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

Zaki Hasnain, Jeremy M. Mason, Karanvir S. Gill, Gus Miranda, Inderbir Singh Gill, Peter Kühn, Paul K. Newton

PLoS ONE 2019 article cited by 83

Source Open-access PDF More in Medicine & Health

importance 5/10 ai assisted Bladder and Urothelial Cancer Treatments oncology stage: research

Problem

Predicting cancer recurrence and survival one, three, and five years after radical cystectomy in bladder cancer patients.

Prognosis after cystectomy determines monitoring schedules and neoadjuvant/adjuvant treatment decisions.

Information theory and machine learning algorithms built predictive models on a prospective dataset of 3503 patients (1971-2016).

Recurrence and survival predicted with greater than 70% sensitivity and specificity at one, three, and five years.

Models may inform patient monitoring schedules and post-cystectomy treatments, per the authors.

Venue	PLoS ONE
Published	2019
Work type	article
Authors	Zaki Hasnain, Jeremy M. Mason, Karanvir S. Gill, Gus Miranda, Inderbir Singh Gill, Peter Kühn, Paul K. Newton
Citations	83 (OpenAlex, at import time)
References	27
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.1371/journal.pone.0210976
Open access	gold
Keywords	machine learning, bladder cancer, recurrence, survival, cystectomy, prognosis

5 / 10

real but narrower advance

Prognosis after cystectomy guides monitoring and treatment decisions for bladder cancer patients; a real but single-cancer clinical advance.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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