Prognostic Significance of Baseline 18F-FDG PET/CT Parameters in Combination with an Artificial Intelligence-Based Pleural Effusion Segmentation Model for Malignant Pleural Effusion

Yaqi Cao; Fan Hu; Shuqian Feng; Hui Xia; Xueyun Tan; Feng Pan; Minglei Li; Sufei Wang; Lian Yang; Yanling Ma; Daquan Meng; Ze Huang; Zhengyan Wang; Mengjia Yi; Qingjia Bao; Xiaoli Lan; Yang Jin

doi:10.1093/bjr/tqag116

Prognostic Significance of Baseline 18F-FDG PET/CT Parameters in Combination with an Artificial Intelligence-Based Pleural Effusion Segmentation Model for Malignant Pleural Effusion

Br J Radiol. 2026 May 20:tqag116. doi: 10.1093/bjr/tqag116. Online ahead of print.

Authors

Yaqi Cao^{1

2

3}, Fan Hu^{4

5}, Shuqian Feng^{4

5}, Hui Xia^{1

2

3}, Xueyun Tan^{1

2

3}, Feng Pan⁶, Minglei Li^{1

2

3}, Sufei Wang¹, Lian Yang⁶, Yanling Ma¹, Daquan Meng¹, Ze Huang⁷, Zhengyan Wang⁷, Mengjia Yi¹, Qingjia Bao⁸, Xiaoli Lan^{4

5}, Yang Jin^{1

2

3}

Affiliations

¹ Department of Respiratory and Critical Care Medicine, Hubei Province Clinical Research Center for Major Respiratory Diseases, Key Laboratory of Respiratory Diseases of National Health Commission, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430022, China.
² Hubei Province Engineering Research Center for Tumor-Targeted Biochemotherapy, MOE Key Laboratory of Biological Targeted Therapy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430022, China.
³ Hubei Province Key Laboratory of Biological Targeted Therapy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430022, China.
⁴ Department of Pathogen Biology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430022, China.
⁵ Hubei Key Laboratory of Molecular Imaging, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430022, China.
⁶ Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430022, China.
⁷ Department of Respiratory Medicine, Suizhou Hospital, Hubei University of Medicine, Suizhou, Hubei, 441300, China.
⁸ State Key Laboratory of Magnetic Resonance Spectroscopy and Imaging, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Wuhan, 430071, China.

PMID: 42162960
DOI: 10.1093/bjr/tqag116

Abstract

Objectives: We aimed to use an artificial intelligence (AI)-based pleural effusion segmentation model on baseline 18F-FDG positron emission tomography/computed tomography (PET/CT) images to investigate the prognostic value of PET/CT-derived parameters for overall survival (OS) among lung cancer patients with malignant pleural effusion (MPE).

Methods: A total of 146 patients with MPEs were recruited. An integrated AI segmentation model combining 3D spatially weighted and 2D classical U-Net segmented pleural effusion for 18F-FDG PET/CT parameter extraction. Cox regression analyses revealed independent 12-month survival predictors. The area under the receiver operating characteristic curve (AUC) and DeLong's test were used to evaluate the discriminant power of the predictors and the LENT score. Bootstrap resampling was employed for internal validation.

Results: The patients comprised 81 males (55.5%) and had a mean age of 61.7 (SD = 11.5) years. The key survival predictors included maximum standardised uptake value (SUVmax), metabolic tumour volume (MTV), and total lesion glycolysis (TLG). The combined PET/CT parameters demonstrated a statistically significant advantage over that of the LENT score for 12-month survival prediction (AUC: 0.849, 95% confidence interval (CI) 0.795-0.903 vs. AUC: 0.732, 95%CI 0.660-0.796). The internal bootstrap validation had an AUC of 0.840 (95% CI: 0.671-0.922) and demonstrated a well-fitting calibration curve.

Conclusions: The baseline 18F-FDG-PET/CT parameters extracted using the deep learning model performed excellent in predicting MPE survival and may complement existing MPE survival models and guide clinical stratified treatment.

Advances in knowledge: AI-integrated 18F-FDG-PET/CT radiomics improved prognostic assessment of MPE, facilitating personalised interventions stratified by survival expectations.

Keywords: 18F-FDG PET/CT; artificial intelligence; machine learning; malignant pleural effusion; prognostic factor.

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