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

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 E...

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Vydáno v:	BMC medical informatics and decision making Ročník 20; číslo 1; s. 3 - 10
Hlavní autoři:	Ryu, Sung Mo, Seo, Sung Wook, Lee, Sun-Ho
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 06.01.2020 BioMed Central Ltd Springer Nature B.V BMC
Témata:	Age Algorithms Artificial intelligence Artificial neural networks Bone Neoplasms Cancer therapies Chemotherapy Chondrosarcoma Clinical decision-making Confidence intervals Diagnosis Epidemiology Health aspects Health Informatics Humans Indexing Information Systems and Communication Service Intelligence gathering knowledge support systems Learning algorithms Machine Learning Management of Computing and Information Systems Medical prognosis Medicine Medicine & Public Health Neural network Neural networks Neural Networks, Computer Patients Pelvis Prediction Prognosis Radiation Radiation (Physics) Radiation therapy Radiotherapy Research Article Statistical analysis Subgroups Surgery Survival Test sets theory Time Training Tumors Windows (intervals) South Korea United States > US Chondrosarcoma Neural network Artificial intelligence Survival Prediction
ISSN:	1472-6947, 1472-6947
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Shrnutí:	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 < 0.001). Conclusions This 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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1472-6947 1472-6947
DOI:	10.1186/s12911-019-1008-4