Early treatment outcome prediction in metastatic castration-resistant prostate cancer utilizing 3-month tumor growth rate ( <i>g</i> -rate) based machine learning model

Authors

Harshraj Leuva (ORCID: https://orcid.org/0000-0002-8860-1382)
Esther Chiadikaobi Ugwueke
Mohamed Azzam (ORCID: https://orcid.org/0000-0002-9007-936X)
Raymond C. Bergan (ORCID: https://orcid.org/0000-0001-8690-4876)
Shibiao Wan (ORCID: https://orcid.org/0000-0003-0661-2684)
Benjamin A. Teply (ORCID: https://orcid.org/0000-0002-7366-6212)
J. Wang
Mengxi Zhou
Antonio Tito Fojo

Institutions

Memorial Sloan Kettering Cancer Center (US)
Columbia University Irving Medical Center (US)
James J. Peters VA Medical Center (US)
University of Nebraska Medical Center (US)
Stony Brook University (US)

Publication Details

Journal: medRxiv
Published: 2026-03-03
DOI: https://doi.org/10.64898/2026.02.26.26346987
Primary Topic: Prostate Cancer Treatment and Research
Type: article
Field-Weighted Citation Impact: 0.00

Funders

National Science Foundation
National Institutes of Health
National Cancer Institute

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article

Early treatment outcome prediction in metastatic castration-resistant prostate cancer utilizing 3-month tumor growth rate ( g -rate) based machine learning model

Harshraj Leuva, Esther Chiadikaobi Ugwueke, Mohamed Azzam, Raymond C. Bergan et al.

Mar 3, 2026

medRxiv

Prostate Cancer Treatment and Research

article

Early treatment outcome prediction in metastatic castration-resistant prostate cancer utilizing 3-month tumor growth rate ( g -rate) based machine learning model

Harshraj Leuva, Esther Chiadikaobi Ugwueke, Mohamed Azzam, Raymond C. Bergan, Shibiao Wan, Benjamin A. Teply, J. Wang, Mengxi Zhou, Antonio Tito Fojo

March 3, 2026article en

Abstract

No abstract available for this paper.

medRxiv

Memorial Sloan Kettering Cancer Center (US), Columbia University Irving Medical Center (US), James J. Peters VA Medical Center (US), University of Nebraska Medical Center (US), Stony Brook University (US)

National Science Foundation, National Institutes of Health, National Cancer Institute

Good health and well-being

Openalex Percentile: Top 34%

Prostate Cancer Treatment and Research

DOI Open Access PDF OpenAlex

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Early treatment outcome prediction in metastatic castration-resistant prostate cancer utilizing 3-month tumor growth rate ( <i>g</i> -rate) based machine learning model — Harshraj Leuva, Esther Chiadikaobi Ugwueke, et al. · medRxiv (2026) | TGRS Research Map | TGRS