Dynamic contrast-enhanced MRI versus ^(18)F-FDG PET/CT: Which is better in differentiation between malignant and benign solitary pulmonary nodules?

Objective： To prospectively compare the discriminative capacity of dynamic contrast enhanced-magnetic resonance imaging（DCE-MRI） with that of^18F-fluorodeoxyglucose（^18F-FDG） positron emission tomography/computed tomography（PET/CT） in the differentiation of malignant and benign solitary pulmonary nodules（SPNs）.Methods： Forty-nine patients with SPNs were included in this prospective study. Thirty-two of the patients had malignant SPNs, while the other 17 had benign SPNs. All these patients underwent DCE-MRI and ^18F-FDG PET/CT examinations. The quantitative MRI pharmacokinetic parameters, including the trans-endothelial transfer constant（K^trans）, redistribution rate constant（Kep）, and fractional volume（Ve）, were calculated using the Extended-Tofts Linear two-compartment model. The ^18F-FDG PET/CT parameter, maximum standardized uptake value（SUV（max））, was also measured. Spearman＇s correlations were calculated between the MRI pharmacokinetic parameters and the SUV（max） of each SPN. These parameters were statistically compared between the malignant and benign nodules. Receiver operating characteristic（ROC） analyses were used to compare the diagnostic capability between the DCE-MRI and ^18F-FDG PET/CT indexes.Results： Positive correlations were found between K^trans and SUV（max）, and between K（ep） and SUV（max）（P〈0.05）.There were significant differences between the malignant and benign nodules in terms of the K^trans, K（ep） and SUV（max） values（P〈0.05）. The areas under the ROC curve（AUC） of K^trans） K（ep） and SUV（max） between the malignant and benign nodules were 0.909, 0.838 and 0.759, respectively. The sensitivity and specificity in differentiating malignant from benign SPNs were 90.6% and 82.4% for K^trans; 87.5% and 76.5% for K（ep）; and 75.0% and 70.6%for SUV（max）, respectively. The sensitivity and specificity of K^trans and K（ep） were higher than those of SUV（max）, but there was no significant difference between them（P〉0.05）.Conclusions： DCE-MRI can be used to differentiate between benign and malignant SPNs and has the advantage of being radiation free.

Survey on deep learning for pulmonary medical imaging

As a promising method in artificial intelligence,deep learning has been proven successful in several domains ranging from acoustics and images to natural language processing.With medical imaging becoming an important part of disease screening and diagnosis,deep learning-based approaches have emerged as powerful techniques in medical image areas.In this process,feature representations are learned directly and automatically from data,leading to remarkable breakthroughs in the medical field.Deep learning has been widely applied in medical imaging for improved image analysis.This paper reviews the major deep learning techniques in this time of rapid evolution and summarizes some of its key contributions and state-of-the-art outcomes.The topics include classification,detection,and segmentation tasks on medical image analysis with respect to pulmonary medical images,datasets,and benchmarks.A comprehensive overview of these methods implemented on various lung diseases consisting of pulmonary nodule diseases,pulmonary embolism,pneumonia,and interstitial lung disease is also provided.Lastly,the application of deep learning techniques to the medical image and an analysis of their future challenges and potential directions are discussed.