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External Validation of the Long Short-Term Memory Artificial Neural Network-Based SCaP Survival Calculator for Prediction of Prostate Cancer Survival

PURPOSE: Decision-making for treatment of newly diagnosed prostate cancer (PCa) is complex due to the multiple initial treatment modalities available. We aimed to externally validate the SCaP (Severance Study Group of Prostate Cancer) Survival Calculator that incorporates a long short-term memory ar...

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Autores principales: Lim, Bumjin, Lee, Kwang Suk, Lee, Young Hwa, Kim, Suah, Min, Choongki, Park, Ju-Young, Lee, Hye Sun, Cho, Jin Seon, Kim, Sun Il, Chung, Byung Ha, Kim, Choung-Soo, Koo, Kyo Chul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korean Cancer Association 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8053858/
https://www.ncbi.nlm.nih.gov/pubmed/33070560
http://dx.doi.org/10.4143/crt.2020.637
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author Lim, Bumjin
Lee, Kwang Suk
Lee, Young Hwa
Kim, Suah
Min, Choongki
Park, Ju-Young
Lee, Hye Sun
Cho, Jin Seon
Kim, Sun Il
Chung, Byung Ha
Kim, Choung-Soo
Koo, Kyo Chul
author_facet Lim, Bumjin
Lee, Kwang Suk
Lee, Young Hwa
Kim, Suah
Min, Choongki
Park, Ju-Young
Lee, Hye Sun
Cho, Jin Seon
Kim, Sun Il
Chung, Byung Ha
Kim, Choung-Soo
Koo, Kyo Chul
author_sort Lim, Bumjin
collection PubMed
description PURPOSE: Decision-making for treatment of newly diagnosed prostate cancer (PCa) is complex due to the multiple initial treatment modalities available. We aimed to externally validate the SCaP (Severance Study Group of Prostate Cancer) Survival Calculator that incorporates a long short-term memory artificial neural network (ANN) model to estimate survival outcomes of PCa according to initial treatment modality. MATERIALS AND METHODS: The validation cohort consisted of clinicopathological data of 4,415 patients diagnosed with biopsy-proven PCa between April 2005 and November 2018 at three institutions. Area under the curves (AUCs) and time-to-event calibration plots were utilized to determine the predictive accuracies of the SCaP Survival Calculator in terms of progression to castration-resistant PCa (CRPC)–free survival, cancer-specific survival (CSS), and overall survival (OS). RESULTS: Excellent discrimination was observed for CRPC-free survival, CSS, and OS outcomes, with AUCs of 0.962, 0.944, and 0.884 for 5-year outcomes and 0.959, 0.928, and 0.854 for 10-year outcomes, respectively. The AUC values were higher for all survival endpoints compared to those of the development cohort. Calibration plots showed that predicted probabilities of 5-year survival endpoints had concordance comparable to those of the observed frequencies. However, calibration performances declined for 10-year predictions with an overall underestimation. CONCLUSION: The SCaP Survival Calculator is a reliable and useful tool for determining the optimal initial treatment modality and for guiding survival predictions for patients with newly diagnosed PCa. Further modifications in the ANN model incorporating cases with more extended follow-up periods are warranted to improve the ANN model for long-term predictions.
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spelling pubmed-80538582021-04-29 External Validation of the Long Short-Term Memory Artificial Neural Network-Based SCaP Survival Calculator for Prediction of Prostate Cancer Survival Lim, Bumjin Lee, Kwang Suk Lee, Young Hwa Kim, Suah Min, Choongki Park, Ju-Young Lee, Hye Sun Cho, Jin Seon Kim, Sun Il Chung, Byung Ha Kim, Choung-Soo Koo, Kyo Chul Cancer Res Treat Original Article PURPOSE: Decision-making for treatment of newly diagnosed prostate cancer (PCa) is complex due to the multiple initial treatment modalities available. We aimed to externally validate the SCaP (Severance Study Group of Prostate Cancer) Survival Calculator that incorporates a long short-term memory artificial neural network (ANN) model to estimate survival outcomes of PCa according to initial treatment modality. MATERIALS AND METHODS: The validation cohort consisted of clinicopathological data of 4,415 patients diagnosed with biopsy-proven PCa between April 2005 and November 2018 at three institutions. Area under the curves (AUCs) and time-to-event calibration plots were utilized to determine the predictive accuracies of the SCaP Survival Calculator in terms of progression to castration-resistant PCa (CRPC)–free survival, cancer-specific survival (CSS), and overall survival (OS). RESULTS: Excellent discrimination was observed for CRPC-free survival, CSS, and OS outcomes, with AUCs of 0.962, 0.944, and 0.884 for 5-year outcomes and 0.959, 0.928, and 0.854 for 10-year outcomes, respectively. The AUC values were higher for all survival endpoints compared to those of the development cohort. Calibration plots showed that predicted probabilities of 5-year survival endpoints had concordance comparable to those of the observed frequencies. However, calibration performances declined for 10-year predictions with an overall underestimation. CONCLUSION: The SCaP Survival Calculator is a reliable and useful tool for determining the optimal initial treatment modality and for guiding survival predictions for patients with newly diagnosed PCa. Further modifications in the ANN model incorporating cases with more extended follow-up periods are warranted to improve the ANN model for long-term predictions. Korean Cancer Association 2021-04 2020-10-06 /pmc/articles/PMC8053858/ /pubmed/33070560 http://dx.doi.org/10.4143/crt.2020.637 Text en Copyright © 2021 by the Korean Cancer Association https://creativecommons.org/licenses/by-nc/4.0/This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Lim, Bumjin
Lee, Kwang Suk
Lee, Young Hwa
Kim, Suah
Min, Choongki
Park, Ju-Young
Lee, Hye Sun
Cho, Jin Seon
Kim, Sun Il
Chung, Byung Ha
Kim, Choung-Soo
Koo, Kyo Chul
External Validation of the Long Short-Term Memory Artificial Neural Network-Based SCaP Survival Calculator for Prediction of Prostate Cancer Survival
title External Validation of the Long Short-Term Memory Artificial Neural Network-Based SCaP Survival Calculator for Prediction of Prostate Cancer Survival
title_full External Validation of the Long Short-Term Memory Artificial Neural Network-Based SCaP Survival Calculator for Prediction of Prostate Cancer Survival
title_fullStr External Validation of the Long Short-Term Memory Artificial Neural Network-Based SCaP Survival Calculator for Prediction of Prostate Cancer Survival
title_full_unstemmed External Validation of the Long Short-Term Memory Artificial Neural Network-Based SCaP Survival Calculator for Prediction of Prostate Cancer Survival
title_short External Validation of the Long Short-Term Memory Artificial Neural Network-Based SCaP Survival Calculator for Prediction of Prostate Cancer Survival
title_sort external validation of the long short-term memory artificial neural network-based scap survival calculator for prediction of prostate cancer survival
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8053858/
https://www.ncbi.nlm.nih.gov/pubmed/33070560
http://dx.doi.org/10.4143/crt.2020.637
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