Comparison and interpretability of machine learning algorithms to predict survival of patients with prostate cancer
- DOI
- 10.1016/j.urolonc.2026.07.015
- Published
- 2026-10
- Container
- Urologic Oncology: Seminars and Original Investigations
- Publisher
- Elsevier BV
- Open access
- unknown
Credibility signals
uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- supportingDOI registered: A matching record was returned by Crossref.
- supportingDOI resolves: A matching record was returned by Crossref.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
Cite this work
BibTeX
@article{allodium:10.1016/j.urolonc.2026.07.015,
title = {Comparison and interpretability of machine learning algorithms to predict survival of patients with prostate cancer},
author = {Isaac E. Kim and Cecile P.G. Meier-Scherling and Steve R. Zhou and Simon J.C. Soerensen and Ismail Ajjawi and Benjamin I. Chung and Eugene Shkolyar and Geoffrey A. Sonn and Joseph C. Liao and Michael S. Leapman},
year = {2026},
journal = {Urologic Oncology: Seminars and Original Investigations},
doi = {10.1016/j.urolonc.2026.07.015},
url = {https://doi.org/10.1016/j.urolonc.2026.07.015}
}RIS
TY - JOUR TI - Comparison and interpretability of machine learning algorithms to predict survival of patients with prostate cancer AU - Isaac E. Kim AU - Cecile P.G. Meier-Scherling AU - Steve R. Zhou AU - Simon J.C. Soerensen AU - Ismail Ajjawi AU - Benjamin I. Chung AU - Eugene Shkolyar AU - Geoffrey A. Sonn AU - Joseph C. Liao AU - Michael S. Leapman PY - 2026 JO - Urologic Oncology: Seminars and Original Investigations DO - 10.1016/j.urolonc.2026.07.015 UR - https://doi.org/10.1016/j.urolonc.2026.07.015 ER -
APA
Kim, I. E., Meier-Scherling, C. P., Zhou, S. R., Soerensen, S. J., Ajjawi, I., Chung, B. I., Shkolyar, E., Sonn, G. A., Liao, J. C., & Leapman, M. S. (2026). Comparison and interpretability of machine learning algorithms to predict survival of patients with prostate cancer. Urologic Oncology: Seminars and Original Investigations. https://doi.org/10.1016/j.urolonc.2026.07.015
Source records
- crossref · retrieved 2026-09-25T11:15:21.113Z