Malignant pleural mesothelioma classification and survival prediction with CT imaging using ResNet.

Journal: European radiology
Published Date: Oct 30, 2025

Abstract

OBJECTIVES: This study aims to achieve accurate differentiation of malignant pleural mesothelioma (MPM) from metastatic pleural disease (MPD) and to predict the overall survival of MPM. MATERIALS AND METHODS: This IRB-approved retrospective study included 385 subjects in total (85 patients with malignant mesothelioma and 290 with MPD secondary to lung adenocarcinoma). A ResNet-3D-18 model was trained on annotated pretreatment CT scans to distinguish MPM from MPD. Using chronological segregation, the training cohort included 70 histologically confirmed mesothelioma and 258 MPD cases, with an independent test cohort of 15 MPM and 32 MPD cases for validation. A multivariate logistic regression model served as the clinical benchmark for comparison. Deep learning features extracted from the trained ResNet model were then assessed for their prognostic utility in MPM patients using a random forest classifier. Model performance was evaluated at both lesion- and patient-levels, with metrics including the area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. RESULTS: The ResNet-3D-18 model demonstrated excellent discriminative performance in differentiating MPM from MPD, with mean AUCs of 0.972 (95% CI 0.947-0.990) and 0.840 (95% CI 0.757-0.929) in the training and independent test cohorts. Compared to the clinical model, the deep learning approach showed higher sensitivity (0.867 vs. 0.533) in the independent test dataset. For overall survival prediction in MPM patients, the random forest classifier achieved an AUC of 0.829 (95% CI 0.663-0.943) in 5-fold cross-validation. CONCLUSIONS: ResNet-3D-18 classification model has excellent abilities in differentiating MPM from MPD, and morphological distinctions between MPM and MPD also contain prognostic information. KEY POINTS: Question The rising global incidence of malignant pleural mesothelioma contrasts with persistent diagnostic challenges. Findings Deep learning-derived discriminative features simultaneously contain prognostic information. Clinical relevance This study bridges the gap between radiological findings and clinical decision-making in MPM, offering a reproducible tool for early diagnosis and personalized prognosis prediction based on CT imaging alone.

Authors

  • Meng Zhou
    College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, People's Republic of China. [email protected].
  • Minghua Li
    Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands.
  • Qian Cao
    Department of Gastroenterology, Sir Run Run Shaw Hospital of Zhejiang University, Hangzhou, China.
  • Zhen Zhang
    School of Pharmacy, Jining Medical University, Rizhao, Shandong, China.
  • Leonard Wee
    Maastricht University Medical Centre, Netherlands.
  • Andre Dekker
    Department of Radiation Oncology (MAASTRO Clinic), Dr. Tanslaan 12, Maastricht, The Netherlands.
  • Ji Zhu
    Department of Statistics, University of Michigan, Ann Arbor, Michigan.
  • Haitao Jiang
    University of Science and Technology of China, No.96, JinZhai Road Baohe District,Hefei, Anhui 230026, PR China.
  • Jiapeng Jiang
    Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, 310022, Hangzhou, China, Zhejiang.
  • Xinyu Miao
    Ningbo Key Laboratory of Biomedical Imaging Probe Materials and Technology, Zhejiang International Cooperation Base of Biomedical Materials Technology and Application, Chinese Academy of Sciences (CAS) Key Laboratory of Magnetic Materials and Devices, Ningbo Cixi Institute of Biomedical Engineering, Zhejiang Engineering Research Center for Biomedical Materials, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, PR China.
  • Weimin Mao
    The Cancer Research Institute, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, China.
  • Meng Yan
    School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
  • Hongyang Lu
    Medtronic Technology Center, Cardiac Rhythm Management, Medtronic (Shanghai) Ltd., Shanghai, China.

Keywords

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