AIMC Topic: Lung Neoplasms

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Using Auto-Segmentation to Reduce Contouring and Dose Inconsistency in Clinical Trials: The Simulated Impact on RTOG 0617.

International journal of radiation oncology, biology, physics Nov 13, 2020
PURPOSE: Contouring inconsistencies are known but understudied in clinical radiation therapy trials. We applied auto-contouring to the Radiation Therapy Oncology Group (RTOG) 0617 dose escalation trial data. We hypothesized that the trial heart doses...
Statistics, Nonparametric Proportional Hazards Models Radiotherapy Dosage Clinical Trials, Phase III as Topic Humans Algorithms Deep Learning Lung Neoplasms Carcinoma, Non-Small-Cell Lung Confidence Intervals Linear Models Heart Organs at Risk Male Middle Aged Tomography, X-Ray Computed Female Aged Aged, 80 and over Adult
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Quantitative Structure-Mutation-Activity Relationship Tests (QSMART) model for protein kinase inhibitor response prediction.

BMC bioinformatics Nov 12, 2020
BACKGROUND: Protein kinases are a large family of druggable proteins that are genomically and proteomically altered in many human cancers. Kinase-targeted drugs are emerging as promising avenues for personalized medicine because of the differential r...
Lung Neoplasms Carcinoma, Non-Small-Cell Lung Mutation Precision Medicine Cell Line, Tumor Protein Interaction Maps Protein Kinase Inhibitors Lapatinib Afatinib MAP Kinase Signaling System Quantitative Structure-Activity Relationship ErbB Receptors Humans Neural Networks, Computer
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Simulated four-dimensional CT for markerless tumor tracking using a deep learning network with multi-task learning.

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) Nov 11, 2020
INTRODUCTION: Our markerless tumor tracking algorithm requires 4DCT data to train models. 4DCT cannot be used for markerless tracking for respiratory-gated treatment due to inaccuracies and a high radiation dose. We developed a deep neural network (D...
Algorithms Neoplasms Deep Learning Lung Neoplasms Four-Dimensional Computed Tomography Humans Neural Networks, Computer
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Artificial intelligence solution to classify pulmonary nodules on CT.

Diagnostic and interventional imaging Nov 7, 2020
PURPOSE: The purpose of this study was to create an algorithm to detect and classify pulmonary nodules in two categories based on their volume greater than 100 mm or not, using machine learning and deep learning techniques.
Humans Artificial Intelligence Lung Neoplasms Tomography, X-Ray Computed Multiple Pulmonary Nodules Deep Learning
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Radiomic Detection of EGFR Mutations in NSCLC.

Cancer research Nov 4, 2020
Radiomics is defined as the use of automated or semi-automated post-processing and analysis of multiple features derived from imaging exams. Extracted features might generate models able to predict the molecular profile of solid tumors. The aim of th...
Area Under Curve Mutation ErbB Receptors Lung Neoplasms Adenocarcinoma Carcinoma, Non-Small-Cell Lung Humans Machine Learning Male Algorithms ROC Curve Female Tomography, X-Ray Computed Reproducibility of Results
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A bilinear convolutional neural network for lung nodules classification on CT images.

International journal of computer assisted radiology and surgery Nov 2, 2020
PURPOSE: Lung cancer is the most frequent cancer worldwide and is the leading cause of cancer-related deaths. Its early detection and treatment at the stage of a lung nodule improve the prognosis. In this study was proposed a new classification appro...
Humans Diagnosis, Computer-Assisted Lung Neoplasms Support Vector Machine Solitary Pulmonary Nodule Neural Networks, Computer Tomography, X-Ray Computed Lung
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The detection of lung cancer using massive artificial neural network based on soft tissue technique.

BMC medical informatics and decision making Oct 31, 2020
BACKGROUND: A proposed computer aided detection (CAD) scheme faces major issues during subtle nodule recognition. However, radiologists have not noticed subtle nodules in beginning stage of lung cancer while a proposed CAD scheme recognizes non subtl...
Solitary Pulmonary Nodule Sensitivity and Specificity Radiographic Image Interpretation, Computer-Assisted Lung Image Interpretation, Computer-Assisted Lung Neoplasms Humans Neural Networks, Computer Tomography, X-Ray Computed
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Prediction of visceral pleural invasion in lung cancer on CT: deep learning model achieves a radiologist-level performance with adaptive sensitivity and specificity to clinical needs.

European radiology Oct 30, 2020
OBJECTIVES: To develop and validate a preoperative CT-based deep learning model for the prediction of visceral pleural invasion (VPI) in early-stage lung cancer.
Deep Learning Lung Neoplasms Tomography, X-Ray Computed Radiologists Sensitivity and Specificity Humans Retrospective Studies
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Using deep learning to model the biological dose prediction on bulky lung cancer patients of partial stereotactic ablation radiotherapy.

Medical physics Oct 26, 2020
PURPOSE: To develop a biological dose prediction model considering tissue bio-reactions in addition to patient anatomy for achieving a more comprehensive evaluation of tumor control and promoting the automatic planning of bulky lung cancer.
Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Intensity-Modulated Radiosurgery Humans Deep Learning Lung Neoplasms
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Non-invasive decision support for NSCLC treatment using PET/CT radiomics.

Nature communications Oct 16, 2020
Two major treatment strategies employed in non-small cell lung cancer, NSCLC, are tyrosine kinase inhibitors, TKIs, and immune checkpoint inhibitors, ICIs. The choice of strategy is based on heterogeneous biomarkers that can dynamically change during...
Progression-Free Survival ErbB Receptors Protein Kinase Inhibitors Humans Male Lung Neoplasms Female Middle Aged Carcinoma, Non-Small-Cell Lung Aged Deep Learning Positron Emission Tomography Computed Tomography Mutation
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