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2000
Volume 21, Issue 3
  • ISSN: 1573-398X
  • E-ISSN: 1875-6387

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.

Methods

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 True Negative, True Positive False Negative, and True Negative False Negative. The logistic regression model demonstrated notable effectiveness, achieving an Area Under the Curve of 0.84 ± 0.02 in the True Positive True Negative analysis and 0.81 ± 0.05 in the True Positive False Negative comparison. In the True Negative 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 (-values of 0.0390 and 0.0249, respectively), and in the liquid pH level between the True Positive and False Negative groups (-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.

© 2025 Bentham Science Publishers
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2024-09-16
2025-07-24

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Machine Learning to Differentiate Malignant and Non-malignant Pleural Effusion Findings
Curr. Respir. Med. Rev. 21, 227 (2025); https://doi.org/10.2174/011573398X306397240903054052
/content/journals/crmr/10.2174/011573398X306397240903054052
/content/journals/crmr/10.2174/011573398X306397240903054052
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  • Article Type:     Research Article
Keyword(s): computed tomography, pleural metastasis; lactate dehydrogenase; machine learning; Pleural effusion; texture analysis

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