Schwab, E., Annaldas, B., Ramesh, N., Lundberg, A., Shelke, V., Xu, X., Gilbertson, C., Byun, J., & Lam, E. T. (2024). Fully Automated CTC Detection, Segmentation and Classification for Multi-Channel IF Imaging. arXiv preprint arXiv:2410.02988.
This research paper aims to develop and validate a fully automated machine learning pipeline for the efficient detection, segmentation, and classification of circulating tumor cells (CTCs) in multi-channel immunofluorescence (IF) images of metastatic breast cancer (mBCa) patients.
The BRIA (BReast cancer Imaging Algorithm) pipeline utilizes a combination of image processing techniques, deep learning, and interpretable feature-based machine learning. It involves cell detection using Laplacian of Gaussian (LoG), nuclear segmentation with Otsu's method, cell segmentation using a 3-channel U-Net, and feature extraction encompassing morphology, intensity, and texture features. A support vector machine (SVM) with an RBF kernel is trained for CTC classification.
The BRIA pipeline offers a clinically valuable tool for assisting in the diagnosis and monitoring of mBCa by automating the laborious process of CTC detection and classification in IF images. Its high sensitivity ensures the identification of nearly all CTCs while significantly reducing the number of candidates requiring manual review by clinicians.
This research contributes to the advancement of automated CTC detection and analysis in liquid biopsies, offering a less invasive alternative to tissue biopsies for mBCa diagnosis and monitoring. The development of a fully automated and interpretable pipeline has the potential to improve the efficiency and accuracy of CTC-based diagnostics.
While the study demonstrates promising results, further validation on larger and more diverse patient cohorts is necessary. Future research could explore the generalization of BRIA to other cancer types and investigate the integration of additional biomarkers for enhanced classification accuracy.
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by Evan Schwab,... at arxiv.org 10-07-2024
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