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Partial Correlation Analysis and Neural-Network-Based Prediction Model for Biochemical Recurrence of Prostate Cancer after Radical Prostatectomy

التفاصيل البيبلوغرافية
العنوان: Partial Correlation Analysis and Neural-Network-Based Prediction Model for Biochemical Recurrence of Prostate Cancer after Radical Prostatectomy
المؤلفون: Jae-Kwon Kim, Sung-Hoo Hong, In-Young Choi
المصدر: Applied Sciences, Vol 13, Iss 2, p 891 (2023)
بيانات النشر: MDPI AG, 2023.
سنة النشر: 2023
المجموعة: LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Biology (General)
LCC:Physics
LCC:Chemistry
مصطلحات موضوعية: prostate cancer, biochemical recurrence, partial correlation analysis, neural network, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
الوصف: Biochemical recurrence (BCR) of prostate cancer occurs when the PSA level increases after treatment. BCR prediction is necessary for successful prostate cancer treatment. We propose a model to predict the BCR of prostate cancer using a partial correlation neural network (PCNN). Our study used data from 1021 patients with prostate cancer who underwent radical prostatectomy at a tertiary hospital. There were nine input variables with BCR as the outcome variable. Feature-sensitive and partial correlation analyses were performed to develop the PCNN. The PCNN provides an NN architecture that is optimized for BCR prediction. The proposed PCNN achieved higher performance in BCR prediction than other machine learning methodologies, with accuracy, sensitivity, and specificity values of 87.16%, 90.80%, and 85.62%, respectively. The enhanced performance of the PCNN is owing to the reduction in unnecessary predictive factors through the correlation between the variables that are used. The PCNN can be used in the clinical treatment stage following prostate treatment. It is expected to be used as a clinical decision-making system in clinical follow-ups for prostate cancer.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2076-3417
Relation: https://www.mdpi.com/2076-3417/13/2/891; https://doaj.org/toc/2076-3417
DOI: 10.3390/app13020891
URL الوصول: https://doaj.org/article/2ac7a017c430442592b83632654914bb
رقم الأكسشن: edsdoj.2ac7a017c430442592b83632654914bb
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20763417
DOI:10.3390/app13020891