Optimizing HER2 assessment in breast cancer: application of automated image analysis

TY - JOUR

T1 - Optimizing HER2 assessment in breast cancer

T2 - application of automated image analysis

AU - Holten-Rossing, Henrik

AU - Møller Talman, Maj-Lis

AU - Kristensson, Martin

AU - Vainer, Ben

PY - 2015/7/1

Y1 - 2015/7/1

N2 - In breast cancer, analysis of HER2 expression is pivotal for treatment decision. This study aimed at comparing digital, automated image analysis with manual reading using the HER2-CONNECT algorithm (Visiopharm) in order to minimize the number of equivocal 2+ scores and the need for reflex fluorescence in situ hybridization (FISH) analysis. Consecutive samples from 462 patients were included. Tissue micro arrays (TMAs) were routinely manufactured including two 2 mm cores from each patient, and each core was assessed in order to ensure the presence of invasive carcinoma. Immunohistochemical staining (IHC) was performed with Roche/Ventana’s HER2 ready-to-use kit. TMAs were scanned in a Zeiss Axio Z1 scanner, and one batch analysis of the HER2-CONNECT algorithm including all core samples was run using Visiopharm’s cloud-based software. The automated reading was compared to conventional manual assessment of HER2 protein expression, together with FISH analysis of HER2 gene amplification for borderline (2+) protein expression samples. Compared to FISH analysis, manual assessment of the HER2 protein expression demonstrated a sensitivity of 85.8 % and a specificity of 86.0 % with 14.0 % equivocal samples. With HER2-CONNECT, sensitivity increased to 100 % and specificity to 95.5 % with less than 4.5 % equivocal. Total agreement when comparing HER2-CONNECT with manual IHC assessment supplemented by FISH for borderline (2+) cases was 93.6 %. Application of automated image analysis for HER2 protein expression instead of manual assessment decreases the need for supplementary FISH testing by 68 %. In the routine diagnostic setting, this would have significant impact on cost reduction and turn-around time.

AB - In breast cancer, analysis of HER2 expression is pivotal for treatment decision. This study aimed at comparing digital, automated image analysis with manual reading using the HER2-CONNECT algorithm (Visiopharm) in order to minimize the number of equivocal 2+ scores and the need for reflex fluorescence in situ hybridization (FISH) analysis. Consecutive samples from 462 patients were included. Tissue micro arrays (TMAs) were routinely manufactured including two 2 mm cores from each patient, and each core was assessed in order to ensure the presence of invasive carcinoma. Immunohistochemical staining (IHC) was performed with Roche/Ventana’s HER2 ready-to-use kit. TMAs were scanned in a Zeiss Axio Z1 scanner, and one batch analysis of the HER2-CONNECT algorithm including all core samples was run using Visiopharm’s cloud-based software. The automated reading was compared to conventional manual assessment of HER2 protein expression, together with FISH analysis of HER2 gene amplification for borderline (2+) protein expression samples. Compared to FISH analysis, manual assessment of the HER2 protein expression demonstrated a sensitivity of 85.8 % and a specificity of 86.0 % with 14.0 % equivocal samples. With HER2-CONNECT, sensitivity increased to 100 % and specificity to 95.5 % with less than 4.5 % equivocal. Total agreement when comparing HER2-CONNECT with manual IHC assessment supplemented by FISH for borderline (2+) cases was 93.6 %. Application of automated image analysis for HER2 protein expression instead of manual assessment decreases the need for supplementary FISH testing by 68 %. In the routine diagnostic setting, this would have significant impact on cost reduction and turn-around time.

KW - Algorithms

KW - Breast Neoplasms

KW - Female

KW - Humans

KW - Image Processing, Computer-Assisted

KW - Immunohistochemistry

KW - In Situ Hybridization, Fluorescence

KW - Receptor, ErbB-2

KW - Reproducibility of Results

KW - Sensitivity and Specificity

U2 - 10.1007/s10549-015-3475-3

DO - 10.1007/s10549-015-3475-3

M3 - Journal article

C2 - 26109345

SN - 0167-6806

VL - 152

SP - 367

EP - 375

JO - Breast Cancer Research and Treatment

JF - Breast Cancer Research and Treatment

IS - 2

ER -