How to Agree on a CTC: Evaluating the Consensus in Circulating Tumor Cell Scoring

Leonie L. Zeune (Corresponding Author), Sanne de Wit, A.M. Sofie Berghuis, Maarten J. IJzerman, Leon W.M.M. Terstappen, Christoph Brune

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Abstract

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.
Original languageEnglish
Pages (from-to)1202-1206
Number of pages5
JournalCytometry. Part A
Volume93
Issue number12
DOIs
Publication statusPublished - 24 Sep 2018

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Circulating Neoplastic Cells
Consensus
Learning
Technology
Physicians

Keywords

  • UT-Hybrid-D
  • Agreement
  • CTC
  • consensus
  • deep learning
  • definition
  • experts
  • ground truth
  • reviewers
  • scoring

Cite this

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abstract = "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.",
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How to Agree on a CTC : Evaluating the Consensus in Circulating Tumor Cell Scoring. / Zeune, Leonie L. (Corresponding Author); de Wit, Sanne; Berghuis, A.M. Sofie; IJzerman, Maarten J.; Terstappen, Leon W.M.M.; Brune, Christoph.

In: Cytometry. Part A, Vol. 93, No. 12, 24.09.2018, p. 1202-1206.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

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T2 - Evaluating the Consensus in Circulating Tumor Cell Scoring

AU - Zeune, Leonie L.

AU - de Wit, Sanne

AU - Berghuis, A.M. Sofie

AU - IJzerman, Maarten J.

AU - Terstappen, Leon W.M.M.

AU - Brune, Christoph

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AB - 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.

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KW - experts

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KW - scoring

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