AI-enabled routine H&E image based prognostic marker for early-stage luminal breast cancer.
Noorul WahabMichael TossIslam M MiligyMostafa JahanifarNehal M AtallahWenqi LuSimon GrahamMohsin BilalAbhir BhaleraoAyat Gamal LashenShorouk MakhloufAsmaa Y IbrahimDavid SneadFayyaz Ul Amir Afsar MinhasShan E Ahmed RazaEmad RakhaNasir M RajpootPublished in: NPJ precision oncology (2023)
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.
Keyphrases
- early stage
- deep learning
- end stage renal disease
- free survival
- ejection fraction
- newly diagnosed
- artificial intelligence
- chronic kidney disease
- machine learning
- patient reported outcomes
- clinical practice
- mesenchymal stem cells
- radiation therapy
- physical activity
- smoking cessation
- young adults
- preterm birth
- clinical evaluation