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Aggressive Lung Adenocarcinoma Subtype Prediction Using FDG-PET/CT Radiomics

W Choi

W Choi*, C Liu , S Riyahi Alam , J Oh , P Adusumilli , W Weber , J Deasy , W Lu , Memorial Sloan Kettering Cancer Center, New York, NY


SU-F-605-5 (Sunday, July 30, 2017) 2:05 PM - 3:00 PM Room: 605

Purpose: To predict the histopathologic subtypes with poor surgery prognosis in early stage lung adenocarcinomas using CT and PET radiomics.

Methods: We retrospectively enrolled 53 patients with stage I lung adenocarcinoma who underwent both diagnostic CT and 18F-fluorodeoxyglucose (FDG) PET/CT before complete surgical resection of the tumors. Tumor segmentation was manually contoured by a physician on both the diagnostic CT and the attenuation CT of PET/CT.A total of 170 radiomics features were extracted on both PET and CT images to design predictive models for two histopathologic endpoints: (1) tumors with solid or micropapillary predominant subtype (aggressiveness), and (2) tumors with micropapillary component more than 5% (MIP5). We used least absolute shrinkage and selection operator (LASSO) as a model building method coupled with a class separability feature selection (CSFS) method. For an unbiased model estimate, a 10-fold cross validation approach was used. The area under the curve (AUC) and prediction accuracy were employed to evaluate the performance of the model. P-values were computed using Wilcoxon rank-sum test.

Results: Of the 53 patients, 9 and 15 had tumors with aggressiveness and MIP5, respectively. For both endpoints, LASSO models with two PET radiomics features achieved the best performance. For aggressiveness, the LASSO model with PET Cluster Shade and PET 2D Variance resulted in 77.6±2.3% accuracy and 0.71±0.02 AUC (P = 0.011). For MIP5, the LASSO model with PET Eccentricity and PET Cluster Shade resulted in 69.6±3.1% accuracy and 0.68±0.04 AUC (P=0.014). The PET Cluster Shade was commonly selected in both models. Cluster shade is a texture feature that measures the skewness of the co-occurrence matrix. Higher PET cluster shade predicted that the tumor was more aggressive and more likely MIP5.

Conclusion: We showed that PET/CT radiomics features can predict tumor aggressiveness.

Funding Support, Disclosures, and Conflict of Interest: This work was supported in part by the National Cancer Institute Grants R01CA172638.

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