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Kidney cancer management 3.0: can artificial intelligence make us better?

作者信息

Lee Matthew, Wei Shuanzeng, Anaokar Jordan, Uzzo Robert, Kutikov Alexander

机构信息

Division of Urologic Oncology.

Department of Pathology.

出版信息

Curr Opin Urol. 2021 Jul 1;31(4):409-415. doi: 10.1097/MOU.0000000000000881.


DOI:10.1097/MOU.0000000000000881
PMID:33882560
Abstract

PURPOSE OF REVIEW: Artificial intelligence holds tremendous potential for disrupting clinical medicine. Here we review the current role of artificial intelligence in the kidney cancer space. RECENT FINDINGS: Machine learning and deep learning algorithms have been developed using information extracted from radiomic, histopathologic, and genomic datasets of patients with renal masses. SUMMARY: Although artificial intelligence applications in medicine are still in their infancy, they already hold immediate promise to improve accuracy of renal mass characterization, grade, and prognostication. As algorithms become more robust and generalizable, artificial intelligence is poised to significantly disrupt kidney cancer care.

摘要

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[2]
Imaging-based deep learning in kidney diseases: recent progress and future prospects.

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[3]
Future of Artificial Intelligence Applications in Cancer Care: A Global Cross-Sectional Survey of Researchers.

Curr Oncol. 2023-3-16

[4]
Automatic Screening of Pediatric Renal Ultrasound Abnormalities: Deep Learning and Transfer Learning Approach.

JMIR Med Inform. 2022-11-2

[5]
Dynamic Predictive Models with Visualized Machine Learning for Assessing the Risk of Lung Metastasis in Kidney Cancer Patients.

J Oncol. 2022-10-14

[6]
Artificial intelligence for renal cancer: From imaging to histology and beyond.

Asian J Urol. 2022-7

[7]
An endorectal ultrasound-based radiomics signature for preoperative prediction of lymphovascular invasion of rectal cancer.

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[8]
Novel Imaging Methods for Renal Mass Characterization: A Collaborative Review.

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[9]
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