Two major treatment strategies employed in non-small cell lung cancer, NSCLC, are tyrosine kinase inhibitors, TKIs, and immune checkpoint inhibitors, ICIs. The choice of strategy is based on heterogeneous biomarkers that can dynamically change during therapy. Thus, there is a compelling need to identify comprehensive biomarkers that can be used longitudinally to help guide therapy choice. Herein, we report a 18F-FDG-PET/CT-based deep learning model, which demonstrates high accuracy in EGFR mutation status prediction across patient cohorts from different institutions. A deep learning score (EGFR-DLS) was significantly and positively associated with longer progression free survival (PFS) in patients treated with EGFR-TKIs, while EGFR-DLS is significantly and negatively associated with higher durable clinical benefit, reduced hyperprogression, and longer PFS among patients treated with ICIs. Thus, the EGFR-DLS provides a non-invasive method for precise quantification of EGFR mutation status in NSCLC patients, which is promising to identify NSCLC patients sensitive to EGFR-TKI or ICI-treatments.

Objectives-Immune-checkpoint blockades have exhibited durable responses and improved long-term survival in a subset of advanced non-small-cell lung cancer (NSCLC) patients. However, highly predictive markers of positive and negative responses to immunotherapy are a significant unmet clinical need. The objective of this study was to identify clinical and computational image-based predictors of rapid disease progression phenotypes in NSCLC patients treated with immune-checkpoint blockades. Materials and Methods-Using time-to-progression (TTP) and/or tumor growth rates, rapid disease progression phenotypes were developed including hyperprogressive disease. The pretreatment baseline predictors that were used to identify these phenotypes included patient demographics, clinical data, driver mutations, hematology data, and computational image-based features (radiomics) that were extracted from pre-treatment computed tomography scans. Synthetic Minority Oversampling Technique (SMOTE) was used to subsample minority groups to *

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the national Lung Screening trial (nLSt) demonstrated that screening with low-dose computed tomography (LDCT) is associated with a 20% reduction in lung cancer mortality. One potential limitation of LDct screening is overdiagnosis of slow growing and indolent cancers. in this study, peritumoral and intratumoral radiomics was used to identify a vulnerable subset of lung patients associated with poor survival outcomes. incident lung cancer patients from the nLSt were split into training and test cohorts and an external cohort of non-screen detected adenocarcinomas was used for further validation. After removing redundant and non-reproducible radiomics features, backward elimination analyses identified a single model which was subjected to Classification and Regression tree to stratify patients into three risk-groups based on two radiomics features (nGtDM Busyness and Statistical Root Mean Square [RMS]). The final model was validated in the test cohort and the cohort of non-screen detected adenocarcinomas. Using a radio-genomics dataset, Statistical RMS was significantly associated with FOXF2 gene by both correlation and two-group analyses. our rigorous approach generated a novel radiomics model that identified a vulnerable high-risk group of early stage patients associated with poor outcomes. these patients may require aggressive follow-up and/or adjuvant therapy to mitigate their poor outcomes. The National Lung Screening Trial (NLST) demonstrated that annual screening with low-dose helical computed tomography (LDCT) compared to chest radiography is associated with a 20% relative reduction in lung cancer mortality among high-risk individuals 1. However, LDCT screening can lead to overdiagnosis and overtreatment of slow growing, indolent cancers that may pose no threat if left untreated 2,3. Prior post-hoc analyses of the NLST have estimated that 18-22.5% of screen-detected cancers would not become symptomatic in a patient's lifetime and would remain as indolent lung cancer 4. At present there is limited data regarding the potential harmful impact of overdiagnosis on lung cancer outcomes; however, studies have suggested overdiagnosis is associated with increased operative mortality, severe disability among survivors, and reduction in longer term disease-free survival attributed to loss of pulmonary reserve 5. Though clinical guidelines provide recommendations for the management of screen-detected nodules, there are limited tools to discriminate between indolent and aggressive lung cancers diagnosed in the lung cancer screening setting 6-9. As such, biomarkers that can classify behavior of screen-detected lung cancers is an unmet clinical need since prior studies have suggested that 10 to 27% of lung cancers are over-diagnosed in lung cancer screening 10-13. Quantitative image features, also known as radiomics 14 , are non-invasive biomarkers that are generated from medical imaging and reflect the underlying tumor pathophysiology and heterogeneity. Radiomics have many advantages over circulating and tissue-based bio...