AIMC Topic: Lung Neoplasms
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Lung Cancer Detection using Probabilistic Neural Network with modified Crow-Search Algorithm.
Asian Pacific journal of cancer prevention : APJCP
Jul 1, 2019
Objective: Lung cancer is a type of malignancy that occurs most commonly among men and the third most common type of malignancy among women. The timely recognition of lung cancer is necessary for decreasing the effect of death rate worldwide. Since t...
Machine learning-based radiomics strategy for prediction of cell proliferation in non-small cell lung cancer.
European journal of radiology
Jun 28, 2019
PURPOSE: To explore the feasibility and performance of machine learning-based radiomics classifier to predict the cell proliferation(Ki-67)in non-small cell lung cancer (NSCLC).
Classification of benign and malignant lung nodules from CT images based on hybrid features.
Physics in medicine and biology
Jun 20, 2019
The classification of benign and malignant lung nodules has great significance for the early detection of lung cancer, since early diagnosis of nodules can greatly increase patient survival. In this paper, we propose a novel classification method for...
Deep Learning-based Image Conversion of CT Reconstruction Kernels Improves Radiomics Reproducibility for Pulmonary Nodules or Masses.
Radiology
Jun 18, 2019
Background Intratumor heterogeneity in lung cancer may influence outcomes. CT radiomics seeks to assess tumor features to provide detailed imaging features. However, CT radiomic features vary according to the reconstruction kernel used for image gene...
Three-dimensional dose prediction for lung IMRT patients with deep neural networks: robust learning from heterogeneous beam configurations.
Medical physics
Jun 17, 2019
PURPOSE: The use of neural networks to directly predict three-dimensional dose distributions for automatic planning is becoming popular. However, the existing methods use only patient anatomy as input and assume consistent beam configuration for all ...
Artificial intelligence and radiomics in pulmonary nodule management: current status and future applications.
Clinical radiology
Jun 12, 2019
Artificial intelligence (AI) has been present in some guise within the field of radiology for over 50 years. The first studies investigating computer-aided diagnosis in thoracic radiology date back to the 1960s, and in the subsequent years, the main ...
Comparison of F-FDG avidity at PET of benign and malignant pure ground-glass opacities: a paradox? Part II: artificial neural network integration of the PET/CT characteristics of ground-glass opacities to predict their likelihood of malignancy.
Clinical radiology
Jun 12, 2019
AIM: To assess the ability of artificial neural networks (ANNs) to predict the likelihood of malignancy of pure ground-glass opacities (GGOs), using observations from computed tomography (CT) and 2-[F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission...
A cascaded dual-pathway residual network for lung nodule segmentation in CT images.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Jun 12, 2019
It is difficult to obtain an accurate segmentation due to the variety of lung nodules in computed tomography (CT) images. In this study, we propose a data-driven model, called the Cascaded Dual-Pathway Residual Network (CDP-ResNet) to improve the seg...
Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT.
Medical physics
Jun 7, 2019
PURPOSE: Computed tomography (CT) is an effective method for detecting and characterizing lung nodules in vivo. With the growing use of chest CT, the detection frequency of lung nodules is increasing. Noninvasive methods to distinguish malignant from...
Multi-Class Neural Networks to Predict Lung Cancer.
Journal of medical systems
May 31, 2019
Lung Cancer is the leading cause of death among all the cancers' in today's world. The survival rate of the patients is 85% if the cancer can be diagnosed during Stage 1. Mining of the patient records can help in diagnosing cancer during Stage 1. Usi...
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