AIMC Topic: Lung Neoplasms
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Artificial intelligence-based graded training of pulmonary nodules for junior radiology residents and medical imaging students.
BMC medical education
Jul 9, 2024
BACKGROUND: To evaluate the efficiency of artificial intelligence (AI)-assisted diagnosis system in the pulmonary nodule detection and diagnosis training of junior radiology residents and medical imaging students.
Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients.
Cell reports methods
Jul 8, 2024
Deep-learning tools that extract prognostic factors derived from multi-omics data have recently contributed to individualized predictions of survival outcomes. However, the limited size of integrated omics-imaging-clinical datasets poses challenges. ...
Combined expert-in-the-loop-random forest multiclass segmentation U-net based artificial intelligence model: evaluation of non-small cell lung cancer in fibrotic and non-fibrotic microenvironments.
Journal of translational medicine
Jul 8, 2024
BACKGROUND: The tumor microenvironment (TME) plays a key role in lung cancer initiation, proliferation, invasion, and metastasis. Artificial intelligence (AI) methods could potentially accelerate TME analysis. The aims of this study were to (1) asses...
Rapid detection of lung cancer based on serum Raman spectroscopy and a support vector machine: a case-control study.
BMC cancer
Jul 2, 2024
BACKGROUND: Early screening and detection of lung cancer is essential for the diagnosis and prognosis of the disease. In this paper, we investigated the feasibility of serum Raman spectroscopy for rapid lung cancer screening.
A multiscale 3D network for lung nodule detection using flexible nodule modeling.
Medical physics
Jul 1, 2024
BACKGROUND: Lung cancer is the most common type of cancer. Detection of lung cancer at an early stage can reduce mortality rates. Pulmonary nodules may represent early cancer and can be identified through computed tomography (CT) scans. Malignant ris...
Machine learning identifies prognostic subtypes of the tumor microenvironment of NSCLC.
Scientific reports
Jul 1, 2024
The tumor microenvironment (TME) plays a fundamental role in tumorigenesis, tumor progression, and anti-cancer immunity potential of emerging cancer therapeutics. Understanding inter-patient TME heterogeneity, however, remains a challenge to efficien...
A deep learning approach for overall survival prediction in lung cancer with missing values.
Computer methods and programs in biomedicine
Jun 28, 2024
BACKGROUND AND OBJECTIVE: In the field of lung cancer research, particularly in the analysis of overall survival (OS), artificial intelligence (AI) serves crucial roles with specific aims. Given the prevalent issue of missing data in the medical doma...
Training immunophenotyping deep learning models with the same-section ground truth cell label derivation method improves virtual staining accuracy.
Frontiers in immunology
Jun 28, 2024
INTRODUCTION: Deep learning (DL) models predicting biomarker expression in images of hematoxylin and eosin (H&E)-stained tissues can improve access to multi-marker immunophenotyping, crucial for therapeutic monitoring, biomarker discovery, and person...
Integrated multi-omics analysis and machine learning to refine molecular subtypes, prognosis, and immunotherapy in lung adenocarcinoma.
Functional & integrative genomics
Jun 27, 2024
Lung adenocarcinoma (LUAD) has a malignant characteristic that is highly aggressive and prone to metastasis. There is still a lack of suitable biomarkers to facilitate the refinement of precision-based therapeutic regimens. We used a combination of 1...
Deep learning model integrating cfDNA methylation and fragment size profiles for lung cancer diagnosis.
Scientific reports
Jun 26, 2024
Detecting aberrant cell-free DNA (cfDNA) methylation is a promising strategy for lung cancer diagnosis. In this study, our aim is to identify methylation markers to distinguish patients with lung cancer from healthy individuals. Additionally, we soug...
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