Objectives. To introduce a new implementation of radiomics analysis for cartilage and subchondral bone of the knee and to compare the performance of the proposed models to classic T2 relaxation time in distinguishing knees predisposed to posttraumatic osteoarthritis (PTOA) after anterior cruciate ligament reconstruction (ACLR) and healthy controls. Methods. 114 patients following ACLR after at least 2 years and 43 healthy controls were reviewed and allocated to training () and testing () cohorts. Radiomics models are built for cartilage and subchondral bone regions of different compartments: lateral femur (LF), lateral tibia (LT), medial femur (MF), and medial tibia (MT) and combined models of four compartments on T2 mapping images. The model performance of discrimination between patients and controls was illustrated with the receiver operating characteristic curve and compared with a classic T2 value-based model. Results. The T2 value model of cartilage yielded moderate predictive performance in discerning patients and controls, with an AUC of 0.731 (95% confidence interval, 0.556–0.875) in the testing cohort, while the radiomics signature of cartilage and subchondral bone of different compartments demonstrated excellent performance, with AUCs of 0.864–0.979. Furthermore, the combined model reported an even better performance, with AUCs of 0.977 (95% confidence interval, 0.919–1.000) for the cartilage and 0.934 (95% confidence interval, 0.865–0.994) for the subchondral bone in the testing cohort. Conclusion. The radiomics features of the cartilage and subchondral bone may be able to provide powerful tools with more sensitive detection than T2 values in differentiating knees at risk for PTOA after ACLR from healthy knees.

中文翻译：

---

软骨及软骨下骨影像组学特征分析区分健康膝前交叉韧带重建术后易发生外伤后骨关节炎的膝关节

目标。介绍一种新的膝关节软骨和软骨下骨放射组学分析方法，并将所提出的模型与经典 T2 松弛时间的性能进行比较，以区分在前交叉韧带重建 (ACLR) 后易患创伤后骨关节炎 (PTOA) 的膝关节和健康膝关节。控制。方法。至少 2 年后接受 ACLR 的 114 名患者和 43 名健康对照接受审查并分配到培训组（)和测试 ()队列。Radiomics 模型是为不同隔室的软骨和软骨下骨区域构建的：股骨外侧 (LF)、胫骨外侧 (LT)、股骨内侧 (MF) 和胫骨内侧 (MT)，以及 T2 映射图像上四个隔室的组合模型。用受试者工作特征曲线说明了区分患者和对照的模型性能，并与经典的基于 T2 值的模型进行了比较。结果. 软骨的 T2 值模型在有辨别力的患者和对照组中产生了中等的预测性能，在测试队列中的 AUC 为 0.731（95% 置信区间，0.556-0.875），而不同隔室的软骨和软骨下骨的放射组学特征表现出优异的性能，AUC 为 0.864–0.979。此外，组合模型报告了更好的性能，测试队列中软骨的 AUC 为 0.977（95% 置信区间，0.919-1.000），软骨下骨的 AUC 为 0.934（95% 置信区间，0.865-0.994）。结论。软骨和软骨下骨的影像组学特征可能能够提供强大的工具，其检测比 T2 值更敏感，以区分 ACLR 后有 PTOA 风险的膝关节与健康膝关节。