Evidence map›Paper›PMID 30785915›Full record

ArticlePloS one2019

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

Zaki Hasnain, Jeremy Mason, Karanvir Gill, Gus Miranda, Inderbir S Gill, Peter Kuhn, Paul K Newton

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

39 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  14. Applications of artificial intelligence in urologic oncology.Investigative and clinical urology · 2024
    Review
  15. Bladder Cancer and Artificial Intelligence: Emerging Applications.The Urologic clinics of North America · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Zaki HasnainDepartment of Aerospace and Mechanical Engineering, Viterbi School of Engineering, University of Southern California, University Park Campus, Los Angeles, CA, United States of America.
Jeremy MasonDepartment 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 GillDepartment 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 MirandaUSC Institute of Urology, Keck School of Medicine, University of Southern California, Health Sciences Campus, Los Angeles, CA, United States of America.ORCID 0000-0001-6110-9024
Inderbir S GillUSC Institute of Urology, Keck School of Medicine, University of Southern California, Health Sciences Campus, Los Angeles, CA, United States of America.
Peter KuhnDepartment of Aerospace and Mechanical Engineering, Viterbi School of Engineering, University of Southern California, University Park Campus, Los Angeles, CA, United States of America.ORCID 0000-0003-2629-4505
Paul K NewtonDepartment of Aerospace and Mechanical Engineering, Viterbi School of Engineering, University of Southern California, University Park Campus, Los Angeles, CA, United States of America.

Funding

NCI NIH HHS HHSN261201500003CNCI NIH HHS HHSN261201500003I
6 · The paper itself

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.

Indexed as

CystectomyDatabases, FactualMachine LearningModels, BiologicalUrinary Bladder NeoplasmsAgedAged, 80 and overDisease-Free SurvivalFemaleHumansMaleMiddle AgedSurvival Rate

Identifiers

PMID30785915
PMCPMC6382101

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.