Classification and Segmentation Algorithm in Benign and Malignant Pulmonary Nodules under Different CT Reconstruction

Lu, Zhiqian; Long, Feixiang; He, Xiaodong

doi:10.1155/2022/3490463

Cited by 2 publications

(1 citation statement)

References 27 publications

(22 reference statements)

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“…In the past few decades, with the rapid development of computer vision and medical imaging technologies, deep learning-based analysis of CT images has shown tremendous potential in the detection and classification of lung nodules [1][2][3][4]. Lung nodules are one of the earliest signs of lung cancer, which is one of the most lethal cancers worldwide.…”

Section: Introductionmentioning

confidence: 99%

Accuracy Study on Deep Learning-Based CT Image Analysis for Lung Nodule Detection and Classification

Cheng,

Li,

et al. 2024

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Early detection and accurate classification of lung nodules are crucial for the treatment of lung cancer. With the widespread application of deep learning technologies in medical imaging analysis, significant progress has been made in the automatic detection and classification of lung nodules from computed tomography (CT) images. However, existing deep learning approaches often face challenges with limited annotated data and generalization across diverse datasets. To address these challenges, this study introduces two innovative methods: a domain-adaptive adversarial network for joint segmentation of lung CT images to enhance model generalization, and an improved deep propagation generation network (DPGN) for few-shot classification of lung CT images to reduce reliance on extensive annotated data. Through these methods, this research aims to improve the accuracy of lung nodule detection and classification, providing more reliable support for clinical diagnosis.

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Section: Introductionmentioning

confidence: 99%

Accuracy Study on Deep Learning-Based CT Image Analysis for Lung Nodule Detection and Classification

Cheng,

Li,

et al. 2024

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Factors influencing the outcome of volumetry tools for pulmonary nodule analysis: a systematic review and attempted meta-analysis

Guedes Pinto,

Penha,

Ravara

et al. 2023

Insights Imaging

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Health systems worldwide are implementing lung cancer screening programmes to identify early-stage lung cancer and maximise patient survival. Volumetry is recommended for follow-up of pulmonary nodules and outperforms other measurement methods. However, volumetry is known to be influenced by multiple factors. The objectives of this systematic review (PROSPERO CRD42022370233) are to summarise the current knowledge regarding factors that influence volumetry tools used in the analysis of pulmonary nodules, assess for significant clinical impact, identify gaps in current knowledge and suggest future research. Five databases (Medline, Scopus, Journals@Ovid, Embase and Emcare) were searched on the 21st of September, 2022, and 137 original research studies were included, explicitly testing the potential impact of influencing factors on the outcome of volumetry tools. The summary of these studies is tabulated, and a narrative review is provided. A subset of studies (n = 16) reporting clinical significance were selected, and their results were combined, if appropriate, using meta-analysis. Factors with clinical significance include the segmentation algorithm, quality of the segmentation, slice thickness, the level of inspiration for solid nodules, and the reconstruction algorithm and kernel in subsolid nodules. Although there is a large body of evidence in this field, it is unclear how to apply the results from these studies in clinical practice as most studies do not test for clinical relevance. The meta-analysis did not improve our understanding due to the small number and heterogeneity of studies testing for clinical significance. Critical relevance statement Many studies have investigated the influencing factors of pulmonary nodule volumetry, but only 11% of these questioned their clinical relevance in their management. The heterogeneity among these studies presents a challenge in consolidating results and clinical application of the evidence. Key points • Factors influencing the volumetry of pulmonary nodules have been extensively investigated. • Just 11% of studies test clinical significance (wrongly diagnosing growth). • Nodule size interacts with most other influencing factors (especially for smaller nodules). • Heterogeneity among studies makes comparison and consolidation of results challenging. • Future research should focus on clinical applicability, screening, and updated technology. Graphical abstract

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Classification and Segmentation Algorithm in Benign and Malignant Pulmonary Nodules under Different CT Reconstruction

Cited by 2 publications

References 27 publications

Accuracy Study on Deep Learning-Based CT Image Analysis for Lung Nodule Detection and Classification

Accuracy Study on Deep Learning-Based CT Image Analysis for Lung Nodule Detection and Classification

Factors influencing the outcome of volumetry tools for pulmonary nodule analysis: a systematic review and attempted meta-analysis

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