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Adaptation of Semiautomated Circulating Tumor Cell CTC Assays for Clinical and Preclinical Research Applications
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Training an automated circulating tumor cell classifier when the true classification is uncertain.

Afroditi Nanou1, Nikolas H Stoecklein2, Daniel Doerr3

  • 1Department of Medical Cell BioPhysics, Faculty of Science and Technology, University of Twente, Enschede 7522 NH, The Netherlands.

PNAS Nexus
|February 19, 2024
PubMed
Summary

A new contrast maximization method improves the identification of circulating tumor cells (CTCs) and tumor-derived extracellular vesicles (tdEVs), enhancing survival prediction in cancer patients.

Keywords:
automated classifiercirculating tumor celllabel uncertaintyprognostic powertumor-derived extracellular vesicle

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Area of Science:

  • Oncology
  • Biotechnology
  • Medical Imaging

Background:

  • Circulating tumor cell (CTC) and tumor-derived extracellular vesicle (tdEV) loads are established prognostic factors in carcinoma patients.
  • Current CTC enumeration methods, relying on operator review, exhibit moderate interoperator agreement due to classification challenges.
  • Accurate identification of CTCs and tdEVs is crucial for predicting patient survival in both metastatic and nonmetastatic cancers.

Purpose of the Study:

  • To compare the efficacy of operator review, ACCEPT automated image processing, and a refined deep-learning algorithm for CTC and tdEV identification.
  • To evaluate the predictive value of these enumeration methods for patient survival.
  • To introduce and assess a contrast maximization technique (CM-CTC and CM-tdEV) for improved event detection.

Main Methods:

  • Utilized 418 benign disease samples, 6,293 nonmetastatic breast, 2,408 metastatic breast, and 698 metastatic prostate cancer samples.
  • Trained, tested, optimized, and evaluated CTC and tdEV enumeration using operator review, ACCEPT-CTC, and a refined deep-learning algorithm (CM-CTC/CM-tdEV).
  • Assessed prognostic performance using hazard ratios (HR) for overall survival.

Main Results:

  • The CM-CTC method demonstrated superior performance in identifying CTCs in metastatic and nonmetastatic breast cancer, yielding higher hazard ratios for overall survival compared to operator review and ACCEPT-CTC.
  • CM-tdEV showed improved performance in tdEV identification, with higher hazard ratios for overall survival compared to ACCEPT-tdEV.
  • The contrast maximization technique proved effective for CTC and tdEV enumeration without requiring domain-specific knowledge.

Conclusions:

  • Contrast maximization is an effective refinement strategy for deep-learning-based CTC and tdEV enumeration.
  • This method enhances the prognostic value of CTC and tdEV loads for predicting survival in cancer patients.
  • The developed technique offers a more objective and potentially more accurate approach to CTC and tdEV analysis.