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.
Highlights
Non-small cell lung cancer (NSCLC) accounts for 85% of lung cancers and adenocarcinoma is the predominant histologic subtype of lung cancer
Al-Kadi et al showed that computed tomography (CT) features with texture analysis can be helpful in differentiating aggressive from nonaggressive NSCLC [6], and Kido et al showed differences between histologic subtypes of peripheral bronchogenic carcinoma using textural parameters on CT [7]
We might expect radiomics to provide noninvasive analysis of lung adenocarcinoma and allow more effective evaluation of tumor aggressiveness based on tumor grade
Summary
Non-small cell lung cancer (NSCLC) accounts for 85% of lung cancers and adenocarcinoma is the predominant histologic subtype of lung cancer. Reflecting the histologic heterogeneity of lung adenocarcinoma, there is an increasing body of evidence that sublobar resection may achieve oncologic outcomes similar to those of lobectomy in early-stage NSCLC [4], some studies have www.impactjournals.com/oncotarget reported the contradictory result that postoperative adjuvant chemotherapy improves the prognosis even in operable early-stage NSCLC [5]. Al-Kadi et al showed that computed tomography (CT) features with texture analysis can be helpful in differentiating aggressive from nonaggressive NSCLC [6], and Kido et al showed differences between histologic subtypes of peripheral bronchogenic carcinoma using textural parameters on CT [7]. We might expect radiomics to provide noninvasive analysis of lung adenocarcinoma and allow more effective evaluation of tumor aggressiveness based on tumor grade
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