Marker Games: Seeking Better Biomarkers?

Electromagnetic Navigation Bronchoscopy Combined Endobronchial Ultrasound in the Diagnosis of Lung Nodules

ABSTRACT

Electromagnetic navigational bronchoscopy (ENB) combined with a radial endobronchial ultrasound probe realizes a combination of magnetic navigation and ultrasound imaging, allowing for the accurate navigation of peripheral lung lesions in real time during surgery. ENB has been evaluated in many studies. However, a comparative report on the feasibility of ENB combined radial endobronchial ultrasound diagnosis in different density lung nodules was small, and few of these studies have reported long-term follow-up results to exclude false negative results. The aim of this study is to explore the applicability of ENB combined radial endobronchial ultrasound in the diagnosis of lung nodules with different densities.Patients underwent biopsy in our medical center from 2016-09 to 2019-03 were divided into 2 groups: the solid nodule group and the subsolid pulmonary nodule group. We collected and analyzed the diagnostic accuracy, the diagnostic yield, the false negative rate and the incidence of complications between these 2 groups.A total of 37 lesions in 25 patients were biopsied, 14 lesions were subsolid pulmonary nodules and 23 were solid nodules. The diagnostic accuracy (success rate to obtain meaningful pathology tissues) was 34/37 (91.8%). Lost to follow-up in 1 case and three cases were undiagnosed. After at least 12 months of follow-up, the total diagnostic yield (true positive rate+ true negative rate) was 27/36 (75%) (P = .006). The false negative rate was 9/19 (47.3%) (P = .26). Complications occurred in 1/36 (2.7%) lesions. For the subsolid pulmonary nodule group, the diagnostic accuracy was 13/14 (92.8%) and the diagnostic yield was 7/14 (50%). For the solid nodule group, the diagnostic accuracy was 21/23 (91.3%), and the diagnostic yield was 20/22 (90.9%).Electromagnetic navigational bronchoscopy combined with radial endobronchial ultrasound in peripheral lung nodule biopsies is safe and effective, especially for solid nodules, but the diagnostic yield in subsolid nodule biopsies remains to be improved.

Phase portrait for high fidelity feature extraction and classification: A surrogate approach

This paper proposes a novel surrogate method of classification of breath sound signals for auscultation through the principal component analysis (PCA), extracting the features of a phase portrait. The nonlinear parameters of the phase portrait like the Lyapunov exponent, the sample entropy, the fractal dimension, and the Hurst exponent help in understanding the degree of complexity arising due to the turbulence of air molecules in the airways of the lungs. Thirty-nine breath sound signals of bronchial breath (BB) and pleural rub (PR) are studied through spectral, fractal, and phase portrait analyses. The fast Fourier transform and wavelet analyses show a lesser number of high-intense, low-frequency components in PR, unlike BB. The fractal dimension and sample entropy values for PR are, respectively, 1.772 and 1.041, while those for BB are 1.801 and 1.331, respectively. This study reveals that the BB signal is more complex and random, as evidenced by the fractal dimension and sample entropy values. The signals are classified by PCA based on the features extracted from the power spectral density (PSD) data and the features of the phase portrait. The PCA based on the features of the phase portrait considers the temporal correlation of the signal amplitudes and that based on the PSD data considers only the signal amplitudes, suggesting that the former method is better than the latter as it reflects the multidimensional aspects of the signal. This appears in the PCA-based classification as 89.6% for BB, a higher variance than the 80.5% for the PR signal, suggesting the higher fidelity of the phase portrait-based classification.