Multiple Pulmonary Nodules

Deep learning for malignancy risk estimation of incidental sub-centimeter pulmonary nodules on CT images.

European radiology 38114849

OBJECTIVES: To establish deep learning models for malignancy risk estimation of sub-centimeter pulmonary nodules incidentally detected by chest CT and managed in clinical settings.

Aged Deep Learning Female Humans Incidental Findings Lung Neoplasms Male Middle Aged Multiple Pulmonary Nodules Radiographic Image Interpretation, Computer-Assisted Risk Assessment Solitary Pulmonary Nodule Tomography, X-Ray Computed

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Attention pyramid pooling network for artificial diagnosis on pulmonary nodules.

PloS one 38753596

The development of automated tools using advanced technologies like deep learning holds great promise for improving the accuracy of lung nodule classification in computed tomography (CT) imaging, ultimately reducing lung cancer mortality rates. Howev...

Algorithms Deep Learning Humans Lung Lung Neoplasms Multiple Pulmonary Nodules Neural Networks, Computer Radiographic Image Interpretation, Computer-Assisted Solitary Pulmonary Nodule Tomography, X-Ray Computed

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Diagnostic value of artificial intelligence based on computed tomography (CT) density in benign and malignant pulmonary nodules: a retrospective investigation.

PeerJ 38188164

OBJECTIVE: To evaluate the diagnostic value of artificial intelligence (AI) in the detection and management of benign and malignant pulmonary nodules (PNs) using computed tomography (CT) density.

Artificial Intelligence Humans Multiple Pulmonary Nodules Retrospective Studies Software Tomography, X-Ray Computed

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Improving Image Quality and Nodule Characterization in Ultra-low-dose Lung CT with Deep Learning Image Reconstruction.

Academic radiology 38429189

RATIONALE AND OBJECTIVE: To investigate the influence of the deep learning image reconstruction (DLIR) on the image quality and quantitative analysis of pulmonary nodules under ultra-low dose lung CT conditions.

Adult Aged Deep Learning Female Humans Lung Neoplasms Male Middle Aged Multiple Pulmonary Nodules Prospective Studies Radiation Dosage Radiographic Image Interpretation, Computer-Assisted Solitary Pulmonary Nodule Tomography, X-Ray Computed

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Enhancing a deep learning model for pulmonary nodule malignancy risk estimation in chest CT with uncertainty estimation.

European radiology 38536463

OBJECTIVE: To investigate the effect of uncertainty estimation on the performance of a Deep Learning (DL) algorithm for estimating malignancy risk of pulmonary nodules.

Aged Algorithms Deep Learning Female Humans Lung Neoplasms Male Middle Aged Multiple Pulmonary Nodules Radiographic Image Interpretation, Computer-Assisted Retrospective Studies Risk Assessment Solitary Pulmonary Nodule Tomography, X-Ray Computed Uncertainty

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LGDNet: local feature coupling global representations network for pulmonary nodules detection.

Medical & biological engineering & computing 38429443

Detection of suspicious pulmonary nodules from lung CT scans is a crucial task in computer-aided diagnosis (CAD) systems. In recent years, various deep learning-based approaches have been proposed and demonstrated significant potential for addressing...

Algorithms Deep Learning Diagnosis, Computer-Assisted Humans Lung Lung Neoplasms Multiple Pulmonary Nodules Neural Networks, Computer Radiographic Image Interpretation, Computer-Assisted Solitary Pulmonary Nodule Tomography, X-Ray Computed

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Clinical value of deep learning image reconstruction on the diagnosis of pulmonary nodule for ultra-low-dose chest CT imaging.

Clinical radiology 38749827

PURPOSE: To compare the image quality and pulmonary nodule detectability between deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V) in ultra-low-dose CT (ULD-CT).

Adult Aged Deep Learning Female Humans Lung Neoplasms Male Middle Aged Multiple Pulmonary Nodules Prospective Studies Radiation Dosage Radiographic Image Interpretation, Computer-Assisted Signal-To-Noise Ratio Solitary Pulmonary Nodule Tomography, X-Ray Computed

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Establishment and validation of multiclassification prediction models for pulmonary nodules based on machine learning.

The clinical respiratory journal 38736274

BACKGROUND: Lung cancer is the leading cause of cancer-related death worldwide. This study aimed to establish novel multiclassification prediction models based on machine learning (ML) to predict the probability of malignancy in pulmonary nodules (PN...

Aged Decision Trees Female Humans Lung Neoplasms Machine Learning Male Middle Aged Multiple Pulmonary Nodules Predictive Value of Tests Retrospective Studies ROC Curve Solitary Pulmonary Nodule Support Vector Machine Tomography, X-Ray Computed

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Impact of deep learning image reconstruction on volumetric accuracy and image quality of pulmonary nodules with different morphologies in low-dose CT.

Cancer imaging : the official publication of the International Cancer Imaging Society 38720391

BACKGROUND: This study systematically compares the impact of innovative deep learning image reconstruction (DLIR, TrueFidelity) to conventionally used iterative reconstruction (IR) on nodule volumetry and subjective image quality (IQ) at highly reduc...

Deep Learning Humans Image Processing, Computer-Assisted Lung Neoplasms Multiple Pulmonary Nodules Phantoms, Imaging Radiation Dosage Radiographic Image Interpretation, Computer-Assisted Solitary Pulmonary Nodule Tomography, X-Ray Computed

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Development and external validation of a multimodal integrated feature neural network (MIFNN) for the diagnosis of malignancy in small pulmonary nodules (≤10 mm).

Biomedical physics & engineering express 38684143

. Current lung cancer screening protocols primarily evaluate pulmonary nodules, yet often neglect the malignancy risk associated with small nodules (≤10 mm). This study endeavors to optimize the management of pulmonary nodules in this population by d...

Aged Algorithms China Deep Learning Early Detection of Cancer Female Humans Lung Neoplasms Male Middle Aged Multiple Pulmonary Nodules Neural Networks, Computer Reproducibility of Results Retrospective Studies Solitary Pulmonary Nodule Tomography, X-Ray Computed

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Deep learning for malignancy risk estimation of incidental sub-centimeter pulmonary nodules on CT images.

Attention pyramid pooling network for artificial diagnosis on pulmonary nodules.

Diagnostic value of artificial intelligence based on computed tomography (CT) density in benign and malignant pulmonary nodules: a retrospective investigation.

Improving Image Quality and Nodule Characterization in Ultra-low-dose Lung CT with Deep Learning Image Reconstruction.

Enhancing a deep learning model for pulmonary nodule malignancy risk estimation in chest CT with uncertainty estimation.

LGDNet: local feature coupling global representations network for pulmonary nodules detection.

Clinical value of deep learning image reconstruction on the diagnosis of pulmonary nodule for ultra-low-dose chest CT imaging.

Establishment and validation of multiclassification prediction models for pulmonary nodules based on machine learning.

Impact of deep learning image reconstruction on volumetric accuracy and image quality of pulmonary nodules with different morphologies in low-dose CT.

Development and external validation of a multimodal integrated feature neural network (MIFNN) for the diagnosis of malignancy in small pulmonary nodules (≤10 mm).

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