Lozano-Montoya Jose, Jimenez-Pastor Ana, Fuster-Matanzo Almudena, Weiss Glen J, Cerda-Alberich Leonor, Veiga-Canuto Diana, Martínez-de-Las-Heras Blanca, Cañete-Nieto Adela, Taschner-Mandl Sabine, Hero Barbara, Simon Thorsten, Ladenstein Ruth, Marti-Bonmati Luis, Alberich-Bayarri Angel
Research & Frontiers in AI Department, Quantitative Imaging Biomarkers in Medicine, Quibim SL, Valencia, Spain.
Medical Studies Department, Quantitative Imaging Biomarkers in Medicine, Quibim Inc., New York, NY, United States.
Front Oncol. 2025 Feb 21;15:1528836. doi: 10.3389/fonc.2025.1528836. eCollection 2025.
Neuroblastoma, the most prevalent solid cancer in children, presents significant biological and clinical heterogeneity. This inherent heterogeneity underscores the need for more precise prognostic markers at the time of diagnosis to enhance patient stratification, allowing for more personalized treatment strategies. In response, this investigation developed a machine learning model using clinical, molecular, and magnetic resonance (MR) radiomics features at diagnosis to predict patient's overall survival (OS) and improve their risk stratification.
PRIMAGE database, including 513 patients (discovery cohort), was used for model training, validation, and testing. Additional 22 patients from different hospitals served as an external independent cohort. Primary tumor segmentation on T2-weighted MR images was semi-automatically edited by an experienced radiologist. From this area, 107 radiomics features were extracted. For the development of the prediction model, radiomics features were harmonized following the nested ComBat methodology and nested cross-validation approach was employed to determine the optimal preprocessing and model configuration.
The discovery cohort yielded a 78.8 ± 4.9 and 77.7 ± 6.1 of C index and time-dependent area under de curve (AUC), respectively, over the test set, with a random survival forest exhibiting the best performance. In the independent cohort, a C-index of 93.4 and a time-dependent AUC of 95.4 were achieved. Interpretability analysis identified lesion heterogeneity, size, and molecular variables as crucial factors in OS prediction. The model stratified neuroblastoma patients into low-, intermediate-, and high-risk categories, demonstrating a superior stratification compared to standard-of-care classification system in both cohorts.
Our results suggested that radiomics features improve current risk stratification systems in patients with neuroblastoma.
神经母细胞瘤是儿童中最常见的实体癌，具有显著的生物学和临床异质性。这种内在的异质性凸显了在诊断时需要更精确的预后标志物，以加强患者分层，从而制定更个性化的治疗策略。为此，本研究开发了一种机器学习模型，利用诊断时的临床、分子和磁共振（MR）影像组学特征来预测患者的总生存期（OS）并改善其风险分层。
PRIMAGE数据库（包括513例患者，即发现队列）用于模型训练、验证和测试。另外22例来自不同医院的患者作为外部独立队列。由经验丰富的放射科医生对T2加权MR图像上的原发肿瘤进行半自动分割。从该区域提取了107个影像组学特征。为了开发预测模型，按照嵌套ComBat方法对影像组学特征进行了标准化处理，并采用嵌套交叉验证方法来确定最佳预处理和模型配置。
在测试集上，发现队列的C指数和曲线下时间依赖面积（AUC）分别为78.8±4.9和77.7±6.1，随机生存森林表现最佳。在独立队列中，C指数为93.4，时间依赖AUC为95.4。可解释性分析确定病变异质性、大小和分子变量是OS预测的关键因素。该模型将神经母细胞瘤患者分为低、中、高风险类别，在两个队列中均显示出比标准治疗分类系统更好的分层效果。
我们的结果表明，影像组学特征改善了神经母细胞瘤患者当前的风险分层系统。
