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14-09-2019 | Methodologies and Application

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

Authors: Stefano Elia, Gianni D’Angelo, Francesco Palmieri, Roberto Sorge, Renato Massoud, Claudio Cortese, Georgia Hardavella, Alessandro De Stefano

Published in: Soft Computing | Issue 10/2020

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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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Title: A machine learning evolutionary algorithm-based formula to assess tumor markers and predict lung cancer in cytologically negative pleural effusions
Authors: Stefano Elia
Gianni D’Angelo
Francesco Palmieri
Roberto Sorge
Renato Massoud
Claudio Cortese
Georgia Hardavella
Alessandro De Stefano
Publication date: 14-09-2019
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 10/2020
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-019-04344-1

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