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).

Elia, S., D’Angelo, G., Palmieri, F., Sorge, R., Massoud, R., Cortese, C., et al. (2019). A machine learning evolutionary algorithm-based formula to assess tumor markers and predict lung cancer in cytologically negative pleural effusions. SOFT COMPUTING [10.1007/s00500-019-04344-1].

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

Elia, Stefano
Writing – Original Draft Preparation
;
Sorge, Roberto
Data Curation
;
Massoud, Renato
Methodology
;
Cortese, Claudio
Membro del Collaboration Group
;
2019-09-14

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).
14-set-2019
Online ahead of print
Rilevanza internazionale
Articolo
Esperti anonimi
Settore MED/21 - CHIRURGIA TORACICA
English
Con Impact Factor ISI
Machine learning; Genetic programming; Genetic algorithm; Evolutionary algorithm; Pleural effusion; Biochemical tumor marker; Thoracentesis; Thoracoscopy; Video-assisted thoracic surgery
Elia, S., D’Angelo, G., Palmieri, F., Sorge, R., Massoud, R., Cortese, C., et al. (2019). A machine learning evolutionary algorithm-based formula to assess tumor markers and predict lung cancer in cytologically negative pleural effusions. SOFT COMPUTING [10.1007/s00500-019-04344-1].
Elia, S; D’Angelo, G; Palmieri, F; Sorge, R; Massoud, R; Cortese, C; Hardavella, G; De Stefano, A
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/232050
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