Spectral CT-based radiomics signature for distinguishing malignant pulmonary nodules from benign

Abstract

Objectives To evaluate the discriminatory capability of spectral CT-based radiomics to distinguish benign from malignant solitary pulmonary solid nodules (SPSNs). Materials and methods A retrospective study was performed including 242 patients with SPSNs who underwent contrast-enhanced dual-layer Spectral Detector CT (SDCT) examination within one month before surgery in our hospital, which were randomly divided into training and testing datasets with a ratio of 7:3. Regions of interest (ROIs) based on 40-65 keV images of arterial phase (AP), venous phases (VP), and 120kVp of SDCT were delineated, and radiomics features were extracted. Then the optimal radiomics-based score in identifying SPSNs was calculated and selected for building radiomics-based model. The conventional model was developed based on significant clinical characteristics and spectral quantitative parameters, subsequently, the integrated model combining radiomics-based model and conventional model was established. The performance of three models was evaluated with discrimination, calibration, and clinical application. Results The 65 keV radiomics-based scores of AP and VP had the optimal performance in distinguishing benign from malignant SPSNs (AUC65keV-AP = 0.92, AUC65keV-VP = 0.88). The diagnostic efficiency of radiomics-based model (AUC = 0.96) based on 65 keV images of AP and VP outperformed conventional model (AUC = 0.86) in the identification of SPSNs, and that of integrated model (AUC = 0.97) was slightly further improved. Evaluation of three models showed the potential for generalizability. Conclusions Among the 40-65 keV radiomics-based scores based on SDCT, 65 keV radiomics-based score had the optimal performance in distinguishing benign from malignant SPSNs. The integrated model combining radiomics-based model based on 65 keV images of AP and VP with Zeff-AP was significantly superior to conventional model in the discrimination of SPSNs.

Dual-layer detector spectral CT-based machine learning models in the differential diagnosis of solitary pulmonary nodules

“…The monoenergetic images with low keV can improve the contrast between iodine-containing pulmonary nodules and surrounding tissues, while CT SPN_40 keV , as the CT values measured by SPN on 40 keV monoenergetic images, can reflect the degree of enhancement of pulmonary nodules under 40 keV monoenergetic images, so it can indirectly reflect the blood supply in the lesions. Many previous studies have shown that the CT values of lesions on 40 keV monoenergetic images can distinguish benign and malignant lesions 16 , 27 , 28 . In a study by Zhang et al to differentiate between solitary lung adenocarcinoma and pulmonary tuberculosis, solitary lung adenocarcinoma had a higher CT 40 keV value in the arterial phase 29 .…”