Journal of Pathology Informatics Journal of Pathology Informatics
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RESEARCH ARTICLE
Year : 2021  |  Volume : 12  |  Issue : 1  |  Page : 24

Digital pathology-based study of cell- and tissue-level morphologic features in serous borderline ovarian tumor and high-grade serous ovarian cancer


1 Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA
2 Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
3 Department of Health Science Research, Mayo Clinic, Rochester, MN, USA

Correspondence Address:
Dr. Chen Wang
Mayo Clinic 200 First St. SW Rochester, MN 55905
USA
Dr. Yajue Huang
Mayo Clinic 200 First St. SW Rochester, MN 55905
USA
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jpi.jpi_76_20

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Background: Serous borderline ovarian tumor (SBOT) and high-grade serous ovarian cancer (HGSOC) are two distinct subtypes of epithelial ovarian tumors, with markedly different biologic background, behavior, prognosis, and treatment. However, the histologic diagnosis of serous ovarian tumors can be subjectively variable and labor-intensive as multiple tumor slides/blocks need to be thoroughly examined to search for these features. Materials and Methods: We developed a novel informatics system to facilitate objective and scalable diagnosis screening for SBOT and HGSOC. The system was built upon Groovy scripts and QuPath to enable interactive annotation and data exchange. Results: The system was used to successfully detect cellular boundaries and extract an expanded set of cellular features representing cell- and tissue-level characteristics. The performance of cell-level classification for both tumor and stroma cells achieved >90% accuracy. The performance of differentiating HGSOC versus SBOT achieved 91%–95% accuracy for 6485 imaging patches which have sufficient tumor and stroma cells (minimum of ten each) and 97% accuracy for classifying patients when aggregating the results to whole-slide image based on consensus. Conclusions: Cellular features digitally extracted from pathological images can be used for cell classification and SBOT v. HGSOC differentiation. Introducing digital pathology into ovarian cancer research could be beneficial to discover potential clinical implications. A larger cohort is required to further evaluate the system.


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