ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024
Unlocking Early Cancer Detection: Leveraging Machine Learning in Cell-Free DNA Analysis for Precision Oncology.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Value of Machine Learning Models for Cell-Free DNA-Based Multi-Cancer Early Detection: A Systematic Review and Meta-Analysis.Technology in cancer research & treatmentPooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study introduces a groundbreaking approach to early cancer detection through the analysis of cell-free DNA (cfDNA), utilizing machine learning algorithms to navigate the complexities of low circulating tumor DNA (ctDNA) fractions and genetic heterogeneity. CfDNA, found in bodily fluids and comprising fragments from apoptotic or necrotic cells, offers a non-invasive means to identify cancer signals. With ctDNA-a subset of cfDNA from cancer cells-serving as a biomarker, the potential for detecting cancer at its earliest stages is vastly improved, enhancing treatment effectiveness and patient prognosis. However, the challenges of distinguishing cancer-specific signatures within cfDNA due to low ctDNA levels and the noise of genetic heterogeneity necessitate advanced methods beyond traditional mutation analysis. Leveraging high-throughput sequencing technologies and the precision of machine learning, our research aims to surmount these obstacles by identifying nuanced cancer signatures within cfDNA sequencing data. Machine learning's capability to model complex data relationships allows for the differentiation of subtle oncogenic patterns from background noise, thereby increasing the diagnostic accuracy of liquid biopsies. This paper outlines our exploration into employing machine learning for early cancer detection via cfDNA, detailing our method of transforming sequencing data into analyzable formats, enhancing signal detection through a sliding window technique, and predicting true tumor-origin fragments. By advancing cfDNA-based cancer diagnostics, this research not only signifies a leap towards more sensitive and specific early-stage cancer detection but also opens avenues for personalized oncology, where treatment strategies are informed by the unique genetic profile unveiled through cfDNA analysis. Our findings underscore the potential of integrating artificial intelligence with liquid biopsy technologies to revolutionize cancer diagnostics, offering new hope for early detection and personalized treatment pathways.
Indexed as
Identifiers
40417566PMC12099344What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.