Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China.
Int J Hyperthermia. 2023;40(1):2226847. doi: 10.1080/02656736.2023.2226847.
To predict the risk of postoperative reintervention for uterine fibroids using clinical-imaging features and T2WI radiomics before high-intensity focused ultrasound (HIFU) ablation.
Among patients with uterine fibroids treated with HIFU from 2019 to 2021, 180 were selected per the inclusion and exclusion criteria (42 reintervention and 138 non-reintervention). All patients were randomly assigned to either the training ( = 125) or validation ( = 55) cohorts. Multivariate analysis was used to determine independent clinical-imaging features of reintervention risk. The Relief and LASSO algorithm were used to select optimal radiomics features. Random forest was used to construct the clinical-imaging model based on independent clinical-imaging features, the radiomics model based on optimal radiomics features, and the combined model incorporating the above features. An independent test cohort of 45 patients with uterine fibroids tested these models. The integrated discrimination index (IDI) was used to compare the discrimination performance of these models.
Age ( < .001), fibroid volume ( = .001) and fibroid enhancement degree ( = .001) were identified as independent clinical-imaging features. The combined model had AUCs of 0.821 (95% CI: 0.712-0.931) and 0.818 (95% CI: 0.694-0.943) in the validation and independent test cohorts, respectively. The predictive performance of the combined model was 27.8% (independent test cohort, < .001) and 29.5% (independent test cohort, = .001) better than the clinical-imaging and radiomics models, respectively.
The combined model can effectively predict the risk of postoperative reintervention for uterine fibroids before HIFU ablation. It is expected to help clinicians to develop accurate, personalized treatment and management plans. Future studies will need to be prospectively validated.
利用高强度聚焦超声（HIFU）消融术前临床-影像特征和 T2WI 放射组学预测子宫肌瘤术后再次干预的风险。
在 2019 年至 2021 年间接受 HIFU 治疗的子宫肌瘤患者中，根据纳入和排除标准选择 180 例（42 例再次干预和 138 例非再次干预）。所有患者被随机分配到训练队列（n=125）或验证队列（n=55）。多变量分析用于确定再次干预风险的独立临床-影像特征。使用 Relief 和 LASSO 算法选择最佳放射组学特征。随机森林用于构建基于独立临床影像特征、基于最佳放射组学特征的放射组学模型以及结合上述特征的联合模型。另一个独立的 45 例子宫肌瘤患者的测试队列用于测试这些模型。综合判别指数（IDI）用于比较这些模型的判别性能。
年龄（ < .001）、肌瘤体积（ = .001）和肌瘤增强程度（ = .001）被确定为独立的临床影像特征。验证和独立测试队列中，联合模型的 AUC 分别为 0.821（95%CI：0.712-0.931）和 0.818（95%CI：0.694-0.943）。联合模型在独立测试队列中的预测性能比临床影像和放射组学模型分别提高了 27.8%（独立测试队列， < .001）和 29.5%（独立测试队列， = .001）。
联合模型可有效预测 HIFU 消融术前子宫肌瘤术后再次干预的风险。预计它将帮助临床医生制定准确的、个性化的治疗和管理计划。未来的研究需要进行前瞻性验证。
