Loughborough William W, Rockall Andrea G, Gagliardi Tanja T, Satchwell Laura, Greenlay Emily, Osborne Piers, Bharwani Nishat, Ind Thomas, Attygalle Ayoma, Lother Dione, Hopkinson Georgina, Jones Robin, Benson Charlotte, Miah Aisha, Sohaib Aslam, Messiou Christina
The Royal Marsden Hospital NHS foundation trust, London UK.
The Royal United Hospitals Bath NHS foundation trust, Bath UK.
Rare Tumors. 2025 Apr 11;17:20363613251327080. doi: 10.1177/20363613251327080. eCollection 2025.
The aim of this study was to construct a diagnostic model from MRI features to distinguish complex leiomyomas/degenerating fibroids (DF) from leiomyosarcoma (LMS). A retrospective case-controlled study was performed comparing MRI features of patients with pathologically proven DF or LMS. MRI in 42 patients with DF (control group) and 46 with LMS (study group) was used to generate a diagnostic model. Imaging features reported in the literature to distinguish these two entities were scored for each uterine mass by two radiologists unaware of the histological diagnosis. Inter observer variation and univariate analysis was undertaken. Imaging characteristics identified on univariate analysis were used to build a multi-variable diagnostic model and sensitivity and specificity of this model calculated. Taking the features identified on the univariate analysis, the final diagnostic model was based on AP length ( = .053), intermediate T2 signal (IT2), volume ( = .002), and nodular border ( = .001). When the model was implemented back into the training dataset it demonstrated a sensitivity of 70.7%, and a specificity of 76.2%. The sensitivity and specificity of radiologist suspicion score was 74.7% and 70.4%. In addition, morphological features showed only poor or moderate inter observer agreement at best. Morphological MRI imaging features alone are not sufficient to obviate the need for pathological confirmation prior to non-surgical management of complex uterine mass lesions. IRAS project ID 251778 Protocol number: CCR 4992 REC reference 19/YH/0134 Date of HRA approval: 29.4.19.
本研究的目的是构建一种基于MRI特征的诊断模型,以区分复杂平滑肌瘤/变性肌瘤(DF)与平滑肌肉瘤(LMS)。进行了一项回顾性病例对照研究,比较经病理证实的DF或LMS患者的MRI特征。使用42例DF患者(对照组)和46例LMS患者(研究组)的MRI数据生成诊断模型。两名对组织学诊断不知情的放射科医生对文献中报道的用于区分这两种病变的影像特征,针对每个子宫肿块进行评分。进行了观察者间变异分析和单变量分析。将单变量分析中确定的影像特征用于构建多变量诊断模型,并计算该模型的敏感性和特异性。根据单变量分析确定的特征,最终诊断模型基于前后径长度(=0.053)、T2加权像中等信号(IT2)、体积(=0.002)和结节状边界(=0.001)。当将该模型应用于训练数据集时,其敏感性为70.7%,特异性为76.2%。放射科医生怀疑评分的敏感性和特异性分别为74.7%和70.4%。此外,形态学特征显示观察者间一致性充其量仅为差或中等。仅靠MRI形态学影像特征不足以在对复杂子宫肿块病变进行非手术治疗前避免病理证实的必要性。IRAS项目ID 251778方案编号:CCR 4992 REC参考号19/YH/0134 HRA批准日期:2019年4月29日