Prediction of immunotherapy response using deep learning of PET/CT images

Abstract

Currently only a fraction of patients with non-small cell lung cancer (NSCLC) experience durable clinical benefit (DCB) from immunotherapy, robust biomarkers to predict response prior to initiation of therapy are an emerging clinical need. PD-L1 expression status from immunohistochemistry is the only clinically approved biomarker, but a non-invasive complimentary approach that could be used when tissues are not available or when the IHC fails and can be assessed longitudinally would have important implications for clinical decision support. In this study, 18F-FDG-PET/CT images and clinical data were curated from 697 NSCLC patients from three institutions. Utilizing PET/CT images, a deeply-learned-score (DLS) was developed by training a small-residual-convolutional-network model to predict the PD-L1 expression status, which was further used to predict DCB, progression-free survival (PFS), and overall survival (OS) in both retrospective and prospective test cohorts of immunotherapy-treated patients with advanced stage NSCLC. This PD-L1 DLS significantly discriminated PD-L1 positive and negative patients (AUC≥0.82 in all cohorts). Further, higher PD-L1 DLS was significantly associated with higher probability of DCB, longer PFS, and longer OS. The DLS combined with clinical characteristics achieved C-indices of 0.86, 0.83 and 0.81 for DCB prediction, 0.73, 0.72 and 0.70 for PFS prediction, and 0.78, 0.72 and 0.75 for OS prediction in the retrospective, prospective and external cohorts, respectively. The DLS provides a non-invasive and promising approach to predict PD-L1 expression and to infer clinical outcomes for immunotherapy-treated NSCLC patients. Additionally, the multivariable models have the potential to guide individual pre-therapy decisions pending in larger prospective trials.

Application of radiomics in diagnosis and treatment of lung cancer

“…Mu et al included 194 patients with advanced NSCLC and extracted multiparameter imaging features from PET, CT, and PET/CT fusion images to distinguish between durable clinical benefit (DCB) and no durable clinical benefit (NDB) based on PFS. The model achieved AUC values of 0.86, 0.83, and 0.81 in the training set, retrospective test set, and prospective test set, respectively (Mu et al, 2020)These studies collectively demonstrate the utility of radiomics analysis in predicting treatment response, distinguishing immune microenvironment characteristics, and identifying patients at risk of adverse outcomes during immunotherapy for lung cancer.…”

Thyroid cancer under the scope of emerging technologies

“…Thus predicting patient response or adverse effects (Jing et al 2020) to those has been an important clinical challenge, for which the power of AI and ML for data fusion can be leveraged, as these are ideally-suited to integrate the complex patterns in single-cell sequencing, multiomics (J. S. Lee and Ruppin 2019;Litchfield et al 2021), clinical images (M. Wu et al 2019), such as radiomics (Mu et al 2020;Butner et al 2020;Trebeschi et al 2019) or histopathological (Hildebrand et al 2021), transcriptomics and genomics data (Litchfield et al 2021).…”