2019 PRESENTER 


Zaibo Li, MD, PhD, MBA
Associate Professor
Ohio State University


Presenting
Predicting Response to Neoadjuvant Chemotherapy Using Machine Learning Models Integrated with Image-based Tumor Microenvironment Features, Niomarkers and Clinical Features in HER2-positive Breast Cancers

Abstract
Background Pathologic complete response (pCR) to anti-HER2 neoadjuvant chemotherapy (NAC) is a presumptive surrogate for disease-free survival in breast cancer (BC) patients. Potential factors associated with pCR have been investigated separately. We aimed to develop machine learning models integrating image-based tumor microenvironment features, biomarkers and clinical features to predict the response to NAC. Methods Sixty-four HER2-positive BC patients treated with anti-HER2 NAC and subsequent resection were included. A multiplex immunohistochemistry (IHC) simultaneously detecting PD-L1, CD8 and CD163 was performed on pretreatment biopsies before NAC to evaluate tumor microenvironment. Various image features were extracted from H&E and IHC whole slide images (WSI). Multiple machine learning models integrated with image-based tumor microenvironment features, biomarkers and clinical features were performed. Results Machine learning models using image-based tumor microenvironment features can predict NAC response, and the predictive power was further improved by integrating biomarkers and clinical features. LASSO-regularized logistic regression outperformed other machine learning models and demonstrated pCR was positively associated with HER2/CEP17 ratio, CD163, CD8 and tumoral PD-L1 expression; while negatively associated with age, PR, ER, and the ratio of stromal area to tissue area. The algorithm is freely available at https://apps.medgen.iupui.edu/rsc/content/41/ as an online web tool. Conclusion Our data demonstrated machine learning models integrated with image-based tumor microenvironment features, biomarkers and clinical features can predict NAC response in HER2-positive BC patients, and the accuracy was improved when integrating all features

Objectives
Machine learning models using image-based tumor microenvironment features can predict NAC response. The predictive power was further improved by integrating biomarkers and clinical features.

Biography
Zaibo Li, MD, Ph.D, MBA is an Associate Professor, the Co-Director of residency program, Associate Director of digital pathology division in the Department of Pathology at The Ohio State University Wexner Medical Center. He is board certified in Anatomic/Clinical Pathology and Cytopathology. His practice and academic interests are breast/gynecological pathology, cytopathology and digital pathology. Dr. Li received his MD from Peking University Health Science Center in China and Ph.D from the University of Rochester. He completed his residency in Anatomic and Clinical Pathology, fellowship in Cytopathology and a second fellowship in Breast/Gynecologic Pathology at the University of Pittsburgh Medical Center in Pittsburgh, PA. Dr. Li has published more than 90 scientific articles and multiple book chapters. Dr. Li is currently served as section editor for Diagnostic Pathology and associate editor for BMC Cancer. Dr. Li is also served in multiple national society committees.


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