The Library
AI-enabled routine H&E image based prognostic marker for early-stage luminal breast cancer
Tools
Wahab, Noorul, Toss, Michael, Miligy, Islam M., Jahanifar, Mostafa, Atallah, Nehal M., Lu, Wenqi, Graham, Simon, Bilal, Mohsin, Bhalerao, Abhir, Lashen, Ayat G., Makhlouf, Shorouk, Ibrahim, Asmaa Y., Snead, David, Minhas, Fayyaz ul Amir Afsar, Raza, Shan E. Ahmed, Rakha, Emad and Rajpoot, Nasir M. (Nasir Mahmood) (2023) AI-enabled routine H&E image based prognostic marker for early-stage luminal breast cancer. npj Precision Oncology, 7 (1). 122. doi:10.1038/s41698-023-00472-y ISSN 2397-768X.
|
PDF
s41698-023-00472-y.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (4Mb) | Preview |
Official URL: https://doi.org/10.1038/s41698-023-00472-y
Abstract
Breast cancer (BC) grade is a well-established subjective prognostic indicator of tumour aggressiveness. Tumour heterogeneity and subjective assessment result in high degree of variability among observers in BC grading. Here we propose an objective Haematoxylin & Eosin (H&E) image-based prognostic marker for early-stage luminal/Her2-negative BReAst CancEr that we term as the BRACE marker. The proposed BRACE marker is derived from AI based assessment of heterogeneity in BC at a detailed level using the power of deep learning. The prognostic ability of the marker is validated in two well-annotated cohorts (Cohort-A/Nottingham: n = 2122 and Cohort-B/Coventry: n = 311) on early-stage luminal/HER2-negative BC patients treated with endocrine therapy and with long-term follow-up. The BRACE marker is able to stratify patients for both distant metastasis free survival (p = 0.001, C-index: 0.73) and BC specific survival (p < 0.0001, C-index: 0.84) showing comparable prediction accuracy to Nottingham Prognostic Index and Magee scores, which are both derived from manual histopathological assessment, to identify luminal BC patients that may be likely to benefit from adjuvant chemotherapy.
Item Type: | Journal Article | ||||||
---|---|---|---|---|---|---|---|
Alternative Title: | Artificial intelligence-enabled routine haematoxylin and eosin image based prognostic marker for early-stage luminal breast cancer | ||||||
Subjects: | R Medicine > R Medicine (General) R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer) |
||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
SWORD Depositor: | Library Publications Router | ||||||
Library of Congress Subject Headings (LCSH): | Breast -- Cancer -- Imaging, Biochemical markers, Artificial intelligence -- Data processing, Artificial intelligence -- Medical applications | ||||||
Journal or Publication Title: | npj Precision Oncology | ||||||
Publisher: | Nature Publishing Group | ||||||
ISSN: | 2397-768X | ||||||
Official Date: | 15 November 2023 | ||||||
Dates: |
|
||||||
Volume: | 7 | ||||||
Number: | 1 | ||||||
Article Number: | 122 | ||||||
DOI: | 10.1038/s41698-023-00472-y | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 17 November 2023 | ||||||
Date of first compliant Open Access: | 17 November 2023 | ||||||
RIOXX Funder/Project Grant: |
|
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year