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Development and validation of [18 F]-PSMA-1007 PET-based radiomics model to predict biochemical recurrence-free survival following radical prostatectomy

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A Correction to this article was published on 24 May 2024

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Abstract

Purpose

Biochemical recurrence (BCR) following radical prostatectomy (RP) is a significant concern for patients with prostate cancer. Reliable prediction models are needed to identify patients at risk for BCR and facilitate appropriate management. This study aimed to develop and validate a clinical-radiomics model based on preoperative [18 F]PSMA-1007 PET for predicting BCR-free survival (BRFS) in patients who underwent RP for prostate cancer.

Materials and methods

A total of 236 patients with histologically confirmed prostate cancer who underwent RP were retrospectively analyzed. All patients had a preoperative [18 F]PSMA-1007 PET/CT scan. Radiomics features were extracted from the primary tumor region on PET images. A radiomics signature was developed using the least absolute shrinkage and selection operator (LASSO) Cox regression model. The performance of the radiomics signature in predicting BRFS was assessed using Harrell’s concordance index (C-index). The clinical-radiomics nomogram was constructed using the radiomics signature and clinical features. The model was externally validated in an independent cohort of 98 patients.

Results

The radiomics signature comprised three features and demonstrated a C-index of 0.76 (95% CI: 0.60–0.91) in the training cohort and 0.71 (95% CI: 0.63–0.79) in the validation cohort. The radiomics signature remained an independent predictor of BRFS in multivariable analysis (HR: 2.48, 95% CI: 1.47–4.17, p < 0.001). The clinical-radiomics nomogram significantly improved the prediction performance (C-index: 0.81, 95% CI: 0.66–0.95, p = 0.007) in the training cohort and (C-index: 0.78 95% CI: 0.63–0.89, p < 0.001) in the validation cohort.

Conclusion

We developed and validated a novel [18 F]PSMA-1007 PET-based clinical-radiomics model that can predict BRFS following RP in prostate cancer patients. This model may be useful in identifying patients with a higher risk of BCR, thus enabling personalized risk stratification and tailored management strategies.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Change history

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The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Nuclear Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, 310003, People’s Republic of China

    Tiancheng Li, Mimi Xu, Shuye Yang, Guolin Wang, Yinuo Liu, Kaifeng Liu, Kui Zhao & Xinhui Su

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  1. Tiancheng Li
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Contributions

Tiancheng Li: data acquisition, literature research, and manuscript writing. Mimi Xu, Yinuo Liu, Guolin Wang and Kaifeng Liu: data acquisition and review, Shuye Yang, Kui Zhao and Xinhui Su: study design and theoretical support. Tiancheng Li and Xinhui Su: design of the research program, review and revision of the manuscript. All the authors agreed on the content of the final manuscript.

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Correspondence to Xinhui Su.

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The study was approved by the Ethics Committee of the First Affiliated Hospital, College of Medicine, Zhejiang University.

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The original online version of this article was revised: The authors regret that the version of Figure 7 that appears in the published original article is incorrect. Both the correct and incorrect Figure 7 are provided in the erratum article.

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Li, T., Xu, M., Yang, S. et al. Development and validation of [18 F]-PSMA-1007 PET-based radiomics model to predict biochemical recurrence-free survival following radical prostatectomy. Eur J Nucl Med Mol Imaging (2024). https://doi.org/10.1007/s00259-024-06734-6

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