AIMC Topic: Liquid Biopsy

Clear Filters Showing 31 to 40 of 60 articles

Subtype-Specific Detection in Stage Ia Breast Cancer: Integrating Raman Spectroscopy, Machine Learning, and Liquid Biopsy for Personalised Diagnostics.

Journal of biophotonics
This study explores the integration of Raman spectroscopy (RS) with machine learning for the early detection and subtyping of breast cancer using blood plasma samples. We performed detailed spectral analyses, identifying significant spectral patterns...

Advances in exosome plasmonic sensing: Device integration strategies and AI-aided diagnosis.

Biosensors & bioelectronics
Exosomes, as next-generation biomarkers, has great potential in tracking cancer progression. They face many detection limitations in cancer diagnosis. Plasmonic biosensors have attracted considerable attention at the forefront of exosome detection, d...

Detecting pulmonary malignancy against benign nodules using noninvasive cell-free DNA fragmentomics assay.

ESMO open
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...

Label-Free Surface-Enhanced Raman Spectroscopy with Machine Learning for the Diagnosis of Thyroid Cancer by Using Fine-Needle Aspiration Liquid Samples.

Biosensors
The incidence of thyroid cancer is increasing worldwide. Fine-needle aspiration (FNA) cytology is widely applied with the use of extracted biological cell samples, but current FNA cytology is labor-intensive, time-consuming, and can lead to the risk ...

Improving platelet-RNA-based diagnostics: a comparative analysis of machine learning models for cancer detection and multiclass classification.

Molecular oncology
Liquid biopsy demonstrates excellent potential in patient management by providing a minimally invasive and cost-effective approach to detecting and monitoring cancer, even at its early stages. Due to the complexity of liquid biopsy data, machine-lear...

Decoding the glycoproteome: a new frontier for biomarker discovery in cancer.

Journal of hematology & oncology
Cancer early detection and treatment response prediction continue to pose significant challenges. Cancer liquid biopsies focusing on detecting circulating tumor cells (CTCs) and DNA (ctDNA) have shown enormous potential due to their non-invasive natu...

Liquid-biopsy proteomics combined with AI identifies cellular drivers of eye aging and disease in vivo.

Cell
Single-cell analysis in living humans is essential for understanding disease mechanisms, but it is impractical in non-regenerative organs, such as the eye and brain, because tissue biopsies would cause serious damage. We resolve this problem by integ...

The impact of misclassification errors on the performance of biomarkers based on next-generation sequencing, a simulation study.

Journal of biopharmaceutical statistics
The development of next-generation sequencing (NGS) opens opportunities for new applications such as liquid biopsy, in which tumor mutation genotypes can be determined by sequencing circulating tumor DNA after blood draws. However, with highly dilute...

Global serum profiling: an opportunity for earlier cancer detection.

Journal of experimental & clinical cancer research : CR
The advances in cancer research achieved in the last 50 years have been remarkable and have provided a deeper knowledge of this disease in many of its conceptual and biochemical aspects. From viewing a tumor as a 'simple' aggregate of mutant cells an...

Uncovering cancer's secrets: could liquid biopsies and artificial intelligence hold the answers?

Future oncology (London, England)
Tweetable abstract How are #liquidbiopsies and #AI pushing us closer to the goal of early cancer detection for all? Plus, what more needs to be done to truly deciphers cancer's secrets? Find out in this editorial by @JadeParkerB.