Training an automated circulating tumor cell classifier when the true classification is uncertain

Afroditi Nanou, Nikolas H Stoecklein, Daniel Doerr, Christiane Driemel, Leon W M M Terstappen, Frank A W Coumans*, Philip Furmanski (Editor)

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Circulating tumor cell (CTC) and tumor-derived extracellular vesicle (tdEV) loads are prognostic factors of survival in patients with carcinoma. The current method of CTC enumeration relies on operator review and, unfortunately, has moderate interoperator agreement (Fleiss’ kappa 0.60) due to difficulties in classifying CTC-like events. We compared operator review, ACCEPT automated image processing, and refined the output of a deep-learning algorithm to identify CTC and tdEV for the prediction of survival in patients with metastatic and nonmetastatic cancers. Operator review is only defined for CTC. Refinement was performed using automatic contrast maximization CM-CTC of events detected in cancer and in benign samples (CM-CTC). We used 418 samples from benign diseases, 6,293 from nonmetastatic breast, 2,408 from metastatic breast, and 698 from metastatic prostate cancer to train,
test, optimize, and evaluate CTC and tdEV enumeration. For CTC identification, the CM-CTC performed best on metastatic/nonmetastatic breast cancer, respectively, with a hazard ratio (HR) for overall survival of 2.6/2.1 vs. 2.4/1.4 for operator CTC and 1.2/0.8 for ACCEPT-CTC. For tdEV identification, CM-tdEV performed best with an HR of 1.6/2.9 vs. 1.5/1.0 with ACCEPT-tdEV. In conclusion, contrast maximization is effective even though it does not utilize domain knowledge.
Original languageEnglish
Number of pages8
JournalPNAS Nexus
Volume3
Issue number2
DOIs
Publication statusPublished - 1 Feb 2024

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