Early Detection of ccRCC

EP: 1.The Evolvement of Imaging in ccRCC

EP: 2.The Biggest Challenges in Distinguishing ccRCC From Benign Renal Masses

EP: 3.Strengths and Weaknesses of Imaging Modalities in ccRCC

EP: 4.Treatment or Lack of Treatment of Small Renal Masses

EP: 5.Decision-Making in Small Renal Masses

EP: 6.Current Unmet Needs in ccRCC Diagnosis

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EP: 7.Early Detection of ccRCC

EP: 8.Key Benefits of PET in ccRCC Risk Stratification

EP: 9.The ZIRCON Trial

EP: 10.The MOA of TLX250

EP: 11.Top-Line Results of the Phase 3 ZIRCON Study

EP: 12.Risk Stratification in ccRCC: The Impact in Patient Populations

Future Directions for Imaging in Kidney Cancer

Main Discussion Topics:

• Potential for deep learning and artificial intelligence (AI) to extract additional information from imaging data sets
• Recognition that computational approaches have limitations
• The future likely requires molecular imaging that assesses tumor expression patterns.

Key Points for Physicians:

• AI and deep learning may identify imaging features not apparent to human readers.
• Computational approaches alone may have limitations.
• Molecular imaging represents the next frontier in renal mass characterization.

Notable Insights:

Although AI approaches show promise, the experts believe the most significant advances will come from molecular imaging techniques that directly assess tumor biology.

Clinical Significance:

Combination of computational approaches and molecular imaging will likely provide the next major advance in renal mass characterization.