AIMC Topic: Lung Neoplasms

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Decision level scheme for fusing multiomics and histology slide images using deep neural network for tumor prognosis prediction.

Scientific reports
Molecular biostatistical workflows in oncology often rely on predictive models that use multimodal data. Advances in deep learning and artificial intelligence technologies have enabled the multimodal fusion of large volumes of multimodal data. Here, ...

Establishing an AI-based diagnostic framework for pulmonary nodules in computed tomography.

BMC pulmonary medicine
BACKGROUND: Pulmonary nodules seen by computed tomography (CT) can be benign or malignant, and early detection is important for optimal management. The existing manual methods of identifying nodules have limitations, such as being time-consuming and ...

Machine Learning-Assisted Multimodal Early Screening of Lung Cancer Based on a Multiplexed Laser-Induced Graphene Immunosensor.

ACS nano
Lung cancer remains the leading cause of cancer-related mortality worldwide, largely due to late-stage diagnosis. Early detection is critical for improving patient outcomes, yet current screening methods, such as low-dose computed tomography (CT), of...

Machine learning for synchronous bone metastasis risk prediction in high grade lung neuroendocrine carcinoma.

Scientific reports
Bone metastasis (BM) is common in high-grade lung neuroendocrine tumors (NETs). This study aimed to use multiple machine learning algorithms to exploring the significant factors associated with synchronous BM in these patients. Patients diagnosed wit...

Predicting time-to-first cancer diagnosis across multiple cancer types.

Scientific reports
Cancer causes over 10 million deaths annually worldwide, with 40.5% of Americans expected to be diagnosed in their lifetime. Early detection is critical; for liver cancer, survival rates improve from 4 to 37% when caught early. However, predicting ti...

The correlation of liquid biopsy genomic data to radiomics in colon, pancreatic, lung and prostatic cancer patients.

European journal of cancer (Oxford, England : 1990)
INTRODUCTION: With the advances in artificial intelligence (AI) and precision medicine, radiomics has emerged as a promising tool in the field of oncology. Radiogenomics integrates radiomics with genomic data, potentially offering a non-invasive meth...

Deep Learning-Based Classification of NSCLC-Derived Extracellular Vesicles Using AFM Nanomechanical Signatures.

Analytical chemistry
Nonsmall cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality, with liquid biopsy emerging as a promising tool for noninvasive diagnostics. Extracellular vesicles (EVs) serve as molecular messengers of the tumor microenvironme...

Metabolic alterations driven by LDHA in CD8 + T cells promote immune evasion and therapy resistance in NSCLC.

Scientific reports
Non-small cell lung cancer (NSCLC) is a leading cause of cancer-related deaths worldwide. Despite advancements in treatment, prognosis for patients with advanced stages remains poor. Metabolic reprogramming in the tumor microenvironment, particularly...

Electronic-Nose Technology for Lung Cancer Detection: A Non-Invasive Diagnostic Revolution.

Lung
BACKGROUND: Lung cancer (LC) remains a leading cause of cancer-related mortality worldwide, primarily due to late-stage diagnosis and the absence of effective early detection methods.