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

Summary

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.

Abstract

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.

Introduction

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 recom....

Protocol

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 diagn.......

Discussion

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.......

Materials

Name	Company	Catalog Number	Comments
MATLAB	MathWorks	2022B	Computing and visualization
Tools for Modeling	Intelligent  Entropy	PulmonaryNodule V1.0	Beijing Intelligent Entropy Science & Technology Co Ltd. Modeling for CT/MRI fusion