(Sunday, 7/12/2020) [Eastern Time (GMT-4)]
Room: AAPM ePoster Library
Purpose:
It is of high clinical importance to identify the primary lesion and its pathological types for patients with brain metastases. Non-small cell lung cancer (NSCLC) is the most likely primary tumor to metastasize in the brain with two major histological types: adenocarcinoma (AD) and squamous cell carcinoma (SCC), which account for nearly 80% of NSCLC. The purpose of this study is to investigate the feasibility and accuracy of differentiating the primary AD and SCC for patients with brain metastases (BM) based on radiomics from brain contrast enhanced computer tomography (CECT) images.
Methods:
BM patients with confirmed histological differentiations from January 2014 and December 2016 were retrospectively reviewed. Radiomics features from manually contoured tumors were extracted using python. Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) logistic regression were applied to select relative radiomics features. Binary logistic regression and support vector machines (SVM) were applied to build models with radiomics features alone and with radiomics features plus age and sex.
Results:
A total of 144 BM patients (94 Male, 50 Female) at a median age of 62 years old (range from 35 to 82) were enrolled in this study with 102 with primary lung AD and 42 with SCC, respectively. Fourteen features were selected from a total of 105 radiomics features for the final model building. The area under curves (AUCs) and accuracy of SVM and binary logistic regression models were 0.765 vs. 0.769, 0.795 vs.0.828, and 0.716 vs. 0.726, 0.768 vs. 0.758, respectively, for models with radiomics features alone and models with radiomics features plus sex and age.
Conclusion: radiomics are promising in differentiating primary AD and SCC to achieve optimal therapeutic management in patients with BM from NSCLC
Keywords
Brain, CT, Quantitative Imaging
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