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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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Incremental value of automatically segmented perirenal adipose tissue for pathological grading of clear cell renal cell carcinoma: a multicenter cohort study.
Shichao Li et al. Int J Surg 2024
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UroPredict: Machine learning model on real-world data for prediction of kidney cancer recurrence (UroCCR-120).
Gaëlle Margue et al. NPJ Precis Oncol 2024 8(1) 45
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Incremental value of radiomics with machine learning to the existing prognostic models for predicting outcome in renal cell carcinoma.
Jiajun Xing et al. Front Oncol 2023 131036734
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Using machine learning to predict lymph node metastasis in patients with renal cell carcinoma: A population-based study.
Yuhan Zhang et al. Front Public Health 2023 111104931
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Artificial intelligence-based multi-class histopathologic classification of kidney neoplasms.
Dibson D Gondim et al. Journal of pathology informatics 2023 14100299
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Artificial intelligence-based prediction of overall survival in metastatic renal cell carcinoma.
Ella Barkan et al. Frontiers in oncology 2023 131021684
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A clinical prediction model for predicting the risk of liver metastasis from renal cell carcinoma based on machine learning.
Ziye Wang et al. Frontiers in endocrinology 2023 131083569
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Machine learning approach for prediction of pT3a upstaging and outcomes of localized RCC (UroCCR-15).
Boulenger de Hauteclocque A et al. BJU international 2023
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Differentiation of clear cell and non-clear-cell renal cell carcinoma through CT-based Radiomics models and nomogram.
Cheng Delu et al. Current medical imaging 2022
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Differentiation of benign from malignant solid renal lesions using CT-based radiomics and machine learning: comparison with radiologist interpretation.
Wentland Andrew L et al. Abdominal radiology (New York) 2022
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Machine learning to improve prognosis prediction of metastatic clear-cell renal cell carcinoma treated with cytoreductive nephrectomy and systemic therapy.
Yang Wenjie et al. Bosnian journal of basic medical sciences 2022
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Deep learning can predict survival directly from histology in clear cell renal cell carcinoma.
Wessels Frederik et al. PloS one 2022 17(8) e0272656
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Using machine learning for mortality prediction and risk stratification in atezolizumab-treated cancer patients: Integrative analysis of eight clinical trials.
Wu Yougen et al. Cancer medicine 2022
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A comprehensive texture feature analysis framework of renal cell carcinoma: pathological, prognostic, and genomic evaluation based on CT images.
Wu Kai et al. European radiology 2021
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A Radiomic-based Machine Learning Algorithm to Reliably Differentiate Benign Renal Masses from Renal Cell Carcinoma.
Nassiri Nima et al. European urology focus 2021
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Evaluation of radiomics and machine learning in identification of aggressive tumor features in renal cell carcinoma (RCC).
Gurbani Sidharth et al. Abdominal radiology (New York) 2021
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Classification of renal tumour using convolutional neural networks to detect oncocytoma.
Pedersen Mikkel et al. European journal of radiology 2020 Oct 133109343
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Deep learning-based and hybrid-type iterative reconstructions for CT: comparison of capability for quantitative and qualitative image quality improvements and small vessel evaluation at dynamic CE-abdominal CT with ultra-high and standard resolutions.
Matsukiyo Ryo et al. Japanese journal of radiology 2020 Oct
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Analysis of Bone Scans in Various Tumor Entities Using a Deep-Learning-Based Artificial Neural Network Algorithm-Evaluation of Diagnostic Performance.
Wuestemann Jan et al. Cancers 2020 Sep 12(9)
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Radiomics and Artificial Intelligence for Renal Mass Characterization.
Lubner Meghan G et al. Radiologic clinics of North America 2020 Sep 58(5) 995-1008
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A CT-based deep learning model for predicting the nuclear grade of clear cell renal cell carcinoma.
Lin Fan et al. European journal of radiology 2020 May 129109079
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Automated classification of solid renal masses on contrast-enhanced computed tomography images using convolutional neural network with decision fusion.
Zabihollahy Fatemeh et al. European radiology 2020 Apr
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MRI Radiomics for the Prediction of Fuhrman Grade in Clear Cell Renal Cell Carcinoma: a Machine Learning Exploratory Study.
Stanzione Arnaldo et al. Journal of digital imaging 2020 Apr
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Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning.
Nazari Mostafa et al. La Radiologia medica 2020 Mar
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Automated Differentiation of Benign Renal Oncocytoma and Chromophobe Renal Cell Carcinoma on Computed Tomography Using Deep Learning.
Baghdadi Amir et al. BJU international 2020 Jan
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Quantitative enhancement thresholds and machine learning algorithms for the evaluation of renal lesions using single-phase split-filter dual-energy CT.
Obmann Markus M et al. Abdominal radiology (New York) 2019 Aug
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Machine learning models to predict the progression from early to late stages of papillary renal cell carcinoma.
Singh Noor Pratap et al. Computers in biology and medicine 2018 10092-99
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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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