2D and 3D CT Radiomics Features Prognostic Performance Comparison in Non-Small Cell Lung Cancer

• Published in: Translational oncology Vol. 10; no. 6; pp. 886 - 894
• Main Authors: Shen, Chen, Liu, Zhenyu, Guan, Min, Song, Jiangdian, Lian, Yucheng, Wang, Shuo, Tang, Zhenchao, Dong, Di, Kong, Lingfei, Wang, Meiyun, Shi, Dapeng, Tian, Jie
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.12.2017; Neoplasia Press; Elsevier
• Subjects: Original article
• ISSN: 1936-5233; 1936-5233
• DOI: 10.1016/j.tranon.2017.08.007

OBJECTIVE: To compare 2D and 3D radiomics features prognostic performance differences in CT images of non-small cell lung cancer (NSCLC). METHOD: We enrolled 588 NSCLC patients from three independent cohorts. Two sets of 463 patients from two different institutes were used as the training cohort. The remaining cohort with 125 patients was set as the validation cohort. A total of 1014 radiomics features (507 2D features and 507 3D features correspondingly) were assessed. Based on the dichotomized survival data, 2D and 3D radiomics indicators were calculated for each patient by trained classifiers. We used the area under the receiver operating characteristic curve (AUC) to assess the prediction performance of trained classifiers (the support vector machine and logistic regression). Kaplan–Meier and Cox hazard survival analyses were also employed. Harrell's concordance index (C-Index) and Akaike's information criteria (AIC) were applied to assess the trained models. RESULTS: Radiomics indicators were built and compared by AUCs. In the training cohort, 2D_AUC=0.653, 3D_AUC=0.671. In the validation cohort, 2D_AUC=0.755, 3D_AUC=0.663. Both 2D and 3D trained indicators achieved significant results (P<.05) in the Kaplan-Meier analysis and Cox regression. In the validation cohort, 2D Cox model had a C-Index=0.683 and AIC=789.047; 3D Cox model obtained a C-Index=0.632 and AIC=799.409. CONCLUSION: Both 2D and 3D CT radiomics features have a certain prognostic ability in NSCLC, but 2D features showed better performance in our tests. Considering the cost of the radiomics features calculation, 2D features are more recommended for use in the current study.