The staging of the central-chest lymph nodes is a major step in the management of lung-cancer patients. For this purpose, the physician uses a device that integrates videobronchoscopy and an endobronchial ultrasound (EBUS) probe. To biopsy a lymph node, the physician first uses videobronchoscopy to navigate through the airways and then invokes EBUS to localize and biopsy the node. Unfortunately, this process proves difficult for many physicians, with the choice of biopsy site found by trial and error. We present a complete image-guided EBUS bronchoscopy system tailored to lymph-node staging. The system accepts a patient’s 3D chest CT scan, an optional PET scan, and the EBUS bronchoscope’s video sources as inputs. System workflow follows two phases: (1) procedure planning and (2) image-guided EBUS bronchoscopy. Procedure planning derives airway guidance routes that facilitate optimal EBUS scanning and nodal biopsy. During the live procedure, the system’s graphical display suggests a series of device maneuvers to perform and provides multimodal visual cues for locating suitable biopsy sites. To this end, the system exploits data fusion to drive a multimodal virtual bronchoscope and other visualization tools that lead the physician through the process of device navigation and localization. A retrospective lung-cancer patient study and follow-on prospective patient study, performed within the standard clinical workflow, demonstrate the system’s feasibility and functionality. For the prospective study, 60/60 selected lymph nodes (100%) were correctly localized using the system, and 30/33 biopsied nodes (91%) gave adequate tissue samples. Also, the mean procedure time including all user interactions was 6 min 43 s All of these measures improve upon benchmarks reported for other state-of-the-art systems and current practice. Overall, the system enabled safe, efficient EBUS-based localization and biopsy of lymph nodes.
Conformational flexibility is an underlying cause of error in all comparisons of protein structure. Using flexible representations, some comparison algorithms can identify subtle functional similarities among distantly related proteins even when they exhibit different backbone conformations. The same techniques are not designed to identify subtle variations among closely related proteins that might cause differences in specificity. In such cases, molecular flexibility obscures structural details that influence the specific recognition of similar but non-identical ligands.To enhance the analysis of ligand binding specificity, this paper presents FAVA (Flexible Aggregate Volumetric Analysis), a conformationally robust tool for comparing similar binding cavities with different binding preferences. FAVA examines a large number of conformational samples to characterize local flexibility using Constructive Solid Geometry. Using molecular dynamics simulations as a source for conformational samples, we used FAVA to analyze a nonredundant sample of serine protease and enolase structures. Different snapshots from the same proteins exhibited significant variations in binding cavity shape. Nonetheless, analysis with FAVA revealed subfamilies with different binding preferences. FAVA also identified amino acids associated with differences in binding preferences, predicting established experimental results. These results illustrate a new approach to flexible comparison that uses sampled conformational data. It reveals that detailed comparisons of very similar proteins, such as those within small ligand binding cavities, are possible even in the presence of conformational flexibility. Identifying influences on specificity in this manner points to new applications of protein engineering and drug design. * equal contribution.
Endobronchial ultrasound (EBUS) is now recommended as a standard procedure for in vivo verification of extraluminal diagnostic sites during cancer-staging bronchoscopy. Yet, physicians vary considerably in their skills at using EBUS effectively. Regarding existing bronchoscopy guidance systems, studies have shown their effectiveness in the lung-cancer management process. With such a system, a patient's X-ray computed tomography (CT) scan is used to plan a procedure to regions of interest (ROIs). This plan is then used during follow-on guided bronchoscopy. Recent clinical guidelines for lung cancer, however, also dictate using positron emission tomography (PET) imaging for identifying suspicious ROIs and aiding in the cancer-staging process. While researchers have attempted to use guided bronchoscopy systems in tandem with PET imaging and EBUS, no true EBUS-centric guidance system exists. We now propose a full multimodal image-based methodology for guiding EBUS. The complete methodology involves two components: 1) a procedure planning protocol that gives bronchoscope movements appropriate for live EBUS positioning; and 2) a guidance strategy and associated system graphical user interface (GUI) designed for image-guided EBUS. We present results demonstrating the operation of the system.
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