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A machine learning evolutionary algorithm-based formula to assess tumor markers and predict lung cancer in cytologically negative pleural effusions

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Abstract

Malignant pleural effusion is diagnostically challenging in presence of negative cytology. The assessment of tumor markers in serum has become a standard tool in cancer diagnosis, while pleural fluid sampling has not met universal consensus. The evaluation of a panel of markers both in serum and pleural fluid may be crucial to improve the diagnostic accuracy. Using a machine learning-based approach, we provide a mathematical formula capable to express the complex relation existing among the expressed markers in serum and pleural effusion and the presence of lung cancer. The formula indicates CEA and CYFRA21-1 in pleural fluid as the best diagnostic markers, with 97% accuracy, 98% sensitivity, 95% specificity, 96% area under curve, 98% positive predictive value, and 92% MCC (Matthews correlation coefficient).

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Abbreviations

CA 15-3:

Carbohydrate antigen 15-3

CA19-9:

Carbohydrate antigen 19-9

CA 125:

Carbohydrate antigen 125

CYFRA 21-1:

Cytocheratin fragment 21-1

CEA:

Carcinoembryonic antigen

NSE:

Neuron-specific enolase

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Funding

This study was partially funded by Italian Ministry of Education, University and Research as a National Interest Research Project (PRIN) No. 20083YAR35 granted to the University of Rome Tor Vergata.

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Authors and Affiliations

  1. Department of Surgical Sciences, Thoracic Surgery Unit, Tor Vergata University, Rome, Italy

    Stefano Elia

  2. Department of Computer Science, University of Salerno, Fisciano, Italy

    Gianni D’Angelo & Francesco Palmieri

  3. Department of System Medicine, Tor Vergata University, Rome, Italy

    Roberto Sorge & Alessandro De Stefano

  4. Department of Experimental Medicine, Tor Vergata University, Rome, Italy

    Renato Massoud & Claudio Cortese

  5. Department of Respiratory Medicine and Allergy, Medical School, King’s College London, London, UK

    Georgia Hardavella

Authors
  1. Stefano Elia
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  2. Gianni D’Angelo
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  4. Roberto Sorge
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  5. Renato Massoud
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  6. Claudio Cortese
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  7. Georgia Hardavella
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  8. Alessandro De Stefano
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Contributions

GD, AD and SE conceived the study and wrote the paper. RM, RS and CC provided data of tumor marker concentrations. AD performed the tumor markers assay. RS and SE performed the statistical analysis, and the feature selection. GD conceived and implemented the classifier based on the genetic programming. Moreover, GD carried out all the genetic programming-based experiments, and conceived the final formula. GD, FP, AD and SE contributed in the interpretation of the meaning of results, and in writing the Discussion and Conclusion sections. GH and FP revised the final draft of manuscript before submission.

Corresponding author

Correspondence to Stefano Elia.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Furthermore, the study design was carried out according to the protocol “Tumor marker in pleural effusion and biopsies” approved by the ethical committee of Fondazione PTV Policlinico Tor Vergata, Rome, Italy, on November 6, 2018 (authorization Nr.171/18), and all patients gave their informed consent.

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Communicated by V. Loia.

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Elia, S., D’Angelo, G., Palmieri, F. et al. A machine learning evolutionary algorithm-based formula to assess tumor markers and predict lung cancer in cytologically negative pleural effusions. Soft Comput 24, 7281–7293 (2020). https://doi.org/10.1007/s00500-019-04344-1

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