Prognostic value of a combined model integrating clinical and PET radiomics parameters in metastatic melanoma: A dual-center retrospective study.

Journal: Annals of nuclear medicine
Published Date: Nov 22, 2025

Abstract

OBJECTIVES: To develop and evaluate the predictive efficacy of a combined model incorporating clinical parameters and PET-based radiomics signature (R-signature) for prognosis in patients with metastatic melanoma. METHODS: A total of 187 metastatic melanoma patients from two centers were included, with the datasets from each center divided into training and validation cohorts, respectively. The optimal machine learning algorithm selected from the six candidates was used to construct the model. Five-fold cross-validation was performed on the training cohort for internal validation, while the external validation cohort was used for independent validation. The area under the receiver operating characteristic curve (AUC) was used to compare the model accuracies. Furthermore, multiparametric models were designed based on results from the Cox proportional hazards model and assessed through calibration curves, concordance index (C-index), and decision curve analysis (DCA) in the training and validation cohorts. RESULTS: The cutoff values for R-signature predicting progression-free survival (PFS) and overall survival (OS) were 0.47 and 0.59, respectively. The combined model showed robust prognostic performance, with C-indices of 0.92 (95%CI: 0.83-0.98) for PFS and 0.99 (95%CI: 0.97-0.99) for OS in the train cohort. Validation cohort confirmed these findings, with C-indices of 0.95 (95%CI: 0.86-0.99) for PFS and 0.97 (95%CI: 0.92-1.00) for OS. Calibration and decision curve analyses supported the clinical value of the combined model. CONCLUSION: PET-based R-signature offers valuable prognostic insight in metastatic melanoma, with the combined model further improving risk stratification. Moreover, the multiparametric models developed in this study exhibited promising potential in accurately stratifying patients based on their survival risk.

Authors

  • Ruihe Lai
    Department of Nuclear Medicine, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, China.
  • Zekun Jiang
    College of Computer Science, Sichuan University, Chengdu, Sichuan, China.
  • Dandan Sheng
    Department of Nuclear Medicine, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China.
  • Yuzhi Geng
    Department of Nuclear Medicine, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China.
  • Qianqian Tan
    Department of Nuclear Medicine, Nanjing Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, China.
  • Chongyang Ding
    Department of Nuclear Medicine, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China.
  • Yue Teng
    Haidian Maternal & Child Health Hospital Nutrition Clinic, Beijing 100080, China.
  • Zhengyang Zhou
    Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou 215123, China; School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China.

Keywords

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