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