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瘤内及瘤周CT影像组学特征预测软骨肉瘤预后的研究
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Abstract:
目的：建立基于CT影像组学的软骨肉瘤瘤内、瘤周以及瘤内瘤周联合的影像组学模型，并评价其对患者无进展生存期(PFS)预测的效能。方法：回顾性收集2009年1月至2023年1月期间诊断为软骨肉瘤的患者，并将来自两个中心的212例软骨肉瘤患者分为训练组(n = 101)和验证组(n = 111)。从CT图像中提取瘤内和瘤周的影像组学特征，分别构建瘤内、瘤周和联合的影像组学模型，并计算其影像组学评分(Rad-score)。通过C指数、受者工作特征曲线下时间依赖面积(AUC)和时间依赖C指数来评估瘤内、瘤周和联合影像组学特征在预测软骨肉瘤患者无进展生存期中的作用。结果：分别使用了11、7和16个影像组学特征来构建瘤内、瘤周和联合影像组学模型。联合影像组学模型表现出最好的预测效能。该模型在训练组中C指数为0.788 (95%置信区间0.711~0.861)，验证组中C指数为0.750 (95%置信区间0.623~0.867)。结论：联合瘤内和瘤周的CT影像组学特征可以更好地预测软骨肉瘤患者的无进展生存期，有助于临床医生为软骨肉瘤患者选择个性化的监测和治疗方案。
Objective: To establish radiomic models based on intratumoral, peritumoral, and combined intratumoral-peritumoral features derived from CT imaging for chondrosarcoma, and to evaluate their efficacy in predicting progression-free survival (PFS) in patients. Methods: A retrospective collection of patients diagnosed with chondrosarcoma from January 2009 to January 2023 was conducted. A total of 212 patients with chondrosarcoma from two centers were divided into a training cohort (n = 101) and a validation cohort (n = 111). Radiomic features from intratumoral and peritumoral regions were extracted from CT images to construct separate intratumoral, peritumoral, and combined radiomic models, and to calculate their radiomic scores (Rad-score). The roles of intratumoral, peritumoral, and combined radiomic features in predicting PFS in chondrosarcoma patients were assessed using the C-index, time-dependent area under the receiver operating characteristic curve (AUC), and time-dependent C-index. Results: Eleven, seven, and sixteen radiomic features were used to construct the intratumoral, peritumoral, and combined radiomic models, respectively. The combined radiomic model demonstrated the best predictive performance. The C-index for this model was 0.788 (95% confidence interval 0.711~0.861) in the training cohort and 0.750 (95% confidence interval 0.623~0.867) in the validation cohort. Conclusion: The combined intratumoral and peritumoral CT radiomic features can better predict PFS in patients with chondrosarcoma, aiding clinicians in selecting personalized monitoring and treatment plans for these patients.
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