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.

BackgroundCurrently, only a fraction of patients with non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICIs) experience a durable clinical benefit (DCB). According to NCCN guidelines, Programmed death-ligand 1 (PD-L1) expression status determined by immunohistochemistry (IHC) of biopsies is the only clinically approved companion biomarker to trigger the use of ICI therapy. Based on prior work showing a relationship between quantitative imaging and gene expression, we hypothesize that quantitative imaging (radiomics) can provide an alternative surrogate for PD-L1 expression status in clinical decision support.Methods18F-FDG-PET/CT images and clinical data were curated from 697 patients with NSCLC from three institutions and these were analyzed using a small-residual-convolutional-network (SResCNN) to develop a deeply learned score (DLS) to predict the PD-L1 expression status. This developed model was further used to predict DCB, progression-free survival (PFS), and overall survival (OS) in two retrospective and one prospective test cohorts of ICI-treated patients with advanced stage NSCLC.ResultsThe PD-L1 DLS significantly discriminated between PD-L1 positive and negative patients (area under receiver operating characteristics curve ≥0.82 in the training, validation, and two external test cohorts). Importantly, the DLS was indistinguishable from IHC-derived PD-L1 status in predicting PFS and OS, suggesting the utility of DLS as a surrogate for IHC. A score generated by combining the DLS with clinical characteristics was able to accurately (C-indexes of 0.70–0.87) predict DCB, PFS, and OS in retrospective training, prospective testing and external validation cohorts.ConclusionHence, we propose DLS as a surrogate or substitute for IHC-determined PD-L1 measurement to guide individual pretherapy decisions pending in larger prospective trials.

1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018.

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Purpose To describe the relationship between conventional magnetic resonance (MR) imaging parameters and MR elastography of the pancreas in association with pancreatic histologic features and occurrence of postoperative pancreatic fistula (POPF). Materials and Methods Patients who underwent preoperative MR imaging (MR elastography and diffusion-, T1-, and T2-weighted imaging) followed by pancreatectomy with pancreaticoenteric anastomosis were included. The relationships between preoperative MR imaging parameters, demographic data, and intraoperative factors with POPF risk were analyzed with logistic regression analyses. The correlation of MR imaging parameters with histologic characteristics was evaluated with multivariate regression analysis. Results A total of 112 patients (64 men, 48 women; median age, 58 years) were evaluated. Forty-two patients (37.5%) developed POPF and 20 (17.9%) developed high-grade POPF (grades B and C). Lower pancreatic stiffness (≤1.43 kPa; odds ratio [OR], 9.196; 95% confidence interval [CI]: 1.92, 43.98), nondilated main pancreatic duct (MPD) diameter (<3 mm; OR, 7.298; 95% CI: 1.51, 35.34), and larger stump area (≥211 mm; OR, 9.210; 95% CI: 1.53, 55.26) were risk factors for POPF. Lower pancreatic stiffness (≤1.27 kPa; OR, 8.389; 95% CI: 1.88, 37.41) was the only independent predictor of high-grade POPF. Log-transformed pancreatic stiffness was independently associated with fibrosis (β = 0.060; 95% CI: 0.052, 0.068), acinar atrophy (β = 0.015; 95% CI: 0.003, 0.028), and lipomatosis (β = -0.016; 95% CI: -0.026, -0.006). Conclusion Preoperative MR assessment of pancreatic stiffness, MPD diameter, and stump area are important predictors of POPF.