Incidence trends, overall survival, and metastasis prediction using multiple machine learning and deep learning techniques in pediatric and adolescent population with osteosarcoma and Ewing's sarcoma: nomogram and webpage.

Journal: Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico

PMID: 39333451

Abstract

OBJECTIVE: The objective of this study was to analyze the incidence and overall survival (OS) of osteosarcoma (OSC) and Ewing's sarcoma (EWS) in a pediatric and adolescent population, employing machine learning (ML) and deep learning (DL) models to predict the likelihood of metastasis.

Authors

Chengyuan Zhou

Department of Oncology, The Affiliated Hospital of Southwest Medical University, 25 TAIPING Street, Luzhou City, 646000, Sichuan Province, China.
Han Li
Hao Zeng

European Laboratory for Non Linear Spectroscopy (LENS), University of Florence, 50019 Sesto Fiorentino, Italy.
Pan Wang

Keywords

Adolescent Bone Neoplasms Child Child, Preschool Deep Learning Female Humans Incidence Infant Infant, Newborn Machine Learning Male Neoplasm Metastasis Nomograms Osteosarcoma Prognosis Sarcoma, Ewing Survival Rate Young Adult

External Resources

View on PubMed Access via DOI PubMed (39333451)

Incidence trends, overall survival, and metastasis prediction using multiple machine learning and deep learning techniques in pediatric and adolescent population with osteosarcoma and Ewing's sarcoma: nomogram and webpage.

Abstract

Authors

Keywords

External Resources

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