For using counts of circulating tumor cells (CTCs) in the clinic to aid a physician's decision, its reported values will need to be accurate and comparable between institutions. Many technologies have become available to enumerate and characterize CTCs, thereby showing a large range of reported values. Here we introduce an Open Source CTC scoring tool to enable comparison of different reviewers and facilitate the reach of a consensus on assigning objects as CTCs. One hundred images generated from two different platforms were used to assess concordance between 15 reviewers and an expert panel. Large differences were observed between reviewers in assigning objects as CTCs urging the need for computer recognition of CTCs. A demonstration of a deep learning approach on the 100 images showed the promise of this technique for future CTC enumeration. © 2018 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.
Keywords: ACCEPT; Agreement; CTC; consensus; deep learning; definition; experts; ground truth; reviewers; scoring.
© 2018 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.