AIMC Topic: Lung Neoplasms

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

Academic radiology Feb 29, 2024
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.
Aged Adult Male Prospective Studies Tomography, X-Ray Computed Female Multiple Pulmonary Nodules Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Middle Aged Deep Learning Lung Neoplasms Humans
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Deep match: A zero-shot framework for improved fiducial-free respiratory motion tracking.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Feb 24, 2024
BACKGROUND AND PURPOSE: Motion management is essential to reduce normal tissue exposure and maintain adequate tumor dose in lung stereotactic body radiation therapy (SBRT). Lung SBRT using an articulated robotic arm allows dynamic tracking during rad...
Movement Radiotherapy, Image-Guided Fiducial Markers Lung Neoplasms Respiration Radiosurgery Humans Algorithms Deep Learning
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Ultra-low dose chest CT with silver filter and deep learning reconstruction significantly reduces radiation dose and retains quantitative information in the investigation and monitoring of lymphangioleiomyomatosis (LAM).

European radiology Feb 22, 2024
PURPOSE: Frequent CT scans to quantify lung involvement in cystic lung disease increases radiation exposure. Beam shaping energy filters can optimize imaging properties at lower radiation dosages. The aim of this study is to investigate whether use o...
Silver Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Radiography, Thoracic Lymphangioleiomyomatosis Middle Aged Deep Learning Lung Neoplasms Female Adult Male Prospective Studies Tomography, X-Ray Computed Humans Algorithms
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Prospective evaluation of deep learning image reconstruction for Lung-RADS and automatic nodule volumetry on ultralow-dose chest CT.

PloS one Feb 22, 2024
PURPOSE: To prospectively evaluate whether Lung-RADS classification and volumetric nodule assessment were feasible with ultralow-dose (ULD) chest CT scans with deep learning image reconstruction (DLIR).
Lung Deep Learning Image Processing, Computer-Assisted Radiation Dosage Lung Neoplasms Radiographic Image Interpretation, Computer-Assisted Female Aged Middle Aged Humans Prospective Studies Tomography, X-Ray Computed
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A deep learning-based framework (Co-ReTr) for auto-segmentation of non-small cell-lung cancer in computed tomography images.

Journal of applied clinical medical physics Feb 19, 2024
PURPOSE: Deep learning-based auto-segmentation algorithms can improve clinical workflow by defining accurate regions of interest while reducing manual labor. Over the past decade, convolutional neural networks (CNNs) have become prominent in medical ...
Image Processing, Computer-Assisted Lung Neoplasms Carcinoma, Non-Small-Cell Lung Algorithms Tomography, X-Ray Computed Deep Learning Humans Neural Networks, Computer
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Comparing Robotic, Thoracoscopic, and Open Segmentectomy: A National Cancer Database Analysis.

The Journal of surgical research Feb 15, 2024
INTRODUCTION: Minimally invasive approaches to lung resection have become widely acceptable and more recently, segmentectomy has demonstrated equivalent oncologic outcomes when compared to lobectomy for early-stage non-small cell lung cancer (NSCLC)....
Humans Pneumonectomy Thoracic Surgery, Video-Assisted Carcinoma, Non-Small-Cell Lung Robotics Robotic Surgical Procedures Retrospective Studies Lung Neoplasms Treatment Outcome
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Potential of radiomics analysis and machine learning for predicting brain metastasis in newly diagnosed lung cancer patients.

Clinical radiology Feb 13, 2024
AIM: To explore the potential of utilising radiomics analysis and machine-learning models that incorporate intratumoural and peritumoural regions of interest (ROIs) for predicting brain metastasis (BM) in newly diagnosed lung cancer patients.
Brain Retrospective Studies Humans Machine Learning Middle Aged Lung Neoplasms Predictive Value of Tests Adult Radiomics Brain Neoplasms Male Tomography, X-Ray Computed Female Aged
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Meta-lasso: new insight on infection prediction after minimally invasive surgery.

Medical & biological engineering & computing Feb 13, 2024
Surgical site infection (SSI) after minimally invasive lung cancer surgery constitutes an important factor influencing the direct and indirect economic implications, patient prognosis, and the 5-year survival rate for early-stage lung cancer patients...
Humans Machine Learning Algorithms Retrospective Studies Middle Aged Lung Neoplasms Minimally Invasive Surgical Procedures Female Surgical Wound Infection Aged Male
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METnet: A novel deep learning model predicting MET dysregulation in non-small-cell lung cancer on computed tomography images.

Computers in biology and medicine Feb 13, 2024
BACKGROUND: Mesenchymal epithelial transformation (MET) is a key molecular target for diagnosis and treatment of non-small cell lung cancer (NSCLC). The corresponding molecularly targeted therapeutics have been approved by Food and Drug Administratio...
Humans Deep Learning Lung Neoplasms Carcinoma, Non-Small-Cell Lung Algorithms Retrospective Studies Tomography, X-Ray Computed
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Overcoming the Challenge of Accurate Segmentation of Lung Nodules: A Multi-crop CNN Approach.

Journal of imaging informatics in medicine Feb 12, 2024
Lung nodules are generated based on the growth of small and round- or oval-shaped cells in the lung, which are either cancerous or non-cancerous. Accurate segmentation of these nodules is crucial for early detection and diagnosis of lung cancer. Howe...
Lung Neoplasms Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Humans Tomography, X-Ray Computed Lung Neural Networks, Computer Algorithms
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