AIMC Topic: Lung Neoplasms
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Predicting cancer content in tiles of lung squamous cell carcinoma tumours with validation against pathologist labels.
Computers in biology and medicine
Dec 4, 2024
BACKGROUND: A growing body of research is using deep learning to explore the relationship between treatment biomarkers for lung cancer patients and cancer tissue morphology on digitized whole slide images (WSIs) of tumour resections. However, these W...
Advancing cancer diagnosis and prognostication through deep learning mastery in breast, colon, and lung histopathology with ResoMergeNet.
Computers in biology and medicine
Dec 4, 2024
Cancer, a global health threat, demands effective diagnostic solutions to combat its impact on public health, particularly for breast, colon, and lung cancers. Early and accurate diagnosis is essential for successful treatment, prompting the rise of ...
Automated derivation of diagnostic criteria for lung cancer using natural language processing on electronic health records: a pilot study.
BMC medical informatics and decision making
Dec 4, 2024
BACKGROUND: The digitisation of healthcare records has generated vast amounts of unstructured data, presenting opportunities for improvements in disease diagnosis when clinical coding falls short, such as in the recording of patient symptoms. This st...
Research Trends of Artificial Intelligence in Lung Cancer: A Combined Approach of Analysis With Latent Dirichlet Allocation and HJ-Biplot Statistical Methods.
Pulmonary medicine
Dec 4, 2024
Lung cancer (LC) remains one of the leading causes of cancer-related mortality worldwide. With recent technological advances, artificial intelligence (AI) has begun to play a crucial role in improving diagnostic and treatment methods. It is crucial t...
CNN-O-ELMNet: Optimized Lightweight and Generalized Model for Lung Disease Classification and Severity Assessment.
IEEE transactions on medical imaging
Dec 2, 2024
The high burden of lung diseases on healthcare necessitates effective detection methods. Current Computer-aided design (CAD) systems are limited by their focus on specific diseases and computationally demanding deep learning models. To overcome these...
Accurate Lung Nodule Segmentation With Detailed Representation Transfer and Soft Mask Supervision.
IEEE transactions on neural networks and learning systems
Dec 2, 2024
Accurate lung lesion segmentation from computed tomography (CT) images is crucial to the analysis and diagnosis of lung diseases, such as COVID-19 and lung cancer. However, the smallness and variety of lung nodules and the lack of high-quality labeli...
External validation of the performance of commercially available deep-learning-based lung nodule detection on low-dose CT images for lung cancer screening in Japan.
Japanese journal of radiology
Nov 30, 2024
PURPOSE: Artificial intelligence (AI) algorithms for lung nodule detection have been developed to assist radiologists. However, external validation of its performance on low-dose CT (LDCT) images is insufficient. We examined the performance of the co...
Radiographic Image Interpretation, Computer-Assisted
Japan
Radiation Dosage
Lung Neoplasms
Multiple Pulmonary Nodules
Solitary Pulmonary Nodule
Early Detection of Cancer
Humans
Middle Aged
Lung
Aged
Deep Learning
Retrospective Studies
Aged, 80 and over
Tomography, X-Ray Computed
Sensitivity and Specificity
Female
Male
Assessment of clinical feasibility:offline adaptive radiotherapy for lung cancer utilizing kV iCBCT and UNet++ based deep learning model.
Journal of applied clinical medical physics
Nov 29, 2024
BACKGROUND: Lung cancer poses a significant global health challenge. Adaptive radiotherapy (ART) addresses uncertainties due to lung tumor dynamics. We aimed to investigate a comprehensively and systematically validated offline ART regimen with high ...
Integrated multi-omics and machine learning reveal a gefitinib resistance signature for prognosis and treatment response in lung adenocarcinoma.
IUBMB life
Nov 29, 2024
Gefitinib resistance (GR) presents a significant challenge in treating lung adenocarcinoma (LUAD), highlighting the need for alternative therapies. This study explores the genetic basis of GR to improve prediction, prevention, and treatment strategie...
Multi-omics characterization and machine learning of lung adenocarcinoma molecular subtypes to guide precise chemotherapy and immunotherapy.
Frontiers in immunology
Nov 28, 2024
BACKGROUND: Lung adenocarcinoma (LUAD) is a heterogeneous tumor characterized by diverse genetic and molecular alterations. Developing a multi-omics-based classification system for LUAD is urgently needed to advance biological understanding.
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