Direct reconstruction of anatomical change in low-dose lung nodule surveillance

2021 ◽  
Vol 8 (02) ◽  
Author(s):  
Jessica D. Flores ◽  
Grace J. Gang ◽  
Hao Zhang ◽  
Cheng T. Lin ◽  
Shui K. Fung ◽  
...  
Keyword(s):  
Low Dose ◽  
Lung Nodule ◽  
Anatomical Change

LUNGRADS AND UPDATE OF LUNG NODULES SCREENING BY LOW DOSE COMPUTED TOMOGRAPHY

2015 ◽  
pp. 12-19
Author(s):  
Thi Ngoc Ha Hoang ◽  
Trong Khoan Le
Keyword(s):  
Lung Cancer ◽  
Low Dose ◽  
False Negative ◽  
Lung Nodule ◽  
Lung Cancer Mortality ◽  
Smoking History ◽  
Low Dose Ct ◽  
History Of ◽  
Chest X Ray ◽  
Low Dose Computed Tomography

Background: A pulmonary nodule is defined as a rounded or irregular opacity, well or poorly defined, measuring up to 3 cm in diameter. Early detection the malignancy of nodules has a significant role in decreasing the mortality, increasing the survival time and consider as early diagnosis lung cancer. The main risk factors are those of current or former smokers, aged 55 to 74 years with a smoking history of at least 1 pack-day. Low dose CT: screening individuals with high risk of lung cancer by low dose CT scans could reduce lung cancer mortality by 20 percent compared to chest X-ray. Radiation dose has to maximum reduced but respect the rule of ALARA (As Low as Resonably Archivable). LungRADS 2014: Classification of American College of Radiology, LungRADS, is a newly application but showed many advantages in comparison with others classification such as increasing positive predict value (PPV), no result of false negative and cost effectiveness. Key words: LungRADS, screening lung nodule, low dose CT, lung cancer

scholarly journals Dosimetry and Comparison between Different CT Protocols (Low Dose, Ultralow Dose, and Conventional CT) for Lung Nodules’ Detection in a Phantom

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Cleverson Alex Leitão ◽  
Gabriel Lucca de Oliveira Salvador ◽  
Priscilla Tazoniero ◽  
Danny Warszawiak ◽  
Cristian Saievicz ◽  
...  
Keyword(s):  
Detection Rate ◽  
Low Dose ◽  
Lung Nodule ◽  
Lung Nodules ◽  
Ultralow Dose ◽  
Thermoluminescent Dosimeters ◽  
Significant Difference ◽  
Nodule Detection ◽  
The Difference ◽  
Conventional Ct

Background. The effects of dose reduction in lung nodule detection need better understanding. Purpose. To compare the detection rate of simulated lung nodules in a chest phantom using different computed tomography protocols, low dose (LD), ultralow dose (ULD), and conventional (CCT), and to quantify their respective amount of radiation. Materials and Methods. A chest phantom containing 93 simulated lung nodules was scanned using five different protocols: ULD (80 kVp/30 mA), LD A (120 kVp/20 mA), LD B (100 kVp/30 mA), LD C (120 kVp/30 mA), and CCT (120 kVp/automatic mA). Four chest radiologists analyzed a selected image from each protocol and registered in diagrams the nodules they detected. Kruskal–Wallis and McNemar’s tests were performed to determine the difference in nodule detection. Equivalent doses were estimated by placing thermoluminescent dosimeters on the surface and inside the phantom. Results. There was no significant difference in lung nodules’ detection when comparing ULD and LD protocols ( p = 0.208 to p = 1.000 ), but there was a significant difference when comparing each one of those against CCT ( p < 0.001 ). The detection rate of nodules with CT attenuation values lower than −600 HU was also different when comparing all protocols against CCT ( p < 0.001 to p = 0.007 ). There was at least moderate agreement between observers in all protocols (κ-value >0.41). Equivalent dose values ranged from 0.5 to 9 mSv. Conclusion. There is no significant difference in simulated lung nodules’ detection when comparing ULD and LD protocols, but both differ from CCT, especially when considering lower-attenuating nodules.

Lung nodule detection in low-dose and thin-slice computed tomography

2008 ◽  
Vol 38 (4) ◽  
pp. 525-534 ◽  
Author(s):  
A. Retico ◽  
P. Delogu ◽  
M.E. Fantacci ◽  
I. Gori ◽  
A. Preite Martinez
Keyword(s):  
Computed Tomography ◽  
Low Dose ◽  
Lung Nodule ◽  
Thin Slice ◽  
Lung Nodule Detection ◽  
Nodule Detection

Investigating the low-dose limits of multidetector CT in lung nodule surveillance

2007 ◽  
Vol 34 (9) ◽  
pp. 3587-3595 ◽  
Author(s):  
N. S. Paul ◽  
J. H. Siewerdsen ◽  
D. Patsios ◽  
T.-B. Chung
Keyword(s):  
Low Dose ◽  
Multidetector Ct ◽  
Lung Nodule ◽  
Dose Limits

scholarly journals 3D CNN with Visual Insights for Early Detection of Lung Cancer Using Gradient-Weighted Class Activation

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Eali Stephen Neal Joshua ◽  
Debnath Bhattacharyya ◽  
Midhun Chakkravarthy ◽  
Yung-Cheol Byun
Keyword(s):  
Low Dose ◽  
Binary Classification ◽  
Lung Nodule ◽  
Fine Grained ◽  
Internal Working ◽  
Nodule Detection ◽  
Classification Tasks ◽  
3D Cnn ◽  
Nodule Classification ◽  
Fold Cross Validation

The 3D convolutional neural network is able to make use of the full nonlinear 3D context information of lung nodule detection from the DICOM (Digital Imaging and Communications in Medicine) images, and the Gradient Class Activation has shown to be useful for tailoring classification tasks and localization interpretation for fine-grained features and visual explanation for the internal working. Gradient-weighted class activation plays a crucial role for clinicians and radiologists in terms of trusting and adopting the model. Practitioners not only rely on a model that can provide high precision but also really want to gain the respect of radiologists. So, in this paper, we explored the lung nodule classification using the improvised 3D AlexNet with lightweight architecture. Our network employed the full nature of the multiview network strategy. We have conducted the binary classification (benign and malignant) on computed tomography (CT) images from the LUNA 16 database conglomerate and database image resource initiative. The results obtained are through the 10-fold cross-validation. Experimental results have shown that the proposed lightweight architecture achieved a superior classification accuracy of 97.17% on LUNA 16 dataset when compared with existing classification algorithms and low-dose CT scan images as well.

WE-D-332-09: Delving the Low-Dose Limits of Lung Nodule Detectability in 320-Slice CT

2008 ◽  
Vol 35 (6Part24) ◽  
pp. 2950-2950
Author(s):  
J Silverman ◽  
J Siewerdsen ◽  
N Paul
Keyword(s):  
Low Dose ◽  
Lung Nodule ◽  
Dose Limits

scholarly journals Ultra-Low-Dose MDCT of the Chest: Influence on Automated Lung Nodule Detection

2008 ◽  
Vol 9 (2) ◽  
pp. 95 ◽  
Author(s):  
Ji Young Lee ◽  
Myung Jin Chung ◽  
Chin A Yi ◽  
Kyung Soo Lee
Keyword(s):  
Low Dose ◽  
Lung Nodule ◽  
Lung Nodule Detection ◽  
Nodule Detection

scholarly journals Detection of pulmonary nodules: a clinical study protocol to compare ultra-low dose chest CT and standard low-dose CT using ASIR-V

BMJ Open ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. e025661 ◽  
Author(s):  
Marie Ludwig ◽  
Emilie Chipon ◽  
Julien Cohen ◽  
Emilie Reymond ◽  
Maud Medici ◽  
...  
Keyword(s):  
Low Dose ◽  
Lung Nodule ◽  
Study Participant ◽  
Lung Cancer Screening ◽  
Chest Ct ◽  
Reference Level ◽  
Patient Identification ◽  
Blind Test ◽  
Informed Consent Form ◽  
Number Of Patients

IntroductionLung cancer screening in individuals at risk has been recommended by various scientific institutions. One of the main concerns for CT screening is repeated radiation exposure, with the risk of inducing malignancies in healthy individuals. Therefore, lowering the radiation dose is one of the main objectives for radiologists. The aim of this study is to demonstrate that an ultra-low dose (ULD) chest CT protocol, using recently introduced hybrid iterative reconstruction (ASiR-V, GE medical Healthcare, Milwaukee, Wisconsin, USA), is as performant as a standard ‘low dose’ (LD) CT to detect non-calcified lung nodules ≥4 mm.Methods and analysisThe total number of patients to include is 150. Those are referred for non-enhanced chest CT for detection or follow-up of lung nodule and will undergo an additional unenhanced ULD CT acquisition, the dose of which is on average 10 times lower than the conventional LD acquisition. Total dose of the entire exam (LD+ULD) is lower than the French diagnostic reference level for a chest CT (6.65 millisievert). ULD CT images will be reconstructed with 50% and 100% ASiR-V and LD CT with 50%. The three sets of images will be read in random order by two pair of radiologists, in a blind test, where patient identification and study outcomes are concealed. Detection rate (sensitivity) is the primary outcome. Secondary outcomes will include concordance of nodule characteristics; interobserver reproducibility; influence of subjects’ characteristics, nodule location and nodule size; and concordance of emphysema, coronary calcifications evaluated by visual scoring and bronchial alterations between LD and ULD CT. In case of discordance, a third radiologist will arbitrate.Ethics and disseminationThe study was approved by the relevant ethical committee. Each study participant will sign an informed consent form.Trial registration numberNCT03305978; Pre-results.

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