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La Radiologia Medica Oct 2005A pulmonary nodule is a frequent and often incidental finding, and still represents a diagnostic challenge for radiologists. Although most solitary nodules are related... (Review)
Review
A pulmonary nodule is a frequent and often incidental finding, and still represents a diagnostic challenge for radiologists. Although most solitary nodules are related to benign disease, some represent stage I lung cancers. and need to be distinguished from benign nodules in a cost-effective manner. The aim of diagnostic assessment should be to allow early treatment of small malignant nodules but avoid unnecessary biopsy or surgery, with their attendant risks, in patients with benign disease. The advent of Multislice Computed Tomography (MSCT) technology has sparked new interest in the non-invasive assessment of pulmonary nodules. Thanks to its ability to scan the whole thoracic volume with thinner collimations, this technology allows a more accurate identification and characterisation of pulmonary nodules, as well as the determination of perfusion patterns and growth rates. In this paper we present an algorithm for the diagnostic workup of incidentally detected small pulmonary nodules in subjects without known malignancy.
Topics: Humans; Lung; Lung Neoplasms; Solitary Pulmonary Nodule; Tomography, X-Ray Computed
PubMed: 16292237
DOI: No ID Found -
Radiologic Clinics of North America Jan 2022Incidental pulmonary nodules are not infrequently identified on computed tomography imaging in the pediatric population and can be a challenge in suggesting appropriate... (Review)
Review
Incidental pulmonary nodules are not infrequently identified on computed tomography imaging in the pediatric population and can be a challenge in suggesting appropriate follow-up recommendations. An evidence-based and practical imaging approach for diagnosis and appropriate directed management is essential for optimal patient care. This article provides an up-to-date review of the pediatric pulmonary nodule literature and suggests a practical algorithm to manage pulmonary nodules in the pediatric population.
Topics: Adolescent; Child; Child, Preschool; Female; Humans; Incidental Findings; Lung; Lung Neoplasms; Magnetic Resonance Imaging; Male; Practice Guidelines as Topic; Radiography; Solitary Pulmonary Nodule; Tomography, X-Ray Computed
PubMed: 34836566
DOI: 10.1016/j.rcl.2021.08.004 -
Journal of the National Cancer Institute Sep 2023Although lung cancer screening with low-dose computed tomography is rolling out in many areas of the world, differentiating indeterminate pulmonary nodules remains a...
BACKGROUND
Although lung cancer screening with low-dose computed tomography is rolling out in many areas of the world, differentiating indeterminate pulmonary nodules remains a major challenge. We conducted one of the first systematic investigations of circulating protein markers to differentiate malignant from benign screen-detected pulmonary nodules.
METHODS
Based on 4 international low-dose computed tomography screening studies, we assayed 1078 protein markers using prediagnostic blood samples from 1253 participants based on a nested case-control design. Protein markers were measured using proximity extension assays, and data were analyzed using multivariable logistic regression, random forest, and penalized regressions. Protein burden scores (PBSs) for overall nodule malignancy and imminent tumors were estimated.
RESULTS
We identified 36 potentially informative circulating protein markers differentiating malignant from benign nodules, representing a tightly connected biological network. Ten markers were found to be particularly relevant for imminent lung cancer diagnoses within 1 year. Increases in PBSs for overall nodule malignancy and imminent tumors by 1 standard deviation were associated with odds ratios of 2.29 (95% confidence interval: 1.95 to 2.72) and 2.81 (95% confidence interval: 2.27 to 3.54) for nodule malignancy overall and within 1 year of diagnosis, respectively. Both PBSs for overall nodule malignancy and for imminent tumors were substantially higher for those with malignant nodules than for those with benign nodules, even when limited to Lung Computed Tomography Screening Reporting and Data System (LungRADS) category 4 (P < .001).
CONCLUSIONS
Circulating protein markers can help differentiate malignant from benign pulmonary nodules. Validation with an independent computed tomographic screening study will be required before clinical implementation.
Topics: Humans; Lung Neoplasms; Proteome; Early Detection of Cancer; Solitary Pulmonary Nodule; Lung; Multiple Pulmonary Nodules
PubMed: 37369027
DOI: 10.1093/jnci/djad122 -
Radiologic Clinics of North America May 2018The number of screening-detected lung nodules is expected to increase as low-dose computed tomography screening is implemented nationally. Standardized guidelines for... (Review)
Review
The number of screening-detected lung nodules is expected to increase as low-dose computed tomography screening is implemented nationally. Standardized guidelines for image acquisition, interpretation, and screen-detected nodule workup are essential to ensure a high standard of medical care and that lung cancer screening is implemented safely and cost effectively. In this article, we review the current guidelines for pulmonary nodule management in the lung cancer screening setting.
Topics: Early Detection of Cancer; Humans; Lung; Lung Neoplasms; Solitary Pulmonary Nodule; Tomography, X-Ray Computed
PubMed: 29622071
DOI: 10.1016/j.rcl.2018.01.003 -
Computational and Mathematical Methods... 2022As cancer with the highest morbidity and mortality in the world, lung cancer is characterized by pulmonary nodules in the early stage. The detection of pulmonary nodules...
As cancer with the highest morbidity and mortality in the world, lung cancer is characterized by pulmonary nodules in the early stage. The detection of pulmonary nodules is an important method for the early detection of lung cancer, which can greatly improve the survival rate of lung cancer patients. However, the accuracy of conventional detection methods for lung nodules is low. With the development of medical imaging technology, deep learning plays an increasingly important role in medical image detection, and pulmonary nodules can be accurately detected by CT images. Based on the above, a pulmonary nodule detection method based on deep learning is proposed. In the candidate nodule detection stage, the multiscale features and Faster R-CNN, a general-purpose detection framework based on deep learning, were combined together to improve the detection of small-sized lung nodules. In the false-positive nodule filtration stage, a 3D convolutional neural network based on multiscale fusion is designed to reduce false-positive nodules. The experiment results show that the candidate nodule detection model based on Faster R-CNN integrating multiscale features has achieved a sensitivity of 98.6%, 10% higher than that of the other single-scale model, the proposed method achieved a sensitivity of 90.5% at the level of 4 false-positive nodules per scan, and the CPM score reached 0.829. The results are higher than methods in other works of literature. It can be seen that the detection method of pulmonary nodules based on multiscale fusion has a higher detection rate for small nodules and improves the classification performance of true and false-positive pulmonary nodules. This will help doctors when making a lung cancer diagnosis.
Topics: Humans; Solitary Pulmonary Nodule; Tomography, X-Ray Computed; Imaging, Three-Dimensional; Radiographic Image Interpretation, Computer-Assisted; Lung; Lung Neoplasms; Multiple Pulmonary Nodules
PubMed: 36590762
DOI: 10.1155/2022/8903037 -
Journal of Thoracic Imaging Mar 2015Pulmonary nodules are commonly detected in computed tomography (CT) chest screening of a high-risk population. The specific visual or quantitative features on CT or... (Review)
Review
Pulmonary nodules are commonly detected in computed tomography (CT) chest screening of a high-risk population. The specific visual or quantitative features on CT or other modalities can be used to characterize the likelihood that a nodule is benign or malignant. Visual features on CT such as size, attenuation, location, morphology, edge characteristics, and other distinctive "signs" can be highly suggestive of a specific diagnosis and, in general, be used to determine the probability that a specific nodule is benign or malignant. Change in size, attenuation, and morphology on serial follow-up CT, or features on other modalities such as nuclear medicine studies or MRI, can also contribute to the characterization of lung nodules. Imaging analytics can objectively and reproducibly quantify nodule features on CT, nuclear medicine, and magnetic resonance imaging. Some quantitative techniques show great promise in helping to differentiate benign from malignant lesions or to stratify the risk of aggressive versus indolent neoplasm. In this article, we (1) summarize the visual characteristics, descriptors, and signs that may be helpful in management of nodules identified on screening CT, (2) discuss current quantitative and multimodality techniques that aid in the differentiation of nodules, and (3) highlight the power, pitfalls, and limitations of these various techniques.
Topics: Fluorodeoxyglucose F18; Humans; Lung; Lung Neoplasms; Magnetic Resonance Imaging; Multimodal Imaging; Multiple Pulmonary Nodules; Positron-Emission Tomography; Radiopharmaceuticals; Solitary Pulmonary Nodule; Tomography, Emission-Computed, Single-Photon; Tomography, X-Ray Computed
PubMed: 25658478
DOI: 10.1097/RTI.0000000000000137 -
BioMed Research International 2022Pulmonary nodules have been found as the main pathological change in the lung. Signs of pulmonary nodule lay the major basis for the recognition of the benign and...
Pulmonary nodules have been found as the main pathological change in the lung. Signs of pulmonary nodule lay the major basis for the recognition of the benign and malignant of pulmonary nodules. The spiculation of pulmonary nodules is one of the main signs. Pulmonary nodules are small in volume, so they are difficult to extract accurately. Moreover, the number of spiculation samples is limited, so it is difficult to build a stable network structure. Thus, a novel pulmonary nodule spiculation recognition algorithm is proposed. MCA (morphological component analysis) model is built to segment pulmonary nodules in accordance with the composition of pulmonary CT images. Subsequently, the maximum density projection mechanism is introduced to characterize the boundary features of pulmonary nodules to the maximum extent. Inspired by time series dynamic programming, this paper proposes DTW (dynamic time warping) distance to measure data similarity. Lastly, a semisupervised generative adversarial network is built to solve the problem of insufficient positive samples, and it is capable of recognizing pulmonary nodule spiculation. As revealed by the experimental result, the proposed algorithm exhibited strong robustness.
Topics: Algorithms; Humans; Lung; Lung Neoplasms; Thorax; Tomography, X-Ray Computed
PubMed: 35782073
DOI: 10.1155/2022/3341924 -
Diagnostic and Interventional Imaging Jan 2023Artificial intelligence (AI) is a broad concept that usually refers to computer programs that can learn from data and perform certain specific tasks. In the recent... (Review)
Review
Artificial intelligence (AI) is a broad concept that usually refers to computer programs that can learn from data and perform certain specific tasks. In the recent years, the growth of deep learning, a successful technique for computer vision tasks that does not require explicit programming, coupled with the availability of large imaging databases fostered the development of multiple applications in the medical imaging field, especially for lung nodules and lung cancer, mostly through convolutional neural networks (CNN). Some of the first applications of AI is this field were dedicated to automated detection of lung nodules on X-ray and computed tomography (CT) examinations, with performances now reaching or exceeding those of radiologists. For lung nodule segmentation, CNN-based algorithms applied to CT images show excellent spatial overlap index with manual segmentation, even for irregular and ground glass nodules. A third application of AI is the classification of lung nodules between malignant and benign, which could limit the number of follow-up CT examinations for less suspicious lesions. Several algorithms have demonstrated excellent capabilities for the prediction of the malignancy risk when a nodule is discovered. These different applications of AI for lung nodules are particularly appealing in the context of lung cancer screening. In the field of lung cancer, AI tools applied to lung imaging have been investigated for distinct aims. First, they could play a role for the non-invasive characterization of tumors, especially for histological subtype and somatic mutation predictions, with a potential therapeutic impact. Additionally, they could help predict the patient prognosis, in combination to clinical data. Despite these encouraging perspectives, clinical implementation of AI tools is only beginning because of the lack of generalizability of published studies, of an inner obscure working and because of limited data about the impact of such tools on the radiologists' decision and on the patient outcome. Radiologists must be active participants in the process of evaluating AI tools, as such tools could support their daily work and offer them more time for high added value tasks.
Topics: Humans; Lung Neoplasms; Artificial Intelligence; Early Detection of Cancer; Neural Networks, Computer; Lung; Solitary Pulmonary Nodule
PubMed: 36513593
DOI: 10.1016/j.diii.2022.11.007 -
The New England Journal of Medicine Sep 2013Major issues in the implementation of screening for lung cancer by means of low-dose computed tomography (CT) are the definition of a positive result and the management... (Clinical Trial)
Clinical Trial
BACKGROUND
Major issues in the implementation of screening for lung cancer by means of low-dose computed tomography (CT) are the definition of a positive result and the management of lung nodules detected on the scans. We conducted a population-based prospective study to determine factors predicting the probability that lung nodules detected on the first screening low-dose CT scans are malignant or will be found to be malignant on follow-up.
METHODS
We analyzed data from two cohorts of participants undergoing low-dose CT screening. The development data set included participants in the Pan-Canadian Early Detection of Lung Cancer Study (PanCan). The validation data set included participants involved in chemoprevention trials at the British Columbia Cancer Agency (BCCA), sponsored by the U.S. National Cancer Institute. The final outcomes of all nodules of any size that were detected on baseline low-dose CT scans were tracked. Parsimonious and fuller multivariable logistic-regression models were prepared to estimate the probability of lung cancer.
RESULTS
In the PanCan data set, 1871 persons had 7008 nodules, of which 102 were malignant, and in the BCCA data set, 1090 persons had 5021 nodules, of which 42 were malignant. Among persons with nodules, the rates of cancer in the two data sets were 5.5% and 3.7%, respectively. Predictors of cancer in the model included older age, female sex, family history of lung cancer, emphysema, larger nodule size, location of the nodule in the upper lobe, part-solid nodule type, lower nodule count, and spiculation. Our final parsimonious and full models showed excellent discrimination and calibration, with areas under the receiver-operating-characteristic curve of more than 0.90, even for nodules that were 10 mm or smaller in the validation set.
CONCLUSIONS
Predictive tools based on patient and nodule characteristics can be used to accurately estimate the probability that lung nodules detected on baseline screening low-dose CT scans are malignant. (Funded by the Terry Fox Research Institute and others; ClinicalTrials.gov number, NCT00751660.).
Topics: Evidence-Based Medicine; Female; Follow-Up Studies; Humans; Logistic Models; Lung; Lung Neoplasms; Male; Models, Statistical; Multiple Pulmonary Nodules; Probability; Prospective Studies; Solitary Pulmonary Nodule; Tomography, X-Ray Computed
PubMed: 24004118
DOI: 10.1056/NEJMoa1214726 -
Respiratory Medicine Aug 2023Pulmonary nodules are often discovered incidentally during CT scans performed for other reasons. While the vast majority of nodules are benign, a small percentage may... (Review)
Review
Pulmonary nodules are often discovered incidentally during CT scans performed for other reasons. While the vast majority of nodules are benign, a small percentage may represent early-stage lung cancer with the potential for curative treatments. With the growing use of CT for both clinical purposes and lung cancer screening, the number of pulmonary nodules detected is expected to increase substantially. Despite well-established guidelines, many nodules do not receive proper evaluation due to a variety of factors, including inadequate coordination of care and financial and social barriers. To address this quality gap, novel approaches such as multidisciplinary nodule clinics and multidisciplinary boards may be necessary. As pulmonary nodules may indicate early-stage lung cancer, it is crucial to adopt a risk-stratified approach to identify potential lung cancers at an early stage, while minimizing the risk of harm and expense associated with over investigation of low-risk nodules. This article, authored by multiple specialists involved in nodule management, delves into the diagnostic approach to lung nodules. It covers the process of determining whether a patient requires tissue sampling or continued surveillance. Additionally, the article provides an in-depth examination of the various biopsy and therapeutic options available for malignant lung nodules. The article also emphasizes the significance of early detection in reducing lung cancer mortality, especially among high-risk populations. Furthermore, it addresses the creation of a comprehensive lung nodule program, which involves smoking cessation, lung cancer screening, and systematic evaluation and follow-up of both incidental and screen-detected nodules.
Topics: Humans; Lung Neoplasms; Early Detection of Cancer; Lung; Multiple Pulmonary Nodules; Tomography, X-Ray Computed; Solitary Pulmonary Nodule
PubMed: 37187432
DOI: 10.1016/j.rmed.2023.107277