AIMC Topic: 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 Lung Neoplasms Lymphocytes, Tumor-Infiltrating Diagnostic Imaging Organs at Risk
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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...
Male Middle Aged Cohort Studies Lung Neoplasms Adenocarcinoma of Lung Female Risk Factors Aged Prognosis Tomography, X-Ray Computed Humans Neural Networks, Computer
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Integration of Deep Learning Radiomics and Counts of Circulating Tumor Cells Improves Prediction of Outcomes of Early Stage NSCLC Patients Treated With Stereotactic Body Radiation Therapy.

International journal of radiation oncology, biology, physics Nov 11, 2021
PURPOSE: We develop a deep learning (DL) radiomics model and integrate it with circulating tumor cell (CTC) counts as a clinically useful prognostic marker for predicting recurrence outcomes of early-stage (ES) non-small cell lung cancer (NSCLC) pati...
Lung Neoplasms Carcinoma, Non-Small-Cell Lung Neoplastic Cells, Circulating Humans Radiosurgery Deep Learning
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Design of lung nodules segmentation and recognition algorithm based on deep learning.

BMC bioinformatics Nov 8, 2021
BACKGROUND: Accurate segmentation and recognition algorithm of lung nodules has great important value of reference for early diagnosis of lung cancer. An algorithm is proposed for 3D CT sequence images in this paper based on 3D Res U-Net segmentation...
Algorithms Tomography, X-Ray Computed Lung Humans Neural Networks, Computer Deep Learning Image Processing, Computer-Assisted Lung Neoplasms
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Deep learning predicts epidermal growth factor receptor mutation subtypes in lung adenocarcinoma.

Medical physics Nov 7, 2021
PURPOSE: This study aimed to explore the predictive ability of deep learning (DL) for the common epidermal growth factor receptor (EGFR) mutation subtypes in patients with lung adenocarcinoma.
Humans Mutation ErbB Receptors Retrospective Studies Adenocarcinoma of Lung Deep Learning Lung Neoplasms
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Identification of Benign and Malignant Lung Nodules in CT Images Based on Ensemble Learning Method.

Interdisciplinary sciences, computational life sciences Nov 2, 2021
BACKGROUND AND OBJECTIVE: Under the background of urgent need for computer-aided technology to provide physicians with objective decision support, aiming at reducing the false positive rate of nodule CT detection in pulmonary nodules detection and im...
Humans Diagnosis, Differential Machine Learning Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Diagnosis, Computer-Assisted Lung Neoplasms Solitary Pulmonary Nodule
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Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images.

Nature communications Nov 2, 2021
Machine-assisted pathological recognition has been focused on supervised learning (SL) that suffers from a significant annotation bottleneck. We propose a semi-supervised learning (SSL) method based on the mean teacher architecture using 13,111 whole...
Humans Neural Networks, Computer Artificial Intelligence Supervised Machine Learning ROC Curve Lung Neoplasms Lymphatic Metastasis Colorectal Neoplasms Deep Learning
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Predictive value of a novel Asian lung cancer screening nomogram based on artificial intelligence and epidemiological characteristics.

Thoracic cancer Oct 28, 2021
BACKGROUND: To develop and validate a risk prediction nomogram based on a deep learning convolutional neural networks (CNN) model and epidemiological characteristics for lung cancer screening in patients with small pulmonary nodules (SPN).
Nomograms Early Detection of Cancer Predictive Value of Tests China Female Artificial Intelligence Lung Neoplasms Risk Factors Retrospective Studies Aged Tomography, X-Ray Computed Humans Epidemiologic Methods Male Neural Networks, Computer Middle Aged
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A deep learning- and CT image-based prognostic model for the prediction of survival in non-small cell lung cancer.

Medical physics Oct 28, 2021
OBJECTIVE: To assist clinicians in arranging personalized treatment, planning follow-up programs and extending survival times for non-small cell lung cancer (NSCLC) patients, a method of deep learning combined with computed tomography (CT) imaging fo...
Carcinoma, Non-Small-Cell Lung Tomography, X-Ray Computed Humans Deep Learning Prognosis Lung Neoplasms Retrospective Studies
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Deep Learning for Lung Cancer Nodal Staging and Real-World Clinical Practice.

Radiology Oct 26, 2021
Lung Neoplasms Humans Neoplasm Staging Deep Learning
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