Lin Lingling, Fu Le, Wu Huawei, Cheng Saiming, Chen Guangquan, Chen Lei, Zhu Jun, Wang Yu, Cheng Jiejun
Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China.
Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Insights Imaging. 2025 Jan 29;16(1):22. doi: 10.1186/s13244-024-01860-z.
To assess the utility of clinical and MRI features in distinguishing ovarian clear cell carcinoma (CCC) from adnexal masses with ovarian-adnexal reporting and data system (O-RADS) MRI scores of 4-5.
This retrospective study included 850 patients with indeterminate adnexal masses on ultrasound. Two radiologists evaluated all preoperative MRIs using the O-RADS MRI risk stratification system. Patients with O-RADS MRI scores of 4-5 were divided into a training set (n = 135, hospital A) and a test set (n = 86, hospital B). Clinical and MRI features were compared between CCC and non-CCC patients. Analysis of variance and support vector machine were used to develop four CCC prediction models. Tenfold cross-validation was applied to determine the hyperparameters. Model performance was evaluated by the area under the curve (AUC) and decision curve.
221 patients were included (30 CCCs, 191 non-CCCs). CA125, HE4, CEA, ROMA, endometriosis, shape, parity, unilocular, component, the growth pattern of mural nodules, high signal on T1WI, number of nodules, the ratio of signal intensity, and the ADC value were significantly different between CCCs and non-CCCs. The kappa and interobserver correlation coefficient of each MRI feature exceeded 0.85. The comprehensive model combining clinical and MRI features had a greater AUC than the clinical model and tumour maker model (0.92 vs 0.66 and 0.78 in the test set; both p < 0.05), displaying improved net benefit.
The comprehensive model combining clinical and MRI features can effectively differentiate CCC from adnexal masses with O-RADS MRI scores of 4-5.
CCC has a high incidence rate in Asians and has limited sensitivity to platinum chemotherapy. This comprehensive model improves CCC prediction ability and clinical applicability for facilitating individualised clinical decision-making.
Identifying ovarian CCC preoperatively is beneficial for treatment planning. Ovarian CCC tends to be high-signal on T1WI, unilocular, big size, with endometriosis and low CEA. This model, integrating clinical and MRI features, can differentiate ovarian CCC from adnexal masses with O-RADS MRI scores 4-5.
评估临床及MRI特征在鉴别卵巢透明细胞癌（CCC）与卵巢影像报告和数据系统（O-RADS）MRI评分为4-5分的附件包块中的作用。
这项回顾性研究纳入了850例超声检查发现附件包块性质不确定的患者。两名放射科医生使用O-RADS MRI风险分层系统对所有术前MRI进行评估。O-RADS MRI评分为4-5分的患者被分为训练集（n = 135，医院A）和测试集（n = 86，医院B）。比较CCC患者与非CCC患者的临床及MRI特征。采用方差分析和支持向量机建立四个CCC预测模型。应用十折交叉验证法确定超参数。通过曲线下面积（AUC）和决策曲线评估模型性能。
共纳入221例患者（30例CCC，191例非CCC）。CA125、HE4、CEA、ROMA、子宫内膜异位症、形态、产次、单房性、成分、壁结节生长方式、T1WI高信号、结节数量、信号强度比值及表观扩散系数（ADC）值在CCC与非CCC患者之间存在显著差异。各MRI特征的kappa值和观察者间相关系数均超过0.85。结合临床和MRI特征的综合模型在测试集中的AUC大于临床模型和肿瘤标志物模型（分别为0.92、0.66和0.78；均p < 0.05），显示出更好的净效益。
结合临床和MRI特征的综合模型能够有效鉴别CCC与O-RADS MRI评分为4-5分的附件包块。
CCC在亚洲人群中发病率较高，对铂类化疗敏感性有限。该综合模型提高了CCC预测能力及临床适用性，有助于促进个体化临床决策。
术前识别卵巢CCC有利于治疗方案的制定。卵巢CCC在T1WI上往往呈高信号、单房、体积较大，伴有子宫内膜异位症且CEA较低。该综合临床和MRI特征的模型能够鉴别卵巢CCC与O-RADS MRI评分为4-5分的附件包块。
