AI Article Synopsis
- About 70% of bladder cancer cases are non-muscle invasive (NMIBC) and inflammation, influenced by factors like smoking, affects treatment outcomes with BCG therapy.
- A study analyzed data from 1,313 NMIBC patients to examine how smoking and systemic inflammation impact the effectiveness of BCG using a machine-learning algorithm.
- The results indicated that both smoking status and specific inflammatory markers can significantly predict the risk of disease progression, highlighting the need for tailored treatment strategies and further research to confirm the findings.
Article Abstract
Background: Approximately 70% of bladder cancer is diagnosed as non-muscle invasive (NMIBC) and inflammation is known to impact the oncological outcomes. Adjuvant intravesical BCG in intermediate/high risk can lower recurrence and progression. The efficacy of intravesical BCG can be impacted by smoking effects on systemic inflammation.
Methods: Our retrospective, multicenter study with data from 1.313 NMIBC patients aimed to assess the impact of smoking and the systemic inflammatory status on BCG response in T1G3 bladder cancer, using a machine-learning CART based algorithm.
Results: In a median of 50-month follow-up (IQR 41-75), 344 patients experienced progression to muscle invasive or metastatic disease and 65 died due to bladder cancer. A CART algorithm has been employed to stratify patients in three prognostic clusters using smoking status, LMR (lymphocytes to monocytes ratio), NLR (neutrophil-to-lymphocyte ratio) and PLR (platelet-to-lymphocyte ratio) as variables. Cox regression models revealed a 1.5-fold (HR 1.66, 95%, CI 1.20-2.29, P=0.002) and three-fold (HR 2.99, 95% CI 2.08-4.30, P<0.001) risk of progression, in intermediate and high risk NMIBC respectively, compared to the low-risk group. The model's concordance index was 0.66.
Conclusions: Our study provides an insight into the influence of smoking on inflammatory markers and BCG response in NMIBC patients. Our machine-learning approach provides clinicians a valuable tool for risk stratification, treatment, and decision-making. Future research in larger prospective cohorts is required for validating these findings.
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| http://dx.doi.org/10.23736/S2724-6051.24.05876-2 | DOI Listing |
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