Li Qiyuan, Wang Ning, Wang Yanmei, Li Xiaoli, Su Qiushi, Zhang Jing, Zhao Xia, Dai Zhengjun, Wang Yao, Sun Li, Xing Xuxiao, Yang Guangjie, Gao Chuanping, Nie Pei
Department of Radiology, The Affiliated Hospital of Qingdao University, No. 16, Jiangsu Road, Qingdao, 266003, Shandong, China.
Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
Insights Imaging. 2024 Jan 17;15(1):9. doi: 10.1186/s13244-023-01582-8.
To evaluate the efficacy of the CT-based intratumoral, peritumoral, and combined radiomics signatures in predicting progression-free survival (PFS) of patients with chondrosarcoma (CS).
In this study, patients diagnosed with CS between January 2009 and January 2022 were retrospectively screened, and 214 patients with CS from two centers were respectively enrolled into the training cohorts (institution 1, n = 113) and test cohorts (institution 2, n = 101). The intratumoral and peritumoral radiomics features were extracted from CT images. The intratumoral, peritumoral, and combined radiomics signatures were constructed respectively, and their radiomics scores (Rad-score) were calculated. The performance of intratumoral, peritumoral, and combined radiomics signatures in PFS prediction in patients with CS was evaluated by C-index, time-dependent area under the receiver operating characteristics curve (time-AUC), and time-dependent C-index (time C-index).
Eleven, 7, and 16 features were used to construct the intratumoral, peritumoral, and combined radiomics signatures, respectively. The combined radiomics signature showed the best prediction ability in the training cohort (C-index, 0.835; 95%; confidence interval [CI], 0.764-0.905) and the test cohort (C-index, 0.800; 95% CI, 0.681-0.920). Time-AUC and time C-index showed that the combined signature outperformed the intratumoral and peritumoral radiomics signatures in the prediction of PFS.
The CT-based combined signature incorporating intratumoral and peritumoral radiomics features can predict PFS in patients with CS, which might assist clinicians in selecting individualized surveillance and treatment plans for CS patients.
Develop and validate CT-based intratumoral, peritumoral, and combined radiomics signatures to evaluate the efficacy in predicting prognosis of patients with CS.
• Reliable prognostic models for preoperative chondrosarcoma are lacking. • Combined radiomics signature incorporating intratumoral and peritumoral features can predict progression-free survival in patients with chondrosarcoma. • Combined radiomics signature may facilitate individualized stratification and management of patients with chondrosarcoma.
评估基于CT的肿瘤内、肿瘤周围及联合放射组学特征在预测软骨肉瘤（CS）患者无进展生存期（PFS）方面的疗效。
本研究回顾性筛选了2009年1月至2022年1月期间诊断为CS的患者，来自两个中心的214例CS患者分别纳入训练队列（机构1，n = 113）和测试队列（机构2，n = 101）。从CT图像中提取肿瘤内和肿瘤周围的放射组学特征。分别构建肿瘤内、肿瘤周围及联合放射组学特征，并计算其放射组学评分（Rad-score）。通过C指数、受试者操作特征曲线下的时间依赖性面积（time-AUC）和时间依赖性C指数（time C-index）评估肿瘤内、肿瘤周围及联合放射组学特征在CS患者PFS预测中的性能。
分别使用11个、7个和16个特征构建肿瘤内、肿瘤周围及联合放射组学特征。联合放射组学特征在训练队列（C指数，0.835；95%；置信区间[CI]，0.764 - 0.905）和测试队列（C指数，0.800；95% CI，0.681 - 0.920）中显示出最佳的预测能力。Time-AUC和time C指数表明，联合特征在PFS预测方面优于肿瘤内和肿瘤周围放射组学特征。
基于CT的结合肿瘤内和肿瘤周围放射组学特征的联合特征可预测CS患者的PFS，这可能有助于临床医生为CS患者选择个体化的监测和治疗方案。
开发并验证基于CT的肿瘤内、肿瘤周围及联合放射组学特征，以评估其在预测CS患者预后方面的疗效。
• 缺乏可靠的术前软骨肉瘤预后模型。• 结合肿瘤内和肿瘤周围特征的联合放射组学特征可预测软骨肉瘤患者的无进展生存期。• 联合放射组学特征可能有助于软骨肉瘤患者的个体化分层和管理。
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