Predicting Nottingham grade in breast cancer digital pathology using a foundation model

Background The Nottingham histologic grade is crucial for assessing severity and predicting prognosis in breast cancer, a prevalent cancer worldwide. Traditional grading systems rely on subjective expert judgment and require extensive pathological expertise, are time-consuming, and often lead to int...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Breast cancer research : BCR Ročník 27; číslo 1; s. 58 - 14
Hlavní autoři:	Kim, Jun Seo, Lee, Jeong Hoon, Yeon, Yousung, An, Doyeon, Kim, Seok Jun, Noh, Myung-Giun, Lee, Suehyun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 19.04.2025 BioMed Central Ltd Springer Nature B.V BMC
Témata:	Algorithms Artificial intelligence Artificial intelligence in breast imaging Automation Biological processes Biomedical and Life Sciences Biomedicine Breast cancer Breast Neoplasms - diagnosis Breast Neoplasms - genetics Breast Neoplasms - mortality Breast Neoplasms - pathology Cancer Cancer Research Cell division Female Gene expression Gene expression data Gene ontology Humans Multiple instance learning Neoplasm Grading - methods Nottingham grade Oncology Pathology Prognosis Statistical analysis Surgical Oncology United Kingdom Gene ontology Biological processes Nottingham grade Breast cancer Gene expression data Multiple instance learning TCGA
ISSN:	1465-542X, 1465-5411, 1465-542X
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!