Association of artificial intelligence–derived collagen disorder architecture (CoDA) features with survival outcome and objective response to immune checkpoint inhibitors in patients with head and neck squamous cell carcinoma.
Authors: Nag Reetoja , Li Haojia , Corredor Germán , Fu Pingfu , Khalighi Sirvan , Steward-Tharp Scott Michael , Patel Mihir R. , Schmitt Nicole C. , Shi Qiuying , Toro Paula , Pathak Tilak , Pandav Krunal , Wasman Jay , Teknos Theodoros Nicholas , Trzcinska Anna Michalina , Patel Monaliben , Pan Quintin , Saba Nabil F. , Lewis Jr. James , Madabhushi Anant 
Published: June 2024

6061 Background: In several cancers, including head and neck squamous cell carcinoma (HNSCC), the immunosuppressive components of the tumor microenvironment (TME) can impact the effectiveness of immune checkpoint inhibitors (ICI). One significant component of the TME is the extracellular matrix, which is rich in collagen fibers. In this work we used machine learning and image analysis approaches on whole slide images (WSIs) to characterize collagen disorder architecture (CoDA) features and evaluated their association with outcomes in HNSCC patients receiving ICI. Methods: WSIs of HNSCC patients treated with ICI were obtained from University Hospitals (S1, n=43) and Emory University (S2, n=31). Tiles from the tumor annotated regions of the WSIs were extracted, and a derivative-of-Gaussian model was used to identify collagen fibers in the stroma of these tiles. Various CoDA features were then calculated as follows: (1) collagen fiber fragmentation measure, (2) collagen fiber bundling percentage, (3) collagen fiber rigidity measure, (4) collagen fiber anisotropy index and (5) collagen fiber density index. CoDA features of S1and S2 were combined and split into 50:50 for training and validation. For survival analysis using overall survival (OS) as endpoint, the median risk score in the training set was applied for risk stratification in the validation set by means of a Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression model. For predictive analysis, CoDA features from patients with objective response (OR) to ICI were identified (S1, non-responder=23, responder=20), (S2, non-responder=23, responder=8). The top features were then selected using the LASSO and combined with a Generalized Linear Model classifier. A 5-fold cross-validation assessed Area Under the Receiver Operating Characteristics Curve (AUC) for predicting OR, with average AUC as the final performance metric in the validation set. Results: For survival analysis, high risk patients in the validation set had worse survival than low risk patients (HR=2.7 (95% CI=1.1-6.6, p=0.02)). For predicting OR, the selected top CoDA features were collagen fiber fragmentation measure, collagen fiber bundling percentage, collagen fiber rigidity measure and collagen fiber density index and the average AUC was 0.64±0.16. More fragmentation of the collagen fibers along with dense thick bundles and straightened fibers were observed in the WSIs of non-responder patients to ICI. Conclusions: High risk CoDA features correlated with worse survival in patients with HNSCC receiving ICI. Also, we established a correlation of specific CoDA features with OR to ICI. The prognostic and predictive value of CoDA deserves additional exploration with confirmatory data from larger, independent multi-site validation.