Deep-Learning-Based Model for the Prediction of Cancer-specific Survival in Patients with Spinal Chordoma.

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Abstract

Spinal chordomas are locally aggressive and frequently recurrent tumors with a poor prognosis. Previous studies focused on Cox regression model to predict the survival of patients with spinal chordoma. We aimed to develop a more effective model based on deep learning for prognosis prediction in spinal chordoma.Patients with the spinal chordoma were gathered from SEER database. Cox regression analysis was conducted to compare the influence of different clinical characteristics on cancer-specific survival. Two deep learning models, namely DeepSurv and NMTLR, were developed, alongside two classic models, for the purpose of comparison. Performance of these models was evaluated by concordance index, Integrated Brier Score, receiver operating characteristic curves, Kaplan-Meier curves, and calibration curves.Two hundred and fifty-eight spinal chordoma patients were included in the current study. The median follow-up time was 94 ± 52 months. Variables used for prognosis prediction consisted of age, primary site, tumor size, histological grade, extension of surgery, tumor invasion and metastasis. Comparing to conventional models, each deep learning model showed superior predictive performance, the C-index on test cohort is 0.830 for DeepSurv and 0.804 for NMTLR respectively. The DeepSurv model represented the best performance among with AUC of 0.843 in predicting 5-year survival and 0.880 in 10-year.The study successfully constructed deep learning model to predict the CSS of spinal chordoma patients and proved that it was more accurate and practical than conventional prediction model. Our deep learning model has the potential to guide clinicians in better care planning and decision-making.Copyright © 2023 Elsevier Inc. All rights reserved.