AIMC Topic: Lung Neoplasms
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Effect of Deep Learning Image Reconstruction Algorithms on Radiomic Features of Pulmonary Nodules in Ultra-Low-Dose CT.
Journal of computer assisted tomography
Aug 2, 2024
OBJECTIVE: The purpose of this study is to explore the impact of deep learning image reconstruction (DLIR) algorithm on the quantification of radiomic features in ultra-low-dose computed tomography (ULD-CT) compared with adaptive statistical iterativ...
Clinical implementation and evaluation of deep learning-assisted automatic radiotherapy treatment planning for lung cancer.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Aug 2, 2024
PURPOSE: The purpose of the study is to investigate the clinical application of deep learning (DL)-assisted automatic radiotherapy planning for lung cancer.
Development of a CT-Based comprehensive model combining clinical, radiomics with deep learning for differentiating pulmonary metastases from noncalcified pulmonary hamartomas: a retrospective cohort study.
International journal of surgery (London, England)
Aug 1, 2024
BACKGROUND: Clinical differentiation between pulmonary metastases and noncalcified pulmonary hamartomas (NCPH) often presents challenges, leading to potential misdiagnosis. However, the efficacy of a comprehensive model that integrates clinical featu...
Evaluating the accuracy of lung-RADS score extraction from radiology reports: Manual entry versus natural language processing.
International journal of medical informatics
Jul 31, 2024
INTRODUCTION: Radiology scoring systems are critical to the success of lung cancer screening (LCS) programs, impacting patient care, adherence to follow-up, data management and reporting, and program evaluation. LungCT ScreeningReporting and Data Sys...
Detecting pulmonary malignancy against benign nodules using noninvasive cell-free DNA fragmentomics assay.
ESMO open
Jul 31, 2024
BACKGROUND: Early screening using low-dose computed tomography (LDCT) can reduce mortality caused by non-small-cell lung cancer. However, ∼25% of the 'suspicious' pulmonary nodules identified by LDCT are later confirmed benign through resection surge...
CT-based deep learning radiomics biomarker for programmed cell death ligand 1 expression in non-small cell lung cancer.
BMC medical imaging
Jul 31, 2024
BACKGROUND: Programmed cell death ligand 1 (PD-L1), as a reliable predictive biomarker, plays an important role in guiding immunotherapy of lung cancer. To investigate the value of CT-based deep learning radiomics signature to predict PD-L1 expressio...
The impact of high-order features on performance of radiomics studies in CT non-small cell lung cancer.
Clinical imaging
Jul 30, 2024
High-order radiomic features have been shown to produce high performance models in a variety of scenarios. However, models trained without high-order features have shown similar performance, raising the question of whether high-order features are wor...
SAFER: sub-hypergraph attention-based neural network for predicting effective responses to dose combinations.
BMC bioinformatics
Jul 30, 2024
BACKGROUND: The potential benefits of drug combination synergy in cancer medicine are significant, yet the risks must be carefully managed due to the possibility of increased toxicity. Although artificial intelligence applications have demonstrated n...
Machine-learning and scRNA-Seq-based diagnostic and prognostic models illustrating survival and therapy response of lung adenocarcinoma.
Genes and immunity
Jul 29, 2024
Lung cancer is a major cause accounting for cancer-related mortalities, with lung adenocarcinoma (LUAD) being the most prevalent subtype. Given the high clinical and cellular heterogeneities of LUAD, accurate diagnosis and prognosis are crucial to av...
RACGAP1 is a pivotal gene in lung adenocarcinoma-associated membranous nephropathy: Based on comprehensive bioinformatics analysis and machine learning.
International immunopharmacology
Jul 27, 2024
BACKGROUND: This study performs a detailed bioinformatics and machine learning analysis to investigate the genetic foundations of membranous nephropathy (MN) in lung adenocarcinoma (LUAD).
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