Research Article: Machine learning models for predicting post-cystectomy recurrence and survival in bladder cancer patients

Date Published: February 20, 2019

Publisher: Public Library of Science

Author(s): Zaki Hasnain, Jeremy Mason, Karanvir Gill, Gus Miranda, Inderbir S. Gill, Peter Kuhn, Paul K. Newton, Masaru Katoh.

http://doi.org/10.1371/journal.pone.0210976

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.

Partial Text

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 cancers invading the sub-epithelial tissue, muscle, peri-vesical fat and adjacent organs, respectively. Radical surgery is the primary treatment method for invasive cancer and may be augmented with other forms of therapy such as chemotherapy to treat more advanced and aggressive cancers [3]. Radical cystectomy, the recommended method for treating invasive BCa [4], is surgical removal of the bladder, regional lymph nodes and adjacent organs (prostate, uterus, etc.) which may contain cancer. Technical precision of this surgical operation can dictate long-term oncologic outcomes, for instance, post-cystectomy survival is higher when negative surgical margins are obtained and more than ten pelvic lymph nodes removed during radical cystectomy [5]. Conversely, cancer recurrence rates are higher with positive margins and removal of less than ten nodes.5 Furthermore, patients with organ-confined disease are less likely to relapse beyond 5 years, and unlikely beyond 10 years after cystectomy, even without adjuvant treatment [6].

Although recurrence and OS are highly associated, preoperative and operative measurements generally do not relate equally to recurrence and OS, and the two outcomes should be assessed separately. The primary predictors of long-term outcomes are pathologic stage and its subgrouping into localized or metastatic conditions. However, the machine learning pipeline developed here can leverage less powerful predictors to improve accuracy of long term predictions. The benefit of having low MI between variables means that each variable offers unique information however the drawback is that each patient needs to be described by many variables and thus the prediction task becomes a higher dimensional problem, for which lack of data can greatly limit predictions of long-term outcomes. Clinical T stage offers a lower resolution signal than the true pathologic T stage, and this loss of information can be particularly impactful in cases where there is an underestimation of disease severity prior to surgery [19].

 

Source:

http://doi.org/10.1371/journal.pone.0210976

 

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