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

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

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.

[Lung cancer screening: Update, news and perspectives]

Lung cancer is the leading cause of cancer death in France and worldwide (20 % of cancer deaths). This mortality is partly linked to an overrepresentation of metastatic stages at diagnosis (approximately 55 % of lung cancers at diagnosis). Low-dose chest CT in a target population to detect early forms accessible to radical treatment has been evaluated through multiple randomized trials (NLST, NELSON, MILD, DANTE…). These trials demonstrated a reduction in lung cancer specific mortality. The current problem is to integrate a CT screening policy CT at a national level, which should be both efficient and cost-effective, while presenting the least harms for the eligible population. Finally, it is necessary to optimize the participation of the eligible population and particularly in the most deprived areas and ensure the proper implementation of smoking cessation measures.

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

Nowadays, lung cancer has remained the most lethal cancer, despite great advances in diagnosis and treatment. However, a large proportion of patients were diagnosed with locally advanced or metastatic disease and have poor prognosis. Immunotherapy and targeted drugs have greatly improved the survival and prognosis of patients with advanced lung cancer. However, how to identify the optimal patients to accept those therapies and how to monitor therapeutic efficacy are still in dispute. In the past few decades, tissue biopsy, including percutaneous fine needle biopsy and surgical excision, has still been the gold standard for examining the gene mutation such as EGFR, ALK, ROS, and PD-1/PD/L1, which can indicate the follow-up treatment. Nevertheless, the biopsy techniques mentioned above were invasive and unrepeatable, which were not suitable for advanced patients. Liquid biopsy, accounting for heterogeneity compared with tissue biopsy, is an alternative technique for monitoring the mutation, and a large quantity of research has demonstrated its feasibility to detect the circulating tumor cell, cell-free DNA, circulating tumor DNA, and extracellular vesicles from peripheral venous blood. The proposal of the concept of precision medicine brings a novel medical model developed with the rapid progress of genome sequencing technology and the cross-application of bioinformation, which was based on personalized medicine. The emerging method of liquid biopsy might contribute to promoting the development of precision medicine. In this review, we intend to describe the liquid biopsy in non-small cell lung cancer in detail in the aspect of screening, diagnosis, monitoring, treatment, and drug resistance.

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

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.