Prediction of non-muscle invasive bladder cancer recurrence using machine learning of quantitative n...
Prediction of non-muscle invasive bladder cancer recurrence using machine learning of quantitative nuclear features
About this item
Full title
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Publisher
New York: Nature Publishing Group US
Journal title
Language
English
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Publisher
New York: Nature Publishing Group US
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Scope and Contents
Contents
Non-muscle invasive bladder cancer (NMIBC) generally has a good prognosis; however, recurrence after transurethral resection (TUR), the standard primary treatment, is a major problem. Clinical management after TUR has been based on risk classification using clinicopathological factors, but these classifications are not complete. In this study, we a...
Alternative Titles
Full title
Prediction of non-muscle invasive bladder cancer recurrence using machine learning of quantitative nuclear features
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TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8964412
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8964412
Other Identifiers
ISSN
0893-3952
E-ISSN
1530-0285
DOI
10.1038/s41379-021-00955-y
