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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)
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Last data update: Apr 25, 2024
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Records 1 - 27 (of 27 Records)
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Construction of a predictive model for bone metastasis from first primary lung adenocarcinoma within 3 cm based on machine learning algorithm: a retrospective study.
Yu Zhang et al. PeerJ 2024 12e17098
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Predictive value of multiple imaging predictive models for spread through air spaces of lung adenocarcinoma: A systematic review and network meta‑analysis.
Cong Liu et al. Oncol Lett 2024 27(3) 122
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Predicting occult lymph node metastasis in solid-predominantly invasive lung adenocarcinoma across multiple centers using radiomics-deep learning fusion model.
Weiwei Tian et al. Cancer Imaging 2024 24(1) 8
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A Machine Learning-Based Model to Predict In-Hospital Mortality of Lung Cancer Patients: A Population-Based Study of 523,959 Cases.
Que N N Tran et al. Adv Respir Med 2023 91(4) 310-323
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Standardized classification of lung adenocarcinoma subtypes and improvement of grading assessment through deep learning.
Kris Lami et al. Am J Pathol 2023
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Deep learning-enhanced radiomics for histologic classification and grade stratification of stage IA lung adenocarcinoma: a multicenter study.
Guotian Pei et al. Front Oncol 2023 131224455
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Value of contrast-enhanced magnetic resonance imaging-T2WI-based radiomic features in distinguishing lung adenocarcinoma from lung squamous cell carcinoma with solid components >8 mm.
Maoyuan Yang et al. Journal of thoracic disease 2023 15(2) 635-648
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Novel nutritional indicator as predictors among subtypes of lung cancer in diagnosis.
Haiyang Li et al. Frontiers in nutrition 2023 101042047
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Establishment and validation of a radiological-radiomics model for predicting high-grade patterns of lung adenocarcinoma less than or equal to 3 cm.
Dong Hao et al. Frontiers in oncology 2022 12964322
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Construction of Pulmonary Nodule CT Radiomics Random Forest Model Based on Artificial Intelligence Software for STAS Evaluation of Stage IA Lung Adenocarcinoma.
Liu Qian et al. Computational and mathematical methods in medicine 2022 20222173412
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ViSTA: A Novel Network Improving Lung Adenocarcinoma Invasiveness Prediction from Follow-Up CT Series.
Zhao Wei et al. Cancers 2022 14(15)
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Multi-lesion radiomics of PET/CT for non-invasive survival stratification and histologic tumor risk profiling in patients with lung adenocarcinoma.
Zhao Meixin et al. European radiology 2022
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Use of deep learning to predict postoperative recurrence of lung adenocarcinoma from preoperative CT.
Sasaki Yuki et al. International journal of computer assisted radiology and surgery 2022
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Machine learning application in personalised lung cancer recurrence and survivability prediction.
Yang Yang et al. Computational and structural biotechnology journal 2022 201811-1820
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Integrative Predictive Models of Computed Tomography Texture Parameters and Hematological Parameters for Lymph Node Metastasis in Lung Adenocarcinomas.
Chen Wenping et al. Journal of computer assisted tomography 2022 46(2) 315-324
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Implementation of artificial intelligence in the histological assessment of pulmonary subsolid nodules.
Deng Jiajun et al. Translational lung cancer research 2022 10(12) 4574-4586
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Effectiveness of convolutional neural networks in the interpretation of pulmonary cytologic images in endobronchial ultrasound procedures.
Lin Ching-Kai et al. Cancer medicine 2021
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Deep Learning Analysis of CT Images Reveals High-Grade Pathological Features to Predict Survival in Lung Adenocarcinoma.
Choi Yeonu et al. Cancers 2021 13(16)
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Comparative analysis of machine learning approaches to classify tumor mutation burden in lung adenocarcinoma using histopathology images.
Sadhwani Apaar et al. Scientific reports 2021 11(1) 16605
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Deep Learning-Based Stage-Wise Risk Stratification for Early Lung Adenocarcinoma in CT Images: A Multi-Center Study.
Gong Jing et al. Cancers 2021 13(13)
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Development and Validation of a Nomogram for Preoperative Prediction of Lymph Node Metastasis in Lung Adenocarcinoma Based on Radiomics Signature and Deep Learning Signature.
Ran Jia et al. Frontiers in oncology 2021 11585942
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Prediction of the Growth Rate of Early-Stage Lung Adenocarcinoma by Radiomics.
Tan Mingyu et al. Frontiers in oncology 2021 11658138
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Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma.
Huang Lin et al. Nature communications 2020 Jul 11(1) 3556
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A cross-modal 3D deep learning for accurate lymph node metastasis prediction in clinical stage T1 lung adenocarcinoma.
Zhao Xingyu et al. Lung cancer (Amsterdam, Netherlands) 2020 Apr 14510-17
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Classifying non-small cell lung cancer types and transcriptomic subtypes using convolutional neural networks.
Yu Kun-Hsing et al. Journal of the American Medical Informatics Association : JAMIA 2020 May 27(5) 757-769
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Identification of a Sixteen-gene Prognostic Biomarker for Lung Adenocarcinoma Using a Machine Learning Method.
Ma Baoshan et al. Journal of Cancer 2020 11(5) 1288-1298
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A Proposal to Reflect Survival Difference and Modify the Staging System for Lung Adenocarcinoma and Squamous Cell Carcinoma: Based on the Machine Learning.
Li Ming et al. Frontiers in oncology 2019 9771
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Page last reviewed:
Feb 1, 2024
Page last updated:
Apr 25, 2024
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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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