Department of Radiology, The Affiliated Hospital of Qingdao University, No. 369, Shanghai Road, 266000, Qingdao, Qingdao, Shandong, China.
GE Healthcare China, Pudong New Town, Shanghai, China.
Cancer Imaging. 2024 Apr 11;24(1):50. doi: 10.1186/s40644-024-00695-7.
The preoperative identification of tumor grade in chondrosarcoma (CS) is crucial for devising effective treatment strategies and predicting outcomes. The study aims to build and validate a CT-based radiomics nomogram (RN) for the preoperative identification of tumor grade in CS, and to evaluate the correlation between the RN-predicted tumor grade and postoperative outcome.
A total of 196 patients (139 in the training cohort and 57 in the external validation cohort) were derived from three different centers. A clinical model, radiomics signature (RS) and RN (which combines significant clinical factors and RS) were developed and validated to assess their ability to distinguish low-grade from high-grade CS with area under the curve (AUC). Additionally, Kaplan-Meier survival analysis was applied to examine the association between RN-predicted tumor grade and recurrence-free survival (RFS) of CS. The predictive accuracy of the RN was evaluated using Harrell's concordance index (C-index), hazard ratio (HR) and AUC.
Size, endosteal scalloping and active periostitis were selected to build the clinical model. Three radiomics features, based on CT images, were selected to construct the RS. Both the RN (AUC, 0.842) and RS (AUC, 0.835) were superior to the clinical model (AUC, 0.776) in the validation set (P = 0.003, 0.040, respectively). A correlation between Nomogram score (Nomo-score, derived from RN) and RFS was observed through Kaplan-Meier survival analysis in the training and test cohorts (log-rank P < 0.050). Patients with high Nomo-score tumors were 2.669 times more likely to suffer recurrence than those with low Nomo-score tumors (HR, 2.669, P < 0.001).
The CT-based RN performed well in predicting both the histologic grade and outcome of CS.
在软骨肉瘤（CS）中术前识别肿瘤分级对于制定有效的治疗策略和预测预后至关重要。本研究旨在建立和验证基于 CT 的放射组学列线图（RN），用于术前识别 CS 中的肿瘤分级，并评估 RN 预测的肿瘤分级与术后结果之间的相关性。
总共从三个不同的中心获得了 196 名患者（训练队列中的 139 名和外部验证队列中的 57 名）。开发和验证了临床模型、放射组学特征（RS）和 RN（将显著的临床因素和 RS 相结合），以评估它们区分低级别和高级别 CS 的能力，曲线下面积（AUC）。此外，Kaplan-Meier 生存分析用于检查 RN 预测的肿瘤分级与 CS 无复发生存（RFS）之间的关联。使用 Harrell 一致性指数（C-index）、风险比（HR）和 AUC 评估 RN 的预测准确性。
选择大小、骨内扇贝和活跃骨膜炎来构建临床模型。基于 CT 图像选择了三个放射组学特征来构建 RS。在验证集中，RN（AUC，0.842）和 RS（AUC，0.835）均优于临床模型（AUC，0.776）（P = 0.003，0.040）。通过训练和测试队列中的 Kaplan-Meier 生存分析观察到 Nomogram 评分（来自 RN）与 RFS 之间的相关性（对数秩 P < 0.050）。Nomo-score 高的肿瘤患者复发的可能性是 Nomo-score 低的肿瘤患者的 2.669 倍（HR，2.669，P < 0.001）。
基于 CT 的 RN 在预测 CS 的组织学分级和结果方面表现良好。
