Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China.
Institute of Urinary Oncology, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China.
Cancer Imaging. 2021 Dec 4;21(1):65. doi: 10.1186/s40644-021-00433-3.
The Ki67 expression is associated with the advanced clinicopathological features and poor prognosis in bladder cancer (BCa). We aimed to develop and validate magnetic resonance imaging (MRI)-based radiomics signatures to preoperatively predict the Ki67 expression status in BCa.
We retrospectively collected 179 BCa patients with Ki67 expression and preoperative MRI. Radiomics features were extracted from T2-weighted (T2WI) and dynamic contrast-enhancement (DCE) images. The synthetic minority over-sampling technique (SMOTE) was used to balance the minority group (low Ki67 expression group) in the training set. Minimum redundancy maximum relevance was used to identify the best features associated with Ki67 expression. Support vector machine and Least Absolute Shrinkage and Selection Operator algorithms (LASSO) were used to construct radiomics signatures in training and SMOTE-training sets, and diagnostic performance was assessed by the area under the curve (AUC) and accuracy. The decision curve analyses (DCA) and calibration curve and were used to investigate the clinical usefulness and calibration of radiomics signatures, respectively. The Kaplan-Meier test was performed to investigate the prognostic value of radiomics-predicted Ki67 expression status.
1218 radiomics features were extracted from T2WI and DCE images, respectively. The SMOTE-LASSO model based on nine features achieved the best predictive performance in the SMOTE-training (AUC, 0.859; accuracy, 80.3%) and validation sets (AUC, 0.819; accuracy, 81.5%) with a good calibration performance and clinical usefulness. Immunohistochemistry-based high Ki67 expression and radiomics-predicted high Ki67 expression based on the SMOTE-LASSO model were significantly associated with poor disease-free survival in training and validation sets (all P < 0.05).
The SMOTE-LASSO model could predict the Ki67 expression status and was associated with survival outcomes of the BCa patients, thereby may aid in clinical decision-making.
Ki67 表达与膀胱癌（BCa）的晚期临床病理特征和不良预后相关。本研究旨在开发和验证基于磁共振成像（MRI）的放射组学特征，以术前预测 BCa 中的 Ki67 表达状态。
我们回顾性收集了 179 例 Ki67 表达和术前 MRI 的 BCa 患者。从 T2 加权（T2WI）和动态对比增强（DCE）图像中提取放射组学特征。使用合成少数过采样技术（SMOTE）平衡训练集中的少数群体（低 Ki67 表达组）。最小冗余最大相关性用于确定与 Ki67 表达相关的最佳特征。支持向量机和最小绝对收缩和选择算子算法（LASSO）分别用于在训练集和 SMOTE 训练集中构建放射组学特征，并通过曲线下面积（AUC）和准确性评估诊断性能。决策曲线分析（DCA）和校准曲线分别用于研究放射组学特征的临床实用性和校准。Kaplan-Meier 检验用于研究放射组学预测的 Ki67 表达状态的预后价值。
分别从 T2WI 和 DCE 图像中提取了 1218 个放射组学特征。基于九个特征的 SMOTE-LASSO 模型在 SMOTE 训练集（AUC，0.859；准确性，80.3%）和验证集（AUC，0.819；准确性，81.5%）中具有最佳的预测性能，具有良好的校准性能和临床实用性。基于免疫组织化学的高 Ki67 表达和基于 SMOTE-LASSO 模型的放射组学预测的高 Ki67 表达在训练集和验证集中均与疾病无复发生存显著相关（均 P < 0.05）。
SMOTE-LASSO 模型可以预测 Ki67 表达状态，并与 BCa 患者的生存结果相关，因此可能有助于临床决策。
