AIMC Topic: Lung Neoplasms
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Impact of artificial intelligence assistance on pulmonary nodule detection and localization in chest CT: a comparative study among radiologists of varying experience levels.
Scientific reports
Sep 28, 2024
The study aimed to evaluate the impact of AI assistance on pulmonary nodule detection rates among radiology residents and senior radiologists, along with assessing the effectiveness of two different commercialy available AI software systems in improv...
A deep learning-informed interpretation of why and when dose metrics outside the PTV can affect the risk of distant metastasis in SBRT NSCLC patients.
Radiation oncology (London, England)
Sep 27, 2024
PURPOSE: Recent papers suggested a correlation between the risk of distant metastasis (DM) and dose outside the PTV, though conclusions in different publications conflicted. This study resolves these conflicts and provides a compelling explanation of...
Development and validation of machine learning models for diagnosis and prognosis of lung adenocarcinoma, and immune infiltration analysis.
Scientific reports
Sep 27, 2024
The aim of our study was to develop robust diagnostic and prognostic models for lung adenocarcinoma (LUAD) using machine learning (ML) techniques, focusing on early immune infiltration. Feature selection was performed on The Cancer Genome Atlas (TCGA...
Length-scale study in deep learning prediction for non-small cell lung cancer brain metastasis.
Scientific reports
Sep 27, 2024
Deep learning-assisted digital pathology has demonstrated the potential to profoundly impact clinical practice, even surpassing human pathologists in performance. However, as deep neural network (DNN) architectures grow in size and complexity, their ...
Impact of the number of dissected lymph nodes on machine learning-based prediction of postoperative lung cancer recurrence: a single-hospital retrospective cohort study.
BMJ open respiratory research
Sep 26, 2024
BACKGROUND: The optimal number of lymph nodes to be dissected during lung cancer surgery to minimise the postoperative recurrence risk remains undetermined. This study aimed to elucidate the impact of the number of dissected lymph nodes on the risk o...
Integrating machine learning and multi-omics analysis to develop an asparagine metabolism immunity index for improving clinical outcome and drug sensitivity in lung adenocarcinoma.
Immunologic research
Sep 25, 2024
Lung adenocarcinoma (LUAD) is a malignancy affecting the respiratory system. Most patients are diagnosed with advanced or metastatic lung cancer due to the fact that most of their clinical symptoms are insidious, resulting in a bleak prognosis. Given...
Evaluating ChatGPT as a patient resource for frequently asked questions about lung cancer surgery-a pilot study.
The Journal of thoracic and cardiovascular surgery
Sep 24, 2024
OBJECTIVE: Chat-based artificial intelligence programs like ChatGPT are reimagining how patients seek information. This study aims to evaluate the quality and accuracy of ChatGPT-generated answers to common patient questions about lung cancer surgery...
Evolution of radiology staff perspectives during artificial intelligence (AI) implementation for expedited lung cancer triage.
Clinical radiology
Sep 21, 2024
AIMS: To investigate radiology staff perceptions of an AI tool for chest radiography triage, flagging findings suspicious for lung cancer to expedite same-day CT chest examination studies.
Radiograph accelerated detection and identification of cancer in the lung (RADICAL): a mixed methods study to assess the clinical effectiveness and acceptability of Qure.ai artificial intelligence software to prioritise chest X-ray (CXR) interpretation.
BMJ open
Sep 20, 2024
INTRODUCTION: Diagnosing and treating lung cancer in early stages is essential for survival outcomes. The chest X-ray (CXR) remains the primary screening tool to identify lung cancers in the UK; however, there is a shortfall of radiologists, while de...
GC-WIR : 3D global coordinate attention wide inverted ResNet network for pulmonary nodules classification.
BMC pulmonary medicine
Sep 20, 2024
PURPOSE: Currently, deep learning methods for the classification of benign and malignant lung nodules encounter challenges encompassing intricate and unstable algorithmic models, limited data adaptability, and an abundance of model parameters.To tack...
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