Objective: The study objective was to investigate if machine learning algorithms can predict whether a lung nodule is benign, adenocarcinoma, or its preinvasive subtype from computed tomography images alone.Methods: A dataset of chest computed tomography scans containing lung nodules was collected with their pathologic diagnosis from several sources. The dataset was split randomly into training (70%), internal validation (15%), and independent test sets (15%) at the patient level. Two machine learning algorithms were developed, trained, and validated. The first algorithm used the support vector machine model, and the second used deep learning technology: a convolutional neural network. Receiver operating characteristic analysis was used to evaluate the performance of the classification on the test dataset.

Purpose To develop and validate a computer tool for automatic and simultaneous segmentation of five body tissues depicted on computed tomography (CT) scans: visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), intermuscular adipose tissue (IMAT), skeletal muscle (SM), and bone. Methods A cohort of 100 CT scans acquired on different subjects were collected from The Cancer Imaging Archive—50 whole‐body positron emission tomography‐CTs, 25 chest, and 25 abdominal. Five different body tissues (i.e., VAT, SAT, IMAT, SM, and bone) were manually annotated. A training‐while‐annotating strategy was used to improve the annotation efficiency. The 10‐fold cross‐validation method was used to develop and validate the performance of several convolutional neural networks (CNNs), including UNet, Recurrent Residual UNet (R2Unet), and UNet++. A grid‐based three‐dimensional patch sampling operation was used to train the CNN models. The CNN models were also trained and tested separately for each body tissue to see if they could achieve a better performance than segmenting them jointly. The paired sample t‐test was used to statistically assess the performance differences among the involved CNN models Results When segmenting the five body tissues simultaneously, the Dice coefficients ranged from 0.826 to 0.840 for VAT, from 0.901 to 0.908 for SAT, from 0.574 to 0.611 for IMAT, from 0.874 to 0.889 for SM, and from 0.870 to 0.884 for bone, which were significantly higher than the Dice coefficients when segmenting the body tissues separately (p < 0.05), namely, from 0.744 to 0.819 for VAT, from 0.856 to 0.896 for SAT, from 0.433 to 0.590 for IMAT, from 0.838 to 0.871 for SM, and from 0.803 to 0.870 for bone. Conclusion There were no significant differences among the CNN models in segmenting body tissues, but jointly segmenting body tissues achieved a better performance than segmenting them separately.

Background Estimating whole‐body composition from limited region‐computed tomography (CT) scans has many potential applications in clinical medicine; however, it is challenging. Purpose To investigate if whole‐body composition based on several tissue types (visceral adipose tissue [VAT], subcutaneous adipose tissue [SAT], intermuscular adipose tissue [IMAT], skeletal muscle [SM], and bone) can be reliably estimated from a chest CT scan only. Methods A cohort of 97 lung cancer subjects who underwent both chest CT scans and whole‐body positron emission tomography‐CT scans at our institution were collected. We used our in‐house software to automatically segment and quantify VAT, SAT, IMAT, SM, and bone on the CT images. The field‐of‐views of the chest CT scans and the whole‐body CT scans were standardized, namely, from vertebra T1 to L1 and from C1 to the bottom of the pelvis, respectively. Multivariate linear regression was used to develop the computer models for estimating the volumes of whole‐body tissues from chest CT scans. Subject demographics (e.g., gender and age) and lung volume were included in the modeling analysis. Ten‐fold cross‐validation was used to validate the performance of the prediction models. Mean absolute difference (MAD) and R‐squared (R2) were used as the performance metrics to assess the model performance. Results The R2 values when estimating volumes of whole‐body SAT, VAT, IMAT, total fat, SM, and bone from the regular chest CT scans were 0.901, 0.929, 0.900, 0.933, 0.928, and 0.918, respectively. The corresponding MADs (percentage difference) were 1.44 ± 1.21 L (12.21% ± 11.70%), 0.63 ± 0.49 L (29.68% ± 61.99%), 0.12 ± 0.09 L (16.20% ± 18.42%), 1.65 ± 1.40 L (10.43% ± 10.79%), 0.71 ± 0.68 L (5.14% ± 4.75%), and 0.17 ± 0.15 L (4.32% ± 3.38%), respectively. Conclusion Our algorithm shows promise in its ability to estimate whole‐body compositions from chest CT scans. Body composition measures based on chest CT scans are more accurate than those based on vertebra third lumbar.

Infections remain a common cause of lung nodules, masses, and cavities. Safe tissue sampling is required to establish a diagnosis, differentiate between malignant and infectious causes, and provide microbiological material for characterization and sensitivity analysis. Tissue samples could be obtained bronchoscopically, percutaneously, or through surgical biopsy. Among these, bronchoscopy is the safest by avoiding the complications of pleural and chest wall puncture including pneumothorax, pain, pleural contamination and empyema, and hemothorax. However, the diagnostic yield with conventional bronchoscopy for small, peripheral lesions is poor. Electromagnetic navigation bronchoscopy (ENB) is a technique where the bronchoscope and working channel are guided through the bronchial tree to accurately reach a peripheral lesion. It dramatically improves on the diagnostic yield of peripheral lesions especially of small lesions, and its role has developed beyond diagnosis to treatment enablement and to direct therapy. Its role in infection is less defined, but it has value especially in the diagnosis of fungal and mycobacterial infections and in cavitating lesions. This review will explore what electromagnetic navigation bronchoscopy is, its use in diagnosis and therapy, and its role in the management of pulmonary infections. The potential for local therapy delivery for infection is also discussed.

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