Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Department of Gynecologic Oncology, Women's Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, People's Republic of China.
Department of Ultrasound, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310016, People's Republic of China.
Phys Med Biol. 2024 Jun 7;69(12). doi: 10.1088/1361-6560/ad4a02.
. Ovarian cancer is the deadliest gynecologic malignancy worldwide. Ultrasound is the most useful non-invasive test for preoperative diagnosis of ovarian cancer. In this study, by leveraging multiple ultrasound images from the same patient to generate personalized, informative statistical radiomic features, we aimed to develop improved ultrasound image-based prognostic models for ovarian cancer.. A total of 2057 ultrasound images from 514 ovarian cancer patients, including 355 patients with epithelial ovarian cancer, from two hospitals in China were collected for this study. The models were constructed using our recently developed Frequency Appearance in Multiple Univariate pre-Screening feature selection algorithm and Cox proportional hazards model.. The models showed high predictive performance for overall survival (OS) and recurrence-free survival (RFS) in both epithelial and nonepithelial ovarian cancer, with concordance indices ranging from 0.773 to 0.794. Radiomic scores predicted 2 year OS and RFS risk groups with significant survival differences (log-rank test,< 1.0 × 10for both validation cohorts). OS and RFS hazard ratios between low- and high-risk groups were 15.994 and 30.692 (internal cohort) and 19.339 and 19.760 (external cohort), respectively. The improved performance of these newly developed prognostic models was mainly attributed to the use of multiple preoperative ultrasound images from the same patient to generate statistical radiomic features, rather than simply using the largest tumor region of interest among them. The models also revealed that the roundness of tumor lesion shape was positively correlated with prognosis for ovarian cancer.The newly developed prognostic models based on statistical radiomic features from ultrasound images were highly predictive of the risk of cancer-related death and possible recurrence not only for patients with epithelial ovarian cancer but also for those with nonepithelial ovarian cancer. They thereby provide reliable, non-invasive markers for individualized prognosis evaluation and clinical decision-making for patients with ovarian cancer.
卵巢癌是全球致死率最高的妇科恶性肿瘤。超声是术前诊断卵巢癌最有用的非侵入性检查方法。在这项研究中，我们利用来自同一位患者的多个超声图像生成个性化、信息丰富的统计放射组学特征，旨在开发基于超声图像的改良卵巢癌预后模型。本研究共纳入来自中国两家医院的 514 名卵巢癌患者的 2057 个超声图像，其中上皮性卵巢癌患者 355 名。我们使用最近开发的多变量单因素前筛选特征选择算法和 Cox 比例风险模型构建模型。在上皮性和非上皮性卵巢癌中，模型对总生存期（OS）和无复发生存期（RFS）均具有较高的预测性能，一致性指数范围为 0.773 至 0.794。放射组学评分可预测 2 年 OS 和 RFS 风险组，且生存差异具有统计学意义（对数秩检验，两组验证队列均<1.0×10-4）。低危组和高危组的 OS 和 RFS 风险比分别为 15.994 和 30.692（内部队列）和 19.339 和 19.760（外部队列）。这些新开发的预后模型性能的提高主要归因于利用来自同一位患者的多个术前超声图像生成统计放射组学特征，而不是简单地使用其中最大的肿瘤感兴趣区。模型还显示肿瘤病变形状的圆形度与卵巢癌的预后呈正相关。基于超声图像统计放射组学特征的新开发的预后模型不仅对上皮性卵巢癌患者，而且对非上皮性卵巢癌患者的癌症相关死亡和复发风险具有高度预测性。因此，这些模型为卵巢癌患者的个体化预后评估和临床决策提供了可靠的、非侵入性的标志物。
