Detection Of Nodules Research Articles

Deep Learning-Based Slice Thickness Reduction for Computer-Aided Detection of Lung Nodules in Thick-Slice CT

Background/Objectives: Computer-aided detection (CAD) systems for lung nodule detection often face challenges with 5 mm computed tomography (CT) scans, leading to missed nodules. This study assessed the efficacy of a deep learning-based slice thickness reduction technique from 5 mm to 1 mm to enhance CAD performance. Methods: In this retrospective study, 687 chest CT scans were analyzed, including 355 with nodules and 332 without nodules. CAD performance was evaluated on nodules, to which all three radiologists agreed. Results: The slice thickness reduction technique significantly improved the area under the receiver operating characteristic curve (AUC) for scan-level analysis from 0.867 to 0.902, with a p-value < 0.001, and nodule-level sensitivity from 0.826 to 0.916 at two false positives per scan. Notably, the performance showed greater improvements on smaller nodules than larger nodules. Qualitative analysis confirmed that nodules mistaken for ground glass on 5 mm scans could be correctly identified as part-solid on the refined 1 mm CT, thereby improving the diagnostic capability. Conclusions: Applying a deep learning-based slice thickness reduction technique significantly enhances CAD performance in lung nodule detection on chest CT scans, supporting the clinical adoption of refined 1 mm CT scans for more accurate diagnoses.

Comparison of AI software tools for automated detection, quantification and categorization of pulmonary nodules in the HANSE LCS trial.

Participant management in a lung cancer screening (LCS) depends on the assigned Lung Imaging Reporting and Data System (Lung-RADS) category, which is based on reliable detection and measurement of pulmonary nodules. The aim of this study was to compare the agreement of two AI-based software tools for detection, quantification and categorization of pulmonary nodules in an LCS program in Northern Germany (HANSE-trial). 946 low-dose baseline CT-examinations were analyzed by two AI software tools regarding lung nodule detection, quantification and categorization and compared to the final radiologist read. The relationship between detected nodule volumes by both software tools was assessed by Pearson correlation (r) and tested for significance using Wilcoxon signed-rank test. The consistency of Lung-RADS classifications between Software tool 1 (S1, Aview v2.5, Coreline Soft, Seoul, Korea) and Software tool 2 (S2, Prototype ''ChestCTExplore'', software version ToDo, Siemens Healthineers, Forchheim, Germany) was evaluated by Cohen's kappa (κ) and percentual agreement (PA).The derived volumes of true positive nodules were strongly correlated (r > 0.95), the volume derived by S2 was significantly higher than by S1 (P < 0.0001, mean difference: 6mm3). Moderate PA (62%) between S1 and S2 was found in the assignment of Lung-RADS classification (κ = 0.45). The PA of Lung-RADS classification to final read was 75% and 55% for S1 and S2, but the incorporation of S1 into the initial nodule detection and segmentation must be considered here. Significant nodule volume differences between AI software tools lead to different Lung-RADS scores in 38% of cases, which may result in altered participant management. Therefore, high performance and agreement of accredited AI software tools are necessary for a future national LCS program.

A systematic review on feature extraction methods and deep learning models for detection of cancerous lung nodules at an early stage -The Recent trends and challenges.

Lung cancer is one of the most common life-threatening worldwide cancers affecting both the male and female populations. The appearance of nodules in the scan image is an early indication of the development of cancer cells in the lung. The Low Dose Computed Tomography screening technique is used for the early detection of cancer nodules. Therefore, with more Computed Tomography (CT) lung profiles, an automated lung nodule analysis system can be utilized through image processing techniques and neural network algorithms. A CT image of the lung consists of many elements such as blood vessels, ribs, nodules, sternum, bronchi, and nodules. These nodules can be both benign and malignant, where the latter leads to lung cancer. Detecting them at an earlier stage can increase life expectancy by up to 5 to 10 years. To analyze only the nodules from the profile, respected features are calculated using image processing techniques. Based on the review, textural features were promising ones in medical image analysis and for solving computer vision problems. The significance of extracting the hidden features (textural) enables the higher performance of deep learning algorithms (DL) predominantly in medical imaging, which has resulted in improvement in accuracy in recent years. Earlier detection of cancerous lung nodules is possible through the combination of multi-featured extraction and classification techniques using image data. In this paper, we discussed the overview of lung cancer along with publicly available datasets for research purposes. The primary objective of the paper is to provide the importance of textural features when combined with different deep-learning models. It gives insights into their advantages, disadvantages, and limitations regarding possible research gaps. The paper compared the recent studies of deep learning models with and without feature extraction and concluded that DL models that include feature extraction were better than the others.

Vermicular and Chunky Graphite Nucleation during Chill Casting: Study on the Dynamics of Ductile Iron Recycling

AbstractThis investigation provides alternative insight into the dynamic behavior of spheroidal graphite structures throughout the remelting process during ductile cast iron recycling. Specifically, this study systematically analyzed the impact of the remelting process on graphite nucleation. Two approaches are included in this research: thermal analysis and the copper–mold chill casting method. Based on these results, different graphite structures nucleate in cast iron in response to the remelting and recasting processes, which correlate with the melting temperature, the cooling method, and the fading effect. Furthermore, it is indicated that the dissolution rate of spheroidal graphite is relatively slower than lamellar graphite, substantiated by the detection of residual graphite nodules in the case of low melting temperature applied during the remelting process. In addition, the formation of vermicular and potentially chunky graphite structures can also be observed to a certain extent during this set of investigations, thus providing further insight into their relation to the ductile cast iron production process. Graphical Abstract

Diagnostic accuracy and image quality evaluation of ultrashort echo time MRI in the lungs.

This study evaluates the diagnostic accuracy of ultrashort echo time (UTE)-MRI for detecting pulmonary nodules and image quality. A total of 46 patients at our hospital underwent unenhanced computed tomography (CT) and UTE-MRI. The image quality and number of nodules detected using CT were used as the gold standards. Three diagnostic radiologists independently recorded the image quality (visibility and sharpness of normal anatomical structures) of the CT and UTE images and the number of pulmonary nodules detected. The diagnostic accuracy, subjective image quality, and consistency between observations were statistically analyzed. Among 46 patients, 36 (78.2%) had pulmonary nodules on CT images, whereas 10 patients (21.7%) had no pulmonary nodules. A total of 48 lung nodules were detected, 3 of which were ground-glass opacities. UTE-MRI revealed 46 lung nodules. Compared with CT, the sensitivity of all MRI readers for detecting lung lesions was 95.8%, and the 3-observer agreement was nearly perfect (P < .001, Kendall Wa [Kender Harmonious Coefficient] = 0.913). The overall image quality score of the observers was high, ranging from good to excellent, and the consistency of the subjective UTE-MRI image quality was good (Kendall Wa = 0.877, P < .001). For tracheal display, the subsegment of the bronchus was displayed, and the wall of the tube was clearly displayed. The difference in the Wa values between the observers was 0.804 (P < .001), indicating strong consistency. For blood vessels, subsegment blood vessels could also be displayed with clear walls and uniform signals (Kendal Wa = 0.823, P < .001), indicating strong consistency. Compared to CT, UTE-MRI can detect pulmonary nodules with a high detection rate, relatively good image quality, and strong consistency between observers. The development of UTE-MRI can provide a novel imaging method for the detection and follow-up of pulmonary nodules and diagnosis of pneumonia by reducing ionizing radiation.

Pulmonary nodules and the psychological harm they can cause: A scoping review

More than 1.6 million pulmonary nodules are diagnosed in the United States each year. Although the majority of nodules are found to be benign, nodule detection and the process of ruling out malignancy can cause patients psychological harm to varying degrees. The present study undertakes a scoping review of the literature investigating pulmonary nodule-related psychological harm as a primary or secondary outcome. Online databases were systematically searched to identify papers published through June 30, 2023, from which 19 publications were reviewed. We examined prevalence by type, measurement, associated factors, and behavioral or clinical consequences. Of the 19 studies reviewed, 11 studies investigated distress, anxiety (n = 6), and anxiety and depression (n = 4). Prevalence of distress was 24.0 %-56.7 %; anxiety 9.9 %-42.1 %, and 14.6 %-27.0 % for depression. A wide range of demographic and social characteristics and clinical factors were associated with nodule-related psychological harm. Outcomes of nodule-related harms included experiencing conflict when deciding about treatment or surveillance, decreased adherence to surveillance, adoption of more aggressive treatment, and lower health-related quality of life. Our scoping review demonstrates that nodule-related psychological harm is common. Findings provide evidence that nodule-related psychological harm can influence clinical decisions and adherence to treatment recommendations. Future research should focus on discerning between nodule-related distress and anxiety; identifying patients at risk; ascertaining the extent of psychological harm on patient behavior and clinical decisions; and developing interventions to assist patients in managing psychological harm for better health-related quality of life and treatment outcomes.

The Correlation Between the Natural Course, Pathologic Properties With Ki-67 Expression in Lung Adenocarcinoma Presenting as Ground-Glass Nodules.

With the increasing use of lung cancer screening, the detection of ground glass nodules (GGNs) has risen. However, the natural course of GGNs and their relationship to pathologic features remains unclear. Differentiating between invasive and pre-invasive lesions based on GGN growth may improve clinical intervention timing. Ki-67, a proliferation marker, holds value in assessing tumor malignancy. This study analyzes the association between GGN growth, pathology, and Ki-67 expression to provide new insights into early-stage lung cancer management. We retrospectively evaluated 183 GGNs with at least two preoperative CT scans. Nodule location, type, natural course, and volume doubling time (VDT) were compared between invasive adenocarcinoma (IAC) and pre-IAC groups. We also assessed differences in Ki-67 expression and correlated VDT with Ki-67 levels. A total of 183 nodules were finally included; gender, nodule location, smoking history, and duration of follow-up did not differ between the IAC group and the pre-IAC group, whereas age was statistically different between the two groups. Of the 183 nodules, 52 showed growth and the predominant pathologic type was IAC, these IACs showed more PSN in nodule type, while the IAC group showed more significant differences in nodule type, nodules growth, and VDT than the pre-IAC group. There were also differences in pathologic type and VDT between different Ki-67 expression groups, and Ki-67 expression gradually increased as VDT decreased. Lung adenocarcinoma (LUAD) presenting as GGNs exhibit distinct natural courses among pathologic subtypes. VDT effectively distinguishes these growth characteristics, with IACs showing shorter VDT. The significant correlation between VDT and Ki-67 expression suggests that combining these parameters may provide valuable insights into the biological behavior and invasiveness of LUAD.

CSSANet: A Channel Shuffle Slice-Aware Network for Pulmonary Nodule Detection

Lung cancer stands as the leading cause of cancer related mortality worldwide. Precise and automated identification of lung nodules through 3D Computed Tomography (CT) scans is an essential part of screening for lung cancer effectively. Due to the small size of pulmonary nodules and the close correlation between neighboring slices of 3D CT images, most of the existing methods only consider the characteristics of a single slice, thus easily lead to insufficient detection accuracy of pulmonary nodules. To solve this problem, this paper proposes a Channel Shuffle Slice-Aware Network (CSSANet), which aims to fully exploit the spatial correlation between slices and effectively utilize the intra-slice features and inter-slice contextual information to achieve accurate detection of lung nodules. Specifically, we design a Group Shuffle Attention module (GSA module) to fuse the inter-slice feature in order to enhance the discrimination and extraction of corresponding shape information of distinct nodules in the same group of slices. Experiments and ablation study on a publicly available LUNA16 dataset demonstrate that the proposed method can enhance the detection sensitivity effectively. The Competition Performance Metric (CPM) score of 89.8% is superior over other representative detection models.

Accuracy and Impact of AI Software for Nodule and Cancer Detection at Lung Cancer Screening CT.

Accuracy and Impact of AI Software for Nodule and Cancer Detection at Lung Cancer Screening CT.

Automatic detection of lung nodules in computed tomography images using U-Net

Automatic detection of lung nodules in computed tomography images using U-Net

Expressive feature representation pyramid network for pulmonary nodule detection

Expressive feature representation pyramid network for pulmonary nodule detection

An artificial intelligence algorithm for the detection of pulmonary ground-glass nodules on spectral detector CT: performance on virtual monochromatic images

BackgroundThis study aims to assess the performance of an established an AI algorithm trained on conventional polychromatic computed tomography (CT) images (CPIs) to detect pulmonary ground-glass nodules (GGNs) on virtual monochromatic images (VMIs), and to screen the optimal virtual monochromatic energy for the clinical evaluation of GGNs.MethodsNon-enhanced chest SDCT images of patients with pulmonary GGNs in our clinic from January 2022 to December 2022 were continuously collected: adenocarcinoma in situ (AIS, n = 40); minimally invasive adenocarcinoma (MIA, n = 44) and invasive adenocarcinoma (IAC, n = 46). A commercial CAD system based on deep convolutional neural networks (DL-CAD) was used to process the CPIs, 40, 50, 60, 70, and 80 keV monochromatic images of 130 spectral CT images. AI-based histogram parameters by logistic regression analysis. The diagnostic performance was evaluated by the receiver operating characteristic (ROC) curves, and Delong’s test was used to compare the CPIs group with the VMIs group.ResultsWhen distinguishing IAC from MIA, the diagnostic efficiency of total mass was obtained at 80 keV, which was superior to those of other energy levels (P < 0.05). And Delong’s test indicated that the differences between the area-under-the-curve (AUC) values of the CPIs group and the VMIs group were not statistically significant (P > 0.05).ConclusionThe AI algorithm trained on CPIs showed consistent diagnostic performance on VMIs. When pulmonary GGNs are encountered in clinical practice, 80 keV could be the optimal virtual monochromatic energy for the identification of preoperative IAC on a non-enhanced chest CT.

Feature Extraction and Identification of Rheumatoid Nodules Using Advanced Image Processing Techniques

Background/Objectives: Accurate detection and classification of nodules in medical images, particularly rheumatoid nodules, are critical due to the varying nature of these nodules, where their specific type is often unknown before analysis. This study addresses the challenges of multi-class prediction in nodule detection, with a specific focus on rheumatoid nodules, by employing a comprehensive approach to feature extraction and classification. We utilized a diverse dataset of nodules, including rheumatoid nodules sourced from the DermNet dataset and local rheumatologists. Method: This study integrates 62 features, combining traditional image characteristics with advanced graph-based features derived from a superpixel graph constructed through Delaunay triangulation. The key steps include image preprocessing with anisotropic diffusion and Retinex enhancement, superpixel segmentation using SLIC, and graph-based feature extraction. Texture analysis was performed using Gray-Level Co-occurrence Matrix (GLCM) metrics, while shape analysis was conducted with Fourier descriptors. Vascular pattern recognition, crucial for identifying rheumatoid nodules, was enhanced using the Frangi filter. A Hybrid CNN–Transformer model was employed for feature fusion, and feature selection and hyperparameter tuning were optimized using Gray Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). Feature importance was assessed using SHAP values. Results: The proposed methodology achieved an accuracy of 85%, with a precision of 0.85, a recall of 0.89, and an F1 measure of 0.87, demonstrating the effectiveness of the approach in detecting and classifying rheumatoid nodules in both binary and multi-class classification scenarios. Conclusions: This study presents a robust tool for the detection and classification of nodules, particularly rheumatoid nodules, in medical imaging, offering significant potential for improving diagnostic accuracy and aiding in the early identification of rheumatoid conditions.

Tube voltage in chest and abdomen DR imaging: Which settings return maximal non-prewhitening model observer with eye filter (NPWE) detectability?

The non-prewhitening computational model observer with eye filter (NPWE) has been shown to reasonably predict human observer performance in general radiography and is an appropriate substitute when real clinical trials are not feasible. In this study, the NPWE model observer is used to detect specific tasks (circular designer nodules) ranging between 1 and 30mm in diameter using chest and abdomen phantom images acquired across the diagnostic energy range (60-125 kVp) with and without an anti-scatter grid. The aim of this study was to derive tube voltage (kVp) settings that return maximal NPWE detectability (d') of designer nodules, for digital radiography (DR) chest and abdomen imaging. Images of a chest phantom (LucAl phantom) and a surrogate for an abdomen (18.5cm PMMA) were acquired across the diagnostic energy range (60-125 kVp) with matched effective dose (for the respective anatomies), with and without an anti-scatter grid, on a general x-ray system. Images were captured using an Agfa DX-D 40C wireless indirect caesium iodide (CsI) imaging panel. Modulation transfer function (MTF), normalized noise power spectrum (NNPS), and contrast (C) were measured in each image and the detectability index d' was calculated for circular designer nodules with diameters ranging from 1 to 30mm (in steps of 1mm). The calculated d' peaked at a nodule diameter of 3mm irrespective of tube voltage, for both chest and abdomen images. A tube voltage of 80kVp returned maximal d' for chest imaging across all nodule diameters both with and without an anti-scatter grid. A tube voltage of 70 kVp returned maximal d' for abdomen imaging. The NPWE observer model has been used to derive tube voltages (kVp) that return maximal detectability (d') of designer nodules for chest and abdomen radiography using a modern DR imaging system. This will provide the medical physicist with a starting point in the task of optimising tube voltage range for chest and abdomen imaging.

Virtual thoracoscopic imaging for accurate pulmonary nodule localization: clinical experience.

The increasing detection of small pulmonary nodules on computed tomography (CT) warrants simple and effective nodule localization methods. We describe our clinical experience using an experimental computer that displays virtual thoracoscopic images. This device constructs three-dimensional images from preoperative CT scans and simulates the deflated lung parenchyma in the lateral decubitus position. Five patients underwent lung resection using this technology. The device provided images that closely resembled actual thoracoscopic images in all cases. This method addresses the limitations of other localization techniques such as allergic reactions and mechanical marker-related complications. The method only requires preoperative CT images, and the process is semi-automatically performed by specifying the nodule location, thoracoscopic camera insertion site, and camera angle. This study is still in the preliminary phase and has several limitations. However, this method has the potential to accurately predict nodule locations and eliminate the many risks associated with other techniques.

Editorial Comment: Computer-Aided Detection of Lung Nodules on Low-Dose Pediatric Chest CT-A Cautionary Tale.

Editorial Comment: Computer-Aided Detection of Lung Nodules on Low-Dose Pediatric Chest CT-A Cautionary Tale.

P4.04C.05 AI-Assisted CXR Analysis in the Detection of Lung Nodules and Incidental Lung Cancers

P4.04C.05 AI-Assisted CXR Analysis in the Detection of Lung Nodules and Incidental Lung Cancers

Software using artificial intelligence for nodule and cancer detection in CT lung cancer screening: systematic review of test accuracy studies

ObjectivesTo examine the accuracy and impact of artificial intelligence (AI) software assistance in lung cancer screening using CT.MethodsA systematic review of CE-marked, AI-based software for automated detection and analysis of...

Multi-View Soft Attention-Based Model for the Classification of Lung Cancer-Associated Disabilities.

Background: The detection of lung nodules at their early stages may significantly enhance the survival rate and prevent progression to severe disability caused by advanced lung cancer, but it often requires manual and laborious efforts for radiologists, with limited success. To alleviate it, we propose a Multi-View Soft Attention-Based Convolutional Neural Network (MVSA-CNN) model for multi-class lung nodular classifications in three stages (benign, primary, and metastatic). Methods: Initially, patches from each nodule are extracted into three different views, each fed to our model to classify the malignancy. A dataset, namely the Lung Image Database Consortium Image Database Resource Initiative (LIDC-IDRI), is used for training and testing. The 10-fold cross-validation approach was used on the database to assess the model's performance. Results: The experimental results suggest that MVSA-CNN outperforms other competing methods with 97.10% accuracy, 96.31% sensitivity, and 97.45% specificity. Conclusions: We hope the highly predictive performance of MVSA-CNN in lung nodule classification from lung Computed Tomography (CT) scans may facilitate more reliable diagnosis, thereby improving outcomes for individuals with disabilities who may experience disparities in healthcare access and quality.

Evaluation of the relationship between thyroid cancer and the concurrent detection of thyroid nodules on the background of primary hyperparathyroidism

Introduction: Primary Hyperparathyroidism (PHPT) is commonly caused by a benign parathyroid adenoma that results in overactivity of the gland and subsequent hypercalcemia due to elevated parathyroid hormone (PTH). This endocrine pathology is associated with thyroid cancer, specifically Papillary Thyroid Cancer (PTC). However, the explanation for their synchronous presentation is unknown. Moreover, it causes diagnostic and treatment challenges that impact patient outcomes. Aims and Objectives: To critically analyze published research to address the relationship between PHPT and thyroid cancer, the role of ultrasound imaging in detecting sinister thyroid nodules in PHPT patients and the features of PHPT that predispose the risk of thyroid malignancy. Study Design: Literature Review Methodology: Electronic database searches of PubMed and CINAHL Plus through EBSCOhost were conducted using the keywords "primary hyperparathyroidism", "thyroid nodules" and "thyroid cancer". Following application of filters and removal of duplicates, 627 relevant results remained. Articles were screened for eligibility based on predetermined selection criteria. Following a review of titles and abstracts, 10 peer-reviewed articles were chosen for further analysis. The studies included were critically appraised using the EBL Critical Appraisal Checklist. Results: 10 articles were examined, 2 were prospective cohort studies and 8 were retrospective cohort studies. All studies involved exhaustive medical chart reviews of patients with PHPT, to investigate the concomitance of thyroid malignancy. Six studies established cervical ultrasound as the optimal method of recognition and preoperative localization of thyroid and parathyroid lesions. Overall, the incidence of thyroid cancer among PHPT patients ranged between 2.9% to 32.9%. Four studies established age, gender and PTH levels as risk factors. Conclusion: The existing literature is consistent with previous studies and purports that individuals with a background fo PHPT are at an increased risk of thyroid cancer. Furthermore the highest likelihood of identifying thyroid cancer is through preoperative localization of parathyroid adenomas by cervical ultrasound, in female patients over the age of 50. Key Findings: Further research is needed to understand the underlying pathogenesis and genetic mechanisms that encompass the relationship between PHPT and thyroid cancer.