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