Automatic lesion detection for narrow-band imaging bronchoscopy.

Purpose: Early detection of cancer is crucial for lung cancer patients, as it determines disease prognosis. Lung cancer typically starts as bronchial lesions along the airway walls. Recent research has indicated that narrow-band imaging (NBI) bronchoscopy enables more effective bronchial lesion detection than other bronchoscopic modalities. Unfortunately, NBI video can be hard to interpret because physicians currently are forced to perform a time-consuming subjective visual search to detect bronchial lesions in a long airway-exam video. As a result, NBI bronchoscopy is not regularly used in practice. To alleviate this problem, we propose an automatic two-stage real-time method for bronchial lesion detection in NBI video and perform a first-of-its-kind pilot study of the method using NBI airway exam video collected at our institution. Approach: Given a patient's NBI video, the first method stage entails a deep-learning-based object detection network coupled with a multiframe abnormality measure to locate candidate lesions on each video frame. The second method stage then draws upon a Siamese network and a Kalman filter to track candidate lesions over multiple frames to arrive at final lesion decisions. Results: Tests drawing on 23 patient NBI airway exam videos indicate that the method can process an incoming video stream at a real-time frame rate, thereby making the method viable for real-time inspection during a live bronchoscopic airway exam. Furthermore, our studies showed a 93% sensitivity and 86% specificity for lesion detection; this compares favorably to a sensitivity and specificity of 80% and 84% achieved over a series of recent pooled clinical studies using the current time-consuming subjective clinical approach. Conclusion: The method shows potential for robust lesion detection in NBI video at a real-time frame rate. Therefore, it could help enable more common use of NBI bronchoscopy for bronchial lesion detection.

Discovery of Nanomolar Inhibitors for Human Dihydroorotate Dehydrogenase Using Structure-Based Drug Discovery Methods.

We used a structure-based drug discovery approach to identify novel inhibitors of human dihydroorotate dehydrogenase (DHODH), which is a therapeutic target for treating cancer and autoimmune and inflammatory diseases. In the case of acute myeloid leukemia, no previously discovered DHODH inhibitors have yet succeeded in this clinical application. Thus, there remains a strong need for new inhibitors that could be used as alternatives to the current standard-of-care. Our goal was to identify novel inhibitors of DHODH. We implemented prefiltering steps to omit PAINS and Lipinski violators at the earliest stages of this project. This enriched compounds in the data set that had a higher potential of favorable oral druggability. Guided by Glide SP docking scores, we found 20 structurally unique compounds from the ChemBridge EXPRESS-pick library that inhibited DHODH with IC50, DHODH values between 91 nM and 2.7 µM. Ten of these compounds reduced MOLM-13 cell viability with IC50, MOLM-13 values between 2.3 and 50.6 µM. Compound 16 (IC50, DHODH = 91 nM) inhibited DHODH more potently than the known DHODH inhibitor, teriflunomide (IC50, DHODH = 130 nM), during biochemical characterizations and presented a promising scaffold for future hit-to-lead optimization efforts. Compound 17 (IC50, MOLM-13 = 2.3 µM) was most successful at reducing survival in MOLM-13 cell lines compared with our other hits. The discovered compounds represent excellent starting points for the development and optimization of novel DHODH inhibitors.

Polymer Complex Fiber: Property, Functionality, and Applications.

Polymer complex fibers (PCFs) are a novel kind of fiber material processed from polymer complexes that are assembled through noncovalent interactions. These can realize the synergy of functional components and miscibility on the molecular level. The dynamic character of noncovalent interactions endows PCFs with remarkable properties, such as reversibility, stimuli responsiveness, self-healing, and recyclability, enabling them to be applied in multidisciplinary fields. The objective of this article is to provide a review of recent progress in the field of PCFs. The classification based on chain interactions will be first introduced followed by highlights of the fabrication technologies and properties of PCFs. The effects of composition and preparation method on fiber properties are also discussed, with some emphasis on utilizing these for rational design. Finally, we carefully summarize recent advanced applications of PCFs in the fields of energy storage and sensors, water treatment, biomedical materials, artificial actuators, and biomimetic platforms. This review is expected to deepen the comprehension of PCF materials and open new avenues for developing PCFs with tailor-made properties for advanced application.

Endobronchial Ultrasound Image Simulation for Image-Guided Bronchoscopy.

BACKGROUND/OBJECTIVE: Accurate disease diagnosis and staging are essential for patients suspected of having lung cancer. The state-of-the-art minimally invasive tools used by physicians to perform these operations are bronchoscopy, for navigating the lung airways, and endobronchial ultrasound (EBUS), for localizing suspect extraluminal cancer lesions. While new image-guided systems enable accurate bronchoscope navigation close to a lesion, no means exists for guiding the final EBUS localization of an extraluminal lesion. We propose an EBUS simulation method to assist with EBUS localization. METHODS: The method draws on a patient's chest computed-tomography (CT) scan to model the ultrasound signal propagation through the tissue media. The method, which is suitable for simulating EBUS images for both radial-probe and convex-probe EBUS devices, entails three steps: 1) image preprocessing, which generates a 2D CT equivalent of the EBUS scan plane; 2) EBUS scan-line computation, which models ultrasound transmission to map the CT plane into a preliminary simulated EBUS image; and 3) image post-processing, which increases realism by introducing simulated EBUS imaging effects and artifacts. RESULTS: Results show that the method produces simulated EBUS images that strongly resemble images generated live by a real device and compares favorably to an existing ultrasound simulation method. It also produces images at a rate greater than real time (i.e., 53 frames/sec). We also demonstrate a successful integration of the method into an image-guided EBUS bronchoscopy system. CONCLUSION/SIGNIFICANCE: The method is effective and practical for procedure planning/preview and follow-on live guidance of EBUS bronchoscopy.

Evaluation of a Telehealth Training Package to Remotely Teach Caregivers to Conduct Discrete-Trial Instruction.

The present study evaluated the effectiveness of using telehealth technologies to remotely train caregivers of children with ASD to conduct discrete-trial instruction (DTI). We used a multiple-baseline-across-participants design to evaluate caregiver correct implementation of the DTI procedures and child emission of independent correct tacts as dependent measures. We observed robust and immediate improvements for all three caregivers and two of three children. Treatment effects were maintained during follow-up and generalization probes. We discuss the benefits of telehealth technologies and other remote treatment applications.

Efficient procedure planning for comprehensive lymph node staging bronchoscopy.

Purpose: For a patient at risk of having lung cancer, accurate disease staging is vital as it dictates disease prognosis and treatment. Accurate staging requires a comprehensive sampling of lymph nodes within the chest via bronchoscopy. Unfortunately, physicians are generally unable to plan and perform sufficiently comprehensive procedures to ensure accurate disease staging. We propose a method for planning comprehensive lymph node staging procedures. Approach: Drawing on a patient's chest CT scan, the method derives a multi-destination tour for efficient navigation to a set of lymph nodes. We formulate the planning task as a traveling salesman problem. To solve the problem, we apply the concept of ant colony optimization (ACO) to derive an efficient airway tour connecting the target nodes. The method has three main steps: (1) CT preprocessing, to define important chest anatomy; (2) graph and staging zone construction, to set up the necessary data structures and clinical constraints; and (3) tour computation, to derive the staging plan. The plan conforms to the world standard International Association for the Study of Lung Cancer (IASLC) lymph node map and recommended clinical staging guidelines. Results: Tests with a patient database indicate that the method derives optimal or near-optimal tours in under a few seconds, regardless of the number of target lymph nodes (mean tour length = 1.4% longer than the optimum). A brute force optimal search, on the other hand, generally cannot reach a solution in under 10 min. for patients exhibiting > 16 nodes, and other methods provide poor solutions. We also demonstrate the method's utility in an image-guided bronchoscopy system. Conclusions: The method provides an efficient computational approach for planning a comprehensive lymph node staging bronchoscopy. In addition, the method shows promise for driving an image-guided bronchoscopy system or robotics-assisted bronchoscopy system tailored to lymph node staging.

Multimodal Registration for Image-Guided EBUS Bronchoscopy.

The state-of-the-art procedure for examining the lymph nodes in a lung cancer patient involves using an endobronchial ultrasound (EBUS) bronchoscope. The EBUS bronchoscope integrates two modalities into one device: (1) videobronchoscopy, which gives video images of the airway walls; and (2) convex-probe EBUS, which gives 2D fan-shaped views of extraluminal structures situated outside the airways. During the procedure, the physician first employs videobronchoscopy to navigate the device through the airways. Next, upon reaching a given node's approximate vicinity, the physician probes the airway walls using EBUS to localize the node. Due to the fact that lymph nodes lie beyond the airways, EBUS is essential for confirming a node's location. Unfortunately, it is well-documented that EBUS is difficult to use. In addition, while new image-guided bronchoscopy systems provide effective guidance for videobronchoscopic navigation, they offer no assistance for guiding EBUS localization. We propose a method for registering a patient's chest CT scan to live surgical EBUS views, thereby facilitating accurate image-guided EBUS bronchoscopy. The method entails an optimization process that registers CT-based virtual EBUS views to live EBUS probe views. Results using lung cancer patient data show that the method correctly registered 28/28 (100%) lymph nodes scanned by EBUS, with a mean registration time of 3.4 s. In addition, the mean position and direction errors of registered sites were 2.2 mm and 11.8∘, respectively. In addition, sensitivity studies show the method's robustness to parameter variations. Lastly, we demonstrate the method's use in an image-guided system designed for guiding both phases of EBUS bronchoscopy.

Measuring Stakeholder Assessments of Postsurgical Facial Scars: A Retrospective Cohort Inter-rater Analysis of Patients, Physicians, and Medical Student Observers.

BACKGROUND: The relationship of postoperative facial scar assessments among patients, physicians, and societal onlookers is not clearly defined. OBJECTIVE: To identify differences in perceived scar outcomes by different stakeholders. METHODS AND MATERIALS: Retrospective cohort study at a single Mohs micrographic surgery (MMS) center during which scars were assessed by: patients, physicians, and medical student observers not involved in patients' care using the Patient and Observer Scar Assessment Scale (v.2). Eighty-one patients graded their scars at 2 visits: 1 to 2 weeks post-MMS and 3 months post-MMS. Deidentified patient photographs were taken at each visit and graded by 4 physicians and 12 observers. RESULTS: At week 1, there was a significant difference in overall opinion of scar appearance between patient and physicians (p = .001) and medical student observers and physicians (p < .001). Physicians graded scars more favorably. At 3 months, there remained a difference in scar evaluations between patient and physicians (p = .005), whereas medical student observers rated scars more similarly to physicians (p = .404). CONCLUSION: Postoperative scar perceptions differ among stakeholders. Physicians must be mindful of this disparity when counseling patients in the perioperative setting to align patient expectations with realistic scar outcomes.

Image-guided EBUS bronchoscopy system for lung-cancer staging.

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.

Autofluorescence Bronchoscopy Video Analysis for Lesion Frame Detection.

Because of the significance of bronchial lesions as indicators of early lung cancer and squamous cell carcinoma, a critical need exists for early detection of bronchial lesions. Autofluorescence bronchoscopy (AFB) is a primary modality used for bronchial lesion detection, as it shows high sensitivity to suspicious lesions. The physician, however, must interactively browse a long video stream to locate lesions, making the search exceedingly tedious and error prone. Unfortunately, limited research has explored the use of automated AFB video analysis for efficient lesion detection. We propose a robust automatic AFB analysis approach that distinguishes informative and uninformative AFB video frames in a video. In addition, for the informative frames, we determine the frames containing potential lesions and delineate candidate lesion regions. Our approach draws upon a combination of computer-based image analysis, machine learning, and deep learning. Thus, the analysis of an AFB video stream becomes more tractable. Using patient AFB video, 99.5%/90.2% of test frames were correctly labeled as informative/uninformative by our method versus 99.2%/47.6% by ResNet. In addition, ≥97% of lesion frames were correctly identified, with false positive and false negative rates ≤3%.Clinical relevance-The method makes AFB-based bronchial lesion analysis more efficient, thereby helping to advance the goal of better early lung cancer detection.

Optimal route planning for image-guided EBUS bronchoscopy.

The staging of the central-chest lymph nodes is a major lung-cancer management procedure. To perform a staging procedure, the physician first uses a patient's 3D X-ray computed-tomography (CT) chest scan to interactively plan airway routes leading to selected target lymph nodes. Next, using an integrated EBUS bronchoscope (EBUS = endobronchial ultrasound), the physician uses videobronchoscopy to navigate through the airways toward a target node's general vicinity and then invokes EBUS to localize the node for biopsy. Unfortunately, during the procedure, the physician has difficulty in translating the preplanned airway routes into safe, effective biopsy sites. We propose an automatic route-planning method for EBUS bronchoscopy that gives optimal localization of safe, effective nodal biopsy sites. To run the method, a 3D chest model is first computed from a patient's chest CT scan. Next, an optimization method derives feasible airway routes that enables maximal tissue sampling of target lymph nodes while safely avoiding major blood vessels. In a lung-cancer patient study entailing 31 nodes (long axis range: [9.0 mm, 44.5 mm]), 25/31 nodes yielded safe airway routes having an optimal tissue sample size = 8.4 mm (range: [1.0 mm, 18.6 mm]) and sample adequacy = 0.42 (range: [0.05, 0.93]). Quantitative results indicate that the method potentially enables successful biopsies in essentially 100% of selected lymph nodes versus the 70-94% success rate of other approaches. The method also potentially facilitates adequate tissue biopsies for nearly 100% of selected nodes, as opposed to the 55-77% tissue adequacy rates of standard methods. The remaining nodes did not yield a safe route within the preset safety-margin constraints, with 3 nodes never yielding a route even under the most lenient safety-margin conditions. Thus, the method not only helps determine effective airway routes and expected sample quality for nodal biopsy, but it also helps point out situations where biopsy may not be advisable. We also demonstrate the methodology in an image-guided EBUS bronchoscopy system, used successfully in live lung-cancer patient studies. During a live procedure, the method provides dynamic real-time sample size visualization in an enhanced virtual bronchoscopy viewer. In this way, the physician vividly sees the most promising biopsy sites along the airway walls as the bronchoscope moves through the airways.

An Evaluation of Real-Time Feedback Delivered Via Telehealth: Training Staff to Conduct Preference Assessments.

Effective, efficient, and accessible staff-training procedures are needed to meet the service-delivery demand for treating individuals diagnosed with autism spectrum disorder. The present study evaluated the effectiveness of delivering real-time feedback via telehealth to train staff to conduct a preference assessment. A nonconcurrent multiple-baseline across-participants design showed that remote real-time feedback was associated with short training time and minimal sessions to achieve mastery. Generalization and maintenance probes indicated these skills were transferable to other preference assessment stimuli and learners.

Efficient Bronchoscopic Video Summarization.

Bronchoscopy enables many minimally invasive chest procedures for diseases such as lung cancer and asthma. Guided by the bronchoscope's video stream, a physician can navigate the complex three-dimensional (3-D) airway tree to collect tissue samples or administer a disease treatment. Unfortunately, physicians currently discard procedural video because of the overwhelming amount of data generated. Hence, they must rely on memory and anecdotal snapshots to document a procedure. We propose a robust automatic method for summarizing an endobronchial video stream. Inspired by the multimedia concept of the video summary and by research in other endoscopy domains, our method consists of three main steps: 1) shot segmentation, 2) motion analysis, and 3) keyframe selection. Overall, the method derives a true hierarchical decomposition, consisting of a shot set and constituent keyframe set, for a given procedural video. No other method to our knowledge gives such a structured summary for the raw, unscripted, unedited videos arising in endoscopy. Results show that our method more efficiently covers the observed endobronchial regions than other keyframe-selection approaches and is robust to parameter variations. Over a wide range of video sequences, our method required on average only 6.5% of available video frames to achieve a video coverage = 92.7%. We also demonstrate how the derived video summary facilitates direct fusion with a patient's 3-D chest computed-tomography scan in a system under development, thereby enabling efficient video browsing and retrieval through the complex airway tree.

Expanding Access to Clinical Services for Toddlers with Autism Spectrum Disorders.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder typically diagnosed in the toddler and preschool years. Intensive early intervention (EI) using applied behavior analytic procedures is the evidenced-based intervention most effective in improving developmental outcomes. Unfortunately, there are numerous barriers to accessing EI services for toddlers with ASD. This article addresses (a) the process of developing an EI program using primarily applied behavior analytic services with multidisciplinary health care providers, (b) a description of the service delivery provided, (c) educational and training programs to increase qualified staff, and (d) advocacy efforts to improve community capacity. The EI program has sustained growth, improved child developmental outcomes, served as a training ground for EI providers, and yielded high parent satisfaction ratings. Suggestions for continued advocacy, education, research, and policy development related to the lack of access to EI for children with ASD is offered for pediatric health care providers.

Evaluation of a telehealth training package to remotely train staff to conduct a preference assessment.

Recent advancements in telecommunication technologies make it possible to conduct a variety of healthcare services remotely (e.g., behavioral-analytic intervention services), thereby bridging the gap between qualified providers and consumers in isolated locations. In this study, web-based telehealth technologies were used to remotely train direct-care staff to conduct a multiple-stimulus-without-replacement preference assessment. The training package included three components: (a) a multimedia presentation; (b) descriptive feedback from previously recorded baseline sessions; and (c) scripted role-play with immediate feedback. A nonconcurrent, multiple-baseline-across-participants design was used to demonstrate experimental control. Training resulted in robust and immediate improvements, and these effects maintained during 1- to 2-month follow-up observations. In addition, participants expressed high satisfaction with the web-based materials and the overall remote-training experience.

The Munroe-Meyer Approach: Continuous Integration of Didactic Instruction, Research, and Clinical Practice.

Increased demand for applied behavior analysis (ABA) services has increased the need for additional masters-level practitioners and doctoral-level academicians and clinical directors. Based on these needs, the University of Nebraska Medical Center's (UNMC) Munroe-Meyer Institute has developed a PhD program. The academic structure at UNMC allowed us to create our PhD program in a relatively quick and efficient manner. Our PhD program has many unique features, including (a) close integration of didactic instruction with clinical and research training provided by leading experts in ABA in which students immediately apply concepts introduced in the classroom during coordinated clinical and research practica; (b) structured grant writing training in which students learn to write and submit an NIH-level grant;

Psychometric Properties of Disaster Event Reaction Items From the Crisis Counseling Individual/Family Encounter Log.

OBJECTIVE: The purpose of this article was to examine the psychometric properties of the Crisis Counseling Assistance and Training Program (CCP) data collection instrument, the Individual/Family Encounter Log (IFEL). Data collected from disaster survivors included how they reacted to events in emotional, behavioral, physical, and cognitive domains. These domains are based on conceptual categorization of event reactions and allow CCP staff to provide survivors with referrals to appropriate behavioral health support resources, if warranted. METHODS: This study explored the factor structure of these survey items to determine how best to use the available information as a screen of disaster-related behavioral health indicators. Specifically, our first research question explored and confirmed the optimal factor structure of the event reaction items, and our second question examined whether the new factor structure was similar across disaster types: hurricanes, tornadoes, floods, and wildfires. Using a factor analytic technique, we tested whether our event reaction outcomes achieved consistent and reliable measurement across different disaster situations. Finally, we assessed how the new subscales were correlated with the type of risk to which CCP disaster survivors were exposed. RESULTS: Our analyses revealed 3 factors: (1) depressive-like, (2) anxiety-like, and (3) somatic. In addition, we found that these factors were coherent for hurricanes, floods, and wildfires, although the basic factor structure was not equivalent for tornadoes. CONCLUSION: Implications for use of the IFEL in disaster preparedness, response, and recovery are discussed. (Disaster Med Public Health Preparedness. 2016;10:822-831).

Methods for 2-D and 3-D Endobronchial Ultrasound Image Segmentation.

Endobronchial ultrasound (EBUS) is now commonly used for cancer-staging bronchoscopy. Unfortunately, EBUS is challenging to use and interpreting EBUS video sequences is difficult. Other ultrasound imaging domains, hampered by related difficulties, have benefited from computer-based image-segmentation methods. Yet, so far, no such methods have been proposed for EBUS. We propose image-segmentation methods for 2-D EBUS frames and 3-D EBUS sequences. Our 2-D method adapts the fast-marching level-set process, anisotropic diffusion, and region growing to the problem of segmenting 2-D EBUS frames. Our 3-D method builds upon the 2-D method while also incorporating the geodesic level-set process for segmenting EBUS sequences. Tests with lung-cancer patient data showed that the methods ran fully automatically for nearly 80% of test cases. For the remaining cases, the only user-interaction required was the selection of a seed point. When compared to ground-truth segmentations, the 2-D method achieved an overall Dice index = 90.0% ±4.9%, while the 3-D method achieved an overall Dice index = 83.9 ± 6.0%. In addition, the computation time (2-D, 0.070 s/frame; 3-D, 0.088 s/frame) was two orders of magnitude faster than interactive contour definition. Finally, we demonstrate the potential of the methods for EBUS localization in a multimodal image-guided bronchoscopy system.

Thoracic cavity definition for 3D PET/CT analysis and visualization.

X-ray computed tomography (CT) and positron emission tomography (PET) serve as the standard imaging modalities for lung-cancer management. CT gives anatomical details on diagnostic regions of interest (ROIs), while PET gives highly specific functional information. During the lung-cancer management process, a patient receives a co-registered whole-body PET/CT scan pair and a dedicated high-resolution chest CT scan. With these data, multimodal PET/CT ROI information can be gleaned to facilitate disease management. Effective image segmentation of the thoracic cavity, however, is needed to focus attention on the central chest. We present an automatic method for thoracic cavity segmentation from 3D CT scans. We then demonstrate how the method facilitates 3D ROI localization and visualization in patient multimodal imaging studies. Our segmentation method draws upon digital topological and morphological operations, active-contour analysis, and key organ landmarks. Using a large patient database, the method showed high agreement to ground-truth regions, with a mean coverage=99.2% and leakage=0.52%. Furthermore, it enabled extremely fast computation. For PET/CT lesion analysis, the segmentation method reduced ROI search space by 97.7% for a whole-body scan, or nearly 3 times greater than that achieved by a lung mask. Despite this reduction, we achieved 100% true-positive ROI detection, while also reducing the false-positive (FP) detection rate by >5 times over that achieved with a lung mask. Finally, the method greatly improved PET/CT visualization by eliminating false PET-avid obscurations arising from the heart, bones, and liver. In particular, PET MIP views and fused PET/CT renderings depicted unprecedented clarity of the lesions and neighboring anatomical structures truly relevant to lung-cancer assessment.