AIMC Topic: Lung Neoplasms

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NSCR-Based DenseNet for Lung Tumor Recognition Using Chest CT Image.

BioMed research international
Nonnegative sparse representation has become a popular methodology in medical analysis and diagnosis in recent years. In order to resolve network degradation, higher dimensionality in feature extraction, data redundancy, and other issues faced when m...

3D deep learning based classification of pulmonary ground glass opacity nodules with automatic segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Classifying ground-glass lung nodules (GGNs) into atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) on diagnostic CT images is important to evaluate the th...

Predicting potential residues associated with lung cancer using deep neural network.

Mutation research
Lung cancer is a prominent type of cancer, which leads to high mortality rate worldwide. The major lung cancers lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC) occur mainly due to somatic driver mutations in proteins and screening of su...

Artificial intelligence-based imaging analytics and lung cancer diagnostics: Considerations for health system leaders.

Healthcare management forum
Lung cancer is a leading cause of cancer death in Canada, and accurate, early diagnosis are critical to improving clinical outcomes. Artificial Intelligence (AI)-based imaging analytics are a promising healthcare innovation that aim to improve the ac...

Immune profile of the tumor microenvironment and the identification of a four-gene signature for lung adenocarcinoma.

Aging
The composition and relative abundances of immune cells in the tumor microenvironment are key factors affecting the progression of lung adenocarcinomas (LUADs) and the efficacy of immunotherapy. Using the cancer gene expression dataset from The Cance...

MD-NDNet: a multi-dimensional convolutional neural network for false-positive reduction in pulmonary nodule detection.

Physics in medicine and biology
Pulmonary nodule false-positive reduction is of great significance for automated nodule detection in clinical diagnosis of low-dose computed tomography (LDCT) lung cancer screening. Due to individual intra-nodule variations and visual similarities be...

Evaluation of a novel deep learning-based classifier for perifissural nodules.

European radiology
OBJECTIVES: To evaluate the performance of a novel convolutional neural network (CNN) for the classification of typical perifissural nodules (PFN).

Predict multicategory causes of death in lung cancer patients using clinicopathologic factors.

Computers in biology and medicine
BACKGROUND: Random forests (RF) is a widely used machine-learning algorithm, and outperforms many other machine learning algorithms in prediction-accuracy. But it is rarely used for predicting causes of death (COD) in cancer patients. On the other ha...

Natural history of pathologically confirmed pulmonary subsolid nodules with deep learning-assisted nodule segmentation.

European radiology
OBJECTIVE: To explore the natural history of pulmonary subsolid nodules (SSNs) with different pathological types by deep learning-assisted nodule segmentation.