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. The official network link is&#160;http://www.natcm.gov.cn/renjiaosi/zhengcewenjian/2021-11-04/23082.html.

For the present study, ethical clearance was obtained from The Ethics Committee of Dongzhimen Hospital, affiliated with Beijing University of Chinese Medicine (DZMEC-KY-2019.90). In this specific case, a methodical description of the research approach is provided, outlining a case involving a 65-year-old female patient with multiple pulmonary nodules. This patient provided informed consent for her diagnosis through digital modeling and authorized the use of her data for scientific research purposes. The model reconstruction function is derived from a commercially available software tool (see Table of Materials).
1. Data preparation and isovoxel transformation
<ol>
	<li>DICOM (Digital Imaging and Communications in Medicine) data preparation and data properties 
	NOTE: The variation in parameters remains relatively unaffected by the research methodology.
	<ol>
		<li>Copy the patient&#39;s DICOM data to a defined working directory.</li>
		<li>Using the file browser, examine each file directory to identify the image sequence with the highest number of scanning layers for analysis.</li>
		<li>Employ the Dicominfo function within MATLAB by providing DICOM files as input parameters. This will enable you to extract essential parameters, such as slice thickness and pixel spacing, directly within the MATLAB environment. 
		NOTE: These parameters hold significant importance in configuring the display rate for the 3D volume. In the case of the dataset utilized in this study, the slice thickness measured 1 mm, the pixel spacing equated to 0.7188 mm, and a total of 387 layers were scanned.</li>
	</ol>
	</li>
	<li>Correct sorting of scanned data 
	NOTE: The sequence of every image should be sorted for volume construction.
	<ol>
		<li>Retrieve the location data for each image using the Dicominfo function. Access the location information by referencing info.SliceLocation within the MATLAB workspace.</li>
		<li>Save the location data into a variable using the SliceLocation function and generate a plot for it (Figure 1).</li>
		<li>Enhance the plot by adding a data point to it using the Data Tips button situated in the upper-right corner of the GUI. This data point should mark the maximum location of the normal sequence, which corresponds to the topmost location in the patient&#39;s imaging (Figure 1).</li>
		<li>Organize all the images by sorting them and then extract the images ranging from the first location to the maximum location. Achieve this by invoking the VolumeResort function.</li>
		<li>Safeguard the volume data, which consists of 512 pixels by 512 pixels by 340 layers, from the valid images along with their sorted index. This information will be valuable for future reference, particularly in the context of identifying important nodules.</li>
	</ol>
	</li>
	<li>Isovoxel transformation 
	&#8203;NOTE: 3D Isovoxel transformation allows subsequent processing to maintain the same display scale in all dimensions.
	<ol>
		<li>Examine the three-dimensional scale of a 3D volume, which is 512 pixels x 512 pixels x 340 layers, using the size function in Matlab.</li>
		<li>To view the 3D-volume (Figure 2) using the Slice_View command function, record the sequence scan range containing the lungs from 60 to 340. Then, simply use the command V1=V0(:,:,60:340) to obtain a 3D-volume containing all the data of the entire lung. The size of V1 is 512 pixels x 512 pixels x 281 layers.</li>
		<li>Utilize the MATLAB command function dicominfo to obtain the slice thickness of the image sequence, which is 1 mm, and the pixel spacing is 0.7188. Calculate the number of z-axes for the isovoxel transformation with the command: round (281 x 1/0.7188). The number of layers for the isovoxel transformation should be 391.</li>
		<li>Use the Matlab command function imresize3 to perform isovoxel transformation on V1. Execute the script using the command V2=imresize3(V1, [512, 512, 391]). Then use the 3D_Slice_View function to view the isovoxel transformed 3D-Volume (Figure 3).</li>
	</ol>
	</li>
</ol>
2. Removal of noise interference caused by Computed Tomography (CT) equipment
NOTE: In Figure 2, the high-intensity signal representing the CT equipment&#39;s patient couch is visible, which can interfere with image segmentation. To eliminate this interference, a spatial filter design is required.
<ol>
	<li>Utilize the Data Tips button in Figure 2 to add continuous data points within the interactive interface. This will allow one to create a line connecting these points, effectively excluding the patient couch. Next, right-click on the Data Tips and select Export Cursor Data to Workspace to export the reference boundary for spatial filtering to the MATLAB workspace (Figure 3). The boundary scatter matrix in this case is named &#39;CI&#39;.</li>
	<li>Invoke the Noise_Clean function to apply spatial filtering to V2, using the input parameter &#39;CI&#39; from the workspace. This operation will yield a 3D volume that removes the interference signal from the CT equipment. Finally, use the Slice_View command function to visualize the resulting volume, as demonstrated in Figure 4.</li>
</ol>
3. Extraction of lung contour
<ol>
	<li>Begin by selecting a slice to serve as a template within the GUI displayed in Figure 4. For instance, choose the 232nd image for the image segmentation design and assign it to a variable &#39;I&#39; using the command I=V2(:,:,232). Then, open the MATLAB Image Segmenter GUI by executing the command imageSegmenter(I), as depicted in Figure 5.</li>
	<li>Figure 5 showcases an array of image segmentation tools. To start, select the Auto Cluster tool from the toolbar at the top and execute the command by clicking the left mouse button. The image will automatically be divided into two classes. Given the denoising process performed in step 2.2, image segmentation at this stage becomes relatively straightforward.</li>
	<li>Next, click on the Show Binary button in the upper right corner to display the image in black and white binary. At this point, the lung region will appear black. To make the lung region white, select the Invert Mask button from the top toolbar and execute the command by clicking the left mouse button.</li>
	<li>To eliminate the white color outside the lung area, select the Clear Borders button on the top toolbar and execute it by clicking with the left mouse button. After this step, only the white-colored lung area will remain. However, any black shadows remaining within the lung area at this point need to be filled. To achieve this, select the Fill Holes button in the toolbar, and the result after clicking the button is shown in Figure 6.</li>
	<li>All the steps involved in lung image segmentation are presented in the GUI of Figure 6 at the lower left corner. By clicking the Export button in the upper right corner, save these automated steps as a function for batch-processing lung region segmentation. In the pop-up Script Editor, click the Save button to save the function in the current working directory.</li>
</ol>
4. 3D reconstruction for the whole lung with multiple pulmonary nodules
NOTE: Taking the dot product of the lung segmentation image of each image with the original image is equivalent to performing 3D spatial filtering on the volume, effectively filtering out interference signals outside the lungs and obtaining the 3D structure of the lungs.
<ol>
	<li>Initiate the 3Dlung_Volume function within the MATLAB workspace. 
	NOTE: This function conducts image segmentation on each image using the output from step 3.5. It then executes a dot product operation between the binary lung mask and the original image to generate a new 3D-volume exclusively containing lung tissue. In the GUI (Figure 7) that appears after the function completes, one can visualize and perform Maximum Intensity Projection (MIP) operations on the entire 3D lung volume.</li>
	<li>Within the GUI, find the first drop-down menu in the top right corner. Select MIP Projection and then choose the jet colormap from the Built-in Colormaps options below. Next, in the drop-down menu located in the top right corner of the fourth view (3D Volume View), select Maximize. This action will yield a whole lung 3D volume (Figure 8) that can be observed from any angle, moved, and manipulated as needed. 
	NOTE: In the human-computer interaction section illustrated in Figure 8, one can adjust the viewing angle freely by holding down the left mouse button and moving it. Scrolling the middle mouse button allows one to zoom in or out.</li>
	<li>For advanced contrast and color enhancement operations, utilize the control panel on the right side of the GUI.</li>
</ol>
5. Focus on the examination of dominant pulmonary nodules
NOTE: In 3D space (Figure 8), the dominant lesion area among multiple pulmonary nodules becomes distinctly visible. The number, size, and concentration of these nodules are critical features of the dominant lesion, offering valuable insights into disease assessment.
<ol>
	<li>Once more, invoke the Slice_View function, but this time input the entire lung&#39;s 3D volume obtained in step 4.2. Within the resulting GUI (Figure 9), use the bottom scroll bar to navigate to the region where the dominant lung nodules are situated, spanning scans 48 to 70.</li>
	<li>Proceed by calling the 3Dlung_Horizon function to conduct 3D reconstruction of the Region of Interest (ROI) encompassing sections 48 to 70 from the whole lung&#39;s 3D volume. This action will generate a GUI interface tailored for visualizing pulmonary nodules, as illustrated in Figure 10. Within this GUI, one can explore the lesion&#39;s detailed features from various angles.</li>
</ol>

Enhancing Pulmonary Nodule Diagnosis Using Whole-Lung 3D Reconstruction

<ol>
	<li>Mazzone, P. 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.">Guidelines for management of incidental pulmonary nodules detected on ct images: from the fleischner society.</a> Radiology. 284 (1), 228-243 (2017).</li><li>Yankelevitz, D. F., Yip, R., Henschke, C. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Impact+of+duration+of+diagnostic+workup+on+prognosis+for+early+lung+cancer.">Impact of duration of diagnostic workup on prognosis for early lung cancer.</a> Journal of Thoracic Oncology. 18 (4), 527-537 (2023).</li><li>Zhao, W., 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=PUNDIT:+Pulmonary+nodule+detection+with+image+category+transformation.">PUNDIT: Pulmonary nodule detection with image category transformation.</a> Medical Physics. 50, 2914-2927 (2023).</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>Gruden, J. 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=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., 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=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., 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=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>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., 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=Applications+of+artificial+intelligence+in+the+thorax:+a+narrative+review+focusing+on+thoracic+radiology.">Applications of artificial intelligence in the thorax: a narrative review focusing on thoracic radiology.</a> Journal of Thoracic Disease. 13 (12), 6943-6962 (2021).</li><li>Schreuder, 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=Artificial+intelligence+for+detection+and+characterization+of+pulmonary+nodules+in+lung+cancer+CT+screening:+ready+for+practice.">Artificial intelligence for detection and characterization of pulmonary nodules in lung cancer CT screening: ready for practice.</a> Translational Lung Cancer Research. 10 (5), 2378-2388 (2021).</li><li>Zheng, S., 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=Automatic+pulmonary+nodule+detection+in+CT+scans+using+convolutional+neural+networks+based+on+maximum+intensity+projection.">Automatic pulmonary nodule detection in CT scans using convolutional neural networks based on maximum intensity projection.</a> IEEE Transactions on Medical Imaging. 39 (3), 797-805 (2019).</li><li>Yabuuchi, 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=Clinical+application+of+radiation+dose+reduction+for+head+and+neck+CT.">Clinical application of radiation dose reduction for head and neck CT.</a> European Journal of Radiology. 107, 209-215 (2018).</li><li>Rana, B., 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=Regions-of-interest+based+automated+diagnosis+of+Parkinson's+disease+using+T1-weighted+MRI.">Regions-of-interest based automated diagnosis of Parkinson's disease using T1-weighted MRI.</a> Expert Systems with Applications. 42 (9), 4506-4516 (2015).</li></ol>

The authors have no conflicts of interest to disclose. The software tool for pulmonary nodule model reconstruction, listed in the Table of Materials of this study, is a commercial software from Beijing Intelligent Entropy Science &amp; Technology Co Ltd. The intellectual property rights of this software tool belong to the company.

This research introduces a unique approach for creating a complete three-dimensional (3D) reconstruction of the entire lung, employing advanced medical image processing techniques to delineate the lung&#39;s 3D shape amidst the context of a full chest scan. This technique offers a more precise and thorough depiction of the spatial arrangement and radiological characteristics of early multiple nodules across the entire lung. This study makes a valuable contribution to enhancing the accuracy and efficacy of diagnostic and treatment strategies for individuals with early multiple pulmonary nodules.
Critical steps 
In this study, several critical steps were identified as essential for the success of the protocol: (1) Sorting and arranging DICOM scan sequence coordinates to generate an accurate 3D volume of the lung scan (step 1.2.2); (2) Isotropic transformation to ensure the correct aspect ratio of the 3D volume, which is crucial for subsequent 3D reconstruction (step 1.3.4); (3) Reconstruction of the entire lung using an early multiple pulmonary nodules model, enabling the identification of the dominant pulmonary nodule area (step 4.1); (4) Detailed visualization and examination of the local area containing the dominant lesion (step 5.2).
Modifications and troubleshooting 
The segmentation of lung tissue structures may be affected by the grayscale threshold offset in the scanning sequence, potentially resulting in inaccurate image segmentation in some scans. In cases of inaccurate segmentation, a separate filter (repeating step 3) can be designed to obtain precise lung tissue contours. Maintaining the highest precision in isovoxel transformation16,17is crucial to ensure the accurate utilization of data. These steps are expected to become more intelligent and automated in the future. With the advancement of large-scale medical imaging models, precise contour identification through computer vision is also an important direction for future development11.
Limitations 
Simplified implementation of lung contour extraction may lead to errors at the boundary of the lung&#39;s 3D contour, potentially affecting the visualization of small nodules near the lung&#39;s edge. However, the impact of this limitation is minimal when visualizing the dominant lesion area in cases of multiple pulmonary nodules.
Significance with respect to existing methods 
Compared to computer vision approaches, this method offers a comprehensive representation of lung tissue structure, including the relationships between multiple pulmonary nodules and lung tissue, while avoiding the issues of false positives and false negatives. Additionally, it effectively filters out signal interference from other tissue structures, leading to more precise and accurate diagnoses with enhanced contrast and clarity.
Future applications 
This 3D visualization method holds substantial potential for various clinical applications, such as facilitating doctor-patient communication, enabling precise diagnosis, supporting data-driven evidence-based classification, aiding in treatment planning, and evaluating prognosis. It can assist in preoperative planning, provide intraoperative navigation for the surgical removal of multiple lung nodules, and monitor changes in nodule size and shape over time to assess treatment effectiveness. Overall, it has the capacity to enhance clinical decision-making in the diagnosis and treatment of multiple pulmonary nodules.

Early multiple pulmonary nodules, which are small, round growths on the lung, can be benign or malignant1,2,3. Although solitary pulmonary nodules are easier to diagnose and treat, patients with early multiple pulmonary nodules face significant diagnostic and treatment challenges. To develop effective treatment plans, it is essential to accurately identify the spatial distribution, size, location, and relationship with surrounding lung tissue of these nodules throughout the whole lung4,5. Traditional diagnostic methods have limitations in accurately identifying early multiple pulmonary nodules.
Recent advancements in medical image processing technology and machine learning algorithms have the potential to improve the accuracy and efficiency of early pulmonary nodule detection and diagnosis. Various approaches have been proposed, such as pattern recognition methods based on machine vision and visualization methods based on maximum intensity projection (MIP)6,7,8,9,10. However, these methods suffer from limitations such as false positives, false negatives11,12,13,14,15, and lack of macroscopic and holistic descriptions of the distribution and spatial features of early multiple pulmonary nodules.
To address these limitations, this study proposes a whole-lung 3D reconstruction method that utilizes medical image processing technology to extract the 3D contour of the lung against the background of the whole chest scan. The method then performs 3D reconstruction of the lung, pulmonary artery, and early multiple pulmonary nodules in 3D space. This approach allows for a more comprehensive and accurate representation of the spatial distribution and radiological features of early multiple nodules throughout the whole lung.
The proposed method involves several key steps. Firstly, the medical images are imported into the 3D image processing software, and the lung region is extracted using a threshold-based segmentation technique. Subsequently, the extracted lung region is separated from the surrounding chest wall and the bony structures of the thoracic vertebrae. The early multiple pulmonary nodules and their relationship with surrounding blood vessels are then reconstructed in 3D space using maximum intensity projection (MIP) algorithms. Finally, the reconstructed 3D model of the lung, pulmonary artery, and nodules is displayed for further analysis.
This method has several advantages over existing methods. Unlike traditional methods that rely on 2D images, this method utilizes 3D volume to provide a more accurate and comprehensive representation of early multiple pulmonary nodules. The method also overcomes the limitations of false positives and false negatives associated with pattern recognition methods and MIP visualization methods. Furthermore, this method provides a macroscopic and holistic description of the distribution and spatial features of early multiple pulmonary nodules, which is essential for developing effective treatment plans.
The proposed method has several potential applications in the diagnosis and treatment of early multiple pulmonary nodules. The accurate identification of the spatial distribution and radiological features of early multiple nodules can aid in the early diagnosis and treatment of lung cancer. Furthermore, the method can be used to monitor the progression of the disease and evaluate the effectiveness of treatment plans.
Pattern recognition methods6,7,8 based on machine vision have shown promise in identifying pulmonary nodules, but suffer from limitations such as false positives and false negatives. MIP visualization methods, on the other hand, provide a more accurate representation of individual nodules, but lack a macroscopic and holistic description of the distribution and spatial features of early multiple nodules. The proposed whole-lung 3D reconstruction method overcomes these limitations and provides a more accurate and comprehensive representation of early multiple pulmonary nodules.
Isovoxel transformation16,17refers to the process of converting 3D images with different voxel sizes into 3D images with uniform voxel sizes. In the field of medical image processing, 3D volumes are often composed of voxels with varying sizes, which can lead to computational and visualization issues. The purpose of isovoxel transformation is to address these issues by resampling and interpolating the voxels in the original 3D volume, resulting in a new 3D image with consistent voxel sizes. This technique finds applications in various medical contexts, including image registration, segmentation, and visualization. Thus, this study proposed a whole-lung 3D reconstruction method that utilizes medical image processing technology to extract the 3D contour of the lung against the background of the whole chest scan. The method provides a more accurate and comprehensive representation of the spatial distribution and radiological features of early multiple nodules throughout the whole lung. This study contributes to the development of more accurate and effective diagnostic and treatment strategies for patients with early multiple pulmonary nodules.

<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>Intelligent 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>

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.

In the data preprocessing stage, DICOM data sorting should be the first step (Figure 1) to ensure the correct scan sequence for each layer during 3D reconstruction. Next, isotropic transformation is performed to ensure the correct aspect ratio of the 3D volume (Figure 2). Afterward, spatial filtering is applied to the original 3D volume (Figure 3) to eliminate interference signals from the patient couch of the CT equipment (Figure 4). To obtain the 3D contour of the entire lung, image segmentation is performed on each scan (Figure 5) to create a binary lung image (Figure 6). Based on the 3D contour of the lung, the entire lung 3D volume is reconstructed (Figure 7) and visualized in 3D (Figure 8). For the dominant lesion area (Figure 9), a separate 3D visualization (Figure 10) can be performed to carefully identify the detailed features of the lesion.
Isovoxel transformation ensures that the same scale is maintained in all dimensions during subsequent processing. Figure 2 displays the slice view after isovoxel transformation. In this graphical user interface (GUI), one can view the complete raw 3D volume data.
Figure 3&#160;and Figure 4 demonstrate the spatial filtering process used to remove bed signal interference from the CT equipment. Without this, images with noisy signals cannot complete the segmentation of lung structures in subsequent steps.
Figure 5&#160;and Figure 6 illustrate the lung contour extraction function, which can automatically extract lung contours, providing the basic conditions for the subsequent 3D reconstruction of lung structures.
Figure 7&#160;and Figure 8 show the 3D reconstruction of the entire lung, revealing the spatial distribution of lung tissues and multiple lung nodules. By eliminating signal interference from tissues outside the lungs, the spatial location, size, and concentration of multiple pulmonary nodules can be accurately depicted.
Figure 9&#160;and Figure 10 display the 3D visualization of the dominant lung nodules of interest. Due to the exclusion of signal interference from outside the lungs, the contrast of the images is improved. The ability to observe the 3D structure from any angle allows physicians to make more accurate judgments about the lesion features of the dominant pulmonary nodules.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65786/65786fig01.jpg" /> 
Figure 1: Location plot of images. The plot displays the location of images based on their file name sequence. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/65786/65786fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/65786/65786fig02.jpg" /> 
Figure 2: GUI for 3D-volume slice view. Graphical User Interface (GUI) for viewing slices of the 3D volume after Isovoxel transformation. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/65786/65786fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/65786/65786fig03.jpg" /> 
Figure 3: Reference boundary scatter matrix. The matrix representing reference boundary scatter for spatial filtering. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/65786/65786fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/65786/65786fig04.jpg" /> 
Figure 4: 3D volume slice view after spatial filtering. View of slices from the 3D volume after applying spatial filtering. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/65786/65786fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/65786/65786fig05.jpg" /> 
Figure 5: Image segmenter GUI. Graphical User Interface (GUI) of the Image Segmenter tool. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/65786/65786fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/65786/65786fig06.jpg" /> 
Figure 6: Result of lung area shadow filling. The resultant image after filling black shadows in the lung area using the &#34;Fill Holes&#34; button. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/65786/65786fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/65786/65786fig07.jpg" /> 
Figure 7: 3D lung reconstruction with multiple pulmonary nodules. 3D reconstruction of the entire lung showing early multiple pulmonary nodules. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/65786/65786fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/65786/65786fig08.jpg" /> 
Figure 8: Interactive GUI for 3D lung volume viewing. Interactive Graphical User Interface (GUI) for viewing and manipulating the entire 3D lung volume. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/65786/65786fig08large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/65786/65786fig09.jpg" /> 
Figure 9: Slice view for navigating dominant pulmonary nodule area. Slice view for navigating the region containing dominant pulmonary nodules within the entire 3D lung volume. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/65786/65786fig09large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 10" class="xfigimg" src="/files/ftp_upload/65786/65786fig10.jpg" /> 
Figure 10: 3D visualization of dominant pulmonary nodule. Three-dimensional visualization of the dominant pulmonary nodule within the lung volume. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/65786/65786fig10large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer at JoVE.com

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.

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, ...

three-dimensional-reconstruction-for-whole-lung-with-early-multiple

Universidad San Sebastian

Research

JoVE Journal

Medicine

1.3K Views.  Beijing University of Chinese Medicine. 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.

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

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 ...

Angiogenesis in the Ischemic Rat Lung

The adult lung is perfused by both the systemic bronchial artery and the entire venous return flowing through the pulmonary arteries. In most lung pathologies, it is the smaller systemic vasculature that responds to a need for enhanced lung perfusion and shows robust neovascularization. Pulmonary vascular ischemia induced by pulmonary artery obstruction has been shown to result in rapid systemic arterial angiogenesis in man as well as in several animal models. Although the histologic assessment of the time course of bronchial artery proliferation in rats was carefully described by Weibel 1, mechanisms responsible for this organized growth of new vessels are not clear. We provide surgical details of inducing left pulmonary artery ischemia in the rat that leads to bronchial neovascularization. Quantification of the extent of angiogenesis presents an additional challenge due to the presence of the two vascular beds within the lung. Methods to determine functional angiogenesis based on labeled microsphere injections are provided.

The lung is perfused by both the systemic bronchial artery and pulmonary arteries. In most lung pathologies, it is the smaller systemic vasculature that shows robust neovascularization. Cessation of pulmonary blood flow promotes brisk bronchial angiogenesis. We provide surgical details of inducing left pulmonary artery ischemia that promotes bronchial neovascularization.

The  adult lung is perfused by both the systemic bronchial artery and the entire  venous return flowing through the pulmonary arteries. In most lung ...

Three-dimensional Co-culture Model for Tumor-stromal Interaction

Cancer progression (initiation, growth, invasion and metastasis) occurs through interactions between malignant cells and the surrounding tumor stromal cells. The tumor microenvironment is comprised of a variety of cell types, such as fibroblasts, immune cells, vascular endothelial cells, pericytes and bone-marrow-derived cells, embedded in the extracellular matrix (ECM). Cancer-associated fibroblasts (CAFs) have a pro-tumorigenic role through the secretion of soluble factors, angiogenesis and ECM remodeling. The experimental models for cancer cell survival, proliferation, migration, and invasion have mostly relied on two-dimensional monocellular and monolayer tissue cultures or Boyden chamber assays. However, these experiments do not precisely reflect the physiological or pathological conditions in a diseased organ. To gain a better understanding of tumor stromal or tumor matrix interactions, multicellular and three-dimensional cultures provide more powerful tools for investigating intercellular communication and ECM-dependent modulation of cancer cell behavior. As a platform for this type of study, we present an experimental model in which cancer cells are cultured on collagen gels embedded with primary cultures of CAFs.

Here we present a protocol to co-culture in three-dimensions, which is useful for investigating multicellular interactions and extracellular matrix-dependent modulation of cancer cell behavior. In this experimental model, cancer cells are cultured on collagen gels embedded with human cancer-associated fibroblasts.

Cancer progression (initiation, growth, invasion and metastasis) occurs through interactions between malignant cells and the surrounding tumor stromal ...

Three-Dimensional (3D) Tumor Spheroid Invasion Assay

Invasion of surrounding normal tissues is generally considered to be a key hallmark of malignant (as opposed to benign) tumors. For some cancers in particular (e.g., brain tumors such as glioblastoma multiforme and squamous cell carcinoma of the head and neck &#8211; SCCHN) it is a cause of severe morbidity and can be life-threatening even in the absence of distant metastases. In addition, cancers which have relapsed following treatment unfortunately often present with a more aggressive phenotype. Therefore, there is an opportunity to target the process of invasion to provide novel therapies that could be complementary to standard anti-proliferative agents. Until now, this strategy has been hampered by the lack of robust, reproducible assays suitable for a detailed analysis of invasion and for drug screening. Here we provide a simple micro-plate method (based on uniform, self-assembling 3D tumor spheroids) which has great potential for such studies. We exemplify the assay platform using a human glioblastoma cell line and also an SCCHN model where the development of resistance against targeted epidermal growth factor receptor (EGFR) inhibitors is associated with enhanced matrix-invasive potential. We also provide two alternative methods of semi-automated quantification: one using an imaging cytometer and a second which simply requires standard microscopy and image capture with digital image analysis.

Three-Dimensional 3D Tumor Spheroid Invasion Assay

Invasion of surrounding normal tissues is a defining characteristic of malignant tumors. We provide here a simple, semi-automated micro-plate assay of invasion into a natural 3D biomatrix that has been exemplified with a number of models of advanced human cancers.

Invasion of surrounding normal tissues is generally considered to be a key hallmark of malignant (as opposed to benign) tumors. For some cancers in ...

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 ...

Whole-Kidney Three-Dimensional Staining with CUBIC

Although conventional pathology provided numerous information about kidney microstructure, it was difficult to know the precise structure of blood vessels, proximal tubules, collecting ducts, glomeruli, and sympathetic nerves in the kidney due to the lack of three-dimensional (3D) information. Optical clearing is a good strategy to overcome this big hurdle. Multiple cells in a whole organ can be analyzed at single-cell resolution by combining tissue clearing and 3D imaging technique. However, cell labeling methods for whole-organ imaging remain underdeveloped. In particular, whole-mount organ staining is challenging because of the difficulty in antibody penetration. The present protocol developed a whole-mount mouse kidney staining for 3D imaging with the CUBIC (Clear, Unobstructed Brain/Body Imaging Cocktails and Computational analysis) tissue clearing method. The protocol has enabled visualizing renal sympathetic denervation after ischemia-reperfusion injury and glomerulomegaly in the early stage of diabetic kidney disease from a comprehensive viewpoint. Thus, this technique can lead to new discoveries in kidney research by providing a macroscopic perspective.

The present protocol describes a tissue clearing method and whole-mount immunofluorescent staining for three-dimensional (3D) kidney imaging. This technique can offer macroscopic perspectives in kidney pathology, leading to new biological discoveries.

Although conventional pathology provided numerous information about kidney microstructure, it was difficult to know the precise structure of blood ...

3D Reconstruction for the Diagnosis and Treatment of Pulmonary Nodules

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.

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.

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

ICE-Based 3D Remodeling for the Left Atrium and Pulmonary Vein

Three-Dimensional Modeling of the Left Atrium and Pulmonary Veins with a Precise Intracardiac Echocardiography Approach

Intracardiac echocardiography (ICE) is a novel tool for estimating cardiac anatomy during pulmonary vein isolation procedures, particularly the left atrium (LA) anatomy and pulmonary vein structures. ICE is widely used to establish a three-dimensional (3D) left atrial structural model during ablation procedures. However, it is unclear whether using ICE in a precise 3D modeling method can provide a more accurate left atrial 3D model and the transseptal approach. This study proposes a protocol to model the left atrium and pulmonary veins with ICE and fast anatomical mapping (FAM) catheter remodeling. It evaluates the accuracy of the models produced using the two methods through observer scoring. We included 50 patients who underwent ICE-based 3D remodeling and 45 who underwent FAM 3D remodeling based on pulmonary vein isolation procedures. The pulmonary vein antrum remodeling is estimated by comparing the antrum area acquired by remodeling and left atrial computed tomography angiography (CTA). The observer scores for the modeling in the ICE and FAM groups were 3.40 &#177; 0.81 and 3.02 &#177; 0.72 (P &#60; 0.05), respectively. The pulmonary vein antrum area obtained using the ICE- and FAM-based methods showed a correlation with the area acquired by left atrium CT. However, the 95% confidence interval bias was narrower in ICE-acquired models than in FAM-acquired models (-238 cm2 to 323 cm2 Vs. -363 cm2 to 386 cm2, respectively) using Bland-Altman analysis. Therefore, precise ICE possesses high accuracy in estimating the left atrial structure, becoming a promising approach for future cardiac structure estimation.

Author Spotlight: Advancements in Intracardiac Echocardiography for Atrial Anatomy Assessment 

Accurate intracardiac echocardiography (ICE) shows significant accuracy in estimating the left atrial structure, a prospective and promising cardiac structure estimation method. Here we propose a protocol for three-dimensional modeling of the left atrium and pulmonary veins with ICE and fast anatomical mapping (FAM) catheter remodeling.

Intracardiac echocardiography (ICE) is a novel tool for estimating cardiac anatomy during pulmonary vein isolation procedures, particularly the left ...

Quantitative Analysis of Liver Stiffness Distribution

A Three-Dimensional Digital Model for Early Diagnosis of Hepatic Fibrosis Based on Magnetic Resonance Elastography

Hepatic fibrosis is an early stage of liver cirrhosis, and there are no better non-invasive and convenient methods for the detection and evaluation of the disease. Despite the good progress made with the liver stiffness map (LSM) based on magnetic resonance elastography (MRE), there are still some limitations that need to be overcome, including manual focus determination, manual selection of regions of interest (ROIs), and discontinuous LSM data without structural information, which makes it impossible to evaluate the liver as a whole. In this study, we propose a novel three-dimensional (3D) digital model for the early diagnosis of hepatic fibrosis based on MRE.
MRE is a non-invasive imaging technique that employs magnetic resonance imaging (MRI) to measure the liver stiffness at the scanning site through human-computer interaction. Studies have indicated a significant positive correlation between the LSM obtained through MRE and the degree of hepatic fibrosis. However, for clinical purposes, a comprehensive and precise quantification of the degree of hepatic fibrosis is necessary. To address this, the concept of Liver Stiffness Distribution (LSD) was proposed in this study, which refers to the 3D stiffness volume of each liver voxel obtained by the alignment of 3D liver tissue images and MRE indicators. This provides a more effective clinical tool for the diagnosis and treatment of hepatic fibrosis.

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI 

The objective of this study was to develop a novel three-dimensional digital model for the early diagnosis of hepatic fibrosis, which includes the stiffness of each voxel in the patient's liver and can thus, be used to calculate the distribution ratio of the patient's liver at different fibrosis stages.

Hepatic fibrosis is an early stage of liver cirrhosis, and there are no better non-invasive and convenient methods for the detection and evaluation of the ...

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 ...