Partial Correlation Analysis and Neural-Network-Based Prediction Model for Biochemical Recurrence of Prostate Cancer after Radical Prostatectomy
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 f...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/2/891 |
| _version_ | 1797446606698577920 |
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| author | Jae-Kwon Kim Sung-Hoo Hong In-Young Choi |
| author_facet | Jae-Kwon Kim Sung-Hoo Hong In-Young Choi |
| author_sort | Jae-Kwon Kim |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T13:43:58Z |
| format | Article |
| id | doaj.art-2ac7a017c430442592b83632654914bb |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T13:43:58Z |
| publishDate | 2023-01-01 |
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| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-2ac7a017c430442592b83632654914bb2023-11-30T21:03:00ZengMDPI AGApplied Sciences2076-34172023-01-0113289110.3390/app13020891Partial Correlation Analysis and Neural-Network-Based Prediction Model for Biochemical Recurrence of Prostate Cancer after Radical ProstatectomyJae-Kwon Kim0Sung-Hoo Hong1In-Young Choi2Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of KoreaDepartment of Urology, Seoul St. Mary’s Hospital, The Catholic University of Korea College of Medicine, Seoul 06591, Republic of KoreaDepartment of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of KoreaBiochemical 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.https://www.mdpi.com/2076-3417/13/2/891prostate cancerbiochemical recurrencepartial correlation analysisneural network |
| spellingShingle | Jae-Kwon Kim Sung-Hoo Hong In-Young Choi Partial Correlation Analysis and Neural-Network-Based Prediction Model for Biochemical Recurrence of Prostate Cancer after Radical Prostatectomy Applied Sciences prostate cancer biochemical recurrence partial correlation analysis neural network |
| title | Partial Correlation Analysis and Neural-Network-Based Prediction Model for Biochemical Recurrence of Prostate Cancer after Radical Prostatectomy |
| title_full | Partial Correlation Analysis and Neural-Network-Based Prediction Model for Biochemical Recurrence of Prostate Cancer after Radical Prostatectomy |
| title_fullStr | Partial Correlation Analysis and Neural-Network-Based Prediction Model for Biochemical Recurrence of Prostate Cancer after Radical Prostatectomy |
| title_full_unstemmed | Partial Correlation Analysis and Neural-Network-Based Prediction Model for Biochemical Recurrence of Prostate Cancer after Radical Prostatectomy |
| title_short | Partial Correlation Analysis and Neural-Network-Based Prediction Model for Biochemical Recurrence of Prostate Cancer after Radical Prostatectomy |
| title_sort | partial correlation analysis and neural network based prediction model for biochemical recurrence of prostate cancer after radical prostatectomy |
| topic | prostate cancer biochemical recurrence partial correlation analysis neural network |
| url | https://www.mdpi.com/2076-3417/13/2/891 |
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