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CT Based Radiomics Data Predicts for Nodal Involvement and Overall Survival in NSCLC

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T Coroller

T Coroller*, p Grossman , E Rios , R Mak , H Aerts , Dana Farber Cancer Institute / Harvard Medical School, Boston, MA


WE-E-17A-4 Wednesday 1:45PM - 3:45PM Room: 17A

Purpose: Recent advances in medical imaging technologies provide opportunities to quantify the tumor phenotype throughout the course of treatment non-invasively. The emerging field of Radiomics addresses this by converting medical images into minable data using a large number of quantitative imaging algorithms. The ability of radiomics to predict clinical outcomes such as survival or nodal involvement, can provide valuable complementary information. This study aims to evaluate the predictability of CT based radiomics features for node stages and survival time.in non-small cell lung cancer (NSCLC) patients.

Method: We included 118 patients with NSCLC in our analysis. Seventy-four quantitative radiomic features describing the tumor phenotype based on intensity, shape, and texture, were extracted from planning CT scans. Association of the radiomics features with node involvement (Yes/No) was assessed using the area under the curve (AUC) of the receiver operating characteristics (ROC). Association with overall survival was assessed using the C-Index (CI).

Results: We found 30 radiomic features that were significantly (FDR corrected) associated with nodal involvement (range AUC=0.73-0.60). Seventeen features were significantly associated survival (range CI=0.56-0.62). Gray Level Non-Uniformity, describing intra-tumor heterogeneity, was the best predictor for both nodal stage (AUC=0.73) and survival time (CI=0.62). It is noteworthy that tumor volume is only the 8th best predictor for both nodal stage (AUC=0.71) and survival (CI =0.61).

Conclusion: Radiomics analysis was able to predict node stage and survival time by looking at complex image features. Although tumor volume has been routinely used to predict outcome, this study shows that radiomic features capturing detailed information of the tumor phenotype can provide better prediction for clinical relevant factors such as nodal involvement and survival.

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