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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1419343

The value of computed tomography-based radiomics for predicting malignant pleural effusions

Provisionally accepted
Zhen-Chuan Xing Zhen-Chuan Xing Hua-Zheng Guo Hua-Zheng Guo *Ziliang Hou Ziliang Hou Hong-Xia Zhang Hong-Xia Zhang *Shuai Zhang Shuai Zhang *
  • Beijing Luhe Hospital, Capital Medical University, Beijing, China

The final, formatted version of the article will be published soon.

    Background: Malignant pleural effusion (MPE) is a common clinical problem that requires cytological and/or histological confirmation obtained by invasive examination to establish a definitive diagnosis. Radiomics is rapidly evolving and can provide a non-invasive tool to identify MPE. Objectives: We aimed to develop a model based on radiomic features extracted from unenhanced chest computed tomography (CT) images and investigate its value in predicting MPE.Method: This retrospective study included patients with pleural effusions between January 2016 and June 2020. All patients underwent a chest CT scanning and medical thoracoscopy after artificial pneumothorax. Cases were divided into a training cohort and a test cohort for modelling and verifying respectively. The Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) were applied to determine the optimal features. We built a radiomics model based on support vector machines (SVM) and evaluated its performance using ROC and calibration curve analysis. Results: Twenty-nine patients with MPE and fifty-two patients with non-MPE were enrolled. A total of 944 radiomic features were quantitatively extracted from each sample and reduced to 14 features for modeling after selection. The AUC of the radiomics model was 0.96 (95% CI: 0.912-0.999) and 0.86 (95% CI: 0.657~1.000) in the training and test cohorts, respectively. The calibration curves for model were in good agreement between predicted and actual data.The radiomics model based on unenhanced chest CT has good performance for predicting MPE and may provide a powerful tool for doctors in clinical decision-making.

    Keywords: Radiomics, Pleural Effusion, X-ray computed tomography, Cancer, machine learning

    Received: 18 Apr 2024; Accepted: 23 Jul 2024.

    Copyright: © 2024 Xing, Guo, Hou, Zhang and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Hua-Zheng Guo, Beijing Luhe Hospital, Capital Medical University, Beijing, China
    Hong-Xia Zhang, Beijing Luhe Hospital, Capital Medical University, Beijing, China
    Shuai Zhang, Beijing Luhe Hospital, Capital Medical University, Beijing, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.