A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
May 19th, 2023
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.
This study aims to create a continuous 3-D reconstruction model for the better characterizing the pulmonary nodules for clinical diagnosis and prognosis evaluation. Deep integration of AI-driving imaging technology, and pulmonary-nodule disease diagnosis and treatment are some of the recent developments in this research field. The combination of medical imaging, natural-language processing, digitization, class of field design and the clinical diagnosis and the treatment-scenario risks are recent advanced in this field.
Gradually, establishing three-dimensional imaging, differentiation features of pulmonary nodules and conducting long-term tracking of the efficacy of traditional Chinese medicine helps to optimize the treatment plan for the prevention and the treatment of disease. The images produced with this digital model are accurate and innovative, avoiding false positives and false negatives, and are not sensitive to scanning equipment. The outcome of this study helps in the clinical evidence-based classic fiction of pulmonary nodules'prognosis, evaluation, and RTP mization of treatment plans based on three-dimensional special features.
Our research focus includes combining digitization, artificial intelligence, imaging and the evaluation and optimization of clinical treatment plans of traditional Chinese medicine to create cost effective and better treatment plans. To begin, obtain written consent from the patient for acquiring the DICOM data. After obtaining the images, transfer the data to the designated working directory in the computer.
Then, 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, the thinner is the scan layer thickness. Using the DICOM info function and DICOM files as the function parameters in the MATLAB environment, obtain the slice thickness and pixel-spacing parameters, which are essential for setting the 3-D volume display rate.
Next, using the function DICOM info, read the location data of each image by giving info. slicelocation as input into the MATLAB workspace. Implement the slice-location function to store the location array for a variable and create a plot of the location array.
Use the data tips button at the top right of the graphical user interface or GUI and add a data tip to the plot on the point representing the maximum location value of the normal sequence. Using the volume-resort function, sort all the images and then extract the images from one to the maximum location value. Store the volumes of the valid images with the sorted index, which will help to trace back the important nodules.
Move the cross hair to the horizontal axis to browse all the images in the coronal axis. Note that the cross hair is in the same spatial coordinates and moving it on one axis will change the location of the images in the other two axes. Use the volume-inspect function to display the axial, coronal and sagittal views of the constructed volume.
In the volume-inspect function, use the default intensity window for the lung and the GUI. Hold the left mouse button to adjust the filter performance and drag it on the axis. Correct data preparation and volume calculation results in lung images appearing in the axial, coronal and sagittal planes.
To reconstruct the three-dimensional digital model of the pulmonary nodules, implement the three-dimensional lung horizon function in the MATLAB workspace. Then, open the graphical user interface, or GUI to check the horizontal, three-dimensional model. Drag the scroll bar on the GUI with the mouse to observe the continuous three-dimensional lung structure, allowing for the clear visualization of various types of pulmonary nodules and their relative spatial relationships with the lung tissue.
Use the zoom function to observe the local features of the lesions and relevant three-dimensional structural output pictures. To measure the size of the nodules, use the mark pixel coordinates button to calculate the distance between two points. The default color bar jet color map displays values from low to high in a blue to red range.
To reset the GUI, right click the color bar and select the standard gray color map. If the filter window is not satisfactory, use the left mouse button to adjust the window level by dragging up and down in the middle of the figure. Drag left and right to adjust the window width to display the accurate filtering range on the color bar.
The continuous three-dimensional reconstruction resulted in the axial view of the pulmonary nodules and the relationship between the nodules and the surrounding lung tissue. Use the three-dimensional nodules function with two parameters, including the slice number and the thoracic volume, to reconstruct a three-dimensional digital model for specific nodules. If the three-dimensional nodules function correctly executes, press and hold the left mouse button and drag it in any direction to alter the perspective of the pulmonary nodules.
It is important to note that the observation angle should account for anatomical considerations and attempt to display both the medical characteristics of the pulmonary nodules, and the relationship between the nodules and the surrounding tissues. Use the zoom and move icons in the upper right corner. Alternatively, use the middle mouse button to zoom in on the model or out of the view.
The GUI displays the coordinate indication in the lower left corner, with the positive direction on the Z axis being the scanning direction in the horizontal position. Utilize the screenshot tool for the operating system to save the necessary three-dimensional projection of the nodules. With this three-dimensional construction, the GUI allows the user to observe the pulmonary nodules of interest from any perspective.
To reconstruct the three-dimensional digital model of pulmonary nodules, implement the three-dimensional lung coronal function in the MATLAB workplace, then open the graphical user interface, or GUI, prepared by the function to check the three-dimensional coronal model. Drag the scroll bar on the GUI with the mouse to observe the continuous three-dimensional lung structure, allowing for the clear visualization of various types of pulmonary nodules and the relative spatial relationships with the lung tissue. Use the zoom function to observe the local features of the lesions and relevant three-dimensional structural output pictures.
To measure the size of the nodules, use the mark pixel coordinates button to calculate the distance between two points. The default color bar jet color map displays values from low to high in a blue to red range. To reset the GUI, right click the color bar and select the standard gray color map.
If the filter window is not satisfactory, use the left mouse button to adjust the window level by dragging up and down in the middle of the figure. Drag left and right to adjust the window width to display the accurate filtering range on the color bar. The continuous three-dimensional reconstruction resulted in the coronal view of the pulmonary nodules and the relationship between the nodules and the surrounding lung tissue.
Once the three-dimensional digital model is prepared, open Adobe Captivate 2019 and create a new screen-recording project. Turn the camera off and wait for a red screen-recording range box to appear for recording only the screen operation. Fit the frame into the model and click the recording button to operate the graphical user interface, or GUI, and generate a digital video file of the screen recording.
After recording the dynamic display of the pulmonary nodules, click the icon in the task bar to return to the operating environment of the software. Next, click on file, distribute and configure the file storage path to save the recorded dynamic video of the three-dimensional digital model of the pulmonary nodules. Then, name the file and save the desired digital video file.
View the full transcript and gain access to thousands of scientific videos
Overview
This study aims to create a continuous 3-D reconstruction model for better characterizing pulmonary nodules, enhancing clinical diagnosis and prognosis evaluation. The integration of AI-driven imaging technology is a significant advancement in this research field.
Key Study Components
Area of Science
- Neuroscience
- Medical Imaging
- Clinical Diagnosis
Background
- Recent developments in pulmonary nodule diagnosis and treatment.
- Deep integration of AI and imaging technologies.
- Importance of accurate imaging to avoid false positives and negatives.
- Long-term tracking of treatment efficacy.
Purpose of Study
- To develop a novel 3D digital model of pulmonary nodules.
- To serve as a communication bridge between physicians and patients.
- To optimize treatment plans based on three-dimensional features.
Methods Used
- 3D reconstruction modeling.
- Integration of medical imaging and natural-language processing.
- Digitization and clinical scenario analysis.
- Long-term tracking methodologies.
Main Results
- Accurate and innovative imaging of pulmonary nodules.
- Reduction of false positives and negatives in diagnosis.
- Enhanced clinical evidence for prognosis evaluation.
- Optimization of treatment plans based on 3D features.
Conclusions
- The digital model significantly aids in clinical diagnosis.
- It provides a reliable tool for pre-diagnosis and prognostic evaluation.
- Establishes a foundation for future advancements in pulmonary nodule treatment.
Frequently Asked Questions
Executive Industry Relevance
Three-dimensional digital modeling of pulmonary nodules advances early diagnostic precision and supports risk-adjusted treatment planning in respiratory disease portfolios. By integrating AI-driven imaging and robust volumetric reconstruction, this approach enhances predictive confidence and reduces diagnostic ambiguity at critical inflection points in the discovery-to-clinic continuum. The method's reproducibility and device-agnostic design position it as a scalable asset for translational and preclinical research pipelines.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Enables detailed spatial characterization of pulmonary nodules for hypothesis-driven target validation.
- Supports mechanistic de-risking by clarifying nodule-tissue relationships in three dimensions.
- Facilitates predictive confidence in distinguishing benign from malignant features using quantitative imaging outputs.
Screening & Assay Development
- Provides standardized, reproducible 3D models for downstream imaging-based screening workflows.
- Delivers quantitative measurements of nodule size and morphology to inform assay development.
- Enables rapid localization and multi-perspective evaluation of nodules, supporting scalable screening readiness.
Translational & Preclinical Research
- Aligns imaging outputs with disease-relevant anatomical and morphological biomarkers for translational continuity.
- Supports longitudinal tracking of treatment efficacy and disease progression in preclinical models.
- Reduces false positive/negative rates, improving risk-adjusted advancement decisions in respiratory portfolios.
Pipeline & Workflow Integration
This 3D digital modeling workflow bridges early discovery, imaging-based screening, and translational research by providing a reproducible, quantitative platform for pulmonary nodule evaluation.
- Discovery Biology: Enhances hypothesis testing and mechanistic de-risking through high-resolution spatial mapping of nodules.
- Screening: Standardizes imaging outputs for reproducible, quantitative comparison across conditions and timepoints.
- Analytics: Enables precise measurement of nodule dimensions and spatial relationships, supporting robust statistical analysis.
- Translational Research: Facilitates continuity from discovery imaging to preclinical and clinical evaluation of disease-relevant features.
- Enterprise Reuse: Offers a device-agnostic, scalable modeling capability for repeated use across respiratory research programs.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence and reduces diagnostic ambiguity in pulmonary nodule assessment.
- Operational Value: Delivers standardized, reproducible, and scalable imaging workflows adaptable to diverse equipment and study designs.
- Strategic Value: Improves go/no-go decision-making and capital efficiency by reducing false diagnostic outcomes.
- Portfolio Impact: Supports risk-adjusted prioritization and advancement of respiratory disease assets.
Implementation Considerations
- Requires expertise in medical imaging, digital modeling, and AI-driven analysis.
- Depends on access to DICOM-compatible imaging infrastructure and computational resources for 3D reconstruction.
- Demands cross-team standardization of imaging protocols and data handling.
- Adaptable to various nodule types and imaging devices, but may require calibration for optimal accuracy.
- Practical limitations include the need for high-quality input data and user training in GUI-based model manipulation.
Biopharma FAQs
Chapters in this video
0:00
Introduction
2:57
Low-Dose Computed Tomography (LDCT) Data Collection for Detecting Pulmonary Nodules
5:13
Digital Model for Horizontal 3D Reconstruction of Pulmonary Nodules
6:44
Constructing a 3D Digital Model for any Specific Nodule
8:03
Digital Model of a Coronal 3D Reconstruction
9:31
Output 3D Video for Dominant Pulmonary Nodules
