Sharaby Israa, Alksas Ahmed, Nashat Ahmed, Balaha Hossam Magdy, Shehata Mohamed, Gayhart Mallorie, Mahmoud Ali, Ghazal Mohammed, Khalil Ashraf, Abouelkheir Rasha T, Elmahdy Ahmed, Abdelhalim Ahmed, Mosbah Ahmed, El-Baz Ayman
Bioengineering Department, University of Louisville, Louisville, KY 40292, USA.
Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt.
Diagnostics (Basel). 2023 Jan 29;13(3):486. doi: 10.3390/diagnostics13030486.
Wilms' tumor, the most prevalent renal tumor in children, is known for its aggressive prognosis and recurrence. Treatment of Wilms' tumor is multimodal, including surgery, chemotherapy, and occasionally, radiation therapy. Preoperative chemotherapy is used routinely in European studies and in select indications in North American trials. The objective of this study was to build a novel computer-aided prediction system for preoperative chemotherapy response in Wilms' tumors. A total of 63 patients (age range: 6 months-14 years) were included in this study, after receiving their guardians' informed consent. We incorporated contrast-enhanced computed tomography imaging to extract the texture, shape, and functionality-based features from Wilms' tumors before chemotherapy. The proposed system consists of six steps: (i) delineate the tumors' images across the three contrast phases; (ii) characterize the texture of the tumors using first- and second-order textural features; (iii) extract the shape features by applying a parametric spherical harmonics model, sphericity, and elongation; (iv) capture the intensity changes across the contrast phases to describe the tumors' functionality; (v) apply features fusion based on the extracted features; and (vi) determine the final prediction as responsive or non-responsive via a tuned support vector machine classifier. The system achieved an overall accuracy of 95.24%, with 95.65% sensitivity and 94.12% specificity. Using the support vector machine along with the integrated features led to superior results compared with other classification models. This study integrates novel imaging markers with a machine learning classification model to make early predictions about how a Wilms' tumor will respond to preoperative chemotherapy. This can lead to personalized management plans for Wilms' tumors.
肾母细胞瘤是儿童中最常见的肾脏肿瘤，以其侵袭性预后和复发而闻名。肾母细胞瘤的治疗是多模式的，包括手术、化疗，偶尔还包括放射治疗。术前化疗在欧洲研究中常规使用，在北美试验中的特定适应症中也会使用。本研究的目的是建立一种用于预测肾母细胞瘤术前化疗反应的新型计算机辅助预测系统。在获得监护人的知情同意后，本研究共纳入了63例患者（年龄范围：6个月至14岁）。我们纳入了对比增强计算机断层扫描成像，以在化疗前从肾母细胞瘤中提取基于纹理、形状和功能的特征。所提出的系统包括六个步骤：(i) 在三个对比期勾勒肿瘤图像；(ii) 使用一阶和二阶纹理特征表征肿瘤的纹理；(iii) 通过应用参数化球谐模型、球形度和伸长率提取形状特征；(iv) 捕捉对比期内的强度变化以描述肿瘤的功能；(v) 基于提取的特征进行特征融合；(vi) 通过调谐的支持向量机分类器确定最终预测为有反应或无反应。该系统的总体准确率为95.24%，灵敏度为95.65%，特异性为94.12%。与其他分类模型相比，使用支持向量机和综合特征可产生更好的结果。本研究将新型成像标志物与机器学习分类模型相结合，以对肾母细胞瘤对术前化疗的反应进行早期预测。这可以为肾母细胞瘤制定个性化的管理计划。
