Wei Wei, Rong Yu, Liu Zhenyu, Zhou Bin, Tang Zhenchao, Wang Shuo, Dong Di, Zang Yali, Guo Yingkun, Tian Jie
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:4130-4133. doi: 10.1109/EMBC.2018.8513351.
In order to predict the 3-year recurrence of advanced ovarian cancer before surgery, we retrospective collected 94 patients to analyze by using a novel radiomics method. A total of 575 3D imaging features used for radiomics analysis were extracted, and 7 features were selected from computed tomography (CT) images that were most strongly associated with 3-year clinical recurrence-free survival (CRFS) probability to build a radiomics signature. The area under the Receiver Operating Characteristic (ROC) curve (AUC) of 0.8567 (95% CI: 0.7251-0.9498) and 0.8533 (95% CI: 0.7231-0.9671) were obtained in the training cohort and validation cohort with the logistic regression classification model respectively. Experimental results show that CT-based radiomics features were closely associated with the recurrence of advanced ovarian cancer. It is possible to prejudge the recurrence of ovarian cancer before surgery.
为了在手术前预测晚期卵巢癌的3年复发情况，我们回顾性收集了94例患者，采用一种新型的放射组学方法进行分析。共提取了575个用于放射组学分析的三维成像特征，并从计算机断层扫描（CT）图像中选取了7个与3年临床无复发生存率（CRFS）概率相关性最强的特征来构建放射组学特征标签。使用逻辑回归分类模型，在训练队列和验证队列中分别获得了受试者工作特征（ROC）曲线下面积（AUC）为0.8567（95%CI：0.7251 - 0.9498）和0.8533（95%CI：0.7231 - 0.9671）。实验结果表明，基于CT的放射组学特征与晚期卵巢癌的复发密切相关。术前预判卵巢癌复发是有可能的。
