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Carcinoma, Non-Small-Cell Lung

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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).
Tumor Microenvironment Male Middle Aged Lung Neoplasms B7-H1 Antigen Antigens, Neoplasm Female Carcinoma, Squamous Cell Aged Aged, 80 and over Adult Carcinoembryonic Antigen Smokers Exome Sequencing Keratin-19 Ex-Smokers Non-Smokers Humans Adenocarcinoma Machine Learning Carcinoma, Non-Small-Cell Lung Lymphocytes, Tumor-Infiltrating Regression Analysis Mutation Forkhead Transcription Factors
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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...
Imaging, Three-Dimensional Lung Neoplasms Carcinoma, Non-Small-Cell Lung Lymphatic Metastasis Predictive Value of Tests Positron Emission Tomography Computed Tomography Aged, 80 and over Adult Sensitivity and Specificity Male Middle Aged Radiopharmaceuticals Neoplasm Metastasis Fluorodeoxyglucose F18 Humans Neural Networks, Computer Retrospective Studies Female Aged
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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...
Time Factors Image Processing, Computer-Assisted Lung Neoplasms Carcinoma, Non-Small-Cell Lung Magnetic Resonance Imaging Artifacts Humans Neural Networks, Computer Retrospective Studies Respiration Signal-To-Noise Ratio
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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.
Lung Neoplasms Retrospective Studies Antineoplastic Combined Chemotherapy Protocols Tomography, X-Ray Computed Nivolumab Female Carcinoma, Non-Small-Cell Lung Humans Docetaxel Survival Rate Randomized Controlled Trials as Topic Machine Learning Clinical Trials, Phase III as Topic Male Clinical Trials, Phase II as Topic Prognosis Gefitinib Disease Progression
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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...
Evidence-Based Medicine Adenocarcinoma of Lung Carcinoma, Squamous Cell Humans Survival Rate Lung Neoplasms Artificial Intelligence Male United States Prognosis Middle Aged Carcinoma, Non-Small-Cell Lung Retrospective Studies China Follow-Up Studies Clinical Decision-Making Decision Support Systems, Clinical Patient Selection Female 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 ...
Neoplasm Staging Neoplasm Grading Workflow Neoplasm Metastasis Microarray Analysis Kaplan-Meier Estimate Deep Learning Lung Neoplasms Computational Biology Support Vector Machine Humans Carcinoma, Non-Small-Cell Lung Reproducibility of Results Area Under Curve Biomarkers, Tumor
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Machine learning helps identifying volume-confounding effects in radiomics.

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) Feb 20, 2020
Highlighting the risk of biases in radiomics-based models will help improve their quality and increase usage as decision support systems in the clinic. In this study we use machine learning-based methods to identify the presence of volume-confounding...
Radiometry Retrospective Studies Squamous Cell Carcinoma of Head and Neck Humans Aged, 80 and over Tomography, X-Ray Computed Carcinoma, Non-Small-Cell Lung Machine Learning Laryngeal Neoplasms Female Cluster Analysis Adult Software Oropharyngeal Neoplasms Carcinoma, Squamous Cell Male Algorithms Regression Analysis Aged Middle Aged Databases, Factual Principal Component Analysis Decision Support Systems, Clinical Lung Neoplasms
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Radiogenomic Models Using Machine Learning Techniques to Predict EGFR Mutations in Non-Small Cell Lung Cancer.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes Feb 17, 2020
BACKGROUND: The purpose of this study was to build radiogenomics models from texture signatures derived from computed tomography (CT) and F-FDG PET-CT (FDG PET-CT) images of non-small cell lung cancer (NSCLC) with and without epidermal growth factor ...
Humans Machine Learning Retrospective Studies Lung Neoplasms Image Interpretation, Computer-Assisted Carcinoma, Non-Small-Cell Lung Fluorodeoxyglucose F18 Predictive Value of Tests ErbB Receptors Positron Emission Tomography Computed Tomography Imaging Genomics Female Mutation Reproducibility of Results Radiopharmaceuticals Aged Sensitivity and Specificity Male Lung
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Prognostic value of anthropometric measures extracted from whole-body CT using deep learning in patients with non-small-cell lung cancer.

European radiology Feb 13, 2020
INTRODUCTION: The aim of the study was to extract anthropometric measures from CT by deep learning and to evaluate their prognostic value in patients with non-small-cell lung cancer (NSCLC).
Male Middle Aged Disease Progression Deep Learning Lung Neoplasms Carcinoma, Non-Small-Cell Lung Muscle, Skeletal Positron Emission Tomography Computed Tomography Neoplasm Staging Body Composition Progression-Free Survival Prognosis Female Reproducibility of Results Aged Adult Survival Rate Humans Proportional Hazards Models Body Surface Area Intra-Abdominal Fat Subcutaneous Fat Whole Body Imaging
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Commentary: Maximum surgery through minimal incisions.

The Journal of thoracic and cardiovascular surgery Nov 30, 2019
Humans Propensity Score Lung Neoplasms Carcinoma, Non-Small-Cell Lung Robotics Robotic Surgical Procedures
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