Predicting Patient Response to Cancer Immunotherapy Using Quantitative Computed Tomography Based Texture Analysis

Abstract

Cancer therapies have evolved continuously, with the newest class being immunotherapies targeting the PD‐L1/PD‐1 pathway. This pathway is often overexpressed in malignancies, which allow the aberrant cells to evade the body’s natural immune response that would normally eliminate them. The novel therapies currently being investigated are monoclonal antibodies that target either the PD‐L1 on the tumor cell or the PD‐1 on the lymphocyte. Considering there are significant toxicities with these therapies, namely gastrointestinal and endocrine adverse effects, a predictive tool that could allow physicians which patients are likely to respond to these immunotherapies could spare patients unnecessary therapy and potential economic harm. Since repetitive imaging of patients with cancer is necessary to monitor treatment response, advanced imaging analysis techniques on standard of care images, such as CT scans may provide insights into tumor patterns that could help to predict treatment response. Quantitative texture analysis (QTA) of computed tomography scans has been used in various settings to examine tissue heterogeneity as a predictive biomarker of response; we hypothesized that QTA may have potential value in predicting tumor response to immunotherapy. We performed a QTA on standard of care CT scans from patients to determine if a unique textural imaging signature could be identified that would serve as a predictive biomarker for response to PD‐L1/PD‐1 therapies in subjects with solid tumor malignancies in the lungs, liver, and lymph nodes. This study examined the diagnostic standard of care CT scans of the chest, abdomen, and pelvis (CT CAP) at baseline and follow‐up, which were acquired as part of routine clinical care for tumor staging and treatment response in 20 subjects whose personal health care information was removed prior to analysis. Regions of interest (ROI) were drawn around all identifiable tumor lesions on baseline CT scans provided that tumors were of reasonable size (>10 mm in diameter) and conspicuity. CT texture analysis was performed on these lesions to obtain a histogram readout of tumor texture based upon tissue densities on a per pixel bases. The output values from the QTA platform provided an estimate of tumor signal properties as expressed as the mean pixel density, standard deviation, entropy, kurtosis, skewness, and mean positive pixel values. Each subject was designated as achieving either a RECIST based treatment response or not. Statistical modeling was then conducted using regression techniques. There was no identifiable signature when examining all of the lesions together, but there were statistically significant correlations noted between QTA and RECIST responses for lung‐based lesions. The QTA derived mean pixel density parameter was a major component of separating out responders from non‐response. Of the 14 lung lesions (8 responder vs. 6 nonresponder) there was a significant difference in the mean density with a threshold cutoff of 11.91 (p < 0.0001). A Mann‐Whitney U‐test was performed on the total data set yielding a Z statistic of 2.6 (p=0.0092). Despite the relatively small number of patients in this initial study, there were promising findings regarding the mean density of lesions, suggesting that texture analysis can be used to predict if patients respond to PD‐L1/PD‐1 inhibitors. Further investigation is warranted in a larger population that can be differentiated by tumor type to validate these results.