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Journal of Thoracic Imaging Mar 2015Fundamental to the diagnosis of lung cancer in computed tomography (CT) scans is the detection and interpretation of lung nodules. As the capabilities of CT scanners... (Review)
Review
Fundamental to the diagnosis of lung cancer in computed tomography (CT) scans is the detection and interpretation of lung nodules. As the capabilities of CT scanners have advanced, higher levels of spatial resolution reveal tinier lung abnormalities. Not all detected lung nodules should be reported; however, radiologists strive to detect all nodules that might have relevance to cancer diagnosis. Although medium to large lung nodules are detected consistently, interreader agreement and reader sensitivity for lung nodule detection diminish substantially as the nodule size falls below 8 to 10 mm. The difficulty in establishing an absolute reference standard presents a challenge to the reliability of studies performed to evaluate lung nodule detection. In the interest of improving detection performance, investigators are using eye tracking to analyze the effectiveness with which radiologists search CT scans relative to their ability to recognize nodules within their search path in order to determine whether strategies might exist to improve performance across readers. Beyond the viewing of transverse CT reconstructions, image processing techniques such as thin-slab maximum-intensity projections are used to substantially improve reader performance. Finally, the development of computer-aided detection has continued to evolve with the expectation that one day it will serve routinely as a tireless partner to the radiologist to enhance detection performance without significant prolongation of the interpretive process. This review provides an introduction to the current understanding of these varied issues as we enter the era of widespread lung cancer screening.
Topics: Humans; Image Processing, Computer-Assisted; Lung; Lung Neoplasms; Radiographic Image Interpretation, Computer-Assisted; Reproducibility of Results; Solitary Pulmonary Nodule; Tomography, X-Ray Computed
PubMed: 25658477
DOI: 10.1097/RTI.0000000000000140 -
Thoracic Surgery Clinics Feb 2023Pulmonary nodules (lesions <3 cm in size) are commonly identified on computed tomographic scans, but radiographic features alone are inadequate to reliably differentiate... (Review)
Review
Pulmonary nodules (lesions <3 cm in size) are commonly identified on computed tomographic scans, but radiographic features alone are inadequate to reliably differentiate between benign and malignant etiologies. Therefore, tissue biopsy remains the standard approach to determine the appropriate treatment course for many patients with pulmonary nodules. Although percutaneous biopsy is highly accurate, it poses substantial risks of procedural complications, including pneumothorax and bleeding. Robotic bronchoscopy has recently been developed to overcome many of the limitations of previous navigational platforms. Here, we explore the currently available systems for robotic bronchoscopy-in particular, electromagnetic-navigation robotic-assisted bronchoscopy and shape-sensing robotic-assisted bronchoscopy.
Topics: Humans; Bronchoscopy; Robotic Surgical Procedures; Electromagnetic Phenomena; Multiple Pulmonary Nodules; Lung; Lung Neoplasms
PubMed: 36372527
DOI: 10.1016/j.thorsurg.2022.08.008 -
Respirology (Carlton, Vic.) Jan 2013Interventional pulmonology (IP) allows comprehensive assessment of patients with benign and malignant airway, lung parenchymal and pleural disease. This relatively new... (Review)
Review
Interventional pulmonology (IP) allows comprehensive assessment of patients with benign and malignant airway, lung parenchymal and pleural disease. This relatively new branch of pulmonary medicine utilizes advanced diagnostic and therapeutic techniques to treat patients with pulmonary diseases. Endobronchial ultrasound revolutionized assessment of pulmonary nodules, mediastinal lymphadenopathy and lung cancer staging allowing minimally invasive, highly accurate assessment of lung parenchymal and mediastinal disease, with both macro- and microscopic tissue characterization including molecular signature analysis. High-spatial resolution, new endobronchial imaging techniques including autofluorescence bronchoscopy, narrow-band imaging, optical coherence tomography and confocal microscopy enable detailed evaluation of airways with increasing role in detection and treatment of malignancies arising in central airways. Precision in peripheral lesion localization has been increased through innovative navigational techniques including navigational bronchoscopy and electromagnetic navigation. Pleural diseases can be assessed with the use of non-invasive pleural ultrasonography, with high sensitivity and specificity for malignant disease detection. Medical pleuroscopy is a minimally invasive technique improving diagnostic safety and precision of pleural disease and pleural effusion assessment. In this review, we discuss the newest advances in diagnostic modalities utilized in IP, indications for their use, their diagnostic accuracy, efficacy, safety and challenges in application of these technologies in assessment of thoracic diseases.
Topics: Diagnostic Techniques, Respiratory System; Humans; Lung; Lung Diseases; Pleural Diseases; Pulmonary Medicine
PubMed: 22712451
DOI: 10.1111/j.1440-1843.2012.02211.x -
Clinical Radiology Nov 2017To evaluate the impact of inspiratory effort and emphysema on reproducibility of pulmonary nodule volumetry.
AIM
To evaluate the impact of inspiratory effort and emphysema on reproducibility of pulmonary nodule volumetry.
MATERIALS AND METHODS
Eighty-eight nodules in 24 patients with emphysema were studied retrospectively. All patients had undergone volumetric inspiratory and end-expiratory thoracic computed tomography (CT) for consideration of bronchoscopic lung volume reduction. Inspiratory and expiratory nodule volumes were measured using commercially available software. Local emphysema extent was established by analysing a segmentation area extended circumferentially around each nodule (quantified as percent of lung with density of -950 HU or less). Lung volumes were established using the same software. Differences in inspiratory and expiratory nodule volumes were illustrated using the Bland-Altman test. The influences of percentage reduction in lung volume at expiration, local emphysema extent, and nodule size on nodule volume variability were tested with multiple linear regression.
RESULTS
The majority of nodules (59/88 [67%]) showed an increased volume at expiration. Mean difference in nodule volume between expiration and inspiration was +7.5% (95% confidence interval: -24.1, 39.1%). No relationships were demonstrated between nodule volume variability and emphysema extent, degree of expiration, or nodule size.
CONCLUSION
Expiration causes a modest increase in volumetry-derived nodule volumes; however, the effect is unpredictable. Local emphysema extent had no significant effect on volume variability in the present cohort.
Topics: Aged; Female; Humans; Inhalation; Lung; Male; Middle Aged; Multiple Pulmonary Nodules; Pulmonary Emphysema; Reproducibility of Results; Respiratory Function Tests; Retrospective Studies; Tomography, X-Ray Computed
PubMed: 28784319
DOI: 10.1016/j.crad.2017.06.117 -
Frontiers in Public Health 2021Malignant pulmonary nodules are one of the main manifestations of lung cancer in early CT image screening. Since lung cancer may have no early obvious symptoms, it is...
Malignant pulmonary nodules are one of the main manifestations of lung cancer in early CT image screening. Since lung cancer may have no early obvious symptoms, it is important to develop a computer-aided detection (CAD) system to assist doctors to detect the malignant pulmonary nodules in the early stage of lung cancer CT diagnosis. Due to the recent successful applications of deep learning in image processing, more and more researchers have been trying to apply it to the diagnosis of pulmonary nodules. However, due to the ratio of nodules and non-nodules samples used in the training and testing datasets usually being different from the practical ratio of lung cancer, the CAD classification systems may easily produce higher false-positives while using this imbalanced dataset. This work introduces a filtering step to remove the irrelevant images from the dataset, and the results show that the false-positives can be reduced and the accuracy can be above 98%. There are two steps in nodule detection. Firstly, the images with pulmonary nodules are screened from the whole lung CT images of the patients. Secondly, the exact locations of pulmonary nodules will be detected using Faster R-CNN. Final results show that this method can effectively detect the pulmonary nodules in the CT images and hence potentially assist doctors in the early diagnosis of lung cancer.
Topics: Humans; Lung; Multiple Pulmonary Nodules; Neural Networks, Computer; Solitary Pulmonary Nodule; Tomography, X-Ray Computed
PubMed: 34095073
DOI: 10.3389/fpubh.2021.671070 -
Zhongguo Fei Ai Za Zhi = Chinese... Jun 2016Thoracoscopic segmentectomy is technically much more meticulous than lobectomy, due to the complicated anotomical variations of segmental bronchi and vessels.... (Review)
Review
Thoracoscopic segmentectomy is technically much more meticulous than lobectomy, due to the complicated anotomical variations of segmental bronchi and vessels. Preoperative three-dimensional computed tomography bronchography and angiography, 3D-CTBA) could reveal the anatomical structures and variations of the segmental bronchi/vessels and locate the pulmonary nodules, which is helpful for surgery planning. Preoperative nodule localization is of vital importance for thoracoscopic segmentectomy. Techniques involved in this procedure include dissection of the targeted arteries, bronchus and intra-segmental veins, retention of the inter-segmental veins, identification of the inter-segmental boarder with the inflation-deflation method and seperation of intra-segmental pulmonary tissues by electrotome and/or endoscopic staplers. The incision margin for malignant nodules should be at least 2 cm or the diameter of the tumor. Meanwhile, sampling of N1 and N2 station lymph nodes and intraoperative frozen section is also necessary. The complication rate of thoracoscopic segmentectomy is comparatively low. The anatomic relationship between pulmonary segments and lobes is that a lobe consists of several irregular cone-shaped segments with the inter-segmental veins lies between the segments. Our center has explored a method to separate pulmonary segments from the lobe on the basis of cone-shaped principle, and we named it "Cone-shaped Segmentectomy". This technique could precisely decide and dissect the targeted bronchi and vessels, and anatomically separate the inter-segmental boarder, which ultimately achieve a completely anatomical segmentectomy.
Topics: Humans; Lung; Pneumonectomy; Thoracoscopy
PubMed: 27335301
DOI: 10.3779/j.issn.1009-3419.2016.06.16 -
Journal of Digital Imaging Jun 2020Accurate and automatic lung nodule segmentation is of prime importance for the lung cancer analysis and its fundamental step in computer-aided diagnosis (CAD) systems....
Accurate and automatic lung nodule segmentation is of prime importance for the lung cancer analysis and its fundamental step in computer-aided diagnosis (CAD) systems. However, various types of nodule and visual similarity with its surrounding chest region make it challenging to develop lung nodule segmentation algorithm. In this paper, we proposed the Deep Deconvolutional Residual Network (DDRN) based approach for the lung nodule segmentation from the CT images. Our approach is based on two key insights. Proposed deep deconvolutional residual network trained end to end and captures the diverse variety of the nodules from the 2D set of the CT images. Summation-based long skip connection from convolutional to deconvolutional part of the network preserves the spatial information lost during the pooling operation and captures the full resolution features. The proposed method is evaluated on the publicly available Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) dataset. Results indicate that our proposed method can successfully segment nodules and achieve the average Dice scores of 94.97%, and Jaccard index of 88.68%.
Topics: Algorithms; Diagnosis, Computer-Assisted; Humans; Lung; Lung Neoplasms; Radiographic Image Interpretation, Computer-Assisted; Solitary Pulmonary Nodule; Tomography, X-Ray Computed
PubMed: 32026218
DOI: 10.1007/s10278-019-00301-4 -
The British Journal of Radiology Feb 2023Non-nodular incidental lung findings can broadly be categorised as airway- or airspace-related abnormalities and diffuse parenchymal abnormalities. Airway-related... (Review)
Review
Non-nodular incidental lung findings can broadly be categorised as airway- or airspace-related abnormalities and diffuse parenchymal abnormalities. Airway-related abnormalities include bronchial dilatation and thickening, foci of low attenuation, emphysema, and congenital variants. Diffuse parenchymal abnormalities relate to the spectrum of diffuse parenchymal lung diseases cover a spectrum from interstitial lung abnormalities (ILAs) and pulmonary cysts to established diffuse parenchymal lung abnormalities such as the idiopathic interstitial pneumonias and cystic lung diseases. In this review, we discuss the main manifestations of these incidental findings, paying attention to their prevalence and importance, descriptors to use when reporting, the limits of what can be considered "normal", and conclude each section with some pragmatic reporting recommendations. We also highlight technical and patient factors which can lead to spurious abnormalities.
Topics: Humans; Lung; Lung Diseases, Interstitial; Tomography, X-Ray Computed; Pulmonary Emphysema; Bronchi
PubMed: 36124681
DOI: 10.1259/bjr.20220207 -
Journal of Digital Imaging Jun 2020This paper presents a systematic review of the literature focused on the lung nodule detection in chest computed tomography (CT) images. Manual detection of lung nodules... (Review)
Review
This paper presents a systematic review of the literature focused on the lung nodule detection in chest computed tomography (CT) images. Manual detection of lung nodules by the radiologist is a sequential and time-consuming process. The detection is subjective and depends on the radiologist's experiences. Owing to the variation in shapes and appearances of a lung nodule, it is very difficult to identify the proper location of the nodule from a huge number of slices generated by the CT scanner. Small nodules (< 10 mm in diameter) may be missed by this manual detection process. Therefore, computer-aided diagnosis (CAD) system acts as a "second opinion" for the radiologists, by making final decision quickly with higher accuracy and greater confidence. The goal of this survey work is to present the current state of the artworks and their progress towards lung nodule detection to the researchers and readers in this domain. This review paper has covered the published works from 2009 to April 2018. Different nodule detection approaches are described elaborately in this work. Recently, it is observed that deep learning (DL)-based approaches are applied extensively for nodule detection and characterization. Therefore, emphasis has been given to convolutional neural network (CNN)-based DL approaches by describing different CNN-based networks.
Topics: Deep Learning; Humans; Lung; Lung Neoplasms; Solitary Pulmonary Nodule; Tomography, X-Ray Computed
PubMed: 31997045
DOI: 10.1007/s10278-020-00320-6 -
Cancer Science Dec 2023Evaluating the accuracy of pulmonary nodule diagnosis avoids repeated low-dose computed tomography (LDCT)/CT scans or invasive examination, yet remains a main clinical...
Evaluating the accuracy of pulmonary nodule diagnosis avoids repeated low-dose computed tomography (LDCT)/CT scans or invasive examination, yet remains a main clinical challenge. Screening for new diagnostic tools is urgent. Herein, we established a nomogram based on the diagnostic signature of five circulating tsRNAs and CT information to predict malignant pulmonary nodules. In total, 249 blood samples of patients with pulmonary nodules were selected from three different lung cancer centers. Five tsRNAs were identified in the discovery and training cohorts and the diagnostic signature was established by the randomForest algorithm (tRF-Ser-TGA-003, tRF-Val-CAC-005, tRF-Ala-AGC-060, tRF-Val-CAC-024, and tiRNA-Gln-TTG-001). A nomogram was developed by combining tsRNA signature and CT information. The high level of accuracy was identified in an internal validation cohort (n = 83, area under the receiver operating characteristic curve [AUC] = 0.930, sensitivity 100.0%, specificity 73.8%) and external validation cohort (n = 66, AUC = 0.943, sensitivity 100.0%, specificity 86.8%). Furthermore, the diagnostic ability of our model discriminating invasive malignant ones from noninvasive lesions was assessed. A robust performance was achieved in the diagnosis of invasive malignant lesions in both training and validation cohorts (discovery cohort: AUC = 0.850, sensitivity 86.0%, specificity 81.4%; internal validation cohort: AUC = 0.784, sensitivity 78.8%, specificity 78.1%; and external validation cohort: AUC = 0.837, sensitivity 85.7%, specificity 84.0%). This novel circulating tsRNA-based diagnostic model has potential significance in predicting malignant pulmonary nodules. Application of the model could improve the accuracy of pulmonary nodule diagnosis and optimize surgical plans.
Topics: Humans; Nomograms; Multiple Pulmonary Nodules; Lung Neoplasms; Tomography, X-Ray Computed; Lung; Retrospective Studies
PubMed: 37770420
DOI: 10.1111/cas.15971