Develop prediction model to help forecast advanced prostate cancer patients' prognosis after surgery using neural network.

ABSTRACT:

Background

The effect of surgery on advanced prostate cancer (PC) is unclear and predictive model for postoperative survival is lacking yet.

Methods

We investigate the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database, to collect clinical features of advanced PC patients. According to clinical experience, age, race, grade, pathology, T, N, M, stage, size, regional nodes positive, regional nodes examined, surgery, radiotherapy, chemotherapy, history of malignancy, clinical Gleason score (composed of needle core biopsy or transurethral resection of the prostate specimens), pathological Gleason score (composed of prostatectomy specimens) and prostate-specific antigen (PSA) are the potential predictive variables. All samples are divided into

SUBMITTER: Li S 

PROVIDER: S-EPMC10991752 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature