Clinical chemistry and laboratory medicine: Machine learning algorithms with body fluid parameters: an interpretable framework for malignant cell screening in cerebrospinal fluid

Ye, Xianfei; Zhao, Xinfeng; Lou, Yinyu; Pan, Hanqi; Chen, Yunying

Machine learning algorithms with body fluid parameters: an interpretable framework for malignant cell screening in cerebrospinal fluid / Ye, Xianfei (Rights reserved)

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fullscreen: Machine learning algorithms with body fluid parameters: an interpretable framework for malignant cell screening in cerebrospinal fluid / Ye, Xianfei (Rights reserved)

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Eingeschränkter Zugang mit Nutzungsbeschränkungen: Das Dokument ist in den Räumen der Zentral- und Landesbibliothek mit dem "Virtuellen Lesesaal der Landesbibliothek" auf allen Internet-Arbeitsplätzen zugreifbar, darf jedoch nicht kopiert, versendet oder in einem Umfang von mehr als 10% ausgedruckt werden. Weitere Informationen.

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Periodical

Title:: Clinical chemistry and laboratory medicine

Publication:: Berlin [u.a.]: De Gruyter

Note:: Gesehen am 22.6.2022; Archivierung/Langzeitarchivierung gewährleistet; 82+C!EBSCO-Split(17-11-09)

Scope:: Online-Ressource

ISSN:: 1437-4331

ZDB-ID:: 1492732-9

VÖBB-Katalog:: 15133150

Previous Title:: European journal of clinical chemistry and clinical biochemistry

Keywords:: Zeitschrift

Classification:: Naturwissenschaften; Medizin

DDC Group:: 610 Medizin; 540 Chemie

Collection:: Naturwissenschaften; Medizin

Copyright:: Rights reserved

Accessibility:: Eingeschränkter Zugang mit Nutzungsbeschränkungen

Article

Author:: Ye, Xianfei; Zhao, Xinfeng; Lou, Yinyu; Pan, Hanqi; Chen, Yunying

Title:: Machine learning algorithms with body fluid parameters: an interpretable framework for malignant cell screening in cerebrospinal fluid

Publication:: Berlin [u.a.]: De Gruyter, 2025

Language:: English

Information:: Objectives: This study aimed to develop and validate a machine learning (ML) model utilizing cerebrospinal fluid (CSF) body fluid parameters from hematology analyzers to screen for malignant cells. Methods: We analyzed 643 consecutive CSF samples from patients with central nervous system symptoms, with 191 samples classified as positive for malignant cells based on cytological examination, for model derivation. Body fluid parameters were measured using the body fluid mode of a hematology analyzer. Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to identify predictive biomarkers, followed by performance evaluations of six ML algorithms. Model interpretability was assessed using SHapley Additive exPlanations (SHAP). The selected model was also externally validated with an additional 136 CSF samples. Results: The median leukocyte (WBC) and total nucleated cell (TNC) counts in the cytology-positive samples were significantly lower than those in the cytology-negative samples (5.4 vs. 31.8 and 7.4 vs. 32.6, respectively, p<0.001). The support vector machine (SVM) model achieved the highest area under the curve (AUC) of 0.899 (SD: 0.035) and the highest sensitivity of 0.827 (SD: 0.059) in internal validation. SHAP analysis identified the percentage of high fluorescence cells and monocytes as the two most significant predictors, both positively correlated with malignant cell outcomes. External validation demonstrated a comparable AUC and sensitivity, confirming the model’s generalizability. Conclusions: We developed an ML model that predicts cytological outcomes in CSF using routinely available body fluid parameters. The model demonstrated consistent performance during external validation.

Scope:: Online-Ressource

Note:: Kein Open Access; Archivierung/Langzeitarchivierung gewährleistet

Keywords:: machine learning ; body fluid ; cerebrospinal fluid ; high fluorescence cells ; malignant cells

Classification:: Naturwissenschaften; Medizin; Sonstiges

URN:: urn:nbn:de:101:1-2508300729435.008207789507

Collection:: Naturwissenschaften; Medizin; Sonstiges

Copyright:: Rights reserved

Accessibility:: Eingeschränkter Zugang mit Nutzungsbeschränkungen

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