Association of AI-informed biomarkers of spatial organization of tumor-infiltrating lymphocytes with loco-regional recurrence in laryngeal squamous cell cancer.

Abstract

6073 Background: Laryngeal Squamous Cell Carcinoma (LaSCC) has varying outcomes based on the stage of cancer patients present with. Currently, a high proportion of patients are diagnosed with advanced-stage LaSCC complicating the treatment landscape and over-treating low risk patients. Risk stratification of LaSCC can help tailor treatment plans. Numerous studies have identified spatial architecture of tumor-infiltrating lymphocytes (TILs) as a prognostic biomarker in oral cavity and oropharyngeal SCC. In this work, we evaluate the prognostic value of an artificial intelligence (AI)-leveraged approach that characterizes the spatial architecture of TILs on digitized hematoxylin and eosin (H&E)-stained slides from patients with LaSCC. Methods: H&E slides from 192 patients with LaSCC were collected from Baylor Medical Center. This dataset was randomly divided into two equal cohorts, A and B. The slides were digitized as whole slide images at 40x magnification. The nuclei of all cells were automatically segmented using a deep-learning model (Hover-Net). Each nucleus was then classified as TIL or non-TIL based on morphological features. TILs and non-TILs were clustered based on proximity, and features related to the density and spatial distribution were extracted. The top features, determined by the least absolute shrinkage and selection operator, were used to train a Cox Proportional Hazards regression model that assigned a risk score for recurrence of cancer to each patient in cohort A. For validation, the model was applied to patients in cohort B. The 25 th percentile training risk score was used as a cutoff for classifying patients as high or low risk. The performance of the model in prognosticating loco-regional recurrence (LRR) was evaluated using survival analysis. Results: Patients in Cohort B identified as “high risk” by the model based on spatial organization of TILs had a significantly shorter survival time. Univariate survival analysis showed this model was prognostic for DFS with a hazard ratio of 2.57 (95% Confidence Interval: 1.12-5.89, p-value=0.048), meaning that patients classified as “high risk” are approximately 2.5 times more likely to develop LLR. Conclusions: We used computational pathology to characterize the architecture of TILs and develop a model to predict risk of LLR in LaSCC. With additional validation, this approach can be used to assist clinicians with making clinical decisions. Multivariate analysis. Variable Reference vs Comparison Pr(>|z|) HR (95% CI) Race Black vs Caucasian 0.93 0.92 (0.40 - 2.31) N N0 vs N+ 0.67 1.80 (0.12 - 26.06) T 1-2 vs 3-4 0.83 1.28 (0.13 - 12.38) Chemo Yes vs No 0.38 3.49 (0.21 - 56.90) Tobacco Yes vs No 0.26 2.08 (0.58 - 7.47) Alcohol Yes vs No 0.43 1.53 (0.54 - 4.35) TIL Architecture Risk High vs Low 0.03 * 0.26 (0.08 - 0.89)