AIMC Topic: Lung Neoplasms

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A comparative study of auto-contouring softwares in delineation of organs at risk in lung cancer and rectal cancer.

Scientific reports Nov 26, 2021
Radiotherapy requires the target area and the organs at risk to be contoured on the CT image of the patient. During the process of organs-at-Risk (OAR) of the chest and abdomen, the doctor needs to contour at each CT image. The delineations of large ...
Software Tomography, X-Ray Computed Deep Learning Image Processing, Computer-Assisted Lung Neoplasms Rectal Neoplasms Humans Radiotherapy, Intensity-Modulated Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage
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Sparsely Connected Autoencoders: A Multi-Purpose Tool for Single Cell omics Analysis.

International journal of molecular sciences Nov 25, 2021
BACKGROUND: Biological processes are based on complex networks of cells and molecules. Single cell multi-omics is a new tool aiming to provide new incites in the complex network of events controlling the functionality of the cell.
Computational Biology Machine Learning Cluster Analysis Single-Cell Analysis Exome Sequencing Humans Adenocarcinoma of Lung Lung Neoplasms Neural Networks, Computer
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Performance of a deep learning-based lung nodule detection system as an alternative reader in a Chinese lung cancer screening program.

European journal of radiology Nov 24, 2021
OBJECTIVE: To evaluate the performance of a deep learning-based computer-aided detection (DL-CAD) system in a Chinese low-dose CT (LDCT) lung cancer screening program.
Deep Learning China Lung Neoplasms Reproducibility of Results Sensitivity and Specificity Humans Solitary Pulmonary Nodule Lung Tomography, X-Ray Computed Early Detection of Cancer Radiographic Image Interpretation, Computer-Assisted
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Utility of mass spectrometry and artificial intelligence for differentiating primary lung adenocarcinoma and colorectal metastatic pulmonary tumor.

Thoracic cancer Nov 23, 2021
BACKGROUND: Rapid intraoperative diagnosis for unconfirmed pulmonary tumor is extremely important for determining the optimal surgical procedure (lobectomy or sublobar resection). Attempts to diagnose malignant tumors using mass spectrometry (MS) hav...
Adenocarcinoma of Lung Female Male Humans Aged Lung Neoplasms Artificial Intelligence Colorectal Neoplasms Mass Spectrometry
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CAD system for lung nodule detection using deep learning with CNN.

Medical & biological engineering & computing Nov 22, 2021
The early detection of pulmonary nodules using computer-aided diagnosis (CAD) systems is very essential in reducing mortality rates of lung cancer. In this paper, we propose a new deep learning approach to improve the classification accuracy of pulmo...
Lung Neoplasms Solitary Pulmonary Nodule Bayes Theorem Radiographic Image Interpretation, Computer-Assisted Lung Diagnosis, Computer-Assisted Deep Learning Humans
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Development and validation of a supervised deep learning algorithm for automated whole-slide programmed death-ligand 1 tumour proportion score assessment in non-small cell lung cancer.

Histopathology Nov 16, 2021
AIMS: Immunohistochemical programmed death-ligand 1 (PD-L1) staining to predict responsiveness to immunotherapy in patients with advanced non-small cell lung cancer (NSCLC) has several drawbacks: a robust gold standard is lacking, and there is substa...
Male Middle Aged Deep Learning Female Aged Aged, 80 and over Lung Neoplasms B7-H1 Antigen Carcinoma, Non-Small-Cell Lung Humans Algorithms
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Clinical suitability of deep learning based synthetic CTs for adaptive proton therapy of lung cancer.

Medical physics Nov 16, 2021
PURPOSE: Adaptive proton therapy (APT) of lung cancer patients requires frequent volumetric imaging of diagnostic quality. Cone-beam CT (CBCT) can provide these daily images, but x-ray scattering limits CBCT-image quality and hampers dose calculation...
Humans Deep Learning Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Proton Therapy Cone-Beam Computed Tomography Image Processing, Computer-Assisted Lung Neoplasms
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A CT-based deep learning model for subsolid pulmonary nodules to distinguish minimally invasive adenocarcinoma and invasive adenocarcinoma.

European journal of radiology Nov 15, 2021
OBJECTIVE: To develop and validate a deep learning nomogram (DLN) model constructed from non-contrast computed tomography (CT) images for discriminating minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC) in patients with subso...
Humans Adenocarcinoma Retrospective Studies Deep Learning Neoplasm Invasiveness Tomography, X-Ray Computed Lung Neoplasms
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[Artificial intelligence and medical imaging].

Bulletin du cancer Nov 12, 2021
The use of artificial intelligence methods for image recognition is one of the most developed branches of the AI field and these technologies are now commonly used in our daily lives. In the field of medical imaging, approaches based on artificial in...
Humans Artificial Intelligence Machine Learning Organs at Risk Lung Neoplasms Lymphocytes, Tumor-Infiltrating Diagnostic Imaging
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Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans.

Communications biology Nov 12, 2021
Deep learning (DL) is a breakthrough technology for medical imaging with high sample size requirements and interpretability issues. Using a pretrained DL model through a radiomics-guided approach, we propose a methodology for stratifying the prognosi...
Adenocarcinoma of Lung Prognosis Middle Aged Tomography, X-Ray Computed Cohort Studies Humans Risk Factors Female Lung Neoplasms Neural Networks, Computer Aged Male
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