Machine learning–assisted classification of lung cancer: the role of sarcopenia, inflammatory biomarkers, and PET/CT anatomical-metabolic parameters

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dc.authorid0000-0003-4815-3674
dc.contributor.authorTanyildizi-Kokkulunk, Handan
dc.contributor.authorAlcin, Goksel
dc.contributor.authorCavdar, Iffet
dc.contributor.authorAkyel, Resit
dc.contributor.authorCiftci-Kusbeci, Tuba
dc.contributor.authorCaliskan, Gonul
dc.date.accessioned2025-10-24T06:57:42Z
dc.date.available2025-10-24T06:57:42Z
dc.date.issued2025
dc.departmentMeslek Yüksekokulları, Meslek Yüksekokulu, Fizyoterapi Programı
dc.description.abstractAccurate differentiation between non-cancerous, benign, and malignant lung cancer remains a diagnostic challenge due to overlapping clinical and imaging characteristics. This study proposes a multimodal machine learning (ML) framework integrating positron emission tomography/computed tomography (PET/CT) anatomic-metabolic parameters, sarcopenia markers, and inflammatory biomarkers to enhance classification performance in lung cancer. A retrospective dataset of 222 patients was analyzed, including demographic variables, functional and morphometric sarcopenia indices, hematological inflammation markers, and PET/CT derived parameters such as maximum and mean standardized uptake value (SUVmax, SUVmean), metabolic tumor volume (MTV), total lesion glycolysis (TLG). Five ML algorithms—Logistic Regression, Multi-Layer Perceptron, Support Vector Machine, Extreme Gradient Boosting, and Random Forest—were evaluated using standardized performance metrics. Synthetic Minority Oversampling Technique was applied to balance class distributions. Feature importance analysis was conducted using the optimal model, and classification was repeated using the top 15 features. Among the models, Random Forest demonstrated superior predictive performance with a test accuracy of 96%, precision, recall, and F1-score of 0.96, and an average AUC of 0.99. Feature importance analysis revealed SUVmax, SUVmean, total lesion glycolysis, and skeletal muscle index as leading predictors. A secondary classification using only the top 15 features yielded even higher test accuracy (97%). These findings underscore the potential of integrating metabolic imaging, physical function, and biochemical inflammation markers in a non-invasive ML-based diagnostic pipeline. The proposed framework demonstrates high accuracy and generalizability and may serve as an effective clinical decision support tool in early lung cancer diagnosis and risk stratification.
dc.identifier.citationTanyildizi-Kokkulunk, H., Alcin, G., Cavdar, I., Akyel, R., Yigit, S., Ciftci-Kusbeci, T., & Caliskan, G. (2025). Machine learning–assisted classification of lung cancer: the role of sarcopenia, inflammatory biomarkers, and PET/CT anatomical-metabolic parameters. Physical and Engineering Sciences in Medicine. ‌
dc.identifier.doi10.1007/s13246-025-01650-x
dc.identifier.endpage13
dc.identifier.issn26624729
dc.identifier.pmid41051469
dc.identifier.scopus2-s2.0-105018484883
dc.identifier.scopusqualityQ2
dc.identifier.scopusqualityQ2
dc.identifier.scopusqualityQ2
dc.identifier.scopusqualityQ2
dc.identifier.scopusqualityQ2
dc.identifier.scopusqualityQ3
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/20.500.12941/343
dc.indekslendigikaynakScopus
dc.indekslendigikaynakScopus
dc.indekslendigikaynakScopus
dc.indekslendigikaynakScopus
dc.indekslendigikaynakScopus
dc.indekslendigikaynakScopus
dc.institutionauthorYiğit, Şafak
dc.institutionauthorid0000-0003-4815-3674
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofPhysical and Engineering Sciences in Medicine
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectInflammatory biomarkers
dc.subjectLung cancer classification
dc.subjectMachine learning
dc.subjectPET/CT
dc.subjectRandom forest
dc.subjectSarcopenia
dc.subjectİnflamatuar biyobelirteçler
dc.subjectAkciğer kanseri sınıflandırması
dc.subjectMakine öğrenimi
dc.subjectPET/BT
dc.subjectRastgele orman
dc.subjectSarkopeni
dc.titleMachine learning–assisted classification of lung cancer: the role of sarcopenia, inflammatory biomarkers, and PET/CT anatomical-metabolic parameters
dc.typeArticle

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