Integrative Stacking Machine Learning Model for Small Cell Lung Cancer Prediction Using Metabolomics Profiling - Published in: Cancers - Article 4225

Sep 17, 2026

Small cell lung cancer (SCLC) is highly aggressive with rapid progression and poor survival, yet early diagnosis remains hindered by invasive, limited techniques. A stacking-based ensemble machine learning framework is introduced using metabolomics profiling data to classify SCLC, non-small cell lung cancer (NSCLC), and healthy controls. The dataset includes 191 SCLC, 173 NSCLC, and 97 controls, with feature selection highlighting positive ions. The model achieves 85.03% accuracy for multi-class (92.47% AUC) and 88.19% accuracy for SCLC vs NSCLC (92.65% AUC), while SHAP identifies key metabolites including benzoic acid, DL-lactate, and L-arginine.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages22

Reading Time55 min

AccessFree to download

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Aalborg Universitet Integrative Stacking Machine Learning Model for Small Cell Lung Cancer Prediction Using Metabolomics Profiling Sumon, Md. Shaheenur Islam; Malluhi, Marwan; Anan, Noushin; AbuHaweeleh, Mohannad Natheef; Krzyslak, Hubert; Vranic, Semir; Chowdhury, Muhammad E. H. ; Pedersen, Shona Published in: Cancers DOI (link to publication from Publisher): 10.3390/cancers16244225 Creative Commons License CC BY 4.0 Publication date: 2024 Document Version Publisher's PDF, also known as Version of record Link to publication from Aalborg University Citation for published version (APA): Sumon, M. S. I. , Malluhi, M. , Anan, N. , AbuHaweeleh, M. N. , Krzyslak, H. , Vranic, S. , Chowdhury, M. E. H. , & Pedersen, S. (2024) . Integrative Stacking Machine Learning Model for Small Cell Lung Cancer Prediction Using Metabolomics Profiling. Cancers, 16(24), Article 4225. [https://doi.org/10.3390/cancers16244225](https://doi.org/10.3390/cancers16244225) General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. -Users may download and print one copy of any publication from the public portal for the purpose of private study or research. -You may not further distribute the material or use it for any profit-making activity or commercial gain -You may freely distribute the URL identifying the publication in the public portal Take down policy If you believe that this document breaches copyright please contact [us at vbn@aub.aau.dk](us at vbn@aub.aau.dk) providing details, and we will remove access to the work immediately and investigate your claim. Article Integrative Stacking Machine Learning Model for Small Cell Lung Cancer Prediction Using Metabolomics Profiling Md. Shaheenur Islam Sumon 1,†, Marwan Malluhi 2,†, Noushin Anan 1, Mohannad Natheef AbuHaweeleh 2, Hubert Krzyslak 3, Semir Vranic 2, Muhammad E. H. Chowdhury 1, * and Shona Pedersen 2, * Citation: Sumon, M.S.I.; Malluhi, M.; Anan, N.; AbuHaweeleh, M.N.; Krzyslak, H.; Vranic, S.; Chowdhury, M.E.H.; Pedersen, S. Integrative Stacking Machine Learning Model for Small Cell Lung Cancer Prediction Using Metabolomics Profiling. Cancers 2024, 16, 4225. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/cancers16244225](10.3390/cancers16244225) Academic Editor: Francesco Petrella Received: 2 November 2024 Revised: 14 December 2024 Accepted: 16 December 2024 Published: 18 December 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . 1 Department of Electrical Engineering, Qatar University, Doha 2713, Qatar; [sumon@qu.edu.qa](sumon@qu.edu.qa) (M.S.I.S.); [noushinas71@gmail.com](noushinas71@gmail.com) (N.A.) 2 College of Medicine, QU Health, Qatar University, Doha 2713, Qatar; [mm2308026@qu.edu.qa](mm2308026@qu.edu.qa) (M.M.); [ma1908120@qu.edu.qa](ma1908120@qu.edu.qa) (M.N.A.); [svranic@qu.edu.qa](svranic@qu.edu.qa) (S.V.) 3 Department of Clinical Biochemistry, Aalborg University Hospital, 9000 Aalborg, Denmark; [h.krzyslak@rn.dk](h.krzyslak@rn.dk) * Correspondence: [spedersen@qu.edu.qa](spedersen@qu.edu.qa) (S.P.); [mchowdhury@qu.edu.qa](mchowdhury@qu.edu.qa) (M.E.H.C.)† These authors contributed equally to this work. Simple Summary: This study investigates lung cancer detection by combining metabolomics and advanced machine learning to identify small cell lung cancer (SCLC) with high accuracy. We analyzed 461 serum samples from publicly available data to create a stacking-b

FAQs

What problem does the study target?

The study targets early and accurate detection of small cell lung cancer (SCLC), which is difficult due to invasive and limited diagnostic techniques.

How is the machine learning model built?

It uses a stacking-based ensemble machine learning approach with metabolomics data, combined with feature selection to identify significant metabolites.

Which metrics and biomarkers are highlighted in the results?

For multi-class classification, the model reaches 85.03% accuracy and 92.47 AUC; for SCLC vs NSCLC, 88.19% accuracy and 92.65 AUC. SHAP analysis points to benzoic acid, DL-lactate, and L-arginine as key predictors.

Sep 17, 2026

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