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Early detection of ...
Early detection of squamous cell carcinoma of the oral tongue using multidimensional plasma protein analysis and interpretable machine learning
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- Gu, Xiaolian, 1976- (författare)
- Umeå universitet,Patologi
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- Salehi, Amir M. (författare)
- Umeå universitet,Patologi,Umeå university,Professor Karin Nylander
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- Wang, Lixiao, 1975- (författare)
- Umeå universitet,Patologi
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- Coates, Philip J. (författare)
- Research Centre for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, Brno, Czech Republic
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- Sgaramella, Nicola (författare)
- Umeå universitet,Patologi,Department of Oral and Maxillo-Facial Surgery, Mater Dei Hospital, Bari, Italy
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- Nylander, Karin (författare)
- Umeå universitet,Patologi
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(creator_code:org_t)
- John Wiley & Sons, 2023
- 2023
- Engelska.
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Ingår i: Journal of Oral Pathology & Medicine. - : John Wiley & Sons. - 0904-2512 .- 1600-0714. ; 52:7, s. 637-643
- Relaterad länk:
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https://doi.org/10.1...
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https://umu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
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- Background: Interpretable machine learning (ML) for early detection of cancer has the potential to improve risk assessment and early intervention.Methods: Data from 261 proteins related to inflammation and/or tumor processes in 123 blood samples collected from healthy persons, but of whom a sub-group later developed squamous cell carcinoma of the oral tongue (SCCOT), were analyzed. Samples from people who developed SCCOT within less than 5 years were classified as tumor-to-be and all other samples as tumor-free. The optimal ML algorithm for feature selection was identified and feature importance computed by the SHapley Additive exPlanations (SHAP) method. Five popular ML algorithms (AdaBoost, Artificial neural networks [ANNs], Decision Tree [DT], eXtreme Gradient Boosting [XGBoost], and Support Vector Machine [SVM]) were applied to establish prediction models, and decisions of the optimal models were interpreted by SHAP.Results: Using the 22 selected features, the SVM prediction model showed the best performance (sensitivity = 0.867, specificity = 0.859, balanced accuracy = 0.863, area under the receiver operating characteristic curve [ROC-AUC] = 0.924). SHAP analysis revealed that the 22 features rendered varying person-specific impacts on model decision and the top three contributors to prediction were Interleukin 10 (IL10), TNF Receptor Associated Factor 2 (TRAF2), and Kallikrein Related Peptidase 12 (KLK12).Conclusion: Using multidimensional plasma protein analysis and interpretable ML, we outline a systematic approach for early detection of SCCOT before the appearance of clinical signs.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
Nyckelord
- machine learning
- interpretable model
- SHAP
- SCCOT
- PLASMA PROTEIN
- Genetics
- genetik
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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