Zhou Ziang, Wang Chao, Xu Yanfeng, Wang Xiaoya, Wang Guanyun, Zhang Hui, Wang Wei, Yang Jigang
Nuclear Medicine Department, Beijing Friendship Hospital, Capital Medical University, 95 Yong'an Road, Xicheng District, Beijing, 100050, China.
Department of Clinical Research, SinoUnion Healthcare Inc, Beijing, China.
Eur J Pediatr. 2025 Jun 7;184(7):399. doi: 10.1007/s00431-025-06233-2.
The purpose of the study is to employ a radiomics approach based on I-MIBG SPECT/CT imaging to predict pathological subtypes in peripheral neuroblastic tumors (pNTs). A retrospective and exploratory study was conducted involving 67 pediatric patients with pNTs, who were randomly divided into training and validation cohorts at a ratio of 7:3. Clinical and radiomics features were selected using univariate feature selection and recursive feature elimination methods. By integrating clinical and radiomics features, a combined model based on logistic regression and a voting classifier incorporating four algorithms were constructed to optimize prediction accuracy. A total of 1702 features were extracted from SPECT and CT features. Ultimately, six clinical and nine radiomic features were included in our analysis. The combined model integrating clinical and radiomic features achieved a macro-average area under the curve (AUC) of 0.871 and an overall accuracy of 80.4% in the training set, and a macro-average AUC of 0.836 with an overall accuracy of 81.0% in the test set. The voting classifier significantly improved performance, achieving a macro-average AUC of 0.968 with an overall accuracy of 87.0% in the training set, and achieved a macro-average AUC of 0.879 in the test set, demonstrating robust stability and high accuracy.
The study demonstrates the potential of radiomics as a non-invasive diagnostic tool for differentiating pathological subtypes of pNTs, which could significantly influence treatment planning and surgical decisions.
• Peripheral neuroblastic tumors are the most common solid tumor in childhood. • Different pathological types exhibit distinctive cytomorphology and prognosis.
•The voting classifier based on clinical and radiomics features has been described in detail. •A non-intrusive diagnostic method for discriminating pathological types of peripheral neuroblastic tumors has been established.
本研究的目的是采用基于碘代间位碘苄胍（I-MIBG）单光子发射计算机断层扫描/计算机断层扫描（SPECT/CT）成像的放射组学方法来预测外周神经母细胞瘤（pNTs）的病理亚型。进行了一项回顾性探索性研究，纳入67例患有pNTs的儿科患者，这些患者以7:3的比例随机分为训练组和验证组。使用单变量特征选择和递归特征消除方法选择临床和放射组学特征。通过整合临床和放射组学特征，构建了基于逻辑回归的组合模型和包含四种算法的投票分类器以优化预测准确性。从SPECT和CT特征中总共提取了1702个特征。最终，我们的分析纳入了6个临床特征和9个放射组学特征。整合临床和放射组学特征的组合模型在训练集中的曲线下面积（AUC）宏平均为0.871，总体准确率为80.4%，在测试集中的AUC宏平均为0.836，总体准确率为81.0%。投票分类器显著提高了性能，在训练集中的AUC宏平均为0.968，总体准确率为87.0%，在测试集中的AUC宏平均为0.879，显示出强大的稳定性和高准确性。
本研究证明了放射组学作为一种非侵入性诊断工具用于区分pNTs病理亚型的潜力，这可能会显著影响治疗计划和手术决策。
•外周神经母细胞瘤是儿童最常见的实体瘤。•不同的病理类型表现出独特的细胞形态和预后。
•详细描述了基于临床和放射组学特征的投票分类器。•建立了一种用于鉴别外周神经母细胞瘤病理类型的非侵入性诊断方法。
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