Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao, 266061, Shandong, China.
School of Basic Medicine, Qingdao University, Qingdao, Shandong, China.
Cancer Imaging. 2024 Aug 6;24(1):103. doi: 10.1186/s40644-024-00744-1.
To develop and validate a radiomics nomogram combining radiomics features and clinical factors for preoperative evaluation of Ki-67 expression status and prognostic prediction in clear cell renal cell carcinoma (ccRCC).
Two medical centers of 185 ccRCC patients were included, and each of them formed a training group (n = 130) and a validation group (n = 55). The independent predictor of Ki-67 expression status was identified by univariate and multivariate regression, and radiomics features were extracted from the preoperative CT images. The maximum relevance minimum redundancy (mRMR) and the least absolute shrinkage and selection operator algorithm (LASSO) were used to identify the radiomics features that were most relevant for high Ki-67 expression. Subsequently, clinical model, radiomics signature (RS), and radiomics nomogram were established. The performance for prediction of Ki-67 expression status was validated using area under curve (AUC), calibration curve, Delong test, decision curve analysis (DCA). Prognostic prediction was assessed by survival curve and concordance index (C-index).
Tumour size was the only independent predictor of Ki-67 expression status. Five radiomics features were finally identified to construct the RS (AUC: training group, 0.821; validation group, 0.799). The radiomics nomogram achieved a higher AUC (training group, 0.841; validation group, 0.814) and clinical net benefit. Besides, the radiomics nomogram provided a highest C-index (training group, 0.841; validation group, 0.820) in predicting prognosis for ccRCC patients.
The radiomics nomogram can accurately predict the Ki-67 expression status and exhibit a great capacity for prognostic prediction in patients with ccRCC and may provide value for tailoring personalized treatment strategies and facilitating comprehensive clinical monitoring for ccRCC patients.
开发并验证一个结合放射组学特征和临床因素的列线图，用于术前评估透明细胞肾细胞癌（ccRCC）中 Ki-67 表达状态和预后预测。
纳入了两个医疗中心的 185 名 ccRCC 患者，每个中心分别形成一个训练组（n=130）和一个验证组（n=55）。通过单因素和多因素回归确定 Ki-67 表达状态的独立预测因子，并从术前 CT 图像中提取放射组学特征。使用最大相关性最小冗余（mRMR）和最小绝对值收缩和选择算子算法（LASSO）选择与高 Ki-67 表达最相关的放射组学特征。随后，建立临床模型、放射组学特征（RS）和放射组学列线图。使用曲线下面积（AUC）、校准曲线、Delong 检验、决策曲线分析（DCA）验证 Ki-67 表达状态预测的性能。通过生存曲线和一致性指数（C-index）评估预后预测。
肿瘤大小是 Ki-67 表达状态的唯一独立预测因子。最终确定了 5 个放射组学特征来构建 RS（AUC：训练组为 0.821，验证组为 0.799）。放射组学列线图获得了更高的 AUC（训练组为 0.841，验证组为 0.814）和临床净获益。此外，放射组学列线图在预测 ccRCC 患者预后方面提供了最高的 C-index（训练组为 0.841，验证组为 0.820）。
放射组学列线图可准确预测 Ki-67 表达状态，并在 ccRCC 患者的预后预测中具有很大的能力，可能为制定个性化治疗策略和促进 ccRCC 患者的综合临床监测提供价值。
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