Quantified treatment effect at the individual level is more indicative for personalized radical prostatectomy recommendation: implications for prostate cancer treatment using deep learning.

Journal: Journal of cancer research and clinical oncology

Published Date: Feb 1, 2024

Abstract

BACKGROUND: There are potential uncertainties and overtreatment existing in radical prostatectomy (RP) for prostate cancer (PCa) patients, thus identifying optimal candidates is quite important.

Authors

Huiqing Pan

School of Medicine, Tongji University, Shanghai, China.
Jiayi Wang

Department of Statistics, Texas A&M University.
Weizhong Shi

Shanghai Hospital Development Center, Shanghai, China.
Ziqin Xu

Department of Biobehavioral Sciences, Columbia University, New York, NY, United States of America.
Enzhao Zhu

School of Medicine, Tongji University, Shanghai, China.

Keywords

Deep Learning Humans Male Proportional Hazards Models Prostate Prostate-Specific Antigen Prostatectomy Prostatic Neoplasms Retrospective Studies

External Resources

View on PubMed Access via DOI PubMed (38302801)

Quantified treatment effect at the individual level is more indicative for personalized radical prostatectomy recommendation: implications for prostate cancer treatment using deep learning.

Abstract

Authors

Keywords

External Resources

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