Results of Second Round Lung Cancer Screening by Low-Dose CT scan - French Cohort Study (DEP-KP80)

Leleu, O.; Basille, Damien; Auquier, M.; Clarot, Caroline; Hoguet, E.; Baud, M.; Lenel, S.; Milleron, B.; Berna, Pascal; Jounieaux, V.

doi:10.1016/j.cllc.2021.09.009

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…However, it is tedious for medical experts to manually analyze CT scans without any error [11]. In the last few years, a low-dose CT scan has been used to screen for lung cancer diseases that has led to a decrease in the mortality rate of lung cancer [12].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of State-of-the-Art CNN Architectures for LUNA16

Naseer

Akram

Masood

et al. 2022

Sensors

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The convolutional neural network (CNN) has become a powerful tool in machine learning (ML) that is used to solve complex problems such as image recognition, natural language processing, and video analysis. Notably, the idea of exploring convolutional neural network architecture has gained substantial attention as well as popularity. This study focuses on the intrinsic various CNN architectures: LeNet, AlexNet, VGG16, ResNet-50, and Inception-V1, which have been scrutinized and compared with each other for the detection of lung cancer using publicly available LUNA16 datasets. Furthermore, multiple performance optimizers: root mean square propagation (RMSProp), adaptive moment estimation (Adam), and stochastic gradient descent (SGD), were applied for this comparative study. The performances of the three CNN architectures were measured for accuracy, specificity, sensitivity, positive predictive value, false omission rate, negative predictive value, and F1 score. The experimental results showed that the CNN AlexNet architecture with the SGD optimizer achieved the highest validation accuracy for CT lung cancer with an accuracy of 97.42%, misclassification rate of 2.58%, 97.58% sensitivity, 97.25% specificity, 97.58% positive predictive value, 97.25% negative predictive value, false omission rate of 2.75%, and F1 score of 97.58%. AlexNet with the SGD optimizer was the best and outperformed compared to the other state-of-the-art CNN architectures.

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Section: Introductionmentioning

confidence: 99%

Performance Analysis of State-of-the-Art CNN Architectures for LUNA16

Naseer

Akram

Masood

et al. 2022

Sensors

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“…In recent years, lung cancer screening has been on the rise in several countries, including the United States, Germany, and France, because of the high health care burden of lung cancer, although such screening results in an increase in the population's radiation dose. 2,[5][6][7] Magnetic resonance imaging (MRI) is not only a radiation-free option for lung imaging, but also offers the advantage of advanced soft tissue characterization and could reduce follow-up examinations. [8][9][10] Even with these inherent advantages, which have contributed to the continued success of MRI in other body regions such as the central nervous and musculoskeletal systems, lung MRI has not been established in the clinical routine, apart from a few exceptional cases.…”

mentioning

confidence: 99%

“…Despite these advantages, it is associated with not inconsiderable radiation exposure, especially with repeated examinations. In recent years, lung cancer screening has been on the rise in several countries, including the United States, Germany, and France, because of the high health care burden of lung cancer, although such screening results in an increase in the population's radiation dose 2,5–7 . Magnetic resonance imaging (MRI) is not only a radiation-free option for lung imaging, but also offers the advantage of advanced soft tissue characterization and could reduce follow-up examinations 8–10 …”

mentioning

confidence: 99%

Lung Nodule Detection With Modern Low-Field MRI (0.55 T) in Comparison to CT

Hinsen,

Nagel,

May

et al. 2024

Invest Radiol

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Objectives The aim of this study was to evaluate the accuracy of modern low-field magnetic resonance imaging (MRI) for lung nodule detection and to correlate nodule size measurement with computed tomography (CT) as reference. Materials and Methods Between November 2020 and July 2021, a prospective clinical trial using low-field MRI at 0.55 T was performed in patients with known pulmonary nodules from a single academic medical center. Every patient underwent MRI and CT imaging on the same day. The primary aim was to evaluate the detection accuracy of pulmonary nodules using MRI with transversal periodically rotated overlapping parallel lines with enhanced reconstruction in combination with coronal half-Fourier acquired single-shot turbo spin-echo MRI sequences. The secondary outcome was the correlation of the mean lung nodule diameter with CT as reference according to the Lung Imaging Reporting and Data System. Nonparametric Mann-Whitney U test, Spearman rank correlation coefficient, and Bland-Altman analysis were applied to analyze the results. Results A total of 46 participants (mean age ± SD, 66 ± 11 years; 26 women) were included. In a blinded analysis of 964 lung nodules, the detection accuracy was 100% for those ≥6 mm (126/126), 80% (159/200) for those ≥4–<6 mm, and 23% (147/638) for those <4 mm in MRI compared with reference CT. Spearman correlation coefficient of MRI and CT size measurement was r = 0.87 (P < 0.001), and the mean difference was 0.16 ± 0.9 mm. Conclusions Modern low-field MRI shows excellent accuracy in lesion detection for lung nodules ≥6 mm and a very strong correlation with CT imaging for size measurement, but could not compete with CT in the detection of small nodules.

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The Implication of Liquid Biopsy in the Non-small Cell Lung Cancer: Potential and Expectation

Ren

Liu

2023

Methods in Molecular Biology

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Results of Second Round Lung Cancer Screening by Low-Dose CT scan - French Cohort Study (DEP-KP80)

Cited by 6 publications

References 24 publications

Performance Analysis of State-of-the-Art CNN Architectures for LUNA16

Performance Analysis of State-of-the-Art CNN Architectures for LUNA16

Lung Nodule Detection With Modern Low-Field MRI (0.55 T) in Comparison to CT

The Implication of Liquid Biopsy in the Non-small Cell Lung Cancer: Potential and Expectation

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