AIMC Topic: Lung Neoplasms

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

Physics in medicine and biology Dec 5, 2020
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...
Humans Lung Neoplasms Solitary Pulmonary Nodule Early Detection of Cancer False Positive Reactions Neural Networks, Computer Tomography, X-Ray Computed Imaging, Three-Dimensional
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Evaluation of a novel deep learning-based classifier for perifissural nodules.

European radiology Dec 2, 2020
OBJECTIVES: To evaluate the performance of a novel convolutional neural network (CNN) for the classification of typical perifissural nodules (PFN).
Multiple Pulmonary Nodules Solitary Pulmonary Nodule Netherlands Humans Deep Learning Lung Neoplasms
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Predict multicategory causes of death in lung cancer patients using clinicopathologic factors.

Computers in biology and medicine Dec 1, 2020
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...
Humans Machine Learning Algorithms Logistic Models Cause of Death Aged Lung Neoplasms
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Development and Validation of a Machine Learning Model to Explore Tyrosine Kinase Inhibitor Response in Patients With Stage IV EGFR Variant-Positive Non-Small Cell Lung Cancer.

JAMA network open Dec 1, 2020
IMPORTANCE: An end-to-end efficacy evaluation approach for identifying progression risk after epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) therapy in patients with stage IV EGFR variant-positive non-small cell lung cancer (...
Machine Learning Prognosis Female Humans Lung Neoplasms Carcinoma, Non-Small-Cell Lung Predictive Value of Tests Neoplasm Staging Male Middle Aged Antineoplastic Agents ErbB Receptors Protein Kinase Inhibitors Antineoplastic Protocols Mutation Survival Analysis Proportional Hazards Models
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Natural history of pathologically confirmed pulmonary subsolid nodules with deep learning-assisted nodule segmentation.

European radiology Nov 21, 2020
OBJECTIVE: To explore the natural history of pulmonary subsolid nodules (SSNs) with different pathological types by deep learning-assisted nodule segmentation.
Solitary Pulmonary Nodule Deep Learning Humans Lung Neoplasms Retrospective Studies Multiple Pulmonary Nodules Tomography, X-Ray Computed
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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 Linear Models Humans Deep Learning Heart Proportional Hazards Models Lung Neoplasms Radiotherapy Dosage Algorithms Adult Organs at Risk Carcinoma, Non-Small-Cell Lung Aged Clinical Trials, Phase III as Topic Tomography, X-Ray Computed Confidence Intervals Male Aged, 80 and over Female Middle Aged
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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...
Humans Lapatinib Cell Line, Tumor MAP Kinase Signaling System Lung Neoplasms Protein Interaction Maps Afatinib ErbB Receptors Neural Networks, Computer Carcinoma, Non-Small-Cell Lung Quantitative Structure-Activity Relationship Protein Kinase Inhibitors Mutation Precision Medicine
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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...
Lung Neoplasms Four-Dimensional Computed Tomography Algorithms Neoplasms Humans Deep Learning 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.
Multiple Pulmonary Nodules Humans Deep Learning Lung Neoplasms Artificial Intelligence Tomography, X-Ray Computed
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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...
Mutation ErbB Receptors Reproducibility of Results Male ROC Curve Carcinoma, Non-Small-Cell Lung Area Under Curve Algorithms Lung Neoplasms Tomography, X-Ray Computed Adenocarcinoma Female Humans Machine Learning
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