Objective:

To identify subgroups of patients with metastatic renal cell carcinoma (mRCC) who have a high probability of response to first-line systemic treatment using machine learning techniques.

Approach:

Key Findings:

• 2,549 patients included, with an overall response rate (ORR) of 36.0%.
• VEGF inhibitors had the lowest ORR at 29.6%, while IO-IO and IO-TKI combinations had ORRs of 39.1% and 50.2%, respectively.
• Five key variables identified: therapy type, nephrectomy, lung metastasis, other sites of metastasis, and age.
• The highest ORR was in patients treated with IO-TKI and nephrectomy (54.9%) and IO-IO with nephrectomy and lung metastasis (59.8%).
• Factors associated with poorer ORR included non-clear cell histology, older age, bone and liver metastases, poor performance status, elevated neutrophils, and poor IMDC risk score.
• Patient demographics included 73.2% male and 13.5% with non-clear cell histology.

Interpretation:

The study suggests that treatment selection for mRCC could be optimized by considering identified hierarchical variables, particularly therapy type, which may enhance clinical decision-making.

Limitations:

• The study is retrospective and may be subject to selection bias, potentially affecting the generalizability of the findings.
• Further validation of the findings is needed in independent cohorts.

Conclusion:

This large-scale ML analysis highlights the potential for improved treatment selection in mRCC based on specific clinical variables.

Attribution Notice

This content is an AI-generated, fully rewritten summary based on a published scholarly article. It does not reproduce the original text and is not a substitute for the original publication. Readers are encouraged to consult the source for full context, data, and methodology.