Image Analysis of Circulating Tumor Cells and Leukocytes Predicts Survival and Metastatic Pattern in Breast Cancer Patients

• Published in: Frontiers in oncology 2022-02, Vol.12, p.725318-725318
• Main Authors: Da Col, Giacomo, Del Ben, Fabio, Bulfoni, Michela, Turetta, Matteo, Gerratana, Lorenzo, Bertozzi, Serena, Beltrami, Antonio Paolo, Cesselli, Daniela
• Format: Article
• Language: English
• Subjects: circulating tumor cells; data science; image analysis; liquid biopsy; machine learning; Oncology
• ISSN: 2234-943X; 2234-943X
• DOI: 10.3389/fonc.2022.725318

The purpose of the present work was to test whether quantitative image analysis of circulating cells can provide useful clinical information targeting bone metastasis (BM) and overall survival (OS >30 months) in metastatic breast cancer (MBC). Starting from cell images of epithelial circulating tumor cells (eCTC) and leukocytes (CD45pos) obtained with DEPArray, we identified the most significant features and applied single-variable and multi-variable methods, screening all combinations of four machine-learning approaches (Naïve Bayes, Logistic regression, Decision Trees, Random Forest). Best predictive features were circularity (OS) and diameter (BM), in both eCTC and CD45pos. Median difference in OS was 15 vs. 43 (months), p = 0.03 for eCTC and 19 vs. 36, p = 0.16 for CD45pos. Prediction for BM showed low accuracy (64%, 53%) but strong positive predictive value PPV (79%, 91%) for eCTC and CD45, respectively. Best machine learning model was Naïve Bayes, showing 46 vs 11 (months), p