Lung Imaging in Cystic Fibrosis

Hughes, David C.; Tiddens, Harm A.W.M.; Wild, Jim M.

doi:10.1111/j.1617-0830.2009.01125.x

Cited by 4 publications

(2 citation statements)

References 74 publications

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“…MRI comprising two or three sequences is helpful in reducing the false-positive rate. It was found that 3T MRI has more difficulty detecting ground glass opacities than 1.5T MRI [ 64 ]. 1.5T MRI with SSFP sequences successfully detected ground glass opacities in 75% of lung fibrosis subjects [ 65 ].…”

Section: Clinical Lung Screening Modalitiesmentioning

confidence: 99%

Screening and Biosensor-Based Approaches for Lung Cancer Detection

Wang

2017

Sensors

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Early diagnosis of lung cancer helps to reduce the cancer death rate significantly. Over the years, investigators worldwide have extensively investigated many screening modalities for lung cancer detection, including computerized tomography, chest X-ray, positron emission tomography, sputum cytology, magnetic resonance imaging and biopsy. However, these techniques are not suitable for patients with other pathologies. Developing a rapid and sensitive technique for early diagnosis of lung cancer is urgently needed. Biosensor-based techniques have been recently recommended as a rapid and cost-effective tool for early diagnosis of lung tumor markers. This paper reviews the recent development in screening and biosensor-based techniques for early lung cancer detection.

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Section: Clinical Lung Screening Modalitiesmentioning

confidence: 99%

Screening and Biosensor-Based Approaches for Lung Cancer Detection

Wang

2017

Sensors

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“…The authors investigated T1-weighted and T2-weighted MRI to detect small lung nodules [ 30 , 31 ]. Compared to 3T 1.5 MRI, 1.5T MRI is much easier to identify ground glass opacities [ 32 ]. Ground glass opacities were successfully detected in 75% of subjects with lung fibrosis who received 1.5T MRI with SSFP sequences [ 33 ].…”

Section: Lung Imaging Techniquesmentioning

confidence: 99%

Deep Learning Techniques to Diagnose Lung Cancer

Wang

2022

Cancers

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Medical imaging tools are essential in early-stage lung cancer diagnostics and the monitoring of lung cancer during treatment. Various medical imaging modalities, such as chest X-ray, magnetic resonance imaging, positron emission tomography, computed tomography, and molecular imaging techniques, have been extensively studied for lung cancer detection. These techniques have some limitations, including not classifying cancer images automatically, which is unsuitable for patients with other pathologies. It is urgently necessary to develop a sensitive and accurate approach to the early diagnosis of lung cancer. Deep learning is one of the fastest-growing topics in medical imaging, with rapidly emerging applications spanning medical image-based and textural data modalities. With the help of deep learning-based medical imaging tools, clinicians can detect and classify lung nodules more accurately and quickly. This paper presents the recent development of deep learning-based imaging techniques for early lung cancer detection.

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Evaluation of Two Chest Tomosynthesis Cystic Fibrosis Scoring Systems Using High-Resolution Computed Tomography Brody Scoring as Reference

Norrlund

Meltzer

Söderman

et al. 2021

Radiation Protection Dosimetry

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Purpose To evaluate two chest tomosynthesis (CTS) scoring systems for cystic fibrosis (CF), one system developed by Vult von Steyern et al. (VvS) and one system based on the Brody scoring system for high-resolution computed tomography (HRCT) (modified Brody (mB)). Brody scoring of HRCT was used as reference. Methods In conjunction with routine control HRCT at clinical follow-up, 10 consecutive adult CF patients underwent CTS for research purposes. Four radiologists scored the CTS examinations using the mB and VvS scoring systems. All scores were compared to the Brody HRCT scores. The agreement between the evaluated CTS scoring systems and the reference HRCT scoring system was determined using Spearman’s rank correlation coefficient and the intraclass correlation coefficient (ICC). Major findings Spearman’s rank correlation coefficient showed strong correlations between HRCT score and both the mB and the VvS CTS total scores (median rs = 0.81 and 0.85, respectively). The ICC showed strong correlation between the CTS scoring systems and the reference: 0.88 for mB and 0.85 for VvS scoring. The median time for scoring was 20 and 10 minutes for the mB and VvS scoring systems, respectively. Conclusions Both evaluated CTS scoring systems correlate well with the reference standard Brody HRCT scoring. The VvS CTS scoring system has a shorter reading time, suggesting its advantage in clinical practice.

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Lung Imaging in Cystic Fibrosis

Cited by 4 publications

References 74 publications

Screening and Biosensor-Based Approaches for Lung Cancer Detection

Screening and Biosensor-Based Approaches for Lung Cancer Detection

Deep Learning Techniques to Diagnose Lung Cancer

Evaluation of Two Chest Tomosynthesis Cystic Fibrosis Scoring Systems Using High-Resolution Computed Tomography Brody Scoring as Reference

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