scholarly journals Pulmonary nodules measurements in CT lung cancer screening

2018 ◽  
Vol 10 (S16) ◽  
pp. S2100-S2102 ◽  
Author(s):  
Marjolein A. Heuvelmans ◽  
Matthijs Oudkerk
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Pulmonary Nodules ◽  
Lung Cancer Screening

scholarly journals Effect of Nodule Characteristics on Variability of Semiautomated Volume Measurements in Pulmonary Nodules Detected in a Lung Cancer Screening Program

Radiology ◽  
2008 ◽  
Vol 248 (2) ◽  
pp. 625-631 ◽  
Author(s):  
Ying Wang ◽  
Rob J. van Klaveren ◽  
Hester J. van der Zaag–Loonen ◽  
Geertruida H. de Bock ◽  
Hester A. Gietema ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Screening Program ◽  
Pulmonary Nodules ◽  
Lung Cancer Screening ◽  
Cancer Screening Program ◽  
Volume Measurements ◽  
Lung Cancer Screening Program

scholarly journals Mobile Low-Dose Computed Tomographic (LDCT) Scanning combined with Remote Reading: a feasible approach to lung cancer screening among rural population

2021 ◽  
Author(s):  
Bojiang Chen ◽  
Jun Shao ◽  
Jinghong Xian ◽  
Pengwei Ren ◽  
Wenxin Luo ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Rural Areas ◽  
Pulmonary Nodules ◽  
Lung Cancer Screening ◽  
Western China ◽  
Control Group ◽  
Local Hospital ◽  
Lung Cancers ◽  
Computed Tomographic

Abstract BackgroundLow-dose computed tomographic (LDCT) screening has been proven to be powerful in detecting lung cancers in early stage. However, it’s hard to carry out in less-developed regions in lacking of facilities and professionals. The feasibility and efficacy of mobile LDCT scanning combined with remote reading by experienced radiologists from superior hospital for lung cancer screening in deprived areas was explored in this study.MethodsA prospective cohort was conducted in rural areas of western China. Residents over 40 years old were invited for lung cancer screening by mobile LDCT scanning combined with remote image reading or local hospital-based LDCT screening. Rates of positive pulmonary nodules and detected lung cancers in the baseline were compared between the two groups.ResultsAmong 8073 candidates with preliminary response, 7251 eligibilities were assigned to the mobile LDCT with remote reading (n = 4527) and local hospital-based LDCT screening (n = 2724) for lung cancer. Basic characteristics of the subjects were almost similar in the two cohorts except that the mean age of participants in mobile group was relatively older than control (61.18 vs. 59.84 years old, P < 0.001). 1778 participants with mobile LDCT scans with remote reading (39.3%) revealed 2570 pulmonary nodules or mass, and 352 subjects in the control group (13.0%) were detected 472 ones (P < 0.001). Proportions of nodules less than 8 mm or subsolid were both more frequent in the mobile LDCT group (83.3% vs. 76.1%, 32.9% vs. 29.8%, respectively; both P < 0.05). In the baseline screening, 26 cases of lung cancer were identified in the mobile LDCT scanning with remote reading cohort, with a lung cancer detection rate of 0.57% (26/4527), which was significantly higher than control (4/2724 = 0.15%, P = 0.006). Moreover, 80.8% (21/26) of lung cancer patients detected by mobile CT with remote reading were in stage I, remarkedly higher than that of 25.0% in control (1/4, P = 0.020).ConclusionMobile LDCT combined with remote reading is probably a potential mode for lung cancer screening in rural areas.Trial registrationNo. of registration trial was ChiCTR-DDD-15007586 (http://www.chictr.org).

scholarly journals New Subsolid Pulmonary Nodules in Lung Cancer Screening: The NELSON Trial

2018 ◽  
Vol 13 (9) ◽  
pp. 1410-1414 ◽  
Author(s):  
Joan E. Walter ◽  
Marjolein A. Heuvelmans ◽  
Uraujh Yousaf-Khan ◽  
Monique D. Dorrius ◽  
Erik Thunnissen ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Pulmonary Nodules ◽  
Lung Cancer Screening

scholarly journals Management of Patients with Solid Pulmonary Nodules Detected in Lung cancer Screening

2019 ◽  
Vol 27 (5) ◽  
pp. 553-562
Author(s):  
V.A. Gombolevskiy ◽  
◽  
A.E. Nikolaev ◽  
A.N. Shapiev ◽  
A.O. Kosolapov ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Pulmonary Nodules ◽  
Lung Cancer Screening

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.

A noninvasive multi-analytic approach for lung cancer screening of patients with pulmonary nodules.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 1550-1550
Author(s):  
Hong Zheng ◽  
Tiancheng Han ◽  
Quanxing Liu ◽  
Dong Zhou ◽  
Li Jiang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Cancer Screening ◽  
Validation Cohort ◽  
Pulmonary Nodules ◽  
Lung Cancer Screening ◽  
Clinical Information ◽  
Ensemble Model ◽  
Independent Validation ◽  
Risk Patients

1550 Background: Low-dose computed tomography (LDCT) is an effective approach for lung cancer screening of high-risk patients with pulmonary nodules, however with varying false positive rates depending on the somewhat subjective judgement of the practice professional. Artificial intelligence derived from machine learning of comprehensive patient profiles, including multi-omics and clinical data, has the potential to provide more objective assessment of patient’s risk in order to aid clinician’s decision making. We have developed a multi-analyte algorithm-based assay (MAAA) that incorporates ctDNA mutation, ctDNA methylation, and protein biomarker profiles evaluated through non-invasive blood-based testing, as well as patient’s clinical information, to improve the diagnostic efficacy of lung cancer. Methods: 98 high-risk patients with pulmonary nodules were enrolled in two independent cohorts (68 for training/testing and 30 for independent validation). The malignancy of the pulmonary nodules were established through pathology of surgical-removed nodules. Prior to surgery, each patient was also subject to cell-free DNA-based sequencing for DNA mutation and DNA methylation profiling, as well as serum protein biomarker profiling. On the training/testing patient cohort, machine-learning-based predictive models were first built for malignancy status prediction based on each type of molecular or clinical features. A final ensemble model was then constructed to incorporate the measurements based on molecular and clinical markers to provide the ultimate recommendation on the malignancy of the pulmonary nodule. The performance of each individual model and the final ensemble model was benchmarked on the training/testing cohort, and also validated on the independent validation cohort. Results: On the 30-patient independent validation cohort, individual prediction models based on clinical information, protein marker, ctDNA mutation, and ctDNA methylation profiles achieved predictive AUC of 0.59, 0.48, 0.71, and 0.84, respectively. The final ensemble model achieved predictive AUC of 0.86, which has strongly indicated that an integrative, algorithm-based approach of multi-analytic molecular and clinical profiles greatly outperforms any single-analytic profiling. Conclusions: Multi-analyte algorithm-based approach can be utilized to assist in lung cancer screening for patients with pulmonary nodules. It has demostrated a high accuracy through independent validation, and has outperformed any single-analyte testing in our study.

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

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