Ability of single anesthesia for combined robotic-assisted bronchoscopy and surgical lobectomy to reduce time between detection and treatment in stage I non-small cell lung cancer.

Background: Background: Early identification, diagnosis, and treatment of lung cancer is associated with improved clinical outcomes. Robotic-assisted bronchoscopy improves the ability to diagnose early stage lung malignancies and, when combined with robotic-assisted lobectomy under single anesthesia, could reduce time from identification to intervention in early stage lung cancer in a select patient population. Methods: Methods: A retrospective case-control single-center study compared patients with radiographic stage I non-small cell carcinoma (NSCCA) undergoing robotic navigational bronchoscopy and surgical resection (N = 22) with historical controls (N = 63). The primary outcome was time from initial radiographic identification of a pulmonary nodule to therapeutic intervention. Secondary outcomes included times between identification to biopsy, biopsy to surgery, and procedural complications. Results: Results: Patients with suspected stage I NSCCA who received single anesthesia for diagnosis and intervention with robotic-assisted bronchoscopy and robotic-assisted lobectomy had shorter times between identification of a pulmonary nodule and intervention compared to controls (65 vs 116 days, P = 0.005). Cases had lower rates of complications (0% vs 5%) and shorter hospitalizations after surgery (3.6 vs 6.2 days, P = 0.017). Conclusion: Conclusion: Our findings support that implementing a multidisciplinary thoracic oncology team and single-anesthesia biopsy-to-surgery approach in management of stage I NSCCA significantly reduced times from identification to intervention, biopsy to intervention, and length of hospital stays in management of lung cancer.

Towards robotic laboratory automation Plug & play: Survey and concept proposal on teaching-free robot integration with the lapp digital twin.

The Laboratory Automation Plug & Play (LAPP) framework is an over-arching reference architecture concept for the integration of robots in life science laboratories. The plug & play nature lies in the fact that manual configuration is not required, including the teaching of the robots. In this paper a digital twin (DT) based concept is proposed that outlines the types of information that must be provided for each relevant component of the system. In particular, for the devices interfacing with the robot, the robot positions must be defined beforehand in a device-attached coordinate system (CS) by the vendor. This CS must be detectable by the vision system of the robot by means of optical markers placed on the front side of the device. With that, the robot is capable of tending the machine by performing the pick-and-place type transportation of standard sample carriers. This basic use case is the primary scope of the LAPP-DT framework. The hardware scope is limited to simple benchtop and mobile manipulators with parallel grippers at this stage. This paper first provides an overview of relevant literature and state-of-the-art solutions, after which it outlines the framework on the conceptual level, followed by the specification of the relevant DT parameters for the robot, for the devices and for the facility. Finally, appropriate technologies and strategies are identified for the implementation.

Towards robotic laboratory automation Plug & Play: The "LAPP" framework.

Increasing the level of automation in pharmaceutical laboratories and production facilities plays a crucial role in delivering medicine to patients. However, the particular requirements of this field make it challenging to adapt cutting-edge technologies present in other industries. This article provides an overview of relevant approaches and how they can be utilized in the pharmaceutical industry, especially in development laboratories. Recent advancements include the application of flexible mobile manipulators capable of handling complex tasks. However, integrating devices from many different vendors into an end-to-end automation system is complicated due to the diversity of interfaces. Therefore, various approaches for standardization are considered in this article, and a concept is proposed for taking them a step further. This concept enables a mobile manipulator with a vision system to "learn" the pose of each device and - utilizing a barcode - fetch interface information from a universal cloud database. This information includes control and communication protocol definitions and a representation of robot actions needed to operate the device. In order to define the movements in relation to the device, devices have to feature - besides the barcode - a fiducial marker as standard. The concept will be elaborated following appropriate research activities in follow-up papers.

The Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview.

The purpose of this manuscript is to provide an overview of the technical specifications and architecture of the Cancer imaging Phenomics Toolkit (CaPTk www.cbica.upenn.edu/captk), a cross-platform, open-source, easy-to-use, and extensible software platform for analyzing 2D and 3D images, currently focusing on radiographic scans of brain, breast, and lung cancer. The primary aim of this platform is to enable swift and efficient translation of cutting-edge academic research into clinically useful tools relating to clinical quantification, analysis, predictive modeling, decision-making, and reporting workflow. CaPTk builds upon established open-source software toolkits, such as the Insight Toolkit (ITK) and OpenCV, to bring together advanced computational functionality. This functionality describes specialized, as well as general-purpose, image analysis algorithms developed during active multi-disciplinary collaborative research studies to address real clinical requirements. The target audience of CaPTk consists of both computational scientists and clinical experts. For the former it provides i) an efficient image viewer offering the ability of integrating new algorithms, and ii) a library of readily-available clinically-relevant algorithms, allowing batch-processing of multiple subjects. For the latter it facilitates the use of complex algorithms for clinically-relevant studies through a user-friendly interface, eliminating the prerequisite of a substantial computational background. CaPTk's long-term goal is to provide widely-used technology to make use of advanced quantitative imaging analytics in cancer prediction, diagnosis and prognosis, leading toward a better understanding of the biological mechanisms of cancer development.

Imaging dose assessment for IGRT in particle beam therapy.

INTRODUCTION: Image-guided advanced photon and particle beam treatments are promising options for improving lung treatments. Extensive use of imaging increases the overall patient dose. The aim of this study was to determine the imaging dose for different IGRT solutions used in photon and particle beam therapy. MATERIAL AND METHODS: Measurements were performed in an Alderson phantom with TLDs. Clinically applied protocols for orthogonal planar kV imaging, stereoscopic imaging, CT scout views, fluoroscopy, CT, 4D-CT and CBCT were investigated at five ion beam centers and one conventional radiotherapy department. The overall imaging dose was determined for a patient undergoing a lung tumor irradiation with institute specific protocols. RESULTS: OAR doses depended on imaging modality and OAR position. Dose values were in the order of 1 mGy for planar and stereoscopic imaging and 10-50 mGy for volumetric imaging, except for one CBCT device leading to lower doses. The highest dose per exam (up to 150 mGy to the skin) was recorded for a 3-min fluoroscopy. DISCUSSION: Modalities like planar kV or stereoscopic imaging result in very low doses (≈ 1 mGy) to the patient. Imaging a moving target during irradiation, low-dose protocols and protocol optimization can reduce the imaging dose to the patient substantially.

Simulating greenhouse gas budgets of four California cropping systems under conventional and alternative management.

Despite the importance of agriculture in California's Central Valley, the potential of alternative management practices to reduce soil greenhouse gas (GHG) emissions has been poorly studied in California. This study aims at (1) calibrating and validating DAYCENT, an ecosystem model, for conventional and alternative cropping systems in California's Central Valley, (2) estimating CO2, N2O, and CH4 soil fluxes from these systems, and (3) quantifying the uncertainty around model predictions induced by variability in the input data. The alternative practices considered were cover cropping, organic practices, and conservation tillage. These practices were compared with conventional agricultural management. The crops considered were beans, corn, cotton, safflower, sunflower, tomato, and wheat. Four field sites, for which at least five years of measured data were available, were used to calibrate and validate the DAYCENT model. The model was able to predict 86-94% of the measured variation in crop yields and 69-87% of the measured variation in soil organic carbon (SOC) contents. A Monte Carlo analysis showed that the predicted variability of SOC contents, crop yields, and N2O fluxes was generally smaller than the measured variability of these parameters, in particular for N2O fluxes. Conservation tillage had the smallest potential to reduce GHG emissions among the alternative practices evaluated, with a significant reduction of the net soil GHG fluxes in two of the three sites of 336 +/- 47 and 550 +/- 123 kg CO2-eq x ha(-1) x yr(-1) (mean +/- SE). Cover cropping had a larger potential, with net soil GHG flux reductions of 752 +/- 10, 1072 +/- 272, and 2201 +/- 82 kg CO2-eq x ha(-1) x yr(-1). Organic practices had the greatest potential for soil GHG flux reduction, with 4577 +/- 272 kg CO2-eq x ha(-1) x yr(-1). Annual differences in weather or management conditions contributed more to the variance in annual GHG emissions than soil variability did. We concluded that the DAYCENT model was successful at predicting GHG emissions of different alternative management systems in California, but that a sound error analysis must accompany the predictions to understand the risks and potentials of GHG mitigation through adoption of alternative practices.