Machine learning models for predicting post-cystectomy recurrence and survival in bladder cancer patients.

Journal: PloS one
Published Date: Feb 20, 2019

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.

Authors

  • Zaki Hasnain
    Department of Aerospace and Mechanical Engineering, Viterbi School of Engineering, University of Southern California, University Park Campus, Los Angeles, CA, United States of America.
  • Jeremy Mason
    Department of Biological Sciences, Dornsife College of Letters, Arts, and Sciences, University of Southern California, University Park Campus, Los Angeles, CA, United States of America.
  • Karanvir Gill
    Department of Biological Sciences, Dornsife College of Letters, Arts, and Sciences, University of Southern California, University Park Campus, Los Angeles, CA, United States of America.
  • Gus Miranda
    USC Institute of Urology, Keck School of Medicine, University of Southern California, Health Sciences Campus, Los Angeles, CA, United States of America.
  • Inderbir S Gill
    Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, University of Southern California Institute of Urology, Los Angeles, California.
  • Peter Kuhn
    Department of Aerospace and Mechanical Engineering, Viterbi School of Engineering, University of Southern California, University Park Campus, Los Angeles, CA, United States of America.
  • Paul K Newton
    Department of Aerospace and Mechanical Engineering, Viterbi School of Engineering, University of Southern California, University Park Campus, Los Angeles, CA, United States of America.

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