Parmar Ambica, Qazi Abdul Aziz, Stundzia Audrius, Sim Hao-Wen, Lewin Jeremy, Metser Ur, O'Malley Martin, Hansen Aaron R
Division of Medical Oncology & Hematology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada.
Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.
Can Urol Assoc J. 2022 Mar;16(3):E113-E119. doi: 10.5489/cuaj.7294.
Neoadjuvant chemotherapy (NAC) for muscle-invasive bladder cancer (MIBC) improves overall survival, but pathological response rates are low. Predictive biomarkers could select those patients most likely to benefit from NAC. Radiomics technology offers a novel, non-invasive method to identify predictive biomarkers. Our study aimed to develop a predictive radiomics signature for response to NAC in MIBC.
An institutional bladder cancer database was used to identify MIBC patients who were treated with NAC followed by radical cystectomy. Patients were classified into responders and non-responders based on pathological response. Bladder lesions on computed tomography images taken prior to NAC were contoured. Extracted radiomics features were used to train a radial basis function support vector machine classifier to learn a prediction rule to distinguish responders from non-responders. The discriminative accuracy of the classifier was then tested using a nested 10-fold cross-validation protocol.
Nineteen patients who underwent NAC followed by radical cystectomy were found to be eligible for analysis. Of these, nine (47%) patients were classified as responders and 10 (53%) as non-responders. Nineteen bladder lesions were contoured. The sensitivity, specificity, and discriminative accuracy were 52.9±9.4%, 69.4±8.6%, and 62.1±6.1%, respectively. This corresponded to an area under the curve of 0.63±0.08 (p=0.20).
Our developed radiomics signature demonstrated modest discriminative accuracy; however, these results may have been influenced by small sample size and heterogeneity in image acquisition. Future research using novel methods for computer-based image analysis on a larger cohort of patients is warranted.
肌肉浸润性膀胱癌（MIBC）的新辅助化疗（NAC）可提高总体生存率，但病理缓解率较低。预测性生物标志物可以筛选出最有可能从NAC中获益的患者。放射组学技术提供了一种识别预测性生物标志物的新型非侵入性方法。我们的研究旨在开发一种用于预测MIBC患者对NAC反应的放射组学特征。
利用一个机构性膀胱癌数据库，识别接受NAC治疗后行根治性膀胱切除术的MIBC患者。根据病理反应将患者分为反应者和无反应者。对NAC前拍摄的计算机断层扫描图像上的膀胱病变进行轮廓勾画。提取的放射组学特征用于训练径向基函数支持向量机分类器，以学习区分反应者和无反应者的预测规则。然后使用嵌套的10倍交叉验证方案测试分类器的判别准确性。
发现19例接受NAC治疗后行根治性膀胱切除术的患者符合分析条件。其中，9例（47%）患者被分类为反应者，10例（53%）为无反应者。勾画了19个膀胱病变的轮廓。敏感性、特异性和判别准确性分别为52.9±9.4%、69.4±8.6%和62.1±6.1%。这对应于曲线下面积为0.63±0.08（p=0.20）。
我们开发的放射组学特征显示出适度的判别准确性；然而，这些结果可能受到样本量小和图像采集异质性的影响。有必要对更多患者群体采用基于计算机的图像分析新方法进行未来研究。
