High-throughput automated scoring of Ki67 in breast cancer tissue microarrays from the Breast Cancer Association Consortium
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High-throughput automated scoring of Ki67 in breast cancer tissue microarrays from the Breast Cancer Association Consortium. / Abubakar, Mustapha; Howat, William J; Daley, Frances; Zabaglo, Lila; McDuffus, Leigh-Anne; Blows, Fiona; Coulson, Penny; Raza Ali, H; Benitez, Javier; Milne, Roger L; Brenner, Herman; Stegmaier, Christa; Mannermaa, Arto; Chang-Claude, Jenny; Rudolph, Anja; Sinn, Peter; Couch, Fergus J; Tollenaar, Rob A E M; Devilee, Peter; Figueroa, Jonine; Sherman, Mark E; Lissowska, Jolanta; Hewitt, Stephen; Eccles, Diana; Hooning, Maartje J; Hollestelle, Antoinette; Wm Martens, John; Hm van Deurzen, Carolien; Investigators, Kconfab; Bolla, Manjeet K; Wang, Qin; Jones, Michael; Schoemaker, Minouk; Broeks, Annegien; van Leeuwen, Flora E; Van't Veer, Laura J; Swerdlow, Anthony J; Orr, Nick; Dowsett, Mitch; Easton, Douglas; Schmidt, Marjanka K; Pharoah, Paul D; Garcia-Closas, Montserrat.
in: J PATHOL CLIN RES, Jahrgang 2, Nr. 3, 07.2016, S. 138-53.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - High-throughput automated scoring of Ki67 in breast cancer tissue microarrays from the Breast Cancer Association Consortium
AU - Abubakar, Mustapha
AU - Howat, William J
AU - Daley, Frances
AU - Zabaglo, Lila
AU - McDuffus, Leigh-Anne
AU - Blows, Fiona
AU - Coulson, Penny
AU - Raza Ali, H
AU - Benitez, Javier
AU - Milne, Roger L
AU - Brenner, Herman
AU - Stegmaier, Christa
AU - Mannermaa, Arto
AU - Chang-Claude, Jenny
AU - Rudolph, Anja
AU - Sinn, Peter
AU - Couch, Fergus J
AU - Tollenaar, Rob A E M
AU - Devilee, Peter
AU - Figueroa, Jonine
AU - Sherman, Mark E
AU - Lissowska, Jolanta
AU - Hewitt, Stephen
AU - Eccles, Diana
AU - Hooning, Maartje J
AU - Hollestelle, Antoinette
AU - Wm Martens, John
AU - Hm van Deurzen, Carolien
AU - Investigators, Kconfab
AU - Bolla, Manjeet K
AU - Wang, Qin
AU - Jones, Michael
AU - Schoemaker, Minouk
AU - Broeks, Annegien
AU - van Leeuwen, Flora E
AU - Van't Veer, Laura J
AU - Swerdlow, Anthony J
AU - Orr, Nick
AU - Dowsett, Mitch
AU - Easton, Douglas
AU - Schmidt, Marjanka K
AU - Pharoah, Paul D
AU - Garcia-Closas, Montserrat
PY - 2016/7
Y1 - 2016/7
N2 - Automated methods are needed to facilitate high-throughput and reproducible scoring of Ki67 and other markers in breast cancer tissue microarrays (TMAs) in large-scale studies. To address this need, we developed an automated protocol for Ki67 scoring and evaluated its performance in studies from the Breast Cancer Association Consortium. We utilized 166 TMAs containing 16,953 tumour cores representing 9,059 breast cancer cases, from 13 studies, with information on other clinical and pathological characteristics. TMAs were stained for Ki67 using standard immunohistochemical procedures, and scanned and digitized using the Ariol system. An automated algorithm was developed for the scoring of Ki67, and scores were compared to computer assisted visual (CAV) scores in a subset of 15 TMAs in a training set. We also assessed the correlation between automated Ki67 scores and other clinical and pathological characteristics. Overall, we observed good discriminatory accuracy (AUC = 85%) and good agreement (kappa = 0.64) between the automated and CAV scoring methods in the training set. The performance of the automated method varied by TMA (kappa range= 0.37-0.87) and study (kappa range = 0.39-0.69). The automated method performed better in satisfactory cores (kappa = 0.68) than suboptimal (kappa = 0.51) cores (p-value for comparison = 0.005); and among cores with higher total nuclei counted by the machine (4,000-4,500 cells: kappa = 0.78) than those with lower counts (50-500 cells: kappa = 0.41; p-value = 0.010). Among the 9,059 cases in this study, the correlations between automated Ki67 and clinical and pathological characteristics were found to be in the expected directions. Our findings indicate that automated scoring of Ki67 can be an efficient method to obtain good quality data across large numbers of TMAs from multicentre studies. However, robust algorithm development and rigorous pre- and post-analytical quality control procedures are necessary in order to ensure satisfactory performance.
AB - Automated methods are needed to facilitate high-throughput and reproducible scoring of Ki67 and other markers in breast cancer tissue microarrays (TMAs) in large-scale studies. To address this need, we developed an automated protocol for Ki67 scoring and evaluated its performance in studies from the Breast Cancer Association Consortium. We utilized 166 TMAs containing 16,953 tumour cores representing 9,059 breast cancer cases, from 13 studies, with information on other clinical and pathological characteristics. TMAs were stained for Ki67 using standard immunohistochemical procedures, and scanned and digitized using the Ariol system. An automated algorithm was developed for the scoring of Ki67, and scores were compared to computer assisted visual (CAV) scores in a subset of 15 TMAs in a training set. We also assessed the correlation between automated Ki67 scores and other clinical and pathological characteristics. Overall, we observed good discriminatory accuracy (AUC = 85%) and good agreement (kappa = 0.64) between the automated and CAV scoring methods in the training set. The performance of the automated method varied by TMA (kappa range= 0.37-0.87) and study (kappa range = 0.39-0.69). The automated method performed better in satisfactory cores (kappa = 0.68) than suboptimal (kappa = 0.51) cores (p-value for comparison = 0.005); and among cores with higher total nuclei counted by the machine (4,000-4,500 cells: kappa = 0.78) than those with lower counts (50-500 cells: kappa = 0.41; p-value = 0.010). Among the 9,059 cases in this study, the correlations between automated Ki67 and clinical and pathological characteristics were found to be in the expected directions. Our findings indicate that automated scoring of Ki67 can be an efficient method to obtain good quality data across large numbers of TMAs from multicentre studies. However, robust algorithm development and rigorous pre- and post-analytical quality control procedures are necessary in order to ensure satisfactory performance.
KW - Journal Article
U2 - 10.1002/cjp2.42
DO - 10.1002/cjp2.42
M3 - SCORING: Journal article
C2 - 27499923
VL - 2
SP - 138
EP - 153
JO - J PATHOL CLIN RES
JF - J PATHOL CLIN RES
SN - 2056-4538
IS - 3
ER -