AIMC Topic: Adenocarcinoma of Lung

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The diagnostic and prognostic value of radiomics and deep learning technologies for patients with solid pulmonary nodules in chest CT images.

BMC cancer Nov 1, 2022
BACKGROUND: Solid pulmonary nodules are different from subsolid nodules and the diagnosis is much more challenging. We intended to evaluate the diagnostic and prognostic value of radiomics and deep learning technologies for solid pulmonary nodules.
Humans Prognosis Lung Neoplasms Adenocarcinoma Adenocarcinoma of Lung Tomography, X-Ray Computed Deep Learning Retrospective Studies
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Measuring pure ground-glass nodules on computed tomography: assessing agreement between a commercially available deep learning algorithm and radiologists' readings.

Acta radiologica (Stockholm, Sweden : 1987) Oct 31, 2022
BACKGROUND: Deep learning algorithms (DLAs) could enable automatic measurements of solid portions of mixed ground-glass nodules (mGGNs) in agreement with the invasive component sizes measured during pathologic examinations. However, the measurement o...
Humans Algorithms Retrospective Studies Adenocarcinoma of Lung Lung Neoplasms Tomography, X-Ray Computed Adenocarcinoma Deep Learning Neoplasm Invasiveness
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Integrative Serum Metabolic Fingerprints Based Multi-Modal Platforms for Lung Adenocarcinoma Early Detection and Pulmonary Nodule Classification.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Oct 18, 2022
Identification of novel non-invasive biomarkers is critical for the early diagnosis of lung adenocarcinoma (LUAD), especially for the accurate classification of pulmonary nodule. Here, a multiplexed assay is developed on an optimized nanoparticle-bas...
United States Government Agencies Humans Early Diagnosis Biomarkers, Tumor Adenocarcinoma of Lung United States Artificial Intelligence
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Development and validation of a deep learning signature for predicting lymph node metastasis in lung adenocarcinoma: comparison with radiomics signature and clinical-semantic model.

European radiology Sep 28, 2022
OBJECTIVE: To develop and validate a deep learning (DL) signature for predicting lymph node (LN) metastasis in patients with lung adenocarcinoma.
Lung Neoplasms Lymph Nodes Deep Learning Semantics Humans Retrospective Studies Adenocarcinoma of Lung Tomography, X-Ray Computed Lymphatic Metastasis
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Deep learning-based tumor microenvironment segmentation is predictive of tumor mutations and patient survival in non-small-cell lung cancer.

BMC cancer Sep 21, 2022
BACKGROUND: Despite the fact that tumor microenvironment (TME) and gene mutations are the main determinants of progression of the deadliest cancer in the world - lung cancer, their interrelations are not well understood. Digital pathology data provid...
Mutation Adenocarcinoma of Lung Hematoxylin Receptor, Platelet-Derived Growth Factor beta Eosine Yellowish-(YS) Deep Learning Lung Neoplasms Carcinoma, Non-Small-Cell Lung Humans Tumor Microenvironment
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A collaborative workflow between pathologists and deep learning for the evaluation of tumour cellularity in lung adenocarcinoma.

Histopathology Sep 12, 2022
AIMS: The reporting of tumour cellularity in cancer samples has become a mandatory task for pathologists. However, the estimation of tumour cellularity is often inaccurate. Therefore, we propose a collaborative workflow between pathologists and artif...
Deep Learning Lung Neoplasms Workflow Humans Artificial Intelligence Pathologists Adenocarcinoma of Lung
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Deep learning reveals cuproptosis features assist in predict prognosis and guide immunotherapy in lung adenocarcinoma.

Frontiers in endocrinology Aug 19, 2022
BACKGROUND: Cuproptosis is a recently found non-apoptotic cell death type that holds promise as an emerging therapeutic modality in lung adenocarcinoma (LUAD) patients who develop resistance to radiotherapy and chemotherapy. However, the Cuproptosis'...
Gene Expression Regulation, Neoplastic Deep Learning Adenocarcinoma of Lung Apoptosis Humans Lung Neoplasms Copper Prognosis Immunotherapy
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Fusion of CT images and clinical variables based on deep learning for predicting invasiveness risk of stage I lung adenocarcinoma.

Medical physics Aug 15, 2022
PURPOSE: To develop a novel multimodal data fusion model by incorporating computed tomography (CT) images and clinical variables based on deep learning for predicting the invasiveness risk of stage I lung adenocarcinoma that manifests as ground-glass...
Tomography, X-Ray Computed Biomarkers, Tumor Humans Lung Neoplasms Deep Learning Neoplasm Invasiveness Adenocarcinoma Retrospective Studies Adenocarcinoma of Lung
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Solid Attenuation Components Attention Deep Learning Model to Predict Micropapillary and Solid Patterns in Lung Adenocarcinomas on Computed Tomography.

Annals of surgical oncology Jul 5, 2022
BACKGROUND: High-grade adenocarcinoma subtypes (micropapillary and solid) treated with sublobar resection have an unfavorable prognosis compared with those treated with lobectomy. We investigated the potential of incorporating solid attenuation compo...
Deep Learning Humans Lung Neoplasms Retrospective Studies Adenocarcinoma Attention Tomography, X-Ray Computed Adenocarcinoma of Lung
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Visualization of the Associations between the CT Features Extracted from a Deep Learning Survival Prediction Model and Histopathologic Risk Factors.

Radiology Jul 5, 2022
Adenocarcinoma of Lung Deep Learning Lung Neoplasms Humans Tomography, X-Ray Computed Risk Factors
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