AIMC Topic: Lung Neoplasms
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Robot-assisted thoracic surgery for lung cancer patients with incomplete fissure.
Surgical endoscopy
May 13, 2022
BACKGROUND: Robot-assisted thoracic surgery has gradually been accepted as an alternative treatment for early-stage non-small-cell lung cancer (NSCLC) owing to its advantages. However, severe incomplete interlobar fissure may lead to a high rate of c...
Prediction of lung cancer using gene expression and deep learning with KL divergence gene selection.
BMC bioinformatics
May 12, 2022
BACKGROUND: Lung cancer is one of the cancers with the highest mortality rate in China. With the rapid development of high-throughput sequencing technology and the research and application of deep learning methods in recent years, deep neural network...
Artificial Intelligence (AI) for Lung Nodules, From the Special Series on AI Applications.
AJR. American journal of roentgenology
May 11, 2022
Interest in artificial intelligence (AI) applications for lung nodules continues to grow among radiologists, particularly with the expanding eligibility criteria and clinical utilization of lung cancer screening CT. AI has been heavily investigated f...
AI recognition of patient race in medical imaging: a modelling study.
The Lancet. Digital health
May 11, 2022
BACKGROUND: Previous studies in medical imaging have shown disparate abilities of artificial intelligence (AI) to detect a person's race, yet there is no known correlation for race on medical imaging that would be obvious to human experts when interp...
An improved CNN-based architecture for automatic lung nodule classification.
Medical & biological engineering & computing
May 7, 2022
Lung cancer is one of the most critical diseases due to its significant death rate compared to all other types of cancer. The early diagnosis of lung cancer that improves the patient's chance of surviving is mostly done in two phases: screening throu...
Validation of a deep learning computer aided system for CT based lung nodule detection, classification, and growth rate estimation in a routine clinical population.
PloS one
May 5, 2022
OBJECTIVE: In this study, we evaluated a commercially available computer assisted diagnosis system (CAD). The deep learning algorithm of the CAD was trained with a lung cancer screening cohort and developed for detection, classification, quantificati...
Auxiliary Diagnosis of Lung Cancer with Magnetic Resonance Imaging Data under Deep Learning.
Computational and mathematical methods in medicine
May 4, 2022
This study was aimed at two image segmentation methods of three-dimensional (3D) U-shaped network (U-Net) and multilevel boundary sensing residual U-shaped network (RUNet) and their application values on the auxiliary diagnosis of lung cancer. In thi...
A novel deep learning prognostic system improves survival predictions for stage III non-small cell lung cancer.
Cancer medicine
May 2, 2022
BACKGROUND: Accurate prognostic prediction plays a crucial role in the clinical setting. However, the TNM staging system fails to provide satisfactory individual survival prediction for stage III non-small cell lung cancer (NSCLC). The performance of...
Using Occlusion-Based Saliency Maps to Explain an Artificial Intelligence Tool in Lung Cancer Screening: Agreement Between Radiologists, Labels, and Visual Prompts.
Journal of digital imaging
Apr 28, 2022
Occlusion-based saliency maps (OBSMs) are one of the approaches for interpreting decision-making process of an artificial intelligence (AI) system. This study explores the agreement among text responses from a cohort of radiologists to describe diagn...
Improved 3D tumour definition and quantification of uptake in simulated lung tumours using deep learning.
Physics in medicine and biology
Apr 27, 2022
In clinical positron emission tomography (PET) imaging, quantification of radiotracer uptake in tumours is often performed using semi-quantitative measurements such as the standardised uptake value (SUV). For small objects, the accuracy of SUV estima...
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