Integrating and validating automated digital imaging analysis of estrogen receptor immunohistochemistry in a fully digital workflow for clinical use

• Ngā kaituhi matua: Saba Shafi (Author), David A. Kellough (Author), Giovanni Lujan (Author), Swati Satturwar (Author), Anil V. Parwani (Author), Zaibo Li (Author)
• Hōputu: Pukapuka
• I whakaputaina: Elsevier, 2022-01-01T00:00:00Z.
• Ngā marau: article
• Urunga tuihono: Connect to this object online.
• Notes: 2153-3539; 10.1016/j.jpi.2022.100122
• Keywords: ER; Digital image analysis; Visiopharm; Breast cancer; Clinical; Computer applications to medicine. Medical informatics; R858-859.7; Pathology; RB1-214
• Source: Journal of Pathology Informatics, Vol 13, Iss , Pp 100122- (2022)

Background: The Visiopharm automated estrogen receptor (ER) digital imaging analysis (DIA) algorithm assesses digitized ER immunohistochemistry (IHC) by segmenting tumor nuclei and detecting stained nuclei automatically. We aimed to integrate and validate this algorithm in a digital pathology workflow for clinical use. Design: The study cohort consisted of a serial collection of 97 invasive breast carcinoma specimens including 73 biopsies and 24 resections. ER IHC slides were scanned into Philips Image Management System (IMS) during our routine digital workflow and digital images were directly streamed into Visiopharm platform and analyzed using automated ER algorithm to obtain the positively stained tumor nuclei and staining intensity. ER DIA scores were compared with pathologists' manual scores. Results: The overall concordance between pathologists' reads and DIA reads was excellent (91/97, 93.8%). Pearson Correlation Coefficient of the percentage of ER positive nuclei between the original reads and VIS reads was 0.72. Six cases (3 ER-negative and 3 ER-positive) had discordant results. All 3 false negative cases had very weak ER staining and no more than 10% positivity. The causes for false positive DIA were mainly pre-analytic/pre-imaging and included intermixed benign glands in tumor area, ductal carcinoma in-situ (DCIS) components, and tissue folding. Conclusions: Automated ER DIA demonstrates excellent concordance with pathologists' scores and accurately discriminates ER positive from negative cases. Furthermore, integrating automated biomarker DIA into a busy clinical digital workflow is feasible and may save time and labor for pathologists.