AIMC Topic: Lung Neoplasms
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Deep learning reconstruction for zero echo time lung magnetic resonance imaging: impact on image quality and lesion detection.
Clinical radiology
Jul 17, 2024
AIMS: This study aimed to examine the impact of deep-learning reconstruction (DLR) on zero echo time (ZTE) lung MRI.
Artificial intelligence-based plasma exosome label-free SERS profiling strategy for early lung cancer detection.
Analytical and bioanalytical chemistry
Jul 17, 2024
As a lung cancer biomarker, exosomes were utilized for in vitro diagnosis to overcome the lack of sensitivity of conventional imaging and the potential harm caused by tissue biopsy. However, given the inherent heterogeneity of exosomes, the challenge...
Metaheuristic integrated machine learning classification of colon cancer using STFT LASSO and EHO feature extraction from microarray gene expressions.
Scientific reports
Jul 17, 2024
The microarray gene expression data poses a tremendous challenge due to their curse of dimensionality problem. The sheer volume of features far surpasses available samples, leading to overfitting and reduced classification accuracy. Thus the dimensio...
Machine Learning for Early Discrimination Between Lung Cancer and Benign Nodules Using Routine Clinical and Laboratory Data.
Annals of surgical oncology
Jul 16, 2024
BACKGROUND: Lung cancer poses a global health threat necessitating early detection and precise staging for improved patient outcomes. This study focuses on developing and validating a machine learning-based risk model for early lung cancer screening ...
Identifying potential targets for preventing cancer progression through the PLA2G1B recombinant protein using bioinformatics and machine learning methods.
International journal of biological macromolecules
Jul 15, 2024
Lung cancer is the deadliest and most aggressive malignancy in the world. Preventing cancer is crucial. Therefore, the new molecular targets have laid the foundation for molecular diagnosis and targeted therapy of lung cancer. PLA2G1B plays a key rol...
Predictive Modeling of Long-Term Prognosis After Resection in Typical Pulmonary Carcinoid: A Machine Learning Perspective.
Cancer investigation
Jul 15, 2024
Typical Pulmonary Carcinoid (TPC) is defined by its slow growth, frequently necessitating surgical intervention. Despite this, the long-term outcomes following tumor resection are not well understood. This study examined the factors impacting Overall...
A position-enhanced sequential feature encoding model for lung infections and lymphoma classification on CT images.
International journal of computer assisted radiology and surgery
Jul 14, 2024
PURPOSE: Differentiating pulmonary lymphoma from lung infections using CT images is challenging. Existing deep neural network-based lung CT classification models rely on 2D slices, lacking comprehensive information and requiring manual selection. 3D ...
Predicting Survival in Patients with Advanced NSCLC Treated with Atezolizumab Using Pre- and on-Treatment Prognostic Biomarkers.
Clinical pharmacology and therapeutics
Jul 12, 2024
Existing survival prediction models rely only on baseline or tumor kinetics data and lack machine learning integration. We introduce a novel kinetics-machine learning (kML) model that integrates baseline markers, tumor kinetics, and four on-treatment...
Enhanced deep learning model for precise nodule localization and recurrence risk prediction following curative-intent surgery for lung cancer.
PloS one
Jul 12, 2024
PURPOSE: Radical surgery is the primary treatment for early-stage resectable lung cancer, yet recurrence after curative surgery is not uncommon. Identifying patients at high risk of recurrence using preoperative computed tomography (CT) images could ...
Deep learning in pulmonary nodule detection and segmentation: a systematic review.
European radiology
Jul 10, 2024
OBJECTIVES: The accurate detection and precise segmentation of lung nodules on computed tomography are key prerequisites for early diagnosis and appropriate treatment of lung cancer. This study was designed to compare detection and segmentation metho...
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