ArticleScientific reports2019
Diagnosing brain tumours by routine blood tests using machine learning.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 40 citations in OpenAlex.
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- Integrative analysis of blood biomarkers and clinical variables improves early detection of aggressive prostate cancer.Scientific reports · 2025Article
- Detection of antibodies in suspected autoimmune encephalitis diseases using machine learning.Scientific reports · 2025Article
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- Differentiating viral and bacterial infections: A machine learning model based on routine blood test values.Heliyon · 2024Article
- Development and validation of a routine blood parameters-based model for screening the occurrence of retinal detachment in high myopia in the context of PPPM.The EPMA journal · 2023Article
- Assessment and prediction of glioblastoma therapy response: challenges and opportunities.Brain : a journal of neurology · 2023Article
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- Detection of factors affecting kidney function using machine learning methods.Scientific reports · 2022Article
- Clinlabomics: leveraging clinical laboratory data by data mining strategies.BMC bioinformatics · 2022Review
- Testing the applicability and performance of Auto ML for potential applications in diagnostic neuroradiology.Scientific reports · 2022Article
- Review
- COVID-19 diagnosis by routine blood tests using machine learning.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Routine blood test results are assumed to contain much more information than is usually recognised even by the most experienced clinicians. Using routine blood tests from 15,176 neurological patients we built a machine learning predictive model for the diagnosis of brain tumours. We validated the model by retrospective analysis of 68 consecutive brain tumour and 215 control patients presenting to the neurological emergency service. Only patients with head imaging and routine blood test data were included in the validation sample. The sensitivity and specificity of the adapted tumour model in the validation group were 96% and 74%, respectively. Our data demonstrate the feasibility of brain tumour diagnosis from routine blood tests using machine learning. The reported diagnostic accuracy is comparable and possibly complementary to that of imaging studies. The presented machine learning approach opens a completely new avenue in the diagnosis of these grave neurological diseases and demonstrates the utility of valuable information obtained from routine blood tests.
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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 OpenQuestion graph.