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This research explores novel non-invasive biomarkers for early diagnosis and risk stratification in NKTCL.

December 8, 2025BloodOpen Access

Untargeted metabolomics and machine learning reveal novel non-invasive biomarker panels for diagnosis and risk stratification in extranodal NK/T-cell lymphoma

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Authors

YLYajun Li KCKailin Chen HZHui Zhou

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Implication

Untargeted metabolomics demonstrates predictive models for diagnosis and risk stratification in NKTCL patients, indicating precision medicine benefits.

Key Points

This research explores novel non-invasive biomarkers for early diagnosis and risk stratification in NKTCL.
Conducted untargeted metabolomic analysis on plasma samples from 821 individuals (247 NKTCL patients and 574 controls)
Utilized machine learning algorithms to construct diagnostic and prognostic models for NKTCL
Evaluated model performance in training and validation cohorts
Identified two novel metabolite biomarker panels for non-invasive early diagnosis and prognosis
Demonstrated excellent performance of the diagnostic model and superior predictive accuracy for the prognostic model
Effectively stratified patients into distinct risk subgroups with differing survival outcomes

Cite This Study

Li et al. (2025) studied this question.

synapsesocial.com/papers/69362f484fa91c937236d69e https://doi.org/10.1182/blood-2025-7082

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Implication

Untargeted metabolomics demonstrates predictive models for diagnosis and risk stratification in NKTCL patients, indicating precision medicine benefits.

Untargeted metabolomics and machine learning reveal novel non-invasive biomarker panels for diagnosis and risk stratification in extranodal NK/T-cell lymphoma

Discussion

Member takes

Implication

Key Points

Cite This Study

Untargeted metabolomics and machine learning reveal novel non-invasive biomarker panels for diagnosis and risk stratification in extranodal NK/T-cell lymphoma

Discussion

Member takes

Implication

Key Points

Cite This Study

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