Chen Xiong, Chen Qinchang, Liu Yuanfang, Qiu Ya, Lv Lin, Zhang Zhengtao, Yin Xuntao, Shu Fangpeng
Department of Paediatric Urology, Guangzhou Women and Children's Medical Center Guangzhou Medical University Guangzhou China.
Department of Paediatric Surgery, Guangzhou Institute of Paediatrics Guangzhou Medical University Guangzhou China.
Cancer Innov. 2024 Jun 28;3(5):e135. doi: 10.1002/cai2.135. eCollection 2024 Oct.
Bone marrow is the leading site for metastasis from neuroblastoma and affects the prognosis of patients with neuroblastoma. However, the accurate diagnosis of bone marrow metastasis is limited by the high spatial and temporal heterogeneity of neuroblastoma. Radiomics analysis has been applied in various cancers to build accurate diagnostic models but has not yet been applied to bone marrow metastasis of neuroblastoma.
We retrospectively collected information from 187 patients pathologically diagnosed with neuroblastoma and divided them into training and validation sets in a ratio of 7:3. A total of 2632 radiomics features were retrieved from venous and arterial phases of contrast-enhanced computed tomography (CT), and nine machine learning approaches were used to build radiomics models, including multilayer perceptron (MLP), extreme gradient boosting, and random forest. We also constructed radiomics-clinical models that combined radiomics features with clinical predictors such as age, gender, ascites, and lymph gland metastasis. The performance of the models was evaluated with receiver operating characteristics (ROC) curves, calibration curves, and risk decile plots.
The MLP radiomics model yielded an area under the ROC curve (AUC) of 0.97 (95% confidence interval [CI]: 0.95-0.99) on the training set and 0.90 (95% CI: 0.82-0.95) on the validation set. The radiomics-clinical model using an MLP yielded an AUC of 0.93 (95% CI: 0.89-0.96) on the training set and 0.91 (95% CI: 0.85-0.97) on the validation set.
MLP-based radiomics and radiomics-clinical models can precisely predict bone marrow metastasis in patients with neuroblastoma.
骨髓是神经母细胞瘤转移的主要部位，影响神经母细胞瘤患者的预后。然而，神经母细胞瘤高度的时空异质性限制了骨髓转移的准确诊断。放射组学分析已应用于多种癌症以建立准确的诊断模型，但尚未应用于神经母细胞瘤的骨髓转移。
我们回顾性收集了187例经病理诊断为神经母细胞瘤患者的信息，并按7:3的比例将他们分为训练集和验证集。从对比增强计算机断层扫描（CT）的静脉期和动脉期提取了总共2632个放射组学特征，并使用九种机器学习方法建立放射组学模型，包括多层感知器（MLP）、极端梯度提升和随机森林。我们还构建了将放射组学特征与年龄、性别、腹水和淋巴结转移等临床预测因素相结合的放射组学-临床模型。使用受试者工作特征（ROC）曲线、校准曲线和风险十分位数图评估模型的性能。
MLP放射组学模型在训练集上的ROC曲线下面积（AUC）为0.97（95%置信区间[CI]：0.95-0.99），在验证集上为0.90（95%CI：0.82-0.95）。使用MLP的放射组学-临床模型在训练集上的AUC为0.93（95%CI：0.89-0.96），在验证集上为0.91（95%CI：0.85-0.97）。
基于MLP的放射组学和放射组学-临床模型可以精确预测神经母细胞瘤患者的骨髓转移。
