Optical coherence tomography for identification of malignant pulmonary nodules based on random forest machine learning algorithm.

OBJECTIVE: To explore the feasibility of using random forest (RF) machine learning algorithm in assessing normal and malignant peripheral pulmonary nodules based on in vivo endobronchial optical coherence tomography (EB-OCT). METHODS: A total of 31 patients with pulmonary nodules were admitted to Department of Respiratory Medicine, Zhongda Hospital, Southeast University, and underwent chest CT, EB-OCT and biopsy. Attenuation coefficient and up to 56 different image features were extracted from A-line and B-scan of 1703 EB-OCT images. Attenuation coefficient and 29 image features with significant p-values were used to analyze the differences between normal and malignant samples. A RF classifier was trained using 70% images as training set, while 30% images were included in the testing set. The accuracy of the automated classification was validated by clinically proven pathological results. RESULTS: Attenuation coefficient and 29 image features were found to present different properties with significant p-values between normal and malignant EB-OCT images. The RF algorithm successfully classified the malignant pulmonary nodules with sensitivity, specificity, and accuracy of 90.41%, 77.87% and 83.51% respectively. CONCLUSION: It is clinically practical to distinguish the nature of pulmonary nodules by integrating EB-OCT imaging with automated machine learning algorithm. Diagnosis of malignant pulmonary nodules by analyzing quantitative features from EB-OCT images could be a potentially powerful way for early detection of lung cancer.

Airway resistance variation correlates with prognosis of critically ill COVID-19 patients: A computational fluid dynamics study.

OBJECTIVE: To evaluate the quantitative changes of respiratory functions for critically ill COVID-19 patients with mechanical ventilation, computational fluid dynamics (CFD) analysis was performed based on patient-specific three-dimensional airway geometry. METHODS: 37 cases of critically ill patients with COVID-19 admitted to the ICU of Huangshi Traditional Chinese Medicine Hospital from February 1st to March 20th, 2020 were retrospectively analyzed. 5 patients whose clinical data met the specific criteria were finally cataloged into death group (2 patients) and survival group (3 patients). The patient-specific three-dimensional airways were reconstructed from the central airways down to the 4th-5th bifurcation of the tracheobronchial tree. The volume changes of bronchi were calculated during the disease progression according to the comparison of two CT scans. Additionally, the changes of air flow resistance were analyzed using numerical simulation of CFD. RESULTS: Pearson correlation analysis demonstrated that there was negative correlation between the change of volume (ΔV) and the change of resistance (ΔR) for all COVID-19 patients (r=-0.7025). For total airway volume, an average decrease of -11.41±15.71% was observed in death group compared to an average increase of 1.86±10.80% in survival group (p=0.0232). For air flow through airways in lower lobe, the resistance increases for death group by 10.97±77.66% and decreases for survival group by -45.49±42.04% (p=0.0246). CONCLUSION: The variation of flow resistance in the airway could be used as a non-invasive functional evaluation for the prognosis and outcome of critically ill patients with COVID-19. The 'virtual' pulmonary function test by integrating follow-up CT scans with patient-derived CFD analysis could be a potentially powerful way in improving the efficiency of treatment for critically ill patients with COVID-19.

[Three-dimensional morphological changes in the temporomandibular joints of patients with anterior disc displacement with reduction].

OBJECTIVES: To investigate the differences in the temporomandibular joints (TMJs) between patients with anterior disc displacement with reduction (ADDwR) and asymptomatic subjects by using 3D morphometric measurements. METHODS: A total of 15 patients with ADDwR and 10 asymptomatic subjects were enrolled. Then, 3D models of the maxilla and mandible were reconstructed using MIMICS 20.0. Nine morphologic parameters of TMJs on both sides were measured on the 3D solid model. The differences in the parameters were analyzed between the patients and the asymptomatic subjects and between the left and right sides of each group. RESULTS: The horizontal and coronal condylar angles on the ipsilateral side of the patients were significantly greater than those of the asymptomatic subjects (P<0.01). Meanwhile, the sagittal ramus angle (SRA), medial joint space, lateral joint space, superior joint space, anterior joint space, and posterior joint space in the patients were significantly lower than those in the asymptomatic subjects (P<0.01). CONCLUSIONS: ADDwR will increase the condylar angles to be significantly greater than the normal level and decrease SRA and articular spaces to be significantly smaller than the normal level. The condyles will be displaced upward, closer to the fossa.