Dataset Information

Novel prognostication of patients with spinal and pelvic chondrosarcoma using deep survival neural networks.

ABSTRACT:

Background

We used the Surveillance, Epidemiology, and End Results (SEER) database to develop and validate deep survival neural network machine learning (ML) algorithms to predict survival following a spino-pelvic chondrosarcoma diagnosis.

Methods

The SEER 18 registries were used to apply the Risk Estimate Distance Survival Neural Network (RED_SNN) in the model. Our model was evaluated at each time window with receiver operating characteristic curves and areas under the curves (AUCs), as was the concordance index (c-index).

Results

The subjects (n?=?1088) were separated into training (80%, n?=?870) and test sets (20%, n?=?218). The training data were randomly sorted into training and validation sets using 5-fold cross validation. The median c-index of the five validation sets was 0.84 (95% confidence interval 0.79-0.87). The median AUC of the five validation subsets was 0.84. This model was evaluated with the previously separated test set. The c-index was 0.82 and the mean AUC of the 30 different time windows was 0.85 (standard deviation 0.02). According to the estimated survival probability (by 62?months), we divided the test group into five subgroups. The survival curves of the subgroups showed statistically significant separation (p?ConclusionsThis study is the first to analyze population-level data using artificial neural network ML algorithms for the role and outcomes of surgical resection and radiation therapy in spino-pelvic chondrosarcoma.

SUBMITTER: Ryu SM

PROVIDER: S-EPMC6945432 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Publications

Novel prognostication of patients with spinal and pelvic chondrosarcoma using deep survival neural networks.

Ryu Sung Mo SM Seo Sung Wook SW Lee Sun-Ho SH

BMC medical informatics and decision making 20200106 1

<h4>Background</h4>We used the Surveillance, Epidemiology, and End Results (SEER) database to develop and validate deep survival neural network machine learning (ML) algorithms to predict survival following a spino-pelvic chondrosarcoma diagnosis.<h4>Methods</h4>The SEER 18 registries were used to apply the Risk Estimate Distance Survival Neural Network (RED_SNN) in the model. Our model was evaluated at each time window with receiver operating characteristic curves and areas under the curves (AU ...[more]

PMID: 31907039

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Dataset Information

Novel prognostication of patients with spinal and pelvic chondrosarcoma using deep survival neural networks.

Background

Methods

Results

Publications

Novel prognostication of patients with spinal and pelvic chondrosarcoma using deep survival neural networks.

Similar Datasets

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