Fully Automated CTC Detection, Segmentation and Classification for Multi-Channel IF Imaging

TL;DR

This paper presents a fully automated machine learning pipeline for detecting, segmenting, and classifying circulating tumor cells in multi-channel immunofluorescence images, significantly aiding clinical analysis of metastatic breast cancer.

Contribution

It introduces a novel production-level system that automates CTC detection and classification with high accuracy, reducing manual workload and improving clinical workflow.

Findings

Achieved over 99% sensitivity and 97% specificity.

Reduced manual review from 14 million cells to 335 CTC candidates.

Successfully deployed in real clinical settings.

Abstract

Liquid biopsies (eg., blood draws) offer a less invasive and non-localized alternative to tissue biopsies for monitoring the progression of metastatic breast cancer (mBCa). Immunofluoresence (IF) microscopy is a tool to image and analyze millions of blood cells in a patient sample. By detecting and genetically sequencing circulating tumor cells (CTCs) in the blood, personalized treatment plans are achievable for various cancer subtypes. However, CTCs are rare (about 1 in 2M), making manual CTC detection very difficult. In addition, clinicians rely on quantitative cellular biomarkers to manually classify CTCs. This requires prior tasks of cell detection, segmentation and feature extraction. To assist clinicians, we have developed a fully automated machine learning-based production-level pipeline to efficiently detect, segment and classify CTCs in multi-channel IF images. We achieve over 99% sensitivity and 97% specificity on 9,533 cells from 15 mBCa patients. Our pipeline has been successfully deployed on real mBCa patients, reducing a patient average of 14M detected cells to only 335 CTC candidates for manual review.