This publication was supported by the Fifth National Traditional Chinese Medicine Clinical Excellent Talents Research Program organized by the National Administration of Traditional Chinese Medicine (http://www.natcm.gov.cn/renjiaosi/zhengcewenjian/2021-11-04/23082.html).&#160;

NOTE: During the data preprocessing stage, the original DICOM data must be sorted and intercepted to ensure compatibility with various devices and consistent results. Adequate adjustable capacity must be reserved for intensity processing, and a continuous 3D perspective is essential for observation. In this protocol, a methodical description of the research approach is provided, detailing a case involving an 84-year-old female patient presenting with pulmonary nodules. This patient provided informed consent for her diagnosis via digital modeling and authorized the utilization of her data for scientific research purposes. The model reconstruction function is derived from the PulmonaryNodule software tool (see the Table of Materials for details). Ethical clearance was obtained from The Ethics Committee of Dongzhimen Hospital, affiliated to Beijing University of Chinese Medicine (DZMEC-KY-2019.90).
1. Data collection and preparation
<ol>
	<li>LDCT data for the detection of pulmonary nodules 
	NOTE: The differences seen in the parameters values do not depend on the research method used.
	<ol>
		<li>Acquire patient consent for the acquisition of DICOM data. Transfer all data to the designated working directory.</li>
		<li>Identify the data directory with the highest number of scanning layers and the thinnest layer thickness to optimize the accuracy based on the file information. Generally, the more DICOM scan files a patient has, the thinner the scan layer thickness.</li>
		<li>By implementing the Dicominfo function and using the DICOM files as the function parameters, obtain the slice thickness and pixel spacing parameters in the MATLAB environment. These parameters are essential for setting the 3D volume display rate. For the example data utilized in this study, the slice thickness was 1 mm, the pixel spacing was 0.5 mm, and a total of 200 layers were scanned.</li>
	</ol>
	</li>
	<li>Correcting the sorting of the scanned data 
	NOTE: The sequence of every image should be sorted for volume construction.
	<ol>
		<li>Read the location data of every image by using the function Dicominfo. Obtain the location by inputting info.SliceLocation into the MATLAB workspace.</li>
		<li>Implement the SliceLocation function to store the location array for a variable, and make a plot of it (Figure 1).</li>
		<li>By using the Data Tips button at the top-right of the GUI, add a data tip to the plot on the point that represents the maximum location value of the normal sequence (the top location of the patient imaging; Figure 1).</li>
		<li>Sort all the images, and extract the images starting at 1 to the maximum location value by implementing the function VolumeResort.</li>
		<li>Store the volumes of the valid images with the sorted index, which will be useful for tracing back to the important nodules.</li>
	</ol>
	</li>
	<li>Inspecting the thoracic volume 
	NOTE: Having a well-defined data-storing structure makes follow-up work more convenient.
	<ol>
		<li>Implement the VolumeInspect function to show three views of the constructed volume. Drag the crosshair intersection up and down in the coronal axis to quickly browse all the images in the horizontal axis (Figure 2).</li>
		<li>Move the crosshair to the horizontal axis to browse all the images in the coronal axis. The crosshair is in the same spatial coordinates in the 3D volume; therefore, moving it on one axis will change the location of the images in the other two axes.</li>
		<li>For the VolumeInspect function, use the default intensity window for the lung in the GUI. Adjust the actual filter performance by holding the left mouse button and dragging in the axis.</li>
	</ol>
	</li>
</ol>
2. Digital model for horizontal 3D reconstruction
NOTE: The 3Dlung_Horizon subprocess performs a thorough examination of pulmonary nodules from a horizontal perspective.
<ol>
	<li>Implement the Build_3Dlung_Horizon function in MATLAB workplace to reconstruct the 3D digital model of the pulmonary nodules under the filter window of the lung, and then open the GUI to check the horizontal 3D model (Figure 3).</li>
	<li>Unlike in step 1.3.2, the GUI in Figure 3 is a continuous 3D lung structure in which various types of pulmonary nodules and their relative spatial relationships with the lung tissue can be seen clearly. When dragging the scroll bar on the GUI with the mouse, the continuous 3D lung structure can be observed.</li>
	<li>The upper-right corner of the GUI in Figure 3 provides icons for zooming in, zooming out, returning to the global view, and marking the coordinates of the pixel selected. Use the zoom function to observe the local features of the lesions and output relevant 3D structural output pictures. Use the Mark pixel coordinates button to calculate the distance between two points in order to measure the size of the nodules.</li>
	<li>The default color bar is the jet colormap, which means that blue to red represents values from low to high. Right-click Color Bar in the pop-up menu to select the common gray colormap and reset the whole GUI.</li>
	<li>If the filter window is not satisfied, use the left mouse button to drag up and down in the middle of the figure to adjust the window level. Drag left and right to adjust the window width, and the corresponding accurate filtering range will be displayed on the color bar.</li>
</ol>
3. Constructing a 3D digital model for any specific nodule
NOTE: The slice number is a parameter of the function 3D_Nodules, which reconstructs a 3D digital model that can be viewed from every perspective.
<ol>
	<li>To determine the slice number, as in Figure 3, check at the top-right of the scroll bar; in Figure 3, the slice number is 70. Use the function Build_3D_Nodules with two parameters, including the slice number and the thoracic volume created in step 1.3, to reconstruct a 3D digital model for specific nodules. This is a user-defined model, as the input slice number is variable and depends on the user.</li>
	<li>If the Build_3D_Nodules function is executed correctly, the user can check the pulmonary nodule located in a certain slice number from various perspectives in the pop-up GUI (Figure 4). To do this, perform the following actions:
	<ol>
		<li>Press and hold the left mouse button, as in the center of Figure 4, and drag it in any direction to change the perspective of the pulmonary nodules. It should be noted that the observation angle should take into account the anatomical considerations and try to show both the medical characteristics of the pulmonary nodules and the relationship between the nodules and the surrounding tissues.</li>
	</ol>
	</li>
	<li>Use the zoom and move icons in the upper-right corner, as done in Figure 3. Additionally, by rolling the middle mouse button, the user can continuously zoom in or zoom out of the view of the model.</li>
	<li>The GUI in Figure 4 shows the coordinate indication of the model in the lower-left corner, where the positive direction on the z-axis is the scanning direction in the horizontal position. Implement the screenshot tool provided by the operating system to save the required 3D projection of the nodules.</li>
</ol>
4. Digital model of a coronal 3D reconstruction
NOTE: The Build_3Dlung_Coronal subprocess is executed to evaluate pulmonary nodules from an alternative coronal perspective, thus aiding clinicians and patients in developing a more precise and inclusive understanding of the location and attributes of the nodules.
<ol>
	<li>Implement the Build_3Dlung_Coronal function in the MATLAB workplace to reconstruct the 3D digital model of pulmonary nodules under the filter window of the lung, and then open the GUI, as prepared by the function, to check the coronal 3D model (Figure 5).</li>
	<li>The GUI in Figure 5 shows a continuous coronal 3D lung structure in which various types of pulmonary nodules and their relative spatial relationships with the lung tissue can be seen clearly. Drag the scroll bar on the GUI with the mouse to observe the continuous coronal 3D lung structure.</li>
	<li>The upper-right corner of the GUI, as shown in Figure 5, also provides icons for zooming in, zooming out, returning to the global view, and marking the coordinates of the pixel selected. Use these functions to observe the local features of the lesions and to generate relevant 3D structural pictures. Mark the pixel coordinates to calculate the distance between two points, which is often used to measure the size of the nodules.</li>
	<li>The default color bar is the jet colormap, in which the colors from blue to red represent values from low to high. Right-click the color bar in the pop-up menu to select the common gray colormap and reset the whole GUI.</li>
	<li>If the filter window is not appropriate, use the left mouse button to drag up and down in the middle of the figure to adjust the window level; drag left and right to adjust the window width, and the corresponding accurate filtering range will be displayed on the color bar.</li>
</ol>
5. Output 3D video for dominant pulmonary nodules
NOTE: Converting the optimal 3D digital model of a pulmonary nodule into a dynamic 3D video enables physicians and patients to better comprehend the condition and make accurate judgments, which is especially critical for formulating effective treatment plans.
<ol>
	<li>In the workspace, prepare the required 3D digital model, and pre-visualize the relative spatial relationships between the pulmonary nodules and the lung tissue to be displayed from various angles (Figure 3 and Figure 4).</li>
	<li>In this study, Adobe Captivate 2019 was used to record all the GUI interaction processes. To begin, open the software, and create a new screen recording project. Turn the camera off, and the red screen recording range box will pop up for recording only the screen operation. In this study, the version 5.1 GUI was included in the box. Click the recording button to operate the GUI, and generate a digital video file of the screen recording.</li>
	<li>After recording the dynamic display of the pulmonary nodules, return to the operating environment of the software by clicking on the icon in the taskbar.</li>
	<li>By using the video publishing feature, save the recorded dynamic video of the 3D digital model of the pulmonary nodules. Click on File &#62; Distribute and configure the file storage path. Name the file, and save the desired digital video file.</li>
</ol>

3D Reconstruction for the Diagnosis and Treatment of Pulmonary Nodules

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J., Lam, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluating+the+patient+with+a+pulmonary+nodule:+A+review.">Evaluating the patient with a pulmonary nodule: A review.</a> JAMA. 327 (3), 264-273 (2022).</li><li>MacMahon, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Guidelines+for+management+of+incidental+pulmonary+nodules+detected+on+CT+images:+From+the+Fleischner+Society+2017.">Guidelines for management of incidental pulmonary nodules detected on CT images: From the Fleischner Society 2017.</a> Radiology. 284 (1), 228-243 (2017).</li><li>Ather, S., Kadir, T., Gleeson, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Artificial+intelligence+and+radiomics+in+pulmonary+nodule+management:+Current+status+and+future+applications.">Artificial intelligence and radiomics in pulmonary nodule management: Current status and future applications.</a> Clinical Radiology. 75 (1), 13-19 (2020).</li><li>Bianconi, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparative+evaluation+of+conventional+and+deep+learning+methods+for+semi-automated+segmentation+of+pulmonary+nodules+on+CT.">Comparative evaluation of conventional and deep learning methods for semi-automated segmentation of pulmonary nodules on CT.</a> Quantitative Imaging in Medicine and Surgery. 11 (7), 3286-3305 (2021).</li><li>Christe, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Computer-aided+diagnosis+of+pulmonary+fibrosis+using+deep+learning+and+CT+images.">Computer-aided diagnosis of pulmonary fibrosis using deep learning and CT images.</a> Investigative Radiology. 54 (10), 627-632 (2019).</li><li>Kim, Y., Park, J. 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S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Incremental+benefit+of+maximum-intensity-projection+images+on+observer+detection+of+small+pulmonary+nodules+revealed+by+multidetector+CT.">Incremental benefit of maximum-intensity-projection images on observer detection of small pulmonary nodules revealed by multidetector CT.</a> American Journal of Roentgenology. 179 (1), 149-157 (2002).</li><li>Guleryuz Kizil, P., Hekimoglu, K., Coskun, M., Akcay, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Diagnostic+importance+of+maximum+intensity+projection+technique+in+the+identification+of+small+pulmonary+nodules+with+computed+tomography.">Diagnostic importance of maximum intensity projection technique in the identification of small pulmonary nodules with computed tomography.</a> Tuberk Toraks. 68 (1), 35-42 (2020).</li><li>Valencia, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Value+of+axial+and+coronal+maximum+intensity+projection+(MIP)+images+in+the+detection+of+pulmonary+nodules+by+multislice+spiral+CT:+Comparison+with+axial+1-mm+and+5-mm+slices.">Value of axial and coronal maximum intensity projection (MIP) images in the detection of pulmonary nodules by multislice spiral CT: Comparison with axial 1-mm and 5-mm slices.</a> European Radiology. 16, 325-332 (2006).</li><li>Jabeen, N., Qureshi, R., Sattar, A., Baloch, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Diagnostic+accuracy+of+maximum+intensity+projection+in+diagnosis+of+malignant+pulmonary+nodules.">Diagnostic accuracy of maximum intensity projection in diagnosis of malignant pulmonary nodules.</a> Cureus. 11 (11), e6120 (2019).</li><li>Naeem, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+maximum+intensity+projection+and+volume+rendering+in+detecting+pulmonary+nodules+on+multidetector+computed+tomography.">Comparison of maximum intensity projection and volume rendering in detecting pulmonary nodules on multidetector computed tomography.</a> Cureus. 13 (3), e14025 (2021).</li><li>Armato, S. 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The software tool for pulmonary nodule model reconstruction, PulmonaryNodule, is commercial software from the Beijing Intelligent Entropy Science &amp; Technology Co Ltd. The intellectual property rights of this software tool belong to the company. The authors have no conflicts of interest to disclose.

Different LDCT devices have significant differences in the DICOM image sequences they output, especially in terms of the file system management. Therefore, to reconstruct the key 3D digital model of a pulmonary nodule in the later stages of the protocol, the data preprocessing step is particularly important. In the data preparation and preprocessing stage (step 1.2.2), the sequence z-axis coordinate can be correctly sorted by using the sequence shown in Figure 1, which can also be used to pr...

The global incidence of pulmonary nodules is variable, but it is generally estimated that about 30% of adults have at least one pulmonary nodule visible on chest radiographs1. The incidence of pulmonary nodules is higher in specific populations, such as heavy smokers and those with a history of lung cancer or other lung diseases. It is important to note that not all pulmonary nodules are malignant, but a thorough evaluation is necessary to rule out malignancy2. The early detection and diagnosis of lung cancer are crucial for improving survival rates, and regular screening with low-dose computed tomography (LDCT) is recommended for high-risk individuals. Many AI-driven pulmonary nodule detection and recognition methods3,4,5,6,7 employ deep learning techniques to capture the radiological features of pulmonary nodules, and these methods can achieve good area under the curve (AUC) performance. However, false positives and false negatives remain a challenge for radiologists and clinicians. The interpretation and expression of features from the perspective of pulmonary nodule classification and examination are still unsatisfactory. At the same time, the 3D reconstruction of pulmonary nodules based on LDCT has gained increasing attention as a digital model for various types of nodules.
The 3D reconstruction of pulmonary nodules is a process that generates a 3D representation of a small growth or lump in the lung. This process typically involves the application of medical image analysis techniques that leverage both medical expertise and data intelligence approaches. The resulting 3D digital model offers a more detailed and accurate depiction of the nodule, enabling the improved visualization and analysis of its size, shape, and spatial relationship with the surrounding lung tissues8,9,10,11,12. Such information can aid in the diagnosis and monitoring of pulmonary nodules, particularly those suspected of being cancerous. By facilitating more precise analysis, the 3D reconstruction of pulmonary nodules has the potential to enhance the accuracy of diagnosis and inform treatment decisions.
Maximum intensity projection (MIP) is a popular technique in the field of 3D reconstruction of pulmonary nodules and is used to create a 2D projection of a 3D image8,9,10,11,12It is particularly useful in the visualization of volumetric data extracted from digital imaging and communications in medicine (DICOM) files scanned by CT. The MIP technique works by selecting the voxels (the smallest units of 3D volume data) with the highest intensity along the viewing direction and projecting them onto a 2D plane. This results in a 2D image that emphasizes the structures with the highest intensity and suppresses those with lower intensity, which makes it easier to identify and analyze relevant features9,10,11,12. However, MIP is not without limitations. For example, the projection process can result in a loss of information, and the resulting 2D image may not accurately represent the 3D structure of the underlying object. Nevertheless, MIP remains a valuable tool for medical imaging and visualization, and its use continues to evolve with advances in technology and computing power11.
In this study, a successive MIP model to visualize pulmonary nodules is developed that is easy to use, user-friendly for radiologists, physicians, and patients, and allows the identification and estimation of the properties of pulmonary nodules. The primary advantages of this processing approach include the following aspects: (1) eliminating false positives and false negatives arising from pattern recognition, which enables a focus on assisting physicians to obtain more comprehensive information on the location, shape, and 3D size of pulmonary nodules, as well as their relationship to the surrounding vasculature; (2) enabling specialist physicians to attain professional knowledge of the characteristics of pulmonary nodules even without the assistance of radiologists; and (3) enhancing both communication efficiency between physicians and patients and prognosis evaluation.

<table><tbody><tr><td>MATLAB</td><td>MathWorks&amp;nbsp;</td><td>2022B</td><td>Computing and visualization&amp;nbsp;</td></tr><tr><td>Tools for Modeling</td><td>&amp;nbsp;Intelligent&lt;br/&gt; &amp;nbsp;Entropy</td><td>PulmonaryNodule V1.0</td><td>Beijing Intelligent Entropy Science &amp;amp; Technology Co Ltd.&lt;br/&gt; Modeling for CT/MRI fusion</td></tr></tbody></table>

a 3d digital model for the diagnosis and treatment of pulmonary nodules

The three-dimensional (3D) reconstruction of pulmonary nodules using medical images has introduced new technical approaches for diagnosing and treating pulmonary nodules, and these approaches are progressively being acknowledged and adopted by physicians and patients. Nonetheless, constructing a relatively universal 3D digital model of pulmonary nodules for diagnosis and treatment is challenging due to device differences, shooting times, and nodule types. The objective of this study is to propose a new 3D digital model of pulmonary nodules that serves as a bridge between physicians and patients and is also a cutting-edge tool for pre-diagnosis and prognostic evaluation. Many AI-driven pulmonary nodule detection and recognition methods employ deep learning techniques to capture the radiological features of pulmonary nodules, and these methods can achieve a good area under-the-curve (AUC) performance. However, false positives and false negatives remain a challenge for radiologists and clinicians. The interpretation and expression of features from the perspective of pulmonary nodule classification and examination are still unsatisfactory. In this study, a method of continuous 3D reconstruction of the whole lung in horizontal and coronal positions is proposed by combining existing medical image processing technologies. Compared with other applicable methods, this method allows users to rapidly locate pulmonary nodules and identify their fundamental properties while also observing pulmonary nodules from multiple perspectives, thereby providing a more effective clinical tool for diagnosing and treating pulmonary nodules.

To make the method applicable to a wider range of devices, the stacking order of each scan needs to be reorganized based on the internal coordinates of the DICOM file system (Figure 1) to generate the correct 3D volume (Figure 2). Based on the accurate volume data, we utilized algorithmic continuous reconstruction of the patient&#39;s lung horizontal and coronal MIPs (Figure 4 and Figure 5) for the prec...

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Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules  

The objective of this study is to develop a novel 3D digital model of pulmonary nodules that serves as a communication bridge between physicians and patients and is also a cutting-edge tool for pre-diagnosis and prognostic evaluation.

A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

The three-dimensional (3D) reconstruction of pulmonary nodules using medical images has introduced new technical approaches for diagnosing and treating ...

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1.7K Views.  Beijing University of Chinese Medicine. Deze studie heeft tot doel een continu 3D-reconstructiemodel te creëren voor het beter karakteriseren van de longknobbels voor klinische diagnose en prognose-evaluatie. Diepe integratie van AI-gestuurde beeldvormingstechnologie en diagnose en behandeling van longknobbelziekte zijn enkele van de recente ontwikkelingen op dit onderzoeksgebied. De combinatie van medische beeldvorming, natuurlijke taalverwerking, digitalisering, klasse van veldontwerp en klinische diagnose en behandelingsscenari...

Video: Author Spotlight: een 3D digitaal model voor de diagnose en behandeling van longknobbeltjes  

Bronchial Thermoplasty:  A Novel Therapeutic Approach to Severe Asthma

Bronchial thermoplasty is a non-drug procedure for severe persistent asthma that delivers thermal energy to the airway wall in a precisely controlled manner to reduce excessive airway smooth muscle. Reducing airway smooth muscle decreases the ability of the airways to constrict, thereby reducing the frequency of asthma attacks. Bronchial thermoplasty is delivered by the Alair System and is performed in three outpatient procedure visits, each scheduled approximately three weeks apart. The first procedure treats the airways of the right lower lobe, the second treats the airways of the left lower lobe and the third and final procedure treats the airways in both upper lobes. After all three procedures are performed the bronchial thermoplasty treatment is complete.
Bronchial thermoplasty is performed during bronchoscopy with the patient under moderate sedation. All accessible airways distal to the mainstem bronchi between 3 and 10 mm in diameter, with the exception of the right middle lobe, are treated under bronchoscopic visualization. Contiguous and non-overlapping activations of the device are used, moving from distal to proximal along the length of the airway, and systematically from airway to airway as described previously. Although conceptually straightforward, the actual execution of bronchial thermoplasty is quite intricate and procedural duration for the treatment of a single lobe is often substantially longer than encountered during routine bronchoscopy. As such, bronchial thermoplasty should be considered a complex interventional bronchoscopy and is intended for the experienced bronchoscopist. Optimal patient management is critical in any such complex and longer duration bronchoscopic procedure. This article discusses the importance of careful patient selection, patient preparation, patient management, procedure duration, postoperative care and follow-up to ensure that bronchial thermoplasty is performed safely.
Bronchial thermoplasty is expected to complement asthma maintenance medications by providing long-lasting asthma control and improving asthma-related quality of life of patients with severe asthma. In addition, bronchial thermoplasty has been demonstrated to reduce severe exacerbations (asthma attacks) emergency rooms visits for respiratory symptoms, and time lost from work, school and other daily activities due to asthma.

Bronchial thermoplasty is a non-drug procedure for severe persistent asthma that delivers thermal energy to the airway wall in a precisely controlled manner to reduce excessive airway smooth muscle. Reducing airway smooth muscle decreases the ability of the airways to constrict, thereby reducing the frequency of asthma attacks.

Bronchial thermoplasty is a non-drug procedure for severe persistent asthma that delivers thermal energy to the airway wall in a precisely controlled ...

Use of Electromagnetic Navigational Transthoracic Needle Aspiration (E-TTNA) for Sampling of Lung Nodules

Lung nodule evaluation represents a clinical challenge especially in patients with intermediate risk for malignancy. Multiple technologies are presently available to sample nodules for pathological diagnosis. Those technologies can be divided into bronchoscopic and non-bronchoscopic interventions. Electromagnetic navigational bronchoscopy is being extensively used for the endobronchial approach to peripheral lung nodules but has been hindered by anatomic challenges resulting in a 70% diagnostic yield. Electromagnetic navigational guided transthoracic needle lung biopsy is novel non-bronchoscopic method that uses a percutaneous electromagnetic tip tracked needle to obtain core biopsy specimens. Electromagnetic navigational transthoracic needle aspiration complements bronchoscopic techniques potentially allowing the provider to maximize the diagnostic yield during one single procedure. This article describes a novel integrated diagnostic approach to pulmonary lung nodules. We propose the use of endobronchial ultrasound transbronchial needle aspiration (EBUS-TBNA) for mediastinal staging; radial EBUS, navigational bronchoscopy and E-TTNA during one single procedure to maximize diagnostic yield and minimize the number of invasive procedures needed to obtain a diagnosis. This manuscript describes in detail how the navigation transthoracic procedure is performed. Additional clinical studies are needed to determine the clinical utility of this novel technology.

Use of Electromagnetic Navigational Transthoracic Needle Aspiration E-TTNA for Sampling of Lung Nodules

We describe the novel use of electromagnetic navigational guided transthoracic needle aspiration for the pathologic assessment of human lung nodules.

Lung nodule evaluation represents a clinical challenge especially in patients with intermediate risk for malignancy. Multiple technologies are presently ...

Using Micro-computed Tomography for the Assessment of Tumor Development and Follow-up of Response to Treatment in a Mouse Model of Lung Cancer

Lung cancer is the most lethal cancer in the world. Intensive research is ongoing worldwide to identify new therapies for lung cancer. Several mouse models of lung cancer are being used to study the mechanism of cancer development and to experiment with various therapeutic strategies. However, the absence of a real-time technique to identify the development of tumor nodules in mice lungs and to monitor the changes in their size in response to various experimental and therapeutic interventions hampers the ability to obtain an accurate description of the course of the disease and its timely response to treatments. In this study, a method using a micro-computed tomography (CT) scanner for the detection of the development of lung tumors in a mouse model of lung adenocarcinoma is described. Next, we show that monthly follow-up with micro-CT can identify dynamic changes in the lung tumor, such as the appearance of additional nodules, increase in the size of previously detected nodules, and decrease in the size or complete resolution of nodules in response to treatment. Finally, the accuracy of this real-time assessment method was confirmed with end-point histological quantification. This technique paves the way for planning and conducting more complex experiments on lung cancer animal models, and it enables us to better understand the mechanisms of carcinogenesis and the effects of different treatment modalities while saving time and resources.

We describe a method for the detection of tumor nodule development in the lungs of an adenocarcinoma mouse model using micro-computed tomography and its use for monitoring changes in nodule size over time and in response to treatment. The accuracy of the assessment was confirmed with end-point histological quantification.

Lung cancer is the most lethal cancer in the world. Intensive research is ongoing worldwide to identify new therapies for lung cancer. Several mouse ...

Isometric Contractility Measurement of the Mouse Mesenteric Artery Using Wire Myography

The wire myograph technique is used to assess the contractility of vascular smooth muscles in response to depolarization, GPCR agonists/inhibitors and drugs. It is widely used in many studies on the physiological functions of vascular smooth muscle, the pathogenesis of vascular diseases such as hypertension, and the development of smooth muscle relaxant drugs. The mouse is a widely used model animal with a large pool of disease models and genetically modified strains. We introduced this method to measure the isometric contraction of mouse mesenteric artery in detail. A 1.4-mm segment of mouse mesenteric resistance artery was isolated and mounted on a myograph chamber by passing two steel wires through its lumen. After equilibration and normalization steps, the vessel segment was potentiated by a high-K+ solution twice prior to the contraction assay. As an example of the application of this method in drug development, we measured the relaxant effect of a novel natural substance, neoliensinine, isolated from a Chinese herb, embryos of the lotus seed (Nelumbo nucifera Gaertn.) on mouse mesenteric arteries. The vessel segments mounted on the myograph chamber were stimulated with a high-K+ solution. When the force tension reached a stable sustained phase, cumulative doses of neoliensinine were added to the chamber. We found that neoliensinine had a dose-dependent relaxant effect on smooth muscle contraction, thus suggesting that it bears potential activity against hypertension. In addition, as the vessel segment can survive at least 4 hours after mounting and maintain contractility induced by the high-K+ solution for many times, we suggest that the wire myograph system may be used for the time-consuming process of drug screening.

The wire myograph technique is used to investigate vascular smooth muscle functions and screen new drugs. We report a detailed protocol for measuring the isometric contractility of the mouse mesenteric artery and for screening new relaxants of vascular smooth muscle.

The wire myograph technique is used to assess the contractility of vascular smooth muscles in response to depolarization, GPCR agonists/inhibitors and ...

Diagnosis and Surgical Treatment of Human Brucellar Spondylodiscitis

Brucellar spondylodiscitis (BS) is the most prevalent and significant osteoarticular presentation of human Brucellosis, which is commonly manifested in pastoral communities. It is difficult to differentially diagnose and usually leads to irreversible neurologic deficits and spinal deformities. The initial diagnosis of BS is based on clinical findings and radiographic assessments, and the confirmed diagnosis should be established by the isolation of Brucella species from the blood and/or the standard tube agglutination test. Differential diagnosis of multifocal BS from either degenerative disc diseases or tuberculosis is especially highlighted. The surgical approach, either endoscopic or open, is demonstrated in detail, accompanied by radiographic evidence of structural compression or severe instability. Further, the crucial surgical steps, including single-stage transforaminal decompression, debridement, interbody fusion, and internal fixation, are explained. Moreover, perioperative care and postoperative rehabilitation are also addressed. Taken together, this clinical algorithm presents a practical guide that has yielded substantially satisfactory outcomes in the past decades, which can also be introduced for large-scale application to manage human BS, especially in endemic regions.

We describe a clinical algorithm, based on decades of front-line experience of diagnosis and surgical treatment of human Brucellar spondylodiscitis in the largest medical center of the China's Xinjiang Pastoral Area.

Brucellar spondylodiscitis (BS) is the most prevalent and significant osteoarticular presentation of human Brucellosis, which is commonly manifested in ...

Basophil Activation Test for Allergy Diagnosis

The basophil activation test (BAT) is a complementary in vitro diagnostic test that can be used in addition to clinical history, skin test (ST), and specific IgE (sIgE) determination in the evaluation of IgE-mediated allergic reactions to food, insect venom, drugs, as well as some forms of chronic urticaria. However, the role of this technique in the diagnostic algorithms is highly variable and not well determined.
BAT is based on the determination of basophil response to allergen/drug cross-linking IgE activation through the measurement of activation markers (such as CD63, CD203c) by flow cytometry. This test can be a useful and complementary tool to avoid controlled challenge tests&#160;to confirm allergy diagnosis, especially in subjects experiencing severe life-threatening reactions. In general, the performance of BAT should be considered if i) the allergen/drug produces false positive results in ST; ii) there is no allergen/drug source to use for ST or sIgE determination; iii) there is discordance between patient history and ST or sIgE determination; iv) symptoms suggest that ST may result in systemic response; v) before considering a CCT to confirm the culprit allergen/drug. The main limitations of the test are related to non-optimal sensitivity, especially in drug allergy, the need to perform the test no longer than 24 h after sample extraction, and the lack of standardization between laboratories in terms of procedures, concentrations, and cell markers.

The basophil activation test is a complementary in vitro diagnostic test for the evaluation of IgE-mediated allergic reactions based on the detection of basophil activation in the presence of a specific stimulus through the measure of activation markers by flow cytometry.

The basophil activation test (BAT) is a complementary in vitro diagnostic test that can be used in addition to clinical history, skin test (ST), and ...

A Rodent Model of The Ross Operation: Syngeneic Pulmonary Artery Graft Implantation in A Systemic Position

The Ross operation for aortic valve disease has regained new interest due to its outstanding long-term results. Nonetheless, when employed as freestanding root replacement, the possible dilation of the pulmonary autograft and subsequent aortic regurgitation is described. Several animal models have been proposed. However, these are usually limited to ex-vivo models or in-vivo experiments with relatively expensive large animal models. In this study, we sought to establish a rodent model of pulmonary artery graft (PAG) implantation in a systemic position. A total of 39 adult Lewis rats were included. Immediately after euthanasia, the pulmonary root was harvested from a donor animal (n=17). Syngeneic recipient (n=17) and sham-operated (n=5) rats were sedated and ventilated. In the recipient group, the PAG was implanted with an end-to-end anastomosis in infra-renal abdominal aortic position. Sham-operated rats underwent only transection and re-anastomosis of the aorta. Animals were followed with serial ultrasound studies for two months and post-mortem histological analysis. The median PAG diameter in the native position was 3.20 mm (IQR=3.18-3.23). At follow-up, the median diameter of the PAG was 4.03 mm (IQR=3.74-4.13) at 1 week, 4.07 mm (IQR=3.80-4.28) at 1 month, and 4.27 mm (IQR=3.90-4.35) at 2 months (p&lt;0.01). Peak systolic velocity was 220.07 mm/s (IQR=210.43-246.41) at 1 week, 430.88 mm/s (IQR=375.28-495.56) at 1 month, and 373.68 mm/s (IQR=305.78-429.81) at 2 months (p=0.02) and did not differ from the sham-operated group at the end of the experiment (p=0.5). Histological analysis did not show any sign of endothelial thrombosis. This study showed that rodent models may allow for the evaluation of the long-term adaptation of the pulmonary root to a high-pressure system. A systemically placed syngeneic PAG implantation represents a simple and feasible platform for the development and evaluation of novel surgical techniques and drug therapies to further improve the outcomes of the Ross operation.

We demonstrate how to establish a murine model of pulmonary root implantation into the descending aorta to simulate the Ross procedure. This model enables the medium/long-term evaluation of pulmonary autograft remodeling in a systemic position, representing the basis of developing therapeutic strategies to promote its adaptation.

The Ross operation for aortic valve disease has regained new interest due to its outstanding long-term results. Nonetheless, when employed as freestanding ...

Drug Treatment by Central Venous Catheter in a Mouse Model of Angiotensin II Induced Abdominal Aortic Aneurysm and Monitoring by 3D Ultrasound

Since pharmaceutical treatment options are lacking in the clinical management of abdominal aortic aneurysm (AAA), animal models, in particular mouse models, are applied to advance the understanding of the disease pathogenesis and to identify potential therapeutic targets. Testing novel drug candidates to block AAA growth in these models generally requires repeated drug administration during the time course of the experiment. Here, we describe a compiled protocol for AAA induction, insertion of an intravenous catheter to facilitate prolonged therapy, and serial AAA monitoring by 3D ultrasound. Aneurysms are induced in apolipoprotein E (ApoE) deficient mice by angiotensin II release over 28 days from osmotic mini-pumps implanted subcutaneously into the mouse back. Subsequently, the surgical procedure for external jugular vein catheterization is conducted to allow for daily intravenous drug treatment or repeated blood sampling via a subcutaneous vascular access button. Despite the two dorsal implants, the monitoring of AAA development is readily facilitated by sequential semi-automated 3D ultrasound analysis, which yields comprehensive information on the expansion of aortic diameter and volume and on aneurysm morphology, as illustrated by experimental examples.

This protocol describes the consecutive implantation of an osmotic pump to induce abdominal aortic aneurysm by angiotensin II release in apolipoprotein E (ApoE) deficient mice and of a vascular access port with a jugular vein catheter for repeated drug treatment. Monitoring of aneurysm development by 3D ultrasound is effectively conducted despite dorsal implants.

Since pharmaceutical treatment options are lacking in the clinical management of abdominal aortic aneurysm (AAA), animal models, in particular mouse ...

Enhancing Pulmonary Nodule Diagnosis Using Whole-Lung 3D Reconstruction

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules

For patients with early multiple pulmonary nodules, it is essential, from a diagnostic perspective, to determine the spatial distribution, size, location, and relationship with surrounding lung tissue of these nodules throughout the entire lung. This is crucial for identifying the primary lesion and developing more scientifically grounded treatment plans for doctors. However, pattern recognition methods based on machine vision are susceptible to false positives and false negatives and, therefore, cannot fully meet clinical demands in this regard. Visualization methods based on maximum intensity projection (MIP) can better illustrate local and individual pulmonary nodules but lack a macroscopic and holistic description of the distribution and spatial features of multiple pulmonary nodules.
Therefore, this study proposes a whole-lung 3D reconstruction method. It extracts the 3D contour of the lung using medical image processing technology against the background of the entire lung and performs 3D reconstruction of the lung, pulmonary artery, and multiple pulmonary nodules in 3D space. This method can comprehensively depict the spatial distribution and radiological features of multiple nodules throughout the entire lung, providing a simple and convenient means of evaluating the diagnosis and prognosis of multiple pulmonary nodules.

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer 

This study introduces a three-dimensional (3D) reconstruction method for the entire lung in patients with early multiple pulmonary nodules. It offers a comprehensive visualization of nodule distribution and their interplay with lung tissue, simplifying the assessment of diagnosis and prognosis for these patients.

For patients with early multiple pulmonary nodules, it is essential, from a diagnostic perspective, to determine the spatial distribution, size, location, ...

Fractal-Based CT Analysis for Lung Cancer Diagnosis and Staging

Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy

Non-invasive assessment of pulmonary nodule malignancy remains a critical challenge in lung cancer diagnosis. Traditional methods often lack precision in differentiating benign from malignant nodules, particularly in the early stages. This study introduces an approach using multifractal spectrum analysis to quantitatively evaluate pulmonary nodule characteristics.
A fractal-based protocol was developed to process computed tomography (CT)-digital imaging and communications in medicine (DICOM) data, enabling three-dimensional (3D) visualization and analysis of pulmonary nodule's multifractal spectrum. The method involves 3D volume reconstruction, precise ROI delineation, and calculation of fractal dimensions across multiple scales. Multifractal spectra were computed for both early-stage and late-stage lung adenocarcinoma nodules, with comparative analysis performed using data tip tool quantification.
Analysis revealed that the fractal dimension of a pulmonary nodule's 3D digital matrix varies continuously with different voxel scales, forming a distinctive multifractal spectrum. Significant differences were observed between early-stage and late-stage nodules. Late-stage nodules demonstrated a wider scale range (longer X-axis) and higher extreme points in their multifractal spectra. These distinctions were quantitatively confirmed, indicating the method's potential for precise staging.
The multifractal spectrum analysis provides a highly significant and precise quantitative method for staging pulmonary nodules, effectively differentiating between benign and malignant cases. This non-invasive technique shows promise for improving early diagnosis and accurate staging of lung cancer, potentially enhancing clinical decision-making in pulmonary oncology.

This study introduces multifractal spectrum analysis for assessing pulmonary nodule malignancy. Using CT-DICOM data, the method calculates fractal dimensions across multiple voxel scales, revealing significant differences between early-stage and late-stage pulmonary nodules.

Non-invasive assessment of pulmonary nodule malignancy remains a critical challenge in lung cancer diagnosis. Traditional methods often lack precision in ...

A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

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