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

Bumjin Lim, Kwang Suk Lee, Young Hwa Lee, Suah Kim, Choongki Min, Ju Young Park, Hye Sun Lee, Jin Seon Cho, Sun Il Kim, Byung Ha Chung, Choung Soo Kim, Kyo Chul Koo

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)558-566
Number of pages9
JournalCancer Research and Treatment
Volume53
Issue number2
DOIs
Publication statusPublished - 2021 Apr

Bibliographical note

Funding Information:
This study was supported by the Research Grant of the National Research Foundation of Korea (2020R1F1A1073833) and the Research Grant of Gangnam Severance Hospital Prostate Cancer Center Research Committee (7523110).

Publisher Copyright:
Copyright © 2021 by the Korean Cancer Association

All Science Journal Classification (ASJC) codes

  • Oncology
  • Cancer Research

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