AIMC Topic: Carcinoma, Non-Small-Cell Lung

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An Artificial Intelligence Model for Predicting 1-Year Survival of Bone Metastases in Non-Small-Cell Lung Cancer Patients Based on XGBoost Algorithm.

BioMed research international Jun 27, 2020
Non-small-cell lung cancer (NSCLC) patients often develop bone metastases (BM), and the overall survival for these patients is usually perishing. However, a model with high accuracy for predicting the survival of NSCLC with BM is still lacking. Here,...
Reproducibility of Results Survival Analysis Humans Logistic Models Multivariate Analysis Aged Artificial Intelligence Nomograms Male Algorithms Carcinoma, Non-Small-Cell Lung ROC Curve Prognosis Bone Neoplasms Lung Neoplasms Female Models, Biological
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Artificial intelligence-based collaborative filtering method with ensemble learning for personalized lung cancer medicine without genetic sequencing.

Pharmacological research Jun 23, 2020
In personalized medicine, many factors influence the choice of compounds. Hence, the selection of suitable medicine for patients with non-small-cell lung cancer (NSCLC) is expensive. To shorten the decision-making process for compounds, we propose a ...
Cell Line, Tumor Lung Neoplasms Carcinoma, Non-Small-Cell Lung Cell Movement Apoptosis Computer Simulation Cost-Benefit Analysis Humans Precision Medicine Artificial Intelligence Clinical Decision-Making Algorithms Animals Databases, Factual Antineoplastic Agents
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Development and Validation of a Deep Learning Model for Non-Small Cell Lung Cancer Survival.

JAMA network open Jun 1, 2020
IMPORTANCE: There is a lack of studies exploring the performance of a deep learning survival neural network in non-small cell lung cancer (NSCLC).
Deep Learning China Cohort Studies Lung Neoplasms United States Young Adult Adult Survival Rate Male Middle Aged Carcinoma, Non-Small-Cell Lung Models, Statistical Neoplasm Staging SEER Program Kaplan-Meier Estimate Female Humans Aged Prognosis Aged, 80 and over
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Prognosis and Treatment of Non-Small Cell Lung Cancer in the Age of Deep Learning.

JAMA network open Jun 1, 2020
Humans Prognosis Deep Learning Lung Neoplasms Carcinoma, Non-Small-Cell Lung Neoplasm Staging
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Assessment of associations between clinical and immune microenvironmental factors and tumor mutation burden in resected nonsmall cell lung cancer by applying machine learning to whole-slide images.

Cancer medicine May 12, 2020
BACKGROUND: It is unclear whether clinical factors and immune microenvironment (IME) factors are associated with tumor mutation burden (TMB) in patients with nonsmall cell lung cancer (NSCLC).
Male Middle Aged Lung Neoplasms B7-H1 Antigen Antigens, Neoplasm Carcinoma, Squamous Cell Aged Aged, 80 and over Adult Tumor Microenvironment Forkhead Transcription Factors Carcinoembryonic Antigen Smokers Exome Sequencing Keratin-19 Ex-Smokers Adenocarcinoma Carcinoma, Non-Small-Cell Lung Lymphocytes, Tumor-Infiltrating Regression Analysis Mutation Humans Non-Smokers Machine Learning Female
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Convolutional Neural Networks in Predicting Nodal and Distant Metastatic Potential of Newly Diagnosed Non-Small Cell Lung Cancer on FDG PET Images.

AJR. American journal of roentgenology Apr 29, 2020
The purpose of this study was to assess, by analyzing features of the primary tumor with F-FDG PET, the utility of deep machine learning with a convolutional neural network (CNN) in predicting the potential of newly diagnosed non-small cell lung can...
Humans Neural Networks, Computer Retrospective Studies Female Sensitivity and Specificity Male Middle Aged Positron Emission Tomography Computed Tomography Aged Radiopharmaceuticals Imaging, Three-Dimensional Aged, 80 and over Neoplasm Metastasis Adult Lung Neoplasms Fluorodeoxyglucose F18 Carcinoma, Non-Small-Cell Lung Lymphatic Metastasis Predictive Value of Tests
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Single patient convolutional neural networks for real-time MR reconstruction: coherent low-resolution versus incoherent undersampling.

Physics in medicine and biology Apr 23, 2020
Accelerated MRI involves undersampling k-space, creating unwanted artifacts when reconstructing the data. While the strategy of incoherent k-space acquisition is proven for techniques such as compressed sensing, it may not be optimal for all techniqu...
Humans Respiration Neural Networks, Computer Signal-To-Noise Ratio Carcinoma, Non-Small-Cell Lung Retrospective Studies Magnetic Resonance Imaging Time Factors Artifacts Image Processing, Computer-Assisted Lung Neoplasms
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Identification of Non-Small Cell Lung Cancer Sensitive to Systemic Cancer Therapies Using Radiomics.

Clinical cancer research : an official journal of the American Association for Cancer Research Mar 20, 2020
PURPOSE: Using standard-of-care CT images obtained from patients with a diagnosis of non-small cell lung cancer (NSCLC), we defined radiomics signatures predicting the sensitivity of tumors to nivolumab, docetaxel, and gefitinib.
Carcinoma, Non-Small-Cell Lung Randomized Controlled Trials as Topic Clinical Trials, Phase II as Topic Nivolumab Docetaxel Clinical Trials, Phase III as Topic Gefitinib Survival Rate Male Disease Progression Lung Neoplasms Antineoplastic Combined Chemotherapy Protocols Machine Learning Prognosis Retrospective Studies Tomography, X-Ray Computed Female Humans
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Real world study for the concordance between IBM Watson for Oncology and clinical practice in advanced non-small cell lung cancer patients at a lung cancer center in China.

Thoracic cancer Mar 19, 2020
BACKGROUND: IBM Watson for Oncology (WFO) provides physicians with evidence-based treatment options. This study was designed to explore the concordance of the suggested therapeutic regimen for advanced non-small cell lung (NSCLC) cancer patients betw...
Male Middle Aged China Decision Support Systems, Clinical Lung Neoplasms Retrospective Studies Clinical Decision-Making Female Carcinoma, Squamous Cell Survival Rate Humans Evidence-Based Medicine Artificial Intelligence Adenocarcinoma of Lung United States Prognosis Carcinoma, Non-Small-Cell Lung Follow-Up Studies Patient Selection Practice Guidelines as Topic
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Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning.

Scientific reports Mar 13, 2020
Non-small cell lung cancer (NSCLC) is one of the most common lung cancers worldwide. Accurate prognostic stratification of NSCLC can become an important clinical reference when designing therapeutic strategies for cancer patients. With this clinical ...
Kaplan-Meier Estimate Humans Neoplasm Staging Lung Neoplasms Reproducibility of Results Neoplasm Grading Computational Biology Biomarkers, Tumor Workflow Support Vector Machine Deep Learning Neoplasm Metastasis Carcinoma, Non-Small-Cell Lung Microarray Analysis Area Under Curve
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