| 18F-PSMA PET/CT影像组学对临床显著前列腺癌的预测及组合模型价值研究 |
| 投稿时间：2024-11-21 修订日期：2024-12-10 点此下载全文 |
| 引用本文：彭岱云,朱仪萌,付静瑜,杨帆,柳江燕.18F-PSMA PET/CT影像组学对临床显著前列腺癌的预测及组合模型价值研究[J].医学研究杂志,2025,54(5):166-173 |
| DOI：
10.11969/j.issn.1673-548X.2025.05.029 |
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| 基金项目:兰州大学第二医院“翠英研究生指导教师”培育计划(CYDSPY202001) |
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| 中文摘要:目的 旨在探讨临床参数联合18F-PSMA PET/CT常规参数与影像组学特征构建的组合模型在早期预测临床显著性前列腺癌(clinically significant prostate cancer,csPCa)中的价值。方法 回顾性分析在兰州大学第二医院或甘肃省人民医院接受18F-PSMA PET/CT检查并有完整病理资料的124例患者。将甘肃省人民医院的96例患者按7∶3随机分为训练集和内部验证集,兰州大学第二医院28例患者为外部验证集。在训练集中,结合皮尔逊相关系数、最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)和10折交叉验证筛选最优临床、PET常规参数和影像组学特征。随后通过逻辑回归算法分别建立了临床PET参数模型、组学模型和三者结合的组合模型。模型性能通过绘制受试者工作特征(receiver operating characteristic, ROC)曲线进行评估,其预测准确性由校准曲线验证,同时结合决策曲线分析(decision curve analysis,DCA)综合评价模型的临床应用价值。结果 筛选出3个临床与PET常规参数,5个影像组学特征分别用于构建临床PET参数模型、组学模型及组合模型。ROC曲线显示3种模型均有良好的预测性能,其中组合模型在训练集及内、外部验证集中均有最高的预测效能(AUC为0.973、0.933、0.813)。校准曲线和DCA曲线显示组合模型在训练集和内、外部验证集中展现出较强的泛化能力和预测一致性。结论 基于临床参数、18F-PSMA PET/CT常规参数及影像组学特征的联合模型,在早期预测csPCa方面具有良好的临床价值。 |
| 中文关键词:临床显著性前列腺癌 18F-PSMA PET/CT 影像组学 逻辑回归模型 |
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| Predicting and Combining Model Value of 18F-PSMA PET/CT Imaging Omics for Clinically Significant Prostate Cancer |
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| Abstract:Objective To investigate the value of a combined model incorporating clinical parameters, conventional metabolic parameters of 18F-PSMA PET/CT, and radiomics features in the early prediction of clinically significant prostate cancer (csPCa). Methods A retrospective analysis was conducted on 124 patients who underwent 18F-PSMA PET/CT and had complete pathological data at the Second Hospital of Lanzhou University or Gansu Provincial People′s Hospital. A total of 96 patients from Gansu Provincial People′s Hospital were randomly divided into a training set and an internal validation set at a 7∶3 ratio, while 28 patients from the Second Hospital of Lanzhou University served as an external validation set. In the training set, clinical parameters and radiomics features were identified through Pearson correlation analysis and optimized using the least absolute shrinkage and selection operator (LASSO) combined with 10-fold cross-validation. Logistic regression was applied to develop separate clinical PET, radiomics, and combined models. Model performance was assessed through receiver operating characteristic (ROC) curve analysis and calibration curves to evaluate predictive accuracy, decision curve analysis (DCA) to assess clinical utility. Results Three clinical and conventional PET parameters and five radiomics features were selected to construct the clinical PET model, radiomics model, and combined model. ROC analysis showed that all three models exhibited good predictive performance, with the combined model achieving the highest performance in the training, internal validation, and external validation sets (AUC were 0.973,0.933, and 0.813, respectively). Calibration curves and DCA indicated that the combined model demonstrated strong generalizability and predictive stability across all datasets. Conclusion The combined model incorporating clinical parameters, conventional metabolic parameters of 18F-PSMA PET/CT, and radiomics features shows good value in the early prediction of csPCa. |
| keywords:Clinically significant prostate cancer 18F-PSMA PET/CT Radiomics Logistic regression model |
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