Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea.
Department of Obstetrics and Gynecology, Institute of Women's Life Medical Science, Yonsei University College of Medicine, Seoul, Korea.
Korean J Radiol. 2021 Sep;22(9):1481-1489. doi: 10.3348/kjr.2020.1477. Epub 2021 May 26.
To construct a CT-based Fagotti scoring system by analyzing the correlations between laparoscopic findings and CT features in patients with advanced ovarian cancer.
This retrospective cohort study included patients diagnosed with stage III/IV ovarian cancer who underwent diagnostic laparoscopy and debulking surgery between January 2010 and June 2018. Two radiologists independently reviewed preoperative CT scans and assessed ten CT features known as predictors of suboptimal cytoreduction. Correlation analysis between ten CT features and seven laparoscopic parameters based on the Fagotti scoring system was performed using Spearman's correlation. Variable selection and model construction were performed by logistic regression with the least absolute shrinkage and selection operator method using a predictive index value (PIV) ≥ 8 as an indicator of suboptimal cytoreduction. The final CT-based scoring system was internally validated using 5-fold cross-validation.
A total of 157 patients (median age, 56 years; range, 27-79 years) were evaluated. Among 120 (76.4%) patients with a PIV ≥ 8, 105 patients received neoadjuvant chemotherapy followed by interval debulking surgery, and the optimal cytoreduction rate was 90.5% (95 of 105). Among 37 (23.6%) patients with PIV < 8, 29 patients underwent primary debulking surgery, and the optimal cytoreduction rate was 93.1% (27 of 29). CT features showing significant correlations with PIV ≥ 8 were mesenteric involvement, gastro-transverse mesocolon-splenic space involvement, diaphragmatic involvement, and para-aortic lymphadenopathy. The area under the receiver operating curve of the final model for prediction of PIV ≥ 8 was 0.72 (95% confidence interval: 0.62-0.82).
Central tumor burden and upper abdominal spread features on preoperative CT were identified as distinct predictive factors for high PIV on diagnostic laparoscopy. The CT-based PIV prediction model might be useful for patient stratification before cytoreduction surgery for advanced ovarian cancer.
通过分析晚期卵巢癌患者腹腔镜检查结果与 CT 特征之间的相关性，构建基于 CT 的 Fagotti 评分系统。
本回顾性队列研究纳入了 2010 年 1 月至 2018 年 6 月期间接受诊断性腹腔镜检查和减瘤术的 III/IV 期卵巢癌患者。两名放射科医生独立评估了术前 CT 扫描，并评估了 10 个已知与不完全肿瘤减灭术相关的 CT 特征。使用 Spearman 相关系数对 10 个 CT 特征与基于 Fagotti 评分系统的 7 个腹腔镜参数之间的相关性进行了分析。使用最小绝对收缩和选择算子法（预测指数值[PIV]≥8 作为不完全肿瘤减灭术的指标）进行逻辑回归变量选择和模型构建。使用 5 折交叉验证对内建 CT 评分系统进行了验证。
共评估了 157 例患者（中位年龄 56 岁；范围 27-79 岁）。在 120 例 PIV≥8 的患者中，105 例患者接受新辅助化疗后行间隔性肿瘤细胞减灭术，最佳肿瘤细胞减灭率为 90.5%（95/105）。在 37 例 PIV<8 的患者中，29 例患者行原发性肿瘤细胞减灭术，最佳肿瘤细胞减灭率为 93.1%（27/29）。与 PIV≥8 显著相关的 CT 特征包括肠系膜受累、胃-横结肠-脾间隙受累、膈肌受累和腹主动脉旁淋巴结病。最终模型预测 PIV≥8 的受试者工作特征曲线下面积为 0.72（95%置信区间：0.62-0.82）。
术前 CT 上的中央肿瘤负荷和上腹部扩散特征被确定为诊断性腹腔镜检查中高 PIV 的独特预测因素。基于 CT 的 PIV 预测模型可能有助于高级卵巢癌肿瘤细胞减灭术前患者分层。
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