Lv Xinna, Li Ye, Ding Chenyu, Qin Lixin, Xu Xiaoyue, Zheng Ziwei, Hou Dailun
Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, China.
Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Eur Radiol Exp. 2025 Apr 26;9(1):43. doi: 10.1186/s41747-025-00581-2.
Early identification of treatment failure can effectively improve the success rate of antituberculosis treatment. This study aimed to construct a predictive model using radiomics based on cavity and cavity periphery to monitor the early treatment efficacy in multidrug-resistant tuberculosis (MDR-TB).
We retrospectively collected data on 350 MDR-TB patients who underwent pretreatment chest computed tomography (CT) and received longer regimens from two hospitals. They were subdivided into training (252 patients from hospital 1) and testing (98 patients from hospital 2) cohorts. According to at least two consecutive sputum culture results within the early sixth months of treatment, patients were divided into high-risk and low-risk groups. Radiomics models were established based on cavity and periphery with a range of 2, 4, 6, 8, and 10 mm. A combined model fused radiomics features of cavity with the best-performing peripheral regions. The performance of these models was evaluated by the receiver operating characteristic area under the curve (AUC) and clinical decision curve analysis.
The cavity model achieved AUCs of 0.858 and 0.809 in the training and testing cohort, respectively. The radiomics model based on 4 mm peripheral region showed superior performance compared to other surrounding areas with AUCs of 0.884 and 0.869 in the two cohorts. The AUCs of the combined model were 0.936 and 0.885 in the two cohorts.
CT radiomics analysis integrating cavity and cavity periphery provided value in identifying MDR-TB patients at high risk of treatment failure. The optimal periphery extent was 4 mm.
The cavity periphery also contains therapy-related information. Radiomics model based on cavity and 4 mm periphery is an effective adjunct to monitor early treatment efficacy for MDR-TB patients that can guide clinical decision.
A combined CT radiomics model integrating cavity with periphery can effectively monitor treatment response. A periphery of 4 mm showed superior performance compared to other peripheral smaller or greater extent. This study provided a surrogate for identifying the high risk of treatment failure in multidrug-resistant tuberculosis patients.
早期识别治疗失败可有效提高抗结核治疗的成功率。本研究旨在构建基于空洞及空洞周边的放射组学预测模型，以监测耐多药结核病（MDR-TB）的早期治疗效果。
我们回顾性收集了350例接受治疗前胸部计算机断层扫描（CT）并在两家医院接受较长疗程治疗的MDR-TB患者的数据。他们被分为训练队列（来自医院1的252例患者）和测试队列（来自医院2的98例患者）。根据治疗前六个月内至少两次连续的痰培养结果，将患者分为高风险组和低风险组。基于范围为2、4、6、8和10毫米的空洞及周边建立放射组学模型。一个联合模型融合了空洞的放射组学特征与表现最佳的周边区域的特征。通过曲线下面积（AUC）的受试者操作特征曲线和临床决策曲线分析来评估这些模型的性能。
空洞模型在训练队列和测试队列中的AUC分别为0.858和0.809。基于4毫米周边区域的放射组学模型与其他周边区域相比表现更优，在两个队列中的AUC分别为0.884和0.869。联合模型在两个队列中的AUC分别为0.936和0.885。
整合空洞及空洞周边的CT放射组学分析在识别有治疗失败高风险的MDR-TB患者方面具有价值。最佳周边范围为4毫米。
空洞周边也包含与治疗相关的信息。基于空洞和4毫米周边的放射组学模型是监测MDR-TB患者早期治疗效果的有效辅助手段，可指导临床决策。
整合空洞与周边的联合CT放射组学模型可有效监测治疗反应。与其他更小或更大范围的周边区域相比，4毫米的周边表现更优。本研究为识别耐多药结核病患者治疗失败的高风险提供了一种替代方法。
