Comparison of Radiomic Models Based on Low-Dose and Standard-Dose CT for Prediction of Adenocarcinomas and Benign Lesions in Solid Pulmonary Nodules

Abstract

Objectives: This study aimed to develop radiomic models based on low-dose CT (LDCT) and standard-dose CT to distinguish adenocarcinomas from benign lesions in patients with solid solitary pulmonary nodules and compare the performance among these radiomic models and Lung CT Screening Reporting and Data System (Lung-RADS). The reproducibility of radiomic features between LDCT and standard-dose CT were also evaluated.

Methods: A total of 141 consecutive pathologically confirmed solid solitary pulmonary nodules were enrolled including 50 adenocarcinomas and 48 benign nodules in primary cohort and 22 adenocarcinomas and 21 benign nodules in validation cohort. LDCT and standard-dose CT scans were conducted using same acquisition parameters and reconstruction method except for radiation dose. All nodules were automatically segmented and 104 original radiomic features were extracted. The concordance correlation coefficient was used to quantify reproducibility of radiomic features between LDCT and standard-dose CT. Radiomic features were selected to build radiomic signature, and clinical characteristics and radiomic signature were combined to develop radiomic nomogram for LDCT and standard-dose CT, respectively. The performance of radiomic models and Lung-RADS was assessed by area under curve (AUC) of receiver operating characteristic curve, sensitivity, and specificity.

Results: Shape and first order features, and neighboring gray tone difference matrix features were highly reproducible between LDCT and standard-dose CT. No significant differences of AUCs were found among radiomic signature and nomogram of LDCT and standard-dose CT in both primary and validation cohort (0.915 vs. 0.919 vs. 0.898 vs. 0.909 and 0.976 vs. 0.976 vs. 0.985 vs. 0.987, respectively). These radiomic models had higher specificity than Lung-RADS (all correct P < 0.05), while there were no significant differences of sensitivity between Lung-RADS and radiomic models.

Conclusions: The diagnostic performance of LDCT-based radiomic models to differentiate adenocarcinomas from benign lesions in solid pulmonary nodules were equivalent to that of standard-dose CT. The LDCT-based radiomic model with higher specificity and lower false-positive rate than Lung-RADS might help reduce overdiagnosis and overtreatment of solid pulmonary nodules in lung cancer screening.

Keywords: benign lesion; low-dose computed tomography; lung adenocarcinoma; lung cancer screening; radiomics; solid pulmonary nodule.

Figure 1

Representative segmentation results and texture feature maps of nodules. (A, C) A 43-year-old female with a granuloma, (B, D) A 75-year-old male with an adenocarcinoma. From left to right: (A, B) segmentation in low-dose CT, neighboring gray tone difference matrix, gray-level run length matrix, gray-level cooccurrence matrix. (C, D) segmentation in standard-dose CT, neighboring gray tone difference matrix, gray level size zone matrix.

Figure 2

Developed radiomic nomograms and calibration curves for predicting the probability of adenocarcinoma. (A) Radiomic nomogram of low-dose CT. (B) Radiomic nomogram of standard-dose CT. (C) Calibration curve of radiomic nomogram of low-dose CT. (D) Calibration curve of radiomic nomogram of standard-dose CT. Rad_score, radiomic score.

Figure 3

Receiver operating characteristic (ROC) curves of the radiomic models and Lung CT Screening Reporting and Data System (Lung-RADS) for differentiating adenocarcinomas from benign nodules. (A) Primary cohort. (B) Validation cohort.

Figure 4

Decision curves of the radiomic models and Lung CT Screening Reporting and Data System (Lung-RADS). The decision curves showed that the model of radiomic signature of low-dose CT, radiomic nomogram of low-dose CT, radiomic signature of standard-dose CT, and radiomic nomogram of standard-dose CT added more net benefit than Lung-RADS in differentiating adenocarcinomas from benign nodules within the range of the threshold probability of 0.02 to 0.84, 0.02 to 0.85, 0.02 to 0.74, and 0.02 to 0.79, respectively.