Renal Cell Carcinoma
What's New
Last Posted: Apr 09, 2024
- 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 - 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 - Referral patterns and genetic testing outcomes in a contemporary hereditary renal cancer clinic.
J McFadden et al. Urol Oncol 2024 - Family History and Risk of Renal Cell Carcinoma: A National Multi-Register Case-Control Study.
Rasmus G Jakobsson et al. J Urol 2023 101097JU0000000000003765 - 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 - 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 - Artificial intelligence-based multi-class histopathologic classification of kidney neoplasms.
Dibson D Gondim et al. Journal of pathology informatics 2023 14100299 - Artificial intelligence-based prediction of overall survival in metastatic renal cell carcinoma.
Ella Barkan et al. Frontiers in oncology 2023 131021684 - ANO4 Expression Is a Potential Prognostic Biomarker in Non-Metastasized Clear Cell Renal Cell Carcinoma.
Ahmed H Al Sharie et al. Journal of personalized medicine 2023 13(2) - 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 - Relative Risk of Bladder and Kidney Cancer in Lynch Syndrome: Systematic Review and Meta-Analysis.
Nassour Anthony-Joe et al. Cancers 2023 15(2) - Machine learning approach for prediction of pT3a upstaging and outcomes of localized RCC (UroCCR-15).
Boulenger de Hauteclocque A et al. BJU international 2023 - 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 - Genitourinary manifestations of Lynch syndrome in the urological practice.
Lonati Chiara et al. Asian journal of urology 2022 9(4) 443-450 - 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 - 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 - Family History of Cancers Increases Risk of Renal Cell Carcinoma in a Chinese Population.
Xing Siwei et al. Cancer management and research 2022 142561-2568 - Deep learning can predict survival directly from histology in clear cell renal cell carcinoma.
Wessels Frederik et al. PloS one 2022 17(8) e0272656 - 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 - A Novel Machine Learning 13-Gene Signature: Improving Risk Analysis and Survival Prediction for Clear Cell Renal Cell Carcinoma Patients.
Terrematte Patrick et al. Cancers 2022 14(9)
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Rare Disease PHGKB is an online, continuously updated, searchable database of published scientific literature, CDC and NIH resources, and other information that address the public health impact and translation of genomic and other precision health discoveries into improved health outcomes related to rare diseases...more
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Selected Rare Diseases
- Alpha-1 Antitrypsin Deficiency
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Disclaimer: Articles listed in the Public Health Knowledge Base are selected by Public Health Genomics Branch to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.
- Page last reviewed:Feb 1, 2024
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