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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- CA 15-3:
Carbohydrate antigen 15-3
Carbohydrate antigen 19-9
- CA 125:
Carbohydrate antigen 125
- CYFRA 21-1:
Cytocheratin fragment 21-1
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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.
Conflict of interest
The authors declare that they have no conflict of interest.
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
- Machine learning
- Genetic programming
- Genetic algorithm
- Evolutionary algorithm
- Pleural effusion
- Biochemical tumor marker
- Video-assisted thoracic surgery