department of Urology, Mayo Clinic Arizona, Phoenix, AZ.

Urology
|July 19, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

Artificial intelligence (AI) enhances kidney cancer care by improving evaluation, management, and outcome predictions. This review highlights AI

Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) is increasingly integrated into medical practice.
  • Its application in kidney cancer research is rapidly advancing.
  • AI offers novel approaches to analyze complex data in oncology.

Purpose of the Study:

  • To conduct a narrative review of artificial intelligence applications in kidney cancer research.
  • To identify key AI methodologies and their impact on clinical practice.
  • To assess the current state and future potential of AI in renal neoplasm management.

Main Methods:

  • A narrative review was performed using the PubMed electronic database.
  • Searches included keywords such as Artificial Intelligence, Machine Learning, Deep Learning, radiomics, pathomics, and kidney/renal neoplasms.

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  • 72 clinically relevant studies were identified and analyzed.

    • Main Results:

    • AI demonstrates significant potential in kidney cancer evaluation and outcome prediction.
    • Studies covered AI applications in imaging, histopathology, and prognostic modeling.
    • Key AI techniques include supervised/unsupervised machine learning, deep learning, and natural language processing.

    Conclusions:

    • Artificial intelligence tools are poised to become integral to clinical decision-making in kidney cancer.
    • Further integration of AI in renal neoplasm research will refine diagnostic and therapeutic strategies.
    • AI advancements promise improved patient outcomes in kidney cancer care.