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Artificial intelligence in differentiating malignant from benign pleural effusion: a step beyond conventional methods
  1. Younhyun Jung1,
  2. Eun Young Kim2
  1. 1School of Computing, Gachon University, Seongnam, South Korea
  2. 2Department of Radiology, Gil Medical Center, Gachon University College of Medicine, Incheon, South Korea
  1. Correspondence to Dr Eun Young Kim, Department of Radiology, Gil Medical Center, Gachon University College of Medicine, Incheon, Korea (the Republic of); oneshot0229{at}gmail.com

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Malignant pleural effusion is a common complication of advanced malignancy. It affects 15% of oncology patients, and lung cancer and breast cancer are the most common causes.1 Identifying of the condition is essential because its presence has implications in the staging, management and prognosis of a patient with established malignancy, and it can serve as the source of initial diagnostic material. However, the diagnosis can be made only after the detection of neoplastic cells or tissue in the pleural space. Although cytology or biopsy is the confirmatory study, their positivity rate is low. Therefore, malignant pleural effusion can remain undiagnosed even after the analysis of a single or even multiple cytological or biopsy specimens.

Demographic information (age, sex, symptom, smoking history) and several laboratory markers as well as imaging studies are used in the diagnosis of malignant or benign pleural effusion in clinical setting. A previous study demonstrated that artificial intelligence (AI) framework based on routinely collected clinical data could significantly improve diagnostic performance in distinguishing malignant from benign pleural effusion.2 They revealed the improved …

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Footnotes

  • Contributors YJ drafted the editorial, and EYK reviewed and revised it. Both authors approved the final submitted version.

  • Funding EYK is supported by the Gachon University Gil Medical Centre (Grant number: FRD2021-11).

  • Competing interests None declared.

  • Provenance and peer review Commissioned; externally peer reviewed.

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