AIMC Topic: Adenocarcinoma of Lung

Clear Filters Showing 71 to 80 of 179 articles

Transformaer-based model for lung adenocarcinoma subtypes.

Medical physics Mar 1, 2024
BACKGROUND: Lung cancer has the highest morbidity and mortality rate among all types of cancer. Histological subtypes serve as crucial markers for the development of lung cancer and possess significant clinical values for cancer diagnosis, prognosis,...
Image Processing, Computer-Assisted Lung Neoplasms Adenocarcinoma of Lung Humans Neural Networks, Computer
View on PubMed DOI

Predicting occult lymph node metastasis in solid-predominantly invasive lung adenocarcinoma across multiple centers using radiomics-deep learning fusion model.

Cancer imaging : the official publication of the International Cancer Imaging Society Jan 12, 2024
BACKGROUND: In solid-predominantly invasive lung adenocarcinoma (SPILAC), occult lymph node metastasis (OLNM) is pivotal for determining treatment strategies. This study seeks to develop and validate a fusion model combining radiomics and deep learni...
Retrospective Studies Radiomics Deep Learning Lung Neoplasms Lymphatic Metastasis Adenocarcinoma of Lung Humans
View on PubMed DOI

Using Vision Transformer for high robustness and generalization in predicting EGFR mutation status in lung adenocarcinoma.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico Jan 9, 2024
BACKGROUND: Lung adenocarcinoma is a common cause of cancer-related deaths worldwide, and accurate EGFR genotyping is crucial for optimal treatment outcomes. Conventional methods for identifying the EGFR genotype have several limitations. Therefore, ...
Aged Adult Male Humans Tomography, X-Ray Computed Female Mutation ErbB Receptors Adenocarcinoma of Lung Middle Aged Deep Learning Lung Neoplasms
View on PubMed DOI

CT-Based Super-Resolution Deep Learning Models with Attention Mechanisms for Predicting Spread Through Air Spaces of Solid or Part-Solid Lung Adenocarcinoma.

Academic radiology Jan 5, 2024
RATIONALE AND OBJECTIVES: Spread through air space (STAS) is a novel invasive pattern of lung adenocarcinoma (LUAD), and preoperative knowledge of STAS status is helpful in choosing an appropriate surgical approach.
Adenocarcinoma of Lung Sensitivity and Specificity Male Middle Aged Female Humans Deep Learning Aged Retrospective Studies Lung Neoplasms Adult Tomography, X-Ray Computed Neoplasm Invasiveness
View on PubMed DOI

Immune, metabolic landscapes of prognostic signatures for lung adenocarcinoma based on a novel deep learning framework.

Scientific reports Jan 4, 2024
Lung adenocarcinoma (LUAD) is a malignant tumor with high lethality, and the aim of this study was to identify promising biomarkers for LUAD. Using the TCGA-LUAD dataset as a discovery cohort, a novel joint framework VAEjMLP based on variational auto...
Biomarkers, Tumor Deep Learning Lung Neoplasms Humans Prognosis Adenocarcinoma of Lung
View on PubMed DOI

CT-Based Deep-Learning Model for Spread-Through-Air-Spaces Prediction in Ground Glass-Predominant Lung Adenocarcinoma.

Annals of surgical oncology Nov 13, 2023
BACKGROUND: Sublobar resection is strongly associated with poor prognosis in early-stage lung adenocarcinoma, with the presence of tumor spread through air spaces (STAS). Thus, preoperative prediction of STAS is important for surgical planning. This ...
Neoplasm Staging Adenocarcinoma of Lung Deep Learning Humans Lung Neoplasms Neoplasm Invasiveness Prognosis Retrospective Studies Tomography, X-Ray Computed
View on PubMed DOI

Prediction of visceral pleural invasion of clinical stage I lung adenocarcinoma using thoracoscopic images and deep learning.

Surgery today Oct 20, 2023
PURPOSE: To develop deep learning models using thoracoscopic images to identify visceral pleural invasion (VPI) in patients with clinical stage I lung adenocarcinoma, and to verify if these models can be applied clinically.
Adenocarcinoma of Lung Middle Aged Lung Neoplasms Neoplasm Staging Pleura Aged Humans Neoplasm Invasiveness Deep Learning Thoracoscopy Sensitivity and Specificity Neural Networks, Computer Predictive Value of Tests ROC Curve Male Viscera Female
View on PubMed DOI

Prognostication of lung adenocarcinomas using CT-based deep learning of morphological and histopathological features: a retrospective dual-institutional study.

European radiology Oct 20, 2023
OBJECTIVES: To develop and validate CT-based deep learning (DL) models that learn morphological and histopathological features for lung adenocarcinoma prognostication, and to compare them with a previously developed DL discrete-time survival model.
Aged Male Middle Aged Deep Learning Lung Neoplasms Adenocarcinoma of Lung Retrospective Studies Tomography, X-Ray Computed Female Humans Prognosis
View on PubMed DOI

Development and Validation of a Deep Learning Radiomics Model to Predict High-Risk Pathologic Pulmonary Nodules Using Preoperative Computed Tomography.

Academic radiology Oct 5, 2023
RATIONALE AND OBJECTIVES: To accurately identify the high-risk pathological factors of pulmonary nodules, our study constructed a model combined with clinical features, radiomics features, and deep transfer learning features to predict high-risk path...
Multiple Pulmonary Nodules Adenocarcinoma of Lung Humans Radiomics Retrospective Studies Deep Learning Tomography, X-Ray Computed Lung Neoplasms
View on PubMed DOI

Deep learning-based solid component measuring enabled interpretable prediction of tumor invasiveness for lung adenocarcinoma.

Lung cancer (Amsterdam, Netherlands) Oct 5, 2023
BACKGROUND: The nature of the solid component of subsolid nodules (SSNs) can indicate tumor pathological invasiveness. However, preoperative solid component assessment still lacks a reference standard.
Humans Lung Neoplasms Adenocarcinoma Neoplasm Invasiveness Retrospective Studies Adenocarcinoma of Lung Tomography, X-Ray Computed Deep Learning
View on PubMed DOI
Popular Topics
Recent Journals