AIMC Topic: Lung Neoplasms

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Machine learning identifies exosome related gene signatures for early prediction of non-small cell lung cancer.

Scientific reports
Non-small cell lung cancer (NSCLC) remains a major health challenge worldwide, mainly due to the lack of effective early diagnostic biomarkers. Exosome-related genes have recently emerged as potential diagnostic markers due to their roles in tumor pr...

Personalized Cancer-Specific Protein-Aptamer Corona for Orthogonal Multiplex Cancer Diagnosis.

Journal of the American Chemical Society
Aptamers are powerful synthetic recognition elements for biosensing, yet their application in complex biofluids, such as human serum, is critically limited by enzymatic degradation. To overcome this fundamental challenge, we introduce a novel analyti...

Automated segmentation of canine pulmonary masses in CT imaging using AI.

The veterinary quarterly
Primary pulmonary lung cancer is rare in dogs, and clinicians increasingly rely on advanced imaging for diagnosis and treatment planning. However, manual lesion segmentation can be time-consuming and subject to operator variability. This retrospectiv...

Subvisual imaging signals as biomarkers of impending lung metastasis: A multicenter pan-cancer study.

European journal of cancer (Oxford, England : 1990)
STUDY AIM: Early detection of distant metastases is crucial, but current imaging detects them only when radiographically visible. This study reported subvisual chest CT signals could serve as early biomarkers for impending lung metastasis before radi...

A machine learning framework for classifying lipids in untargeted metabolomics using mass-to-charge ratios and retention times.

Metabolomics : Official journal of the Metabolomic Society
INTRODUCTION: The identification of unknown metabolites remains a major challenge in untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS). This process typically depends on comparing mass spectral or chromatographic data to r...

AI-driven 3D CT imaging prediction model for improving preoperative detection of visceral pleural invasion in early-stage lung cancer.

PloS one
Visceral pleural invasion (VPI) is a critical prognostic factor in early-stage non-small-cell lung cancer (NSCLC), significantly affecting patient outcomes. Conventional computed tomography (CT) often fails to diagnose VPI accurately. This retrospect...

Pathology image-based predictive model for individual survival time of early-stage lung adenocarcinoma patients.

Scientific reports
The tumor microenvironment (TME) is associated with tumor prognosis, immunotherapy response, and prognosis in patients. Here, we hypothesized that the entire TME in pathology image is associated with the survival time prediction. To address this hypo...

Gut microbiota predictive of the efficacy of consolidation immunotherapy and chemoradiotherapy toxicity in lung cancer.

Med (New York, N.Y.)
BACKGROUND: Gut microbiota (GM) predict responses to immune checkpoint inhibitors (ICIs) in patients with advanced lung cancer. However, its role in patients with locally advanced lung cancer undergoing chemoradiotherapy (CRT) combined with consolida...

Secure and interpretable lung cancer prediction model using mapreduce private blockchain federated learning and XAI.

Scientific reports
Lung cancer continues to be one of the most widespread and deadly cancer diagnoses that affects humans worldwide. Early detection of lung cancer leads to decreased mortality rates; however, several challenges hinder the development and deployment of ...

Predictive radiomicsĀ based ensemble machine learning approach in CT lung nodule diagnosis.

Journal of the Egyptian National Cancer Institute
BACKGROUND: Computed tomography imaging, a non-invasive tool, is used around the globe by medical professionals to identify and diagnose lung cancer; a lethal disease with high rates of occurrence and mortality globally. Radiomics extracted from medi...