A Radiomics Signature in Preoperative Predicting Degree of Tumor Differentiation in Patients with Non–small Cell Lung Cancer

Publication date: December 2018

Source: Academic Radiology, Volume 25, Issue 12

Author(s): Xin Chen, Mengjie Fang, Di Dong, Xinhua Wei, Lingling Liu, Xiangdong Xu, Xinqing Jiang, Jie Tian, Zaiyi Liu

Rationale and Objectives

Poorly differentiated non–small cell lung cancer (NSCLC) indicated a poor prognosis and well-differentiated NSCLC indicates a noninvasive nature and good prognosis. The purpose of this study was to build and validate a radiomics signature to predict the degree of tumor differentiation (DTD) for patients with NSCLC.

Materials and Methods

A total of 487 patients with pathologically diagnosed NSCLC were retrospectively included in our study. Five hundred ninety-one radiomics features were extracted from each tumor from the contrast-enhanced computed tomography images. A minimum redundancy maximum relevance algorithm and a logistic regression model were used for dimension reduction, feature selection, and radiomics signature building. The performance of the radiomics signature was assessed using receiver operating characteristic analysis, and the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated to quantify the association between a signature and DTD. An independent validation set contained 184 consecutive patients with NSCLC.

Results

A nine-radiomics-feature-based signature was built and it could differentiate low and high DTDs in the training set (AUC = 0.763, sensitivity = 0.750, specificity = 0.665, and accuracy = 0.687), and the radiomics signature had good discrimination performance in the validation set (AUC = 0.782, sensitivity = 0.608, specificity = 0.752, and accuracy = 0.712).

Conclusions

A radiomics signature based on contrast-enhanced computed tomography imaging is a potentially useful imaging biomarker for differentiating low from high DTD in patients with NSCLC.

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