Abstract

PurposeThe 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.Methods and materialsWe 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.Results1218 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).ConclusionsThe 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.

Highlights

  • Bladder cancer (BCa) is the 10th most prevalent cancer with high risk of malignant progression, metastasis and recurrence [1]

  • Immunohistochemistry-based high Ki67 expression and radiomics-predicted high Ki67 expression based on the Synthetic minority over-sampling technique (SMOTE)-Least Absolute Shrinkage and Selection Operator algorithms (LASSO) model were significantly associated with poor disease-free survival in training and validation sets

  • A meta-analysis study has reported that high expression of Ki67 was a risk factor for progressionfree survival in non-muscleinvasive bladder cancer (NMIBC) patients treated with transurethral resection and Bacillus Calmette-Guérin intravesical immunotherapy [9]

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Summary

Introduction

Bladder cancer (BCa) is the 10th most prevalent cancer with high risk of malignant progression, metastasis and recurrence [1]. BCa can be classified into non-muscleinvasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) based on the muscle invasion status. Approximately 75% of BCa patients represent NMIBC (Ta, Tis, T1) while the remaining 25% accounts for MIBC (stage from T2 to T4) [2]. Previous studies have demonstrated that high Ki67 expression is associated with higher T stage, higher tumor grade, lymph nodes invasion, lymphovascular invasion, and poorer prognosis in BCa [4,5,6,7,8]. A meta-analysis study has reported that high expression of Ki67 was a risk factor for progressionfree survival in NMIBC patients treated with transurethral resection and Bacillus Calmette-Guérin intravesical immunotherapy [9]. A noninvasive and accurate tool is needed to preoperatively predict the Ki67 expression in BCa patients more comprehensively and accurately

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