Early detection of squamous cell carcinoma of the oral tongue using multidimensional plasma protein analysis and interpretable machine learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00209805%3A_____%2F23%3A00079324" target="_blank" >RIV/00209805:_____/23:00079324 - isvavai.cz</a>
Result on the web
<a href="https://onlinelibrary.wiley.com/doi/10.1111/jop.13461" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1111/jop.13461</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1111/jop.13461" target="_blank" >10.1111/jop.13461</a>
Alternative languages
Result language
angličtina
Original language name
Early detection of squamous cell carcinoma of the oral tongue using multidimensional plasma protein analysis and interpretable machine learning
Original language description
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.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
30204 - Oncology
Result continuities
Project
<a href="/en/project/EF16_019%2F0000868" target="_blank" >EF16_019/0000868: Molecular, cellular and clinical approach to healthy ageing</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of oral pathology & medicine
ISSN
0904-2512
e-ISSN
1600-0714
Volume of the periodical
52
Issue of the periodical within the volume
7
Country of publishing house
US - UNITED STATES
Number of pages
7
Pages from-to
637-643
UT code for WoS article
001026127400001
EID of the result in the Scopus database
2-s2.0-85164698201