AIMC Topic: Lung Neoplasms

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Deep-Learning Model of ResNet Combined with CBAM for Malignant-Benign Pulmonary Nodules Classification on Computed Tomography Images.

Medicina (Kaunas, Lithuania) Jun 5, 2023
: Lung cancer remains a leading cause of cancer mortality worldwide. Accurately classifying benign pulmonary nodules and malignant ones is crucial for early diagnosis and improved patient outcomes. The purpose of this study is to explore the deep-lea...
Deep Learning Lung Neoplasms Humans Retrospective Studies Tomography, X-Ray Computed
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Imaging of lung cancer.

Current problems in cancer Jun 4, 2023
Lung cancer is the leading cause of cancer-related mortality globally. Imaging is essential in the screening, diagnosis, staging, response assessment, and surveillance of patients with lung cancer. Subtypes of lung cancer can have distinguishing imag...
Magnetic Resonance Imaging Positron-Emission Tomography Humans Tomography, X-Ray Computed Neoplasm Staging Artificial Intelligence Lung Neoplasms
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Can incorrect artificial intelligence (AI) results impact radiologists, and if so, what can we do about it? A multi-reader pilot study of lung cancer detection with chest radiography.

European radiology Jun 2, 2023
OBJECTIVE: To examine whether incorrect AI results impact radiologist performance, and if so, whether human factors can be optimized to reduce error.
Humans Artificial Intelligence Lung Neoplasms Radiography Radiologists Retrospective Studies Pilot Projects
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Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study.

The Lancet. Digital health May 31, 2023
BACKGROUND: Only around 20-30% of patients with non-small-cell lung cancer (NCSLC) have durable benefit from immune-checkpoint inhibitors. Although tissue-based biomarkers (eg, PD-L1) are limited by suboptimal performance, tissue availability, and tu...
Carcinoma, Non-Small-Cell Lung B7-H1 Antigen Retrospective Studies Deep Learning United States Immune Checkpoint Inhibitors Humans Lung Neoplasms
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Deep learning-based radiomics: pacing immunotherapy in lung cancer.

The Lancet. Digital health May 31, 2023
Immunotherapy Humans Deep Learning Lung Neoplasms
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The artificial intelligence and machine learning in lung cancer immunotherapy.

Journal of hematology & oncology May 24, 2023
Since the past decades, more lung cancer patients have been experiencing lasting benefits from immunotherapy. It is imperative to accurately and intelligently select appropriate patients for immunotherapy or predict the immunotherapy efficacy. In rec...
Tumor Microenvironment Lung Neoplasms B7-H1 Antigen Immunotherapy Artificial Intelligence Machine Learning Humans
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Deep learning-based markerless lung tumor tracking in stereotactic radiotherapy using Siamese networks.

Medical physics May 23, 2023
BACKGROUND: Radiotherapy (RT) is involved in about 50% of all cancer patients, making it a very important treatment modality. The most common type of RT is external beam RT, which consists of delivering the radiation to the tumor from outside the bod...
Radiotherapy Planning, Computer-Assisted Respiration Radiotherapy, Intensity-Modulated Radiosurgery Cone-Beam Computed Tomography Lung Neoplasms Humans Deep Learning
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Artificial intelligence predicts lung cancer radiotherapy response: A meta-analysis.

Artificial intelligence in medicine May 20, 2023
BACKGROUND: Artificial intelligence (AI) technology has clustered patients based on clinical features into sub-clusters to stratify high-risk and low-risk groups to predict outcomes in lung cancer after radiotherapy and has gained much more attention...
Prospective Studies Humans Lung Neoplasms Artificial Intelligence PubMed Databases, Factual
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Radiomics and artificial intelligence for precision medicine in lung cancer treatment.

Seminars in cancer biology May 19, 2023
Lung cancer is the leading cause of cancer-related deaths worldwide. It exhibits, at the mesoscopic scale, phenotypic characteristics that are generally indiscernible to the human eye but can be captured non-invasively on medical imaging as radiomic ...
Machine Learning Precision Medicine Humans Artificial Intelligence Lung Neoplasms Diagnostic Imaging
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Deep learning PET/CT-based radiomics integrates clinical data: A feasibility study to distinguish between tuberculosis nodules and lung cancer.

Thoracic cancer May 14, 2023
BACKGROUND: Radiomic diagnosis models generally consider only a single dimension of information, leading to limitations in their diagnostic accuracy and reliability. The integration of multiple dimensions of information into the deep learning model h...
Humans Reproducibility of Results Tuberculosis Deep Learning Positron Emission Tomography Computed Tomography Lung Neoplasms Feasibility Studies
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