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AI-enabled routine H E image based prognostic marker for early-stage luminal breast cancer
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-b...
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Published in: | NPJ precision oncology 2023-11, Vol.7 (1), p.1-13 |
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creator | Noorul Wahab Michael Toss Islam M. Miligy Mostafa Jahanifar Nehal M. Atallah Wenqi Lu Simon Graham Mohsin Bilal Abhir Bhalerao Ayat G. Lashen Shorouk Makhlouf Asmaa Y. Ibrahim David Snead Fayyaz Minhas Shan E. Ahmed Raza Emad Rakha Nasir Rajpoot |
description | 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 |
doi_str_mv | 10.1038/s41698-023-00472-y |
format | article |
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Miligy ; Mostafa Jahanifar ; Nehal M. Atallah ; Wenqi Lu ; Simon Graham ; Mohsin Bilal ; Abhir Bhalerao ; Ayat G. Lashen ; Shorouk Makhlouf ; Asmaa Y. Ibrahim ; David Snead ; Fayyaz Minhas ; Shan E. Ahmed Raza ; Emad Rakha ; Nasir Rajpoot</creator><creatorcontrib>Noorul Wahab ; Michael Toss ; Islam M. Miligy ; Mostafa Jahanifar ; Nehal M. Atallah ; Wenqi Lu ; Simon Graham ; Mohsin Bilal ; Abhir Bhalerao ; Ayat G. Lashen ; Shorouk Makhlouf ; Asmaa Y. Ibrahim ; David Snead ; Fayyaz Minhas ; Shan E. Ahmed Raza ; Emad Rakha ; Nasir Rajpoot</creatorcontrib><description>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.</description><identifier>EISSN: 2397-768X</identifier><identifier>DOI: 10.1038/s41698-023-00472-y</identifier><language>eng</language><publisher>Nature Portfolio</publisher><ispartof>NPJ precision oncology, 2023-11, Vol.7 (1), p.1-13</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Noorul Wahab</creatorcontrib><creatorcontrib>Michael Toss</creatorcontrib><creatorcontrib>Islam M. 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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.</description><issn>2397-768X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNqtjU1OwzAQRi0kJCroBVjNBQxOnPpniVBRu2eB2FjjZBqlOHE1The5PQFxBFaf9PTpPSEeK_VUKe2eS1MZ76SqtVSqsbVcbsSm1t5Ka9zHndiWclZKVW5X1cZsxOfLUdKEMVEHnK_zMBEcYA_DiD1BxLLyC-d-ymUeWhiRv4jhlBkIOS2yzD-_dB2HCRNEJiwztDi1xA_i9oSp0PZv78Xxbf_-epBdxnO48JrgJWQcwi_I3AfkNZIooCeDjnat17GhDmPTRYfWO-WtbazR_-n6BiuSYrU</recordid><startdate>20231101</startdate><enddate>20231101</enddate><creator>Noorul Wahab</creator><creator>Michael Toss</creator><creator>Islam M. 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title | AI-enabled routine H E image based prognostic marker for early-stage luminal breast cancer |
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