Machine Learning to Differentiate Malignant and Non-malignant Pleural Effusion Findings

Abstract

Introduction: This study developed a method using machine learning techniques to differentiate between malignant and non-malignant pleural effusions, analyzing texture parameters in computed tomography scans. Method: The study involved forty-one patients, with their computed tomography examinations classified into three groups: True Positive - patients with both cytopathological analysis and pleural biopsy indicating malignancy; True Negative - patients with negative results in both tests; and False Negative - patients with negative cytopathological analysis but positive pleural biopsy results. Four machine learning methods were applied across three analyses: True Positive versus True Negative, True Positive versus False Negative, and True Negative versus False Negative. The logistic regression model demonstrated notable effectiveness, achieving an Area Under the Curve of 0.84 ± 0.02 in the True Positive versus True Negative analysis and 0.81 ± 0.05 in the True Positive versus False Negative comparison. In the True Negative versus False Negative analysis, the Naive Bayes model achieved an Area Under the Curve of 0.72 ± 0.02. Results: Statistically significant differences were observed in the liquid Lactate Dehydrogenase and protein content between the True Positive and True Negative groups (p-values of 0.0390 and 0.0249, respectively), and in the liquid pH level between the True Positive and False Negative groups (p-value of 0.0254). The use of textural features in combination with machine learning techniques provided a reliable classification for investigating suspected pleural effusion findings. This method represents a potential tool for assisting in clinical diagnosis and decision-making, enhancing the accuracy of pleural effusion assessments. Conclusion: In conclusion, our approach not only improves diagnostic accuracy but also offers a faster and non-invasive alternative, significantly benefiting clinical decision-making and patient care.