Hybrid capture-based NGS was performed by Foundation Medicine and Gaurdant 360 TM if tissue was not available. Result: 178 consecutive NSCLC patients were included in this study. Median age at diagnosis was 63±12.1 years. 83% had adenocarcinoma. NGS was performed upfront in 45.5% (81/178) and after 1st line failure in 54.5% (97/178). Treatment decision was taken toward targeted therapy subsequent to NGS analysis in 34% (61/178) of patients (29 and 32 respectively) with an objective response rate of 54%. Overall survival (OS) was evaluated for 51% (31/61) with a median of 12.2±14.1 months. For patients who performed upfront NGS, OS ranged between 1.8 to 32.5 months, with a median OS of 13.8 months. For patients who performed NGS on progression, OS ranged between 1.7 to 77.1, with a median OS of 12.7 months. Conclusion: Comprehensive tissue and liquid-based NGS have revealed targeted treatment options for one third of the patients. Overall Survival of patients treated with tailored therapy was positively impacted by earlier performed NGS.
segmentation. The second dataset (B), also comprising 640 subjects, included all malignant nodules in A but benign nodules were randomly selected following the empirical size distribution of the whole NLST dataset. Therefore, nodule size cannot be a discriminative factor in A but would be in B. Two nodule stratification algorithms were developed using Texture Analysis combined with Machine Learning (Support Vector Regression) integrating 20 variables including 3D Haralick, Gabor and Shape features, from A and B respectively using five-fold cross validation and the performance compared measuring Area-Under-the-Curve (AUC). Result: The average AUC for the algorithm trained on dataset A was 0.70 whereas using size alone on the same dataset gave an AUC of 0.50. The AUC was 0.91 for the algorithm trained on B. Conclusion: On this data, Texture Analysis with Machine Learning contributes 0.20 AUC points to classification performance. Artificial Intelligence based risk classification can identify radiological features that are predictive of solid nodule malignancy that are independent of nodule size.
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