AIMC Topic: Lung Neoplasms

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Artificial intelligence-assisted quantitative CT parameters in predicting the degree of risk of solitary pulmonary nodules.

Annals of medicine Sep 19, 2024
INTRODUCTION: Artificial intelligence (AI) shows promise for evaluating solitary pulmonary nodules (SPNs) on computed tomography (CT). Accurately determining cancer invasiveness can guide treatment. We aimed to investigate quantitative CT parameters ...
Solitary Pulmonary Nodule Neoplasm Invasiveness Retrospective Studies Middle Aged Tomography, X-Ray Computed Lung Neoplasms Female Carcinoma, Non-Small-Cell Lung Aged Aged, 80 and over Adult Humans Male Artificial Intelligence
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Human lung cancer classification and comprehensive analysis using different machine learning techniques.

Microscopy research and technique Sep 18, 2024
Lung cancer is the most common causes of death among all cancer-related diseases. A lung scan examination of the patient is the primary diagnostic technique. This scan analysis pertains to an MRI, CT, or X-ray. The automated classification of lung ca...
Humans Tomography, X-Ray Computed Lung Machine Learning Algorithms Image Processing, Computer-Assisted Decision Trees Lung Neoplasms Bayes Theorem Support Vector Machine
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Deep Learning Model for Pathological Grading and Prognostic Assessment of Lung Cancer Using CT Imaging: A Study on NLST and External Validation Cohorts.

Academic radiology Sep 17, 2024
RATIONALE AND OBJECTIVES: To develop and validate a deep learning model for automated pathological grading and prognostic assessment of lung cancer using CT imaging, thereby providing surgeons with a non-invasive tool to guide surgical planning.
Carcinoma, Non-Small-Cell Lung Neoplasm Grading Tomography, X-Ray Computed Deep Learning Female Cohort Studies Reproducibility of Results Male Lung Neoplasms Humans Aged Middle Aged Prognosis Sensitivity and Specificity
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Leveraging immuno-fluorescence data to reduce pathologist annotation requirements in lung tumor segmentation using deep learning.

Scientific reports Sep 16, 2024
The main bottleneck in training a robust tumor segmentation algorithm for non-small cell lung cancer (NSCLC) on H&E is generating sufficient ground truth annotations. Various approaches for generating tumor labels to train a tumor segmentation model ...
Algorithms Deep Learning Image Processing, Computer-Assisted Pathologists Lung Neoplasms Carcinoma, Non-Small-Cell Lung Humans
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Use machine learning to predict pulmonary metastasis of esophageal cancer: a population-based study.

Journal of cancer research and clinical oncology Sep 16, 2024
BACKGROUND: This study aims to establish a predictive model for assessing the risk of esophageal cancer lung metastasis using machine learning techniques.
Aged Male Middle Aged Lung Neoplasms Machine Learning Female Risk Factors Esophageal Neoplasms SEER Program Humans
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An enhanced Garter Snake Optimization-assisted deep learning model for lung cancer segmentation and classification using CT images.

Journal of medical engineering & technology Sep 16, 2024
An early detection of lung tumors is critical for better treatment results, and CT scans can reveal lumps in the lungs which are too small to be picked up by conventional X-rays. CT imaging has advantages, but it also exposes a person to radiation fr...
Algorithms Tomography, X-Ray Computed Deep Learning Image Processing, Computer-Assisted Humans Lung Neoplasms
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Pulmonary nodule visualization and evaluation of AI-based detection at various ultra-low-dose levels using photon-counting detector CT.

Acta radiologica (Stockholm, Sweden : 1987) Sep 15, 2024
BACKGROUND: Radiation dose should be as low as reasonably achievable. With the invention of photon-counting detector computed tomography (PCD-CT), the radiation dose may be considerably reduced.
Artificial Intelligence Tomography, X-Ray Computed Lung Lung Neoplasms Humans Phantoms, Imaging Photons Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Radiation Dosage
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Artificial Intelligence Algorithm Can Predict Lymph Node Malignancy from Endobronchial Ultrasound Transbronchial Needle Aspiration Images for Non-Small Cell Lung Cancer.

Respiration; international review of thoracic diseases Sep 14, 2024
INTRODUCTION: Endobronchial ultrasound transbronchial needle aspiration (EBUS-TBNA) for lung cancer staging is operator dependent, resulting in high rates of non-diagnostic lymph node (LN) samples. We hypothesized that an artificial intelligence (AI)...
Algorithms Neoplasm Staging Lymphatic Metastasis Male Female Endoscopic Ultrasound-Guided Fine Needle Aspiration Predictive Value of Tests Middle Aged Aged Lymph Nodes Humans Lung Neoplasms Carcinoma, Non-Small-Cell Lung Neural Networks, Computer Artificial Intelligence
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Systems Biology and Machine Learning Identify Genetic Overlaps Between Lung Cancer and Gastroesophageal Reflux Disease.

Omics : a journal of integrative biology Sep 13, 2024
One Health and planetary health place emphasis on the common molecular mechanisms that connect several complex human diseases as well as human and planetary ecosystem health. For example, not only lung cancer (LC) and gastroesophageal reflux disease ...
Humans Machine Learning Lung Neoplasms Molecular Docking Simulation Algorithms Systems Biology Computational Biology Gene Expression Profiling Gastroesophageal Reflux Gene Regulatory Networks
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Predicting invasion in early-stage ground-glass opacity pulmonary adenocarcinoma: a radiomics-based machine learning approach.

BMC medical imaging Sep 13, 2024
BACKGROUND: To design a pulmonary ground-glass nodules (GGN) classification method based on computed tomography (CT) radiomics and machine learning for prediction of invasion in early-stage ground-glass opacity (GGO) pulmonary adenocarcinoma.
Adenocarcinoma of Lung Middle Aged Lung Neoplasms Neoplasm Invasiveness Retrospective Studies Tomography, X-Ray Computed Female Aged Humans Sensitivity and Specificity Radiomics Machine Learning Male
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