Assessing the influence of smoking on inflammatory markers in bacillus Calmette Guérin response among bladder cancer patients: a novel machine-learning approach.
Matteo Ferro, Octavian S Tataru, Giuseppe Fallara, Cristian Fiori, Matteo Manfredi, Francesco Claps, Rodolfo Hurle, Nicolò M Buffi, Giovanni Lughezzani, Massimo Lazzeri, Achille Aveta, Savio D Pandolfo, Biagio Barone, Felice Crocetto, Pasquale Ditonno, Giuseppe Lucarelli, Francesco Lasorsa, Giuseppe Carrieri, Gian M Busetto, Ugo G Falagario, Francesco Del Giudice, Martina Maggi, Francesco Cantiello, Marco Borghesi, Carlo Terrone, Pierluigi Bove, Alessandro Antonelli, Alessandro Veccia, Andrea Mari, Stefano Luzzago, Raul Gherasim, Ciprian Todea-Moga, Andrea Minervini, Gennaro Musi, Francesco A Mistretta, Roberto Bianchi, Marco Tozzi, Francesco Soria, Paolo Gontero, Michele Marchioni, Letizia M Janello, Daniela Terracciano, Giorgio I Russo, Luigi Schips, Sisto Perdonà, Riccardo Autorino, Michele Catellani, Chiara Sighinolfi, Emanuele Montanari, Savino M DI Stasi, Francesco Porpiglia, Bernardo Rocco, Ottavio de Cobelli, Roberto Contieri
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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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