A mathematical model for predicting tumor recurrence within 24 months following surgery in patients with T1 high-grade bladder cancer treated with BCG immunotherapy

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Jacob Rubinstein
Tomer Bar-On
Zaher Bahouth
Roy Mano
Ohad Shoshany
Jack Baniel
Ofer Nativ
Sarel Halachmi


bladder cancer, decision tree model, neutrophils-to-lymphocytes ratio


Objectives: Aggressive bladder cancer has a high rate of recurrence and progression. Treatment of T1 high-grade (T1HG) bladder cancer lesions is challenging. Current prognostic models reasonably predict progression; however, additional prognostic markers are required to accurately predict recurrence. The aim of the study was to develop a prediction model for risk of recurrence in individual patients with T1HG bladder cancer treated with intravesical BCG.

Patients and Methods: Medical records of 115 patients with T1HG bladder cancer treated with adjuvant intravesical BCG immunotherapy from two different hospitals were reviewed. Mathematical algorithms were applied to identify parameters that could accurately predict recurrence within 24 months after surgery.

Results: Overall recurrence rate at 24 months after surgery was 49%. Of all clinical and pathological parameters evaluated, the best predictor of recurrence within 24 months after surgery was neutrophils-to-lymphocytes ratio. This ratio predicted recurrence with 86% sensitivity and 62% specificity in the whole database. The main limitations of our study are its retrospective nature and the small patient number.

Conclusions: Neutrophils-to-lymphocytes ratio was found to be superior to traditional parameters for the prediction of recurrence within 24 months following surgery in patients with T1HG bladder cancer treated with intravesical BCG immunotherapy. Stratifying patients using risk factors can help determine the appropriate follow-up and treatment for each individual patient.



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