scholarly journals Current Practice in the Management of Pulmonary Nodules Detected on Computed Tomography Chest Scans

2019 ◽  
Vol 2019 ◽  
pp. 1-6 ◽  
Author(s):  
Clarus Leung ◽  
Tawimas Shaipanich
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Surgical Resection ◽  
Pulmonary Nodule ◽  
Current Practice ◽  
Pulmonary Nodules ◽  
Lung Cancer Screening ◽  
Time Interval ◽  
Positive Finding

Lung cancer is associated with high mortality. It can present as one or more pulmonary nodules identified on computed tomography (CT) chest scans. The National Lung Screening Trial has shown that the use of low-dose CT chest screening can reduce deaths due to lung cancer. High adherence to appropriate follow-up of positive results, including imaging or interventional approaches, is an important aspect of pulmonary nodule management. Our study is one of the first to evaluate the current practice in managing pulmonary nodules and to explore potential causes for nonadherence to follow-up. This is a retrospective analysis at St. Paul’s Hospital, a tertiary healthcare center in Vancouver, British Columbia, Canada. We first identified CT chest scans between January 1 to June 30, 2014, that demonstrated one or more pulmonary nodules equal to or greater than 6 mm in diameter. We then looked for evidence of interventional (surgical resection or biopsy, or bronchoscopy for transbronchial biopsy and cytology) and radiological follow-up of the pulmonary nodule by searching on the province-wide CareConnect eHealth Viewer patient database. A total of 1614 CT reports were analyzed and 139 (8.6%) had a positive finding. Out of the 97 patients who received follow-up, 54.6% (N = 53) was referred for a repeat CT chest scan and 36.1% (N = 35) and 9.3% (N = 9) were referred for interventional biopsy and surgical resection, respectively. In our study, 30.2% (N = 42) of the patients with pulmonary nodules were nonadherent to follow-up. Despite the radiologist’s recommendation for follow-up within a certain time interval, only 36% had repeat imaging in a timely manner. Our findings reflect the current practice in the management of pulmonary nodules and suggest that there is a need for improvement at our academic center. Adherence to follow-up is important for the potentially near-future implementation of lung cancer screening.

Tracking time interval changes of pulmonary nodules on follow-up 3D CT images via image-based risk score of lung cancer

2013 ◽  
Author(s):  
Y. Kawata ◽  
N. Niki ◽  
H. Ohmatsu ◽  
M. Kusumoto ◽  
T. Tsuchida ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Risk Score ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Time Interval ◽  
3D Ct ◽  
Tracking Time

scholarly journals Volumetric lung cancer screening reduces unnecessary low-dose computed tomography scans: results from a single-centre prospective trial on 4,119 subjects

2021 ◽  
Author(s):  
Gianluca Milanese ◽  
Federica Sabia ◽  
Roberta Eufrasia Ledda ◽  
Stefano Sestini ◽  
Alfonso Vittorio Marchiano' ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Cancer Screening ◽  
Low Dose ◽  
Prospective Trial ◽  
Pulmonary Nodules ◽  
Lung Cancer Screening ◽  
Single Centre ◽  
Kappa Test ◽  
Low Dose Computed Tomography

Purpose. To compare low-dose computed tomography (LDCT) outcome and volume-doubling time (VDT) derived from measured volume (MV) and estimated volume (EV) of pulmonary nodules (PN) detected in a single-centre lung cancer screening trial. Materials and Methods. MV, EV and VDT were obtained for prevalent pulmonary nodules detected at the baseline round of the bioMILD trial. LDCT outcome (based on bioMILD thresholds) and VDT categories were simulated on a PN- and a screenees-based analysis. Weighted Cohen's kappa test was used to assess the agreement between diagnostic categories as per MV and EV. Results. 1,583 screenees displayed 2,715 pulmonary nodules. On a PN-based analysis 40.1% PNs would have been included in different LDCT categories if measured by MV or EV. Agreement between MV and EV was moderate (κ = 0.49) and fair (κ = 0.37) for LDCT outcome and VDT categories, respectively. On a screenees-based analysis, 46% pulmonary nodules would have been included in different LDCT categories if measured by MV or EV. Agreement between MV and EV was moderate (κ = 0.52) and fair (κ = 0.34) for LDCT outcome and VDT categories, respectively. Conclusions. Within a simulated lung cancer screening based on recommendation by estimated volumetry, the number of LDCT performed for the evaluation of pulmonary nodules would be higher as compared to the prospective volumetric management.

Imaging Of Lung Disease, Part II: ­Cavities, Cysts, Nodules, And Masses

2018 ◽  
Author(s):  
Gerald W. Staton Jr ◽  
Eugene A Berkowitz ◽  
Adam Bernheim
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Lung Disease ◽  
Interstitial Pneumonia ◽  
Pulmonary Nodules ◽  
Lung Cancer Screening ◽  
Cystic Lung ◽  
Cystic Lung Disease ◽  
Clinical Scenarios

Cavitary lesions may occur in the setting of pulmonary infection, neoplasm, or vasculitis.  Cystic lung disease must be differentiated from emphysema and is seen in lymphangioleiomyomatosis, Langerhans cell histiocytosis (LCH), and lymphoid interstitial pneumonia (LIP).  Pulmonary nodules are routinely encountered on chest imaging and may be due to benign or malignant etiologies.  There are follow-up algorithms that provide recommendations for solid and sub-solid nodules in certain clinical scenarios.  Nodules characteristics (such as size, morphology, and number [solitary versus multiple]) and patient characteristics (including age, oncology history, and cigarette smoking status) are important to consider in formulating a differential diagnosis and follow-up plan.  Lung cancer screening computed tomography (CT) is now a recommended screening test for high-risk patients who meet certain eligibility requirements, and should be reported according to the Lung Imaging Reporting and Data System (Lung-RADS). This review contains 28 figures, 3 tables and 26 references Keywords: Cavitary Lung Disease, Granulomatosis with Polyangiitis, Cystic Lung Disease, Lymphoid Interstitial Pneumonia, Pulmonary Emphysema, Pulmonary Nodules, Pulmonary Granulomatous Disease, Arteriovenous Malformation, Lung Cancer Screening, Pulmonary Fungal Infection

scholarly journals Towards a close computed tomography monitoring approach for screen detected subsolid pulmonary nodules?

2014 ◽  
Vol 45 (3) ◽  
pp. 765-773 ◽  
Author(s):  
Ernst T. Scholten ◽  
Pim A. de Jong ◽  
Bartjan de Hoop ◽  
Rob van Klaveren ◽  
Saskia van Amelsvoort-van de Vorst ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Pulmonary Nodules ◽  
Lung Cancer Screening ◽  
Maximum Diameter ◽  
Cancer Screening Trial ◽  
Study Population ◽  
Safe Approach ◽  
Screening Trial ◽  
Measured Mass

Pulmonary subsolid nodules (SSNs) have a high likelihood of malignancy, but are often indolent. A conservative treatment approach may therefore be suitable. The aim of the current study was to evaluate whether close follow-up of SSNs with computed tomography may be a safe approach.The study population consisted of participants of the Dutch-Belgian lung cancer screening trial (Nederlands Leuvens Longkanker Screenings Onderzoek; NELSON). All SSNs detected during the trial were included in this analysis. Retrospectively, all persistent SSNs and SSNs that were resected after first detection were segmented using dedicated software, and maximum diameter, volume and mass were measured. Mass doubling time (MDT) was calculated.In total 7135 volunteers were included in the current analysis. 264 (3.3%) SSNs in 234 participants were detected during the trial. 147 (63%) of these SSNs in 126 participants disappeared at follow-up, leaving 117 persistent or directly resected SSNs in 108 (1.5%) participants available for analysis. The median follow-up time was 95 months (range 20–110 months). 33 (28%) SSNs were resected and 28 of those were (pre-) invasive. None of the non-resected SSNs progressed into a clinically relevant malignancy.Persistent SSNs rarely developed into clinically manifest malignancies unexpectedly. Close follow-up with computed tomography may be a safe option to monitor changes.

scholarly journals Automated Pulmonary Nodule Classification in Computed Tomography Images Using a Deep Convolutional Neural Network Trained by Generative Adversarial Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Yuya Onishi ◽  
Atsushi Teramoto ◽  
Masakazu Tsujimoto ◽  
Tetsuya Tsukamoto ◽  
Kuniaki Saito ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Lung Cancer ◽  
Pulmonary Nodule ◽  
Classification Accuracy ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Benign Nodules

Lung cancer is a leading cause of death worldwide. Although computed tomography (CT) examinations are frequently used for lung cancer diagnosis, it can be difficult to distinguish between benign and malignant pulmonary nodules on the basis of CT images alone. Therefore, a bronchoscopic biopsy may be conducted if malignancy is suspected following CT examinations. However, biopsies are highly invasive, and patients with benign nodules may undergo many unnecessary biopsies. To prevent this, an imaging diagnosis with high classification accuracy is essential. In this study, we investigate the automated classification of pulmonary nodules in CT images using a deep convolutional neural network (DCNN). We use generative adversarial networks (GANs) to generate additional images when only small amounts of data are available, which is a common problem in medical research, and evaluate whether the classification accuracy is improved by generating a large amount of new pulmonary nodule images using the GAN. Using the proposed method, CT images of 60 cases with confirmed pathological diagnosis by biopsy are analyzed. The benign nodules assessed in this study are difficult for radiologists to differentiate because they cannot be rejected as being malignant. A volume of interest centered on the pulmonary nodule is extracted from the CT images, and further images are created using axial sections and augmented data. The DCNN is trained using nodule images generated by the GAN and then fine-tuned using the actual nodule images to allow the DCNN to distinguish between benign and malignant nodules. This pretraining and fine-tuning process makes it possible to distinguish 66.7% of benign nodules and 93.9% of malignant nodules. These results indicate that the proposed method improves the classification accuracy by approximately 20% in comparison with training using only the original images.

scholarly journals Optimizing the timing of diagnostic testing after positive findings in lung cancer screening: a proof of concept radiomics study

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Zixing Wang ◽  
Ning Li ◽  
Fuling Zheng ◽  
Xin Sui ◽  
Wei Han ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Cancer Diagnosis ◽  
Diagnostic Testing ◽  
Delayed Diagnosis ◽  
Pulmonary Nodules ◽  
Lung Cancer Screening ◽  
Lung Cancer Diagnosis ◽  
Risk Patients

Abstract Background The timeliness of diagnostic testing after positive screening remains suboptimal because of limited evidence and methodology, leading to delayed diagnosis of lung cancer and over-examination. We propose a radiomics approach to assist with planning of the diagnostic testing interval in lung cancer screening. Methods From an institute-based lung cancer screening cohort, we retrospectively selected 92 patients with pulmonary nodules with diameters ≥ 3 mm at baseline (61 confirmed as lung cancer by histopathology; 31 confirmed cancer-free). Four groups of region-of-interest-based radiomic features (n = 310) were extracted for quantitative characterization of the nodules, and eight features were proven to be predictive of cancer diagnosis, noise-robust, phenotype-related, and non-redundant. A radiomics biomarker was then built with the random survival forest method. The patients with nodules were divided into low-, middle- and high-risk subgroups by two biomarker cutoffs that optimized time-dependent sensitivity and specificity for decisions about diagnostic workup within 3 months and about repeat screening after 12 months, respectively. A radiomics-based follow-up schedule was then proposed. Its performance was visually assessed with a time-to-diagnosis plot and benchmarked against lung RADS and four other guideline protocols. Results The radiomics biomarker had a high time-dependent area under the curve value (95% CI) for predicting lung cancer diagnosis within 12 months; training: 0.928 (0.844, 0.972), test: 0.888 (0.766, 0.975); the performance was robust in extensive cross-validations. The time-to-diagnosis distributions differed significantly between the three patient subgroups, p < 0.001: 96.2% of high-risk patients (n = 26) were diagnosed within 10 months after baseline screen, whereas 95.8% of low-risk patients (n = 24) remained cancer-free by the end of the study. Compared with the five existing protocols, the proposed follow-up schedule performed best at securing timely lung cancer diagnosis (delayed diagnosis rate: < 5%) and at sparing patients with cancer-free nodules from unnecessary repeat screenings and examinations (false recommendation rate: 0%). Conclusions Timely management of screening-detected pulmonary nodules can be substantially improved with a radiomics approach. This proof-of-concept study’s results should be further validated in large programs.

Study the value of lung low dose computed tomography in early detection the malignant pulmonary nodule

2020 ◽  
pp. 7-15
Author(s):  
Ha Hoang ◽  
Tien Doan Dung ◽  
Khoan Le Trong
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Early Detection ◽  
Pulmonary Nodule ◽  
Pulmonary Nodules ◽  
Diagnostic Value ◽  
Cross Sectional Study ◽  
Solitary Pulmonary Nodules ◽  
Cross Sectional ◽  
The Right

Background: Early diagnosis of the malignant pulmonary nodules plays an important role in decreasing the mortality, increasing the lifetime and considering as early detection of lung cancer. Objectives: To describe the characteristics and diagnostic value of the malignant suspected signs of pulmonary nodule. Materials and methods: A descriptive cross-sectional study on 33 patients with localized pulmonary nodule which has indications of biopsy or surgery at Hospital of Hue University of Medicine and Pharmacy from 05/2017 to 08/2018. Results: A majority of pulmonary nodules were found in the right upper lobe with 42.4%; solitary pulmonary nodules made up the majority of 75.8%. (Nodules > 21.5 mm: 57.6%; nodules ≤ 21.5 mm: 42.4%; solid nodules: 97% and mixed nodules: 3%, round shape: 42.4% and polygons: 57.6%; irregular margin: 78.8%; regular margin: 21.2%; eccentric and stippled calcification: 18.2%; non-calcification: 81.8%; air-bronchogram in nodules: 39.4%; air-bronchogram (-): 60.6%; fat containing pulmonary nodules: 6.1%, malignant and benign confirmed by biopsy: 39.3% and 60.7% respectively. The sensitivity and specificity of features included size > 21.5 mm; air-bronchogram in nodules, polygons for malignant nodules diagnosis are 81.6%; 92.3%; 76.9% and 60%, 65%, 85% respectively. Conclusions: Three features of nodules: Size ≥ 21.5 mm; air-bronchogram and polygons are suggestive malignant characteristics. The combination of two or more characteristics have the sensitive 92.3% and specific 80% Keywords: pulmonary nodule, thoracic computed tomography, lung cancer.

scholarly journals Plasma Metabolomics Study in Lung Cancer Screening and Differential Diagnosis

2021 ◽  
Author(s):  
Zixu Liu ◽  
Lei Guo ◽  
Shuai Liu ◽  
Minjun Du ◽  
Yicheng Liang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Differential Diagnosis ◽  
Pulmonary Nodule ◽  
Primary Lung Cancer ◽  
Pulmonary Nodules ◽  
Lung Cancer Screening ◽  
Population Group ◽  
Healthy People ◽  
Healthy Population ◽  
Validation Set

Abstract [Objectives] By studying the plasma metabolomics of patients with different pulmonary nodules and healthy people, we can find the difference in plasma low-molecular metabolites among them. [Methods] Patients with pulmonary nodules admitted to our department were divided into three groups: pulmonary metastatic carcinoma (PMC), benign pulmonary nodules (BPN), and primary lung cancer (PLC). Meanwhile healthy people were enrolled as healthy population group (HPG). PLC and HPG were equally divided into the Discovery Set and Validation set. [Results] Five significant low-molecular metabolites were found by comparison of four groups as a whole. Four to six metabolites were selected by comparison of the three pulmonary nodule groups with healthy people respectively. The AUC of ROC of these metabolites were all＞0.93. Each pairwise comparison within the three pulmonary nodule groups all found three metabolites, whose AUC of ROC were all＞0.83. From the comparison of PLC and HPG in the discovery set, six metabolites were selected. Their AUC of ROC were all greater than 0.95 in the validation set, indicating that they had a strong ability to differentiate between primary lung cancer and healthy people. [Conclusions] We can find the significant changes of some low-molecular metabolites among three pulmonary nodules and healthy people. These metabolites had the potential to be biomarkers for screening and differential diagnosis of lung cancer.

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