Son Hye Min, Kim See Hyung, Kwon Bo Ra, Kim Mi Jeong, Kim Chan Sun, Cho Seung Hyun
1 Department of Radiology, Dongsan Hospital, Keimyung University, Daegu, Republic of Korea.
2 Department of Radiology, National University Hospital, Kyungbook National University Hospital, Daegu, Republic of Korea.
Acta Radiol. 2017 Apr;58(4):498-504. doi: 10.1177/0284185116658683. Epub 2016 Jul 22.
Background Cytoreduction is important as a survival predictor in advanced ovarian cancer. Purpose To determine the prediction of suboptimal resection (SOR) in advanced ovarian cancer based on clinical and computed tomography (CT) parameters. Material and Methods Between 2007 and 2015, 327 consecutive patients with FIGO stage III-IV ovarian cancer and preoperative CT were included. During 2007-2012, patients were assigned to a derivation dataset ( n = 220) and the others were assigned to a validation dataset ( n = 107). Clinical parameters were reviewed and two radiologists assessed the presence or absence of tabulated parameters on CT images. Logistic regression analyses based on area under the receiver-operating characteristic curve (AUROC) were performed to identify variables predicting SOR, and generated simple score using Cox proportional hazards model. Results There was no statistical difference in patients' characteristics in both datasets, except for residual disease ( P = 0.001). Optimal resection improved from 45.0% (99/220) in the derivation dataset to 64.4% (69/107) in the validation dataset. Logistic regression identified that Eastern Cooperative Oncology Group-performance status (ECOG-PS 2), involvements of peritoneum, diaphragm, bowel mesentery and suprarenal lymph nodes, and pleural effusion were independent variables of SOR. Overall AUROC for score predicting SOR was 0.761 with sensitivity, specificity, and positive and negative predictive values of 70.6%, 73.2%, 68.7%, and 91.9%, respectively. In the derivation dataset, AUROC was 0.792, with sensitivity of 71.4% and specificity of 74.3%, and AUROC of 0.758 with sensitivity of 69.2% and specificity of 72.8% in the validation dataset. Conclusion CT may be a useful preoperative predictor of SOR in advanced ovarian cancer.
肿瘤细胞减灭术是晚期卵巢癌生存预测的重要指标。目的：基于临床和计算机断层扫描（CT）参数确定晚期卵巢癌次优切除（SOR）的预测情况。材料与方法：纳入2007年至2015年连续收治的327例国际妇产科联盟（FIGO）Ⅲ - Ⅳ期卵巢癌患者及术前行CT检查者。2007年至2012年期间的患者被分配至推导数据集（n = 220），其余患者被分配至验证数据集（n = 107）。回顾临床参数，两名放射科医生评估CT图像上列出参数的有无。基于受试者操作特征曲线下面积（AUROC）进行逻辑回归分析以识别预测SOR的变量，并使用Cox比例风险模型生成简单评分。结果：除残留病灶外（P = 0.001），两个数据集患者的特征无统计学差异。最佳切除率从推导数据集中的45.0%（99/220）提高到验证数据集中的64.4%（69/107）。逻辑回归分析确定东部肿瘤协作组体能状态（ECOG - PS 2）、腹膜、膈肌、肠系膜和肾上腺淋巴结受累以及胸腔积液是SOR的独立变量。预测SOR评分的总体AUROC为0.761，敏感性、特异性、阳性预测值和阴性预测值分别为70.6%、73.2%、68.7%和91.9%。在推导数据集中，AUROC为0.792，敏感性为71.4%，特异性为74.3%；在验证数据集中，AUROC为0.758，敏感性为69.2%，特异性为72.8%。结论：CT可能是晚期卵巢癌术前SOR的有用预测指标。
