Pathologic stratification of operable lung adenocarcinoma using radiomics features extracted from dual energy CT images

Abstract

PurposeTo evaluate the usefulness of surrogate biomarkers as predictors of histopathologic tumor grade and aggressiveness using radiomics data from dual-energy computed tomography (DECT), with the ultimate goal of accomplishing stratification of early-stage lung adenocarcinoma for optimal treatment.ResultsPathologic grade was divided into grades 1, 2, and 3. Multinomial logistic regression analysis revealed i-uniformity and 97.5th percentile CT attenuation value as independent significant factors to stratify grade 2 or 3 from grade 1. The AUC value calculated from leave-one-out cross-validation procedure for discriminating grades 1, 2, and 3 was 0.9307 (95% CI: 0.8514–1), 0.8610 (95% CI: 0.7547–0.9672), and 0.8394 (95% CI: 0.7045–0.9743), respectively.Materials and MethodsA total of 80 patients with 91 clinically and radiologically suspected stage I or II lung adenocarcinoma were prospectively enrolled. All patients underwent DECT and F-18-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT, followed by surgery. Quantitative CT and PET imaging characteristics were evaluated using a radiomics approach. Significant features for a tumor aggressiveness prediction model were extracted and used to calculate diagnostic performance for predicting all pathologic grades.ConclusionsQuantitative radiomics values from DECT imaging metrics can help predict pathologic aggressiveness of lung adenocarcinoma.

Radiomics predicts survival of patients with advanced non‑small cell lung cancer undergoing PD‑1 blockade using Nivolumab

“…In recent years, numerous studies have examined the potential clinical utility of radiomics features calculated from computed tomography (CT) images of NSCLC, correlated with tumor histology (24)(25)(26), staging (27), patient prognosis (19,(28)(29)(30) and genetic mutations (31)(32)(33).…”

Predicting the Development of Normal-Appearing White Matter With Radiomics in the Aging Brain: A Longitudinal Clinical Study

“…This process can reveal subtle microstructural alterations in the tissues by incorporating and analyzing the signal intensities of neighboring voxels ( Albuquerque et al, 2015 ; Yip and Aerts, 2016 ). As an emerging quantitative imaging method, radiomics yield additional insights into the disease, such as tumor heterogeneity, when compared with traditional imaging techniques ( Bae et al, 2016 ). In addition, texture analysis has been applied in cross-sectional studies of patients with small vessel diseases, which suggests that radiomics may be a feasible technique to investigate the microstructural changes of NAWM ( Tozer et al, 2018 ).…”

The development and validation of a radiomic nomogram for the preoperative prediction of lung adenocarcinoma

“…Radiomic signatures may help to mining bioinformatics behind lung cancer on medical image, for instance, tumor staging [24], gene expression patterns [25], treatment response [26,27], and patient survival [28,29]. Current determination of whether radiomic features can improve the prediction of pulmonary nodules as being malignant as opposed to conventional visual assessment on CT is a hot topic [30,31], but most studies have examined nodules smaller than 30 mm in diameter. In this study, 210 SPN less than 10 mm with surgery-proven malignancy or benign status were included for radiomic analysis.…”