Department of Radiology, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, No. 3333, Binshneg Rd, Hangzhou, China.
Wenzhou Medical University, Wenzhou, China.
BMC Pediatr. 2024 May 9;24(1):321. doi: 10.1186/s12887-024-04775-2.
Radiologic volumetric evaluation of Wilms' tumor (WT) is an important indicator to guide treatment decisions. However, due to the heterogeneity of the tumors, radiologists have main-guard differences in diagnosis that can lead to misdiagnosis and poor treatment. The aim of this study was to explore whether CT-based outlining of WT foci can be automated using deep learning.
We included CT intravenous phase images of 105 patients with WT and double-blind outlining of lesions by two radiologists. Then, we trained an automatic segmentation model using nnUnet. The Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD) were used to assess the performance. Next, we optimized the automatic segmentation results based on the ratio of the three-dimensional diameter of the lesion to improve the performance of volumetric assessment.
The DSC and HD was 0.83 ± 0.22 and 10.50 ± 8.98 mm. The absolute difference and percentage difference in tumor size was 72.27 ± 134.84 cm and 21.08% ± 30.46%. After optimization according to our method, it decreased to 40.22 ± 96.06 cm and 10.16% ± 9.70%.
We introduce a novel method that enhances the accuracy of predicting WT volume by integrating AI automated outlining and 3D tumor diameters. This approach surpasses the accuracy of using AI outcomes alone and has the potential to enhance the clinical evaluation of pediatric patients with WT. By intertwining AI outcomes with clinical data, this method becomes more interpretive and offers promising applications beyond Wilms tumor, extending to other pediatric diseases.
Wilms 瘤（WT）的放射学容积评估是指导治疗决策的重要指标。然而，由于肿瘤的异质性，放射科医生在诊断上存在主要差异，可能导致误诊和治疗效果不佳。本研究旨在探讨是否可以使用深度学习自动进行基于 CT 的 WT 病灶勾画。
我们纳入了 105 例 WT 患者的 CT 静脉相图像，并由两名放射科医生进行了病变的双盲勾画。然后，我们使用 nnUnet 训练了一个自动分割模型。使用 Dice 相似系数（DSC）和 95%的 Hausdorff 距离（HD）来评估性能。接下来，我们根据病变的三维直径比优化自动分割结果，以提高容积评估的性能。
DSC 和 HD 分别为 0.83±0.22 和 10.50±8.98mm。肿瘤大小的绝对差值和百分比差值分别为 72.27±134.84cm 和 21.08%±30.46%。根据我们的方法进行优化后，差值降至 40.22±96.06cm 和 10.16%±9.70%。
我们提出了一种新方法，通过整合 AI 自动勾画和 3D 肿瘤直径，提高了预测 WT 体积的准确性。该方法优于仅使用 AI 结果的准确性，有可能增强对儿科 WT 患者的临床评估。通过将 AI 结果与临床数据相结合，该方法更具可解释性，并在 Wilms 肿瘤之外具有广阔的应用前景，可扩展到其他儿科疾病。
