title: Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer.
abstract: Tissue biomarker scoring by pathologists is central to defining the appropriate therapy for patients with cancer. Yet, inter-pathologist variability in the interpretation of ambiguous cases can affect diagnostic accuracy. Modern artificial intelligence methods such as deep learning have the potential to supplement pathologist expertise to ensure constant diagnostic accuracy. We developed a computational approach based on deep learning that automatically scores HER2, a biomarker that defines patient eligibility for anti-HER2 targeted therapies in breast cancer. In a cohort of 71 breast tumour resection samples, automated scoring showed a concordance of 83% with a pathologist. The twelve discordant cases were then independently reviewed, leading to a modification of diagnosis from initial pathologist assessment for eight cases. Diagnostic discordance was found to be largely caused by perceptual differences in assessing HER2 expression due to high HER2 staining heterogeneity. This study provides evidence that deep learning aided diagnosis can facilitate clinical decision making in breast cancer by identifying cases at high risk of misdiagnosis.
authors.affiliation: Personalised Healthcare &Biomarkers, IMED Biotech Unit, AstraZeneca, HODGKIN, C/o B310 Cambridge Science Park, Milton Road, Cambridge, CB4 0WG, United Kingdom.
authors.forename: Michel E
authors.initials: ME
authors.lastname: Vandenberghe
authors.affiliation: Personalised Healthcare &Biomarkers, IMED Biotech Unit, AstraZeneca, HODGKIN, C/o B310 Cambridge Science Park, Milton Road, Cambridge, CB4 0WG, United Kingdom.
authors.forename: Marietta L J
authors.initials: ML
authors.lastname: Scott
authors.affiliation: Personalised Healthcare &Biomarkers, IMED Biotech Unit, AstraZeneca, HODGKIN, C/o B310 Cambridge Science Park, Milton Road, Cambridge, CB4 0WG, United Kingdom.
authors.forename: Paul W
authors.initials: PW
authors.lastname: Scorer
authors.affiliation: Pathology, Drug Safety &Metabolism, IMED Biotech Unit, AstraZeneca, Pepparedsleden 1, 431 50 Mölndal, Sweden.
authors.forename: Magnus
authors.initials: M
authors.lastname: Söderberg
authors.affiliation: Personalised Healthcare &Biomarkers, IMED Biotech Unit, AstraZeneca, HODGKIN, C/o B310 Cambridge Science Park, Milton Road, Cambridge, CB4 0WG, United Kingdom.
authors.forename: Denis
authors.initials: D
authors.lastname: Balcerzak
authors.affiliation: Personalised Healthcare &Biomarkers, IMED Biotech Unit, AstraZeneca, HODGKIN, C/o B310 Cambridge Science Park, Milton Road, Cambridge, CB4 0WG, United Kingdom.
authors.forename: Craig
authors.initials: C
authors.lastname: Barker
mesh_terms: D000074322:Antineoplastic Agents, Immunological / Q000627:therapeutic use; D014408:Biomarkers, Tumor / Q000037:antagonists & inhibitors / Q000378:metabolism*; D001943:Breast Neoplasms / Q000175:diagnosis / Q000188:drug therapy / Q000378:metabolism*; D015331:Cohort Studies; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D005260:Female; D006801:Humans; D007150:Immunohistochemistry; D000069550:Machine Learning; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000037:antagonists & inhibitors / Q000378:metabolism*; D015203:Reproducibility of Results; D000068878:Trastuzumab / Q000627:therapeutic use
chemical_list: D000074322:Antineoplastic Agents, Immunological; D014408:Biomarkers, Tumor; D018719:Erb-b2 Receptor Tyrosine Kinases; D000068878:Trastuzumab