RESUMO
BACKGROUND: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is considered a promising method in lung lesion assessment. METHODS: Sixty-four patients with single pulmonary lesions (SPLs) received DCE-MRI at 3.0 T. Of them, 49 cases were diagnosed with lung cancer, and 15 with benign pulmonary nodules (8 inflammatory nodules, 5 tuberculosis, and 2 abscesses). SPLs were quantitatively analyzed to determine the pulmonary lesions-related perfusion parameters, including reflux constant (Kep), volume transfer constant (Ktrans), the maximum slope of increase (MaxSlope), extravascular extracellular space volume fraction (Ve), apparent diffusion coefficient (ADC), the initial area in the signal intensity-time curve (IAUGC), and contrast-enhancement ratio (CER). In addition, a Student's t-test was conducted to calculate statistical significance regarding the quantitatively analyzed perfusion parameters in benign SPLs compared to malignant SPLs. The area under (AUC) the receiver operating characteristic (ROC) curve was studied to investigate the performance of perfusion parameters in diagnosing lung cancer. RESULTS: Values of Ktrans, Kep, Ve, MaxSlope, and IAUGC increased within malignant nodules relative to benign nodules (Ktrans: 0.21 ±0.08 vs. 0.73 ±0.40, P = 0.0001; Kep: 1.21 ±0.66 vs. 1.83 ±0.90, P = 0.0163; Ve: 0.24 ±0.08 vs. 0.47 ±0.18, P < 0.0001; MaxSlope: 0.09 ±0.14 vs. 0.28 ±0.29, P = 0.0166; IAUGC: 0.18 ±0.09 vs. 0.55 ±0.34, P = 0.0001). Meanwhile, malignant nodules presented higher ADC than benign nodules (0.0016 ±0.0006 vs. 0.0012 ±0.0003, P = 0.0019). Ktrans and IAUGC showed the best diagnostic performance with AUCs [1.0, 95%CI (0.99-1.0); 0.93, 95%CI(0.85-1.0), respectively]. CONCLUSION: Malignant pulmonary lesions had higher values of Ktrans, Ve, Kep, MaxSlope, and IAUGC compared to benign pulmonary lesions. Overall, perfusion parameters of DCE-MRI facilitate discrimination between benign from malignant pulmonary nodules.
RESUMO
Based on 18 hospitals including the Chinese People's Liberation Army General Hospital and Peking University People's Hospital, and based on the "Specifications for Perioperative Data", explore the construction and application of perioperative multi-center data centers in the era of medical big data. The use of data ferry technology avoids hidden safety hazards in hospitals, realizes the integration and sharing of perioperative medical data of various medical institutions, and forms a complete data chain combining patient medical data and follow-up data.
