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Public Health Genomics and Precision Health Knowledge Base (v9.0)
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Precision Health Database|Search|Public Health Genomics and Precision Health Knowledge Base (PHGKB)
Rare Diseases
Last data update: Apr 19, 2024
. (Total: 63627 Documents since 2012)
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Evaluation of the neoadjuvant chemotherapy response in osteosarcoma using the MRI DWI-based machine learning radiomics nomogram.
Lu Zhang et al. Front Oncol 2024 141345576
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Application of interpretable machine learning algorithms to predict distant metastasis in osteosarcoma.
Bai Bing-Li et al. Cancer medicine 2022
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Automated prediction of the neoadjuvant chemotherapy response in osteosarcoma with deep learning and an MRI-based radiomics nomogram.
Zhong Jingyu et al. European radiology 2022
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Correlation of histopathology and multi-modal magnetic resonance imaging in childhood osteosarcoma: Predicting tumor response to chemotherapy.
Teo Ka Yaw et al. PloS one 2022 17(2) e0259564
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Effectiveness of Learning Systems from Common Image File Types to Detect Osteosarcoma Based on Convolutional Neural Networks (CNNs) Models.
Loraksa Chanunya et al. Journal of imaging 2022 8(1)
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Machine Learning-Based Radiomics Nomogram With Dynamic Contrast-Enhanced MRI of the Osteosarcoma for Evaluation of Efficacy of Neoadjuvant Chemotherapy.
Zhang Lu et al. Frontiers in oncology 2021 11758921
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Machine learning-based CT radiomics features for the prediction of pulmonary metastasis in osteosarcoma.
Pereira Helcio Mendonça et al. The British journal of radiology 2021 20201391
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Predictive model for the 5-year survival status of osteosarcoma patients based on the SEER database and XGBoost algorithm.
Jiang Jiuzhou et al. Scientific reports 2021 11(1) 5542
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A systematic review of radiomics in osteosarcoma: utilizing radiomics quality score as a tool promoting clinical translation.
Zhong Jingyu et al. European radiology 2020 Sep
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Feasibility of multi-parametric magnetic resonance imaging combined with machine learning in the assessment of necrosis of osteosarcoma after neoadjuvant chemotherapy: a preliminary study.
Huang Bingsheng et al. BMC cancer 2020 Apr 20(1) 322
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Prediction of Chemotherapy Response of Osteosarcoma Using Baseline 18 F-FDG Textural Features Machine Learning Approaches with PCA.
Jeong Su Young et al. Contrast media & molecular imaging 2019 20193515080
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Co-Expression Network Analysis Identified Gene Signatures in Osteosarcoma as a Predictive Tool for Lung Metastasis and Survival.
Zhang Honghua et al. Journal of Cancer 2019 10(16) 3706-3716
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Page last reviewed:
Feb 1, 2024
Page last updated:
Apr 19, 2024
Content source:
Public Health Genomics Branch in the Division of Blood Disorders and Public Health Genomics
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National Center on Birth Defects and Developmental Disabilities
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