Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Ovarian Cancer Program, Department of Gynecologic Oncology, Zhongshan Hospital, Shanghai, China.
Acad Radiol. 2023 Jun;30(6):1118-1128. doi: 10.1016/j.acra.2022.06.021. Epub 2022 Jul 29.
To investigate the value of magnetic resonance imaging (MRI) including diffusion-weighted imaging (DWI) findings in predicting mesenchymal transition (MT) high-grade serous ovarian cancer (HGSOC).
Patients with HGSOC were enrolled from May 2017 to December 2020, who underwent pelvic MRI including DWI (b = 0,1000 s/mm) before surgery, and were assigned to the MT HGSOC or non-MT HGSOC group according to histopathology results. Clinical characteristics and MRI features including DWI-based histogram metrics were assessed and compared between the two groups. Univariate and multivariate analyses were performed to identify the significant variables associated with MT HGSOC - these variables were then incorporated into a predictive nomogram, and ROC curve analysis was subsequently carried out to evaluate diagnostic performance.
A total of 81 consecutive patients were recruited for pelvic MRI before surgery, including 37 (45.7%) MT patients and 44 (54.3%) non-MT patients. At univariate analysis, the features significantly related to MT HGSOC were identified as absence of discrete primary ovarian mass, pouch of Douglas implants, ovarian mass size, tumor volume, mean, SD, median, and 95th percentile apparent diffusion coefficient (ADC) values (all p < 0.05). At multivariate analysis, the absence of discrete primary ovarian mass {odds ratio (OR): 46.477; p = 0.025}, mean ADC value ≤ 1.105 (OR: 1.023; p = 0.009), and median ADC value ≤ 1.038 (OR: 0.982; p = 0.034) were found to be independent risk factors associated with MT HGSOC. The combination of all independent criteria yielded the largest AUC of 0.82 with a sensitivity of 83.87% and specificity of 66.67%, superior to any of the single predictor alone (p ≤ 0.012). The predictive C-index nomogram performance of the combination was 0.82.
The combination of absence of discrete primary ovarian mass, lower mean ADC value, and median ADC value may be helpful for preoperatively predicting MT HGSOC.
探讨磁共振成像（MRI）包括弥散加权成像（DWI）在预测卵巢高级别浆液性癌（HGSOC）间质转化（MT）中的价值。
回顾性分析 2017 年 5 月至 2020 年 12 月期间因 HGSOC 行盆腔 MRI（包括 DWI，b 值为 0、1000 s/mm 2 ）检查且术后经组织病理学证实的患者。根据组织病理学结果将患者分为 MT-HGSOC 组和非 MT-HGSOC 组，评估并比较两组患者的临床特征及 MRI 特征，包括基于 DWI 的直方图指标。采用单因素和多因素分析确定与 MT-HGSOC 相关的显著变量，然后将这些变量纳入预测列线图，并进行 ROC 曲线分析以评估诊断效能。
共纳入 81 例行术前盆腔 MRI 检查的连续患者，其中 37 例（45.7%）为 MT-HGSOC 患者，44 例（54.3%）为非 MT-HGSOC 患者。单因素分析显示，与 MT-HGSOC 相关的特征包括无离散原发卵巢肿块、Douglas 窝种植、卵巢肿块大小、肿瘤体积、平均、标准差、中位数和 95%分位数表观弥散系数（ADC）值（均 P < 0.05）。多因素分析显示，无离散原发卵巢肿块（OR：46.477；P = 0.025）、平均 ADC 值≤1.105（OR：1.023；P = 0.009）和中位数 ADC 值≤1.038（OR：0.982；P = 0.034）是与 MT-HGSOC 相关的独立危险因素。所有独立标准的组合具有最大 AUC（0.82），敏感性为 83.87%，特异性为 66.67%，优于任何单一预测指标（均 P ≤ 0.012）。组合的预测 C 指数列线图性能为 0.82。
无离散原发卵巢肿块、较低的平均 ADC 值和中位数 ADC 值的组合可能有助于术前预测 MT-HGSOC。
