Abstract
Currently in patients with bladder cancer, various clinical evaluations (imaging, operative findings at transurethral resection and radical cystectomy, pathology) are collectively used to determine disease status and prognosis, and recommend neoadjuvant, definitive and adjuvant treatments. We analyze the predictive power of these measurements in forecasting two key long-term outcomes following radical cystectomy, i.e., cancer recurrence and survival. Information theory and machine learning algorithms are employed to create predictive models using a large prospective, continuously collected, temporally resolved, primary bladder cancer dataset comprised of 3503 patients (1971-2016). Patient recurrence and survival one, three, and five years after cystectomy can be predicted with greater than 70% sensitivity and specificity. Such predictions may inform patient monitoring schedules and post-cystectomy treatments. The machine learning models provide a benchmark for predicting oncologic outcomes in patients undergoing radical cystectomy and highlight opportunities for improving care using optimal preoperative and operative data collection.
Highlights
Bladder cancer (BCa) is the 6th most common cancer in the U.S, with an estimated 79,030 new cases and 16,870 deaths in 2017 [1] and has a 5-year relative survival rate of 79% [2]
There is an exponential decay in survival by age groups in the five-year period post-cystectomy, which suggests the burden of BCa diminishes significantly within five years for patients undergoing radical cystectomy (Fig 2)
Since 2010, there is a surge in stage over-estimation, with a corresponding decrease in underestimation; overall concordance between the two staging measures has remained relatively constant over the decades studied (Fig A in S1 File)
Summary
Bladder cancer (BCa) is the 6th most common cancer in the U.S, with an estimated 79,030 new cases and 16,870 deaths in 2017 [1] and has a 5-year relative survival rate of 79% [2]. BCa staging is based on the TNM system (tumor, nodes, metastasis). In BCa, the “T” stage is dictated by how deep the tumor invades into the various layers of the bladder wall. Ta represents a noninvasive papillary tumor, while T1, T2, T3 and T4 stages represent more aggressive. Machine learning models for predicting bladder cancer recurrence and survival. Urology and the University of Southern California Michelson Center for Convergent Biosciences
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