Diagnosis Of Lung Nodules Research Articles

Detection, measurement, and diagnosis of lung nodules by ultra-low-dose CT in lung cancer screening: a systematic review

Detection, measurement, and diagnosis of lung nodules by ultra-low-dose CT in lung cancer screening: a systematic review

Interpretable rough neural network for lung nodule diagnosis

Computer-aided diagnosis (CAD) systems based on deep learning have shown significant potential in lung nodule diagnosis, providing substantial assistance to medical professionals. However, the inherent lack of interpretability in deep learning models and the uncertainty of annotations limit their widespread application. We propose that uncertain annotations actually imply additional valuable information that can enhance both model performance and interpretability. To address these challenges, we have developed a novel soft computing methodology integrating rough sets with deep neural networks. Firstly, this methodology employs rough sets to process uncertain region of interest (ROI) annotations into upper and lower approximations. Secondly, a novel rough neuron is designed to predict these approximations. Thirdly, the newly proposed region-constraint strategy embeds interpretable radiological domain knowledge into the neural network. Finally, this methodology proposes interpretation curves and regional consistency metrics to quantitatively evaluate the model’s interpretability. We conducted extensive comparison experiments on LIDC-IDRI and LNDb public benchmarks. Detailed experimental results demonstrate that by maximally retaining uncertain samples, the proposed method achieves classification accuracies of 84.6% and 89.74%, and mean absolute errors of 0.4988 and 0.5208 in attribute prediction, representing improvements of 3.4% and 2.5%, respectively, over the backbone networks.

Robotic One Anesthetic Diagnosis and Treatment: A Novel Program for Lung Cancer Care

BackgroundEarly diagnosis and treatment of lung cancer can improve patient outcomes. To reduce the delay between diagnosis and treatment, we initiated a Robotic One Anesthetic Diagnosis and Treatment (ROADAT) program for lung nodules that combined robotic navigational bronchoscopy, liquid rapid on-site evaluation (ROSE), and surgical resection in the same setting. MethodsA retrospective review was conducted of patients who underwent the ROADAT procedure from December 1, 2021, through January 31, 2024. These patients were compared with sequential controls from the same time window, who would have been ROADAT candidates but underwent diagnosis and treatment using a standard lung nodule workup. ResultsThere were 36 patients in the ROADAT group and 35 controls. Of the 36 ROADAT patients, 31 (86%) underwent lung resections. When final pathology was available, ROSE was concordant in 91% (29 of 32) of cases. Compared with robotic lobectomies alone, ROADAT procedures that resulted in lobectomies added 73 minutes (P = .003). However, total operating room time for when lobectomy and biopsy were done in separate settings was 54 minutes shorter in the ROADAT group (P = .028). Direct (ROADAT, $19,244; controls, $21,737; P = .004) procedure costs and total (ROADAT, $26,668; controls, $29,882; P = .005) procedure costs were lower in the ROADAT group for patients who underwent lobectomy. Time from detection to resection was 35 days shorter in the ROADAT group (ROADAT, 82 days; controls, 117 days; P = .001). ConclusionsThe ROADAT procedure reduced time from nodule detection to surgical resection compared with the standard approach. Liquid-based ROSE had high concordance with final pathology. Total operative time, direct costs, and total costs were lower for the ROADAT procedure.

Lung nodule detection using Eyrie Flock-based Deep Convolutional Neural Network

PROBLEM: Lung cancer is a dangerous and deadly disease with high mortality and reduced survival rates. However, the lung nodule diagnosis performance is limited by its heterogeneity in terms of texture, shape, and intensity. Furthermore, the high degree of resemblance between the lung nodules and the tissues that surround the lung nodules makes the building of a reliable detection model more difficult. Moreover, there are several methods for diagnosing and grading lung nodules; still, the accuracy of detection with the variations in intensity is a challenging task. AIM & METHODS: For the detection of lung nodules and grading, this research proposes an Eyrie Flock Optimization-based Deep Convolutional Neural Network (Eyrie Flock-DeepCNN). The proposed Eyrie Flock Optimization integrates the fishing characteristics of Eyrie’s and the flocking characteristics of Tusker to accelerate the convergence speed which inturns enhance the training process and improve the generalization performance of the DeepCNN model. In the Eyrie Flock optimization, two optimal issues are considered: (i) segmenting the lung nodule and (ii) fine-tuning hyperparameters of Deep CNN. RESULTS: The capability of the newly developed method is evaluated by the terms of Specificity, Sensitivity, and Accuracy, attaining 98.96%, 95.21%, and 94.12%, respectively. CONCLUSION: Efficiently utilized the Deep CNN along with the help of the Eyrie Flock optimization algorithm which enhances the efficiency of the classifier and convergence of the model.

Chinese expert consensus on diagnosis and treatment of pulmonary nodules(2024)

Lung cancer is the leading cause of the incidence and mortality of malignant tumors in China. The 5-year survival rate released for China in 2018 was 19.7%. The 5-year survival rate for stage Ⅰ patients is 77%-92%. Early diagnosis and treatment of lung cancer are key to improving the 5-year overall survival rate and prognosis of lung cancer patients. Therefore, experts from the Academic Group of Lung Cancer in Chinese Thoracic Society and Chinese Alliance Against Lung Cancer Expert Group formulated the Chinese Expert Consensus on the Diagnosis and Treatment of Lung Nodules in 2015 to standardize the diagnosis and treatment of lung nodules. In 2018, this consensus was updated to formulate the Chinese Expert Consensus on Diagnosis and Treatment of Lung Nodules (2018 edition), and widely applied in multiple branch centers of Chinese Alliance Against Lung Cancer, proposing the Intelligent Treatment of Million Early Lung Cancer Project. Based on applied experience of the expert consensus in recent years, with reference to the latest evidence has been updated, Chinese Expert Consensus on Diagnosis and Treatment of Lung Nodules (2024 edition) was formulated. The updated content of this consensus mainly includes the following aspects: (1) Define the screening age of high-risk lung cancer populations in China based on the national conditions; (2) Propose definition of "difficult-to-determine pulmonary nodules" to avoid delaying the diagnosis and treatment; (3) Evaluate pulmonary nodules assisted by artificial intelligence (AI) imaging-assisted diagnostic system and propose human-machine MDT to avoid the limitations of AI; (4) Evaluate pulmonary nodules by routine and individualized evaluations for different populations, and make recommendations based on evidence-based management guidelines for different types and sizes of pulmonary nodules. In the updated consensus, 18 consensus points were recommended as a reference for clinical management of pulmonary nodules to improve the 5-year overall survival rate and the prognosis of lung cancer in China.

Retraction Note: An early prediction and classification of lung nodule diagnosis on CT images based on hybrid deep learning techniques

Retraction Note: An early prediction and classification of lung nodule diagnosis on CT images based on hybrid deep learning techniques

The application progress of Convolutional Neural Networks (CNN) in lung nodule diagnosis

The application progress of Convolutional Neural Networks (CNN) in lung nodule diagnosis

Improving lung nodule segmentation in thoracic CT scans through the ensemble of 3D U-Net models.

The current study explores the application of 3D U-Net architectures combined with Inception and ResNet modules for precise lung nodule detection through deep learning-based segmentation technique. This investigation is motivated by the objective of developing a Computer-Aided Diagnosis (CAD) system for effective diagnosis and prognostication of lung nodules in clinical settings. The proposed method trained four different 3D U-Net models on the retrospective dataset obtained from AIIMS Delhi. To augment the training dataset, affine transformations and intensity transforms were utilized. Preprocessing steps included CT scan voxel resampling, intensity normalization, and lung parenchyma segmentation. Model optimization utilized a hybrid loss function that combined Dice Loss and Focal Loss. The model performance of all four 3D U-Nets was evaluated patient-wise using dice coefficient and Jaccard coefficient, then averaged to obtain the average volumetric dice coefficient (DSCavg) and average Jaccard coefficient (IoUavg) on a test dataset comprising 53 CT scans. Additionally, an ensemble approach (Model-V) was utilized featuring 3D U-Net (Model-I), ResNet (Model-II), and Inception (Model-III) 3D U-Net architectures, combined with two distinct patch sizes for further investigation. The ensemble of models obtained the highest DSCavg of 0.84 ± 0.05 and IoUavg of 0.74 ± 0.06 on the test dataset, compared against individual models. It mitigated false positives, overestimations, and underestimations observed in individual U-Net models. Moreover, the ensemble of models reduced average false positives per scan in the test dataset (1.57 nodules/scan) compared to individual models (2.69-3.39 nodules/scan). The suggested ensemble approach presents a strong and effective strategy for automatically detecting and delineating lung nodules, potentially aiding CAD systems in clinical settings. This approach could assist radiologists in laborious and meticulous lung nodule detection tasks in CT scans, improving lung cancer diagnosis and treatment planning.

Bronchoscopy with and without needle-based confocal laser endomicroscopy for peripheral lung nodule diagnosis: protocol for a multicentre randomised controlled trial (CLEVER trial)

IntroductionDespite many technological advances, the diagnostic yield of bronchoscopic peripheral lung nodule analysis remains limited due to frequent mispositioning. Needle-based confocal laser endomicroscopy (nCLE) enables real-time microscopic feedback on needle...

A design of interactive review for computer aided diagnosis of pulmonary nodules based on active learning

Automatic detection of pulmonary nodule based on computer tomography (CT) images can significantly improve the diagnosis and treatment of lung cancer. However, there is a lack of effective interactive tools to record the marked results of radiologists in real time and feed them back to the algorithm model for iterative optimization. This paper designed and developed an online interactive review system supporting the assisted diagnosis of lung nodules in CT images. Lung nodules were detected by the preset model and presented to doctors, who marked or corrected the lung nodules detected by the system with their professional knowledge, and then iteratively optimized the AI model with active learning strategy according to the marked results of radiologists to continuously improve the accuracy of the model. The subset 5-9 dataset of the lung nodule analysis 2016(LUNA16) was used for iteration experiments. The precision, F1-score and MioU indexes were steadily improved with the increase of the number of iterations, and the precision increased from 0.213 9 to 0.565 6. The results in this paper show that the system not only uses deep segmentation model to assist radiologists, but also optimizes the model by using radiologists' feedback information to the maximum extent, iteratively improving the accuracy of the model and better assisting radiologists.

Tongue image feature correlation analysis in benign lung nodules and lung cancer

ObjectiveTo analyze the differences in the correlation of tongue image indicators among patients with benign lung nodules and lung cancer. MethodsFrom July 1, 2020 to March 31, 2022, clinical information of lung cancer patients and benign lung nodules patients was collected at the Oncology Department of Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine and the Physical Examination Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, respectively. We obtained tongue images from patients with benign lung nodules and lung cancer using the TFDA-1 digital tongue diagnosis instrument, and analyzed these images with the TDAS V2.0 software. The extracted indicators included color space parameters in the Lab system for both the tongue body (TB) and tongue coating (TC) (TB/TC-L, TB/TC-a, and TB/TC-b), textural parameters [TB/TC-contrast (CON), TB/TC-angular second moment (ASM), TB/TC-entropy (ENT), and TB/TC-MEAN], as well as TC parameters (perAll and perPart). The bivariate correlation of TB and TC features was analyzed using Pearson’s or Spearman’s correlation analysis, and the overall correlation was analyzed using canonical correlation analysis (CCA). ResultsSamples from 307 patients with benign lung nodules and 276 lung cancer patients were included after excluding outliers and extreme values. Simple correlation analysis indicated that the correlation of TB-L with TC-L, TB-b with TC-b, and TB-b with perAll in lung cancer group was higher than that in benign nodules group. Moreover, the correlation of TB-a with TC-a, TB-a with perAll, and the texture parameters of the TB (TB-CON, TB-ASM, TB-ENT, and TB-MEAN) with the texture parameters of the TC (TC-CON, TC-ASM, TC-ENT, and TC-MEAN) in benign nodules group was higher than lung cancer group. CCA further demonstrated a strong correlation between the TB and TC parameters in lung cancer group, with the first and second pairs of typical variables in benign nodules and lung cancer groups indicating correlation coefficients of 0.918 and 0.817 (P < 0.05), and 0.940 and 0.822 (P < 0.05), respectively. ConclusionBenign lung nodules and lung cancer patients exhibited differences in correlation in the L, a, and b values of the TB and TC, as well as the perAll value of the TC, and the texture parameters (TB/TC-CON, TB/TC-ASM, TB/TC-ENT, and TB/TC-MEAN) between the TB and TC. Additionally, there were differences in the overall correlation of the TB and TC between the two groups. Objective tongue diagnosis indicators can effectively assist in the diagnosis of benign lung nodules and lung cancer, thereby providing a scientific basis for the early detection, diagnosis, and treatment of lung cancer.

Progress in Computer-aided Diagnosis of Lung Nodules based on CT Images

The objective of this study is to review the research progress of computer-aided diagnosis of pulmonary nodules based on CT images. This is done in order to address the challenges posed by the increasing incidence and difficulty of diagnosis of pulmonary nodules. Through an in-depth analysis of the key technologies, algorithms and application cases in the diagnosis of pulmonary nodules, we sought to identify how computer technology can be used to improve the accuracy and efficiency of diagnosis. The study found that CT images have the advantages of high resolution and multi-dimensional reconstruction in the detection of pulmonary nodules. However, interpreting CT images still requires specialized medical knowledge. Computer-aided diagnosis technology can assist doctors to identify lung nodules more accurately, especially for nodules with similar density to the surrounding tissue. This can improve the sensitivity and specificity of diagnosis. In conclusion, the computer-aided diagnosis system based on CT images provides substantial support for the accurate diagnosis of pulmonary nodules, which is beneficial for the improvement of patient health management and the formulation of treatment plans.

Solitary pulmonary capillary hemangioma – An underrecognized rare tumor. Report of 32 new cases with literature review

ObjectiveTo explore the clinical, imaging, pathologic characteristics and differential diagnosis of solitary pulmonary capillary hemangioma (SPCH). MethodsThirty two cases of SPCH were collected and studied, with literature review. ResultsThis study included 13 males and 19 females, with a male-to-female ratio of 1:1.5. The age ranged from 26 to 70 years (median age of 43 years). All patients were asymptomatic at presentation. Lung nodules were incidentally discovered during chest computed tomography (CT). Imaging features included 21 cases with partial solid nodules (PSN), 7 cases with ground-glass nodules (GGN), and 4 cases with solid nodules (SN). Eleven cases were in the left lung lower basal segment, 11 cases in the right lung lower basal segment, 6 cases in the right lung upper anterior segment, and 4 cases in the right lung middle lateral segment. The lower basal segments of the lungs were involved in 22 (11 in each lung) cases (22/32, 68 %). The tumors ranged from 6 to 18 mm (average 10 mm). Macroscopically, 16 cases had clear boundaries, while 16 cases had unclear boundaries, and gray-red or dark brown on cut surfaces. Intraoperative frozen section was performed in 27 cases, with diagnosis of SPCH in 12 and pneumonia or inflammatory lesion in 15. Microscopically, the nodules were composed of densely proliferated and dilated capillaries. The capillary walls were lined with a single layer of flat endothelial cells, without atypical features. Collapsed alveolar septa were replaced by a large number of capillaries. All cases showed proliferating capillaries spreading into the walls of small veins/arteries and bronchi, with 3 cases showing dilated capillaries protruding into the bronchiolar lumens as polyp-like structures. Twenty-six cases (26/32, 81 %) showed proliferating capillaries passed over the interlobular septa. Twenty-six cases (26/32, 81 %) showed irregular intimal thickening of small muscular arteries in the peripheral areas of the lesions, with the thickened intima being cellular or fibrous. In twenty-seven cases (27/32, 84 %) the lesions were located in the subpleura, with 6 cases involving the pleura. ConclusionSPCH is a rare benign lung tumor that mostly occurs in the lung lower basal segments with predominance in females. It usually appears as a ground-glass nodule on CT and is very similar to early-stage lung cancer. Accurate diagnosis requires collaboration of radiologists, surgeons, and pathologists. SPCH should be regarded as an important differential diagnosis of small incidental lung nodules.

Classification of Chest CT Lung Nodules Using Collaborative Deep Learning Model.

Early detection of lung cancer through accurate diagnosis of malignant lung nodules using chest CT scans offers patients the highest chance of successful treatment and survival. Despite advancements in computer vision through deep learning algorithms, the detection of malignant nodules faces significant challenges due to insufficient training datasets. This study introduces a model based on collaborative deep learning (CDL) to differentiate between cancerous and non-cancerous nodules in chest CT scans with limited available data. The model dissects a nodule into its constituent parts using six characteristics, allowing it to learn detailed features of lung nodules. It utilizes a CDL submodel that incorporates six types of feature patches to fine-tune a network previously trained with ResNet-50. An adaptive weighting method learned through error backpropagation enhances the process of identifying lung nodules, incorporating these CDL submodels for improved accuracy. The CDL model demonstrated a high level of performance in classifying lung nodules, achieving an accuracy of 93.24%. This represents a significant improvement over current state-of-the-art methods, indicating the effectiveness of the proposed approach. The findings suggest that the CDL model, with its unique structure and adaptive weighting method, offers a promising solution to the challenge of accurately detecting malignant lung nodules with limited data. This approach not only improves diagnostic accuracy but also contributes to the early detection and treatment of lung cancer, potentially saving lives.

Diagnostic Performance of MRI for the Detection of Pulmonary Nodules: A Systematic Review and Meta-Analysis.

Purpose To perform a meta-analysis of the diagnostic performance of MRI for the detection of pulmonary nodules, with use of CT as the reference standard. Materials and Methods PubMed, Embase, Scopus, and other databases were systematically searched for studies published from January 2000 to March 2023 evaluating the performance of MRI for diagnosis of lung nodules measuring 4 mm or larger, with CT as reference. Studies including micronodules, nodules without size stratification, or those from which data for contingency tables could not be extracted were excluded. Primary outcomes were the per-lesion sensitivity of MRI and the rate of false-positive nodules per patient (FPP). Subgroup analysis by size and meta-regression with other covariates were performed. The study protocol was registered in the International Prospective Register of Systematic Reviews, or PROSPERO (no. CRD42023437509). Results Ten studies met inclusion criteria (1354 patients and 2062 CT-detected nodules). Overall, per-lesion sensitivity of MRI for nodules measuring 4 mm or larger was 87.7% (95% CI: 81.1, 92.2), while the FPP rate was 12.4% (95% CI: 7.0, 21.1). Subgroup analyses demonstrated that MRI sensitivity was 98.5% (95% CI: 90.4, 99.8) for nodules measuring at least 8-10 mm and 80.5% (95% CI: 71.5, 87.1) for nodules less than 8 mm. Conclusion MRI demonstrated a good overall performance for detection of pulmonary nodules measuring 4 mm or larger and almost equal performance to CT for nodules measuring at least 8-10 mm, with a low rate of FPP. Systematic review registry no. CRD42023437509 Keywords: Lung Nodule, Lung Cancer, Lung Cancer Screening, MRI, CT Supplemental material is available for this article. © RSNA, 2024.

Navigational Bronchoscopy vs CT Scan-Guided Transthoracic Needle Biopsy for the Diagnosis of Indeterminate Lung Nodules: Protocol and Rationale for the Navigation Endoscopy to Reach Indeterminate Lung Nodules vs Transthoracic Needle Aspiration, a Randomized Controlled Study Multicenter Randomized Trial

Navigational Bronchoscopy vs CT Scan-Guided Transthoracic Needle Biopsy for the Diagnosis of Indeterminate Lung Nodules: Protocol and Rationale for the Navigation Endoscopy to Reach Indeterminate Lung Nodules vs Transthoracic Needle Aspiration, a Randomized Controlled Study Multicenter Randomized Trial

136TiP A cohort study on the diagnosis of benign or malignant lung nodules based on synapse 3D software AI devascularization technique

136TiP A cohort study on the diagnosis of benign or malignant lung nodules based on synapse 3D software AI devascularization technique

Effective lung nodule detection using deep CNN with dual attention mechanisms

Novel methods are required to enhance lung cancer detection, which has overtaken other cancer-related causes of death as the major cause of cancer-related mortality. Radiologists have long-standing methods for locating lung nodules in patients with lung cancer, such as computed tomography (CT) scans. Radiologists must manually review a significant amount of CT scan pictures, which makes the process time-consuming and prone to human error. Computer-aided diagnosis (CAD) systems have been created to help radiologists with their evaluations in order to overcome these difficulties. These systems make use of cutting-edge deep learning architectures. These CAD systems are designed to improve lung nodule diagnosis efficiency and accuracy. In this study, a bespoke convolutional neural network (CNN) with a dual attention mechanism was created, which was especially crafted to concentrate on the most important elements in images of lung nodules. The CNN model extracts informative features from the images, while the attention module incorporates both channel attention and spatial attention mechanisms to selectively highlight significant features. After the attention module, global average pooling is applied to summarize the spatial information. To evaluate the performance of the proposed model, extensive experiments were conducted using benchmark dataset of lung nodules. The results of these experiments demonstrated that our model surpasses recent models and achieves state-of-the-art accuracy in lung nodule detection and classification tasks.

Image-Guided VATS in the Hybrid Operation Room Facilitates Early Diagnosis and Concurrent Treatment of Subcentimeter Nonpalpable Lung Nodules.

As lung cancer screening increases, the detection of small, nonpalpable lung lesions is on the rise. The hybrid operation room (OR), which combines percutaneous or endobronchial fiducial placement with on-table computed tomography (CT) and fluoroscopic guidance, improves localization and facilitates the diagnosis and treatment of smaller, nonpalpable lung nodules with greater accuracy. In 35 consecutive months, 55 veterans underwent 60 image-guided video-assisted thoracic surgery procedures for lesion resection. Of the cases, 36% were found during lung cancer screening. All patients received their care in the hybrid OR, where cone-beam CT scan technology was used to place an average of 1.6 fiducials percutaneously (n = 55) or via augmented navigational bronchoscopy (n = 5). A total of 66 lesions were resected. The median lesion size was 8 mm with an interquartile range of 6 to 14. The patients underwent nonanatomical resection with lymph node dissection using radiologic guidance. When indicated, an anatomical resection was subsequently performed. Of 47 total non-small cell lung cancer lesions, 83% were diagnosed at stage IA1 or IA2. The median surgical margin was 15 mm; the margin was usually 1.5 times as wide as the lesion. The hybrid OR technology gives a 3-dimensional assessment of the small lung lesions, allowing for a tissue-saving resection while achieving good surgical margins. During lung cancer screening, smaller, nonpalpable lung nodules are frequently found. This technology allows resection of subcentimeter lesions, which would otherwise be unresectable at this early stage, possibly improving survival.

Evaluating artificial intelligence's role in lung nodule diagnostics: A survey of radiologists in two pilot tertiary hospitals in China.

This study assesses the perceptions and attitudes of Chinese radiologists concerning the application of artificial intelligence (AI) in the diagnosis of lung nodules. An anonymous questionnaire, consisting of 26 questions addressing the usability of AI systems and comprehensive evaluation of AI technology, was distributed to all radiologists affiliated with Beijing Anzhen Hospital and Beijing Tsinghua Changgung Hospital. The data collection was conducted between July 19, and 21, 2023. Of the 90 respondents, the majority favored the AI system's convenience and usability, reflected in "good" system usability scale (SUS) scores (Mean ± standard deviation [SD]: 74.3 ± 11.9). General usability was similarly well-received (Mean ± SD: 76.0 ± 11.5), while learnability was rated as "acceptable" (Mean ± SD: 67.5 ± 26.4). Most radiologists noted increased work efficiency (Mean Likert scale score: 4.6 ± 0.6) and diagnostic accuracy (Mean Likert scale score: 4.2 ± 0.8) with the AI system. Views on AI's future impact on radiology careers varied (Mean ± SD: 3.2 ± 1.4), with a consensus that AI is unlikely to replace radiologists entirely in the foreseeable future (Mean ± SD: 2.5 ± 1.1). Radiologists at two leading Beijing hospitals generally perceive the AI-assisted lung nodule diagnostic system positively, citing its user-friendliness and effectiveness. However, the system's learnability requires enhancement. While AI is seen as beneficial for work efficiency and diagnostic accuracy, its long-term career implications remain a topic of debate.