Song Honghao, Wang Xiaoqing, Wang Hongwei, Guo Feng, Wu Rongde, Liu Wei
Department of Pediatric Surgery, Shandong Provincial Hospital, Shandong University, Jinan, China.
Department of Pediatric Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Transl Pediatr. 2024 Mar 27;13(3):417-426. doi: 10.21037/tp-23-508. Epub 2024 Mar 11.
The clinical manifestations of Wilms tumor and non-Wilms tumor in children are similar, and the only way to confirm the diagnosis is by postoperative pathology. Computed tomography (CT) is one of the main methods for preoperative diagnosis of the two, but it is also difficult to distinguish because it is easily affected by the subjective influence and the experience of the radiologists.
The CT images of 82 children with renal tumors admitted to the Department of Pediatric Urology, Shandong Provincial Hospital from January 2011 to March 2022 were retrospectively analyzed. First, we drew the two-dimensional (2D) region of interest (ROI) of the largest cross-section on the corticomedullary phase (CMP) and nephrogenic phase (NP) images, and extracted seven types of 107 features in the ROI. Then, the texture features with similarity greater than 95% and repetition less than 90% were screened out, and the remaining texture features were further screened by analysis of variance (ANOVA) and recursive feature elimination (RFE). Finally, 15 texture feature were used to build the machine learning (ML) models. We used the synthetic minority oversampling technique (SMOTE) and 10-fold cross-validation to build ML models and verified them in the training, testing, and internal validation sets. The area under the receiver-operating characteristic curve (AUC) and calibration curve were used to evaluate the diagnostic performance.
We collected 77 CMP and 81 NP images, which were randomly divided into the training set and the testing set according to the ratio of 7:3. In the internal validation of CMP, the Mean-PCC-ANOVA-5-AE pipeline model achieved the highest AUC 0.792 [95% confidence interval (CI): 0.653-0.930], and its accuracy (ACC), sensitivity (SEN), and specificity (SPE) were 0.833, 0.539 and 0.927, respectively. Correspondingly, in NP, the Mean-PCC-ANOVA-2-LR pipeline model achieved the highest AUC 0.655 (95% CI: 0.485-0.82) in the internal validation. The ACC, SEN, and SPE were 0.696, 0.539, and 0.744, respectively.
The ML models based on CT images have good diagnostic efficiency in differentiating Wilms tumors from non-Wilms tumors in children.
儿童肾母细胞瘤和非肾母细胞瘤的临床表现相似，确诊的唯一方法是术后病理检查。计算机断层扫描（CT）是术前诊断这两种疾病的主要方法之一，但由于易受放射科医生主观因素和经验的影响，也难以区分。
回顾性分析2011年1月至2022年3月山东省立医院小儿泌尿外科收治的82例肾肿瘤患儿的CT图像。首先，在皮质髓质期（CMP）和肾实质期（NP）图像上绘制最大横截面的二维（2D）感兴趣区域（ROI），并在ROI中提取7种类型的107个特征。然后，筛选出相似度大于95%且重复性小于90%的纹理特征，其余纹理特征通过方差分析（ANOVA）和递归特征消除（RFE）进一步筛选。最后，使用15个纹理特征构建机器学习（ML）模型。我们使用合成少数过采样技术（SMOTE）和10折交叉验证来构建ML模型，并在训练集、测试集和内部验证集中进行验证。采用受试者操作特征曲线（AUC）下面积和校准曲线评估诊断性能。
我们收集了77幅CMP图像和81幅NP图像，并按照7:3的比例随机分为训练集和测试集。在CMP的内部验证中，Mean-PCC-ANOVA-5-AE管道模型的AUC最高为0.792[95%置信区间（CI）：0.653-0.930]，其准确率（ACC）、灵敏度（SEN）和特异度（SPE）分别为0.833、0.539和0.927。相应地，在NP中Mean-PCC-ANOVA-2-LR管道模型在内部验证中的AUC最高为0.655（95%CI：0.485-0.82）。ACC、SEN和SPE分别为0.696、0.539和0.744。
基于CT图像的ML模型在鉴别儿童肾母细胞瘤和非肾母细胞瘤方面具有良好的诊断效率
