Background/Objectives: Osteosarcoma (OS) is the most common malignant bone tumor in children and adolescents; the survival rate is as low as 24%. Accurate prediction of clinical outcomes remains a challenge due to tumor heterogeneity and the complexity of pediatric cases. This study aims to improve predictions of progressive disease, therapy response, relapse, and survival in pediatric OS using MRI-based radiomics and machine learning methods.Methods: Pre-treatment contrast-enhanced coronal T1-weighted MR scans were collected from 63 pediatric OS patients, with an additional nine external cases used for validation. Three strategies were considered for target region segmentation (whole-tumor, tumor sampling, and bone/soft tissue) and used for MRI-based radiomics. These were then combined with clinical features to predict OS clinical outcomes.Results: The mean age of OS patients was 11.8 ± 3.5 years. Most tumors were located in the femur (65%). Osteoblastic subtype was the most common histological classification (79%). The majority of OS patients (79%) did not have evidence of metastasis at diagnosis. Progressive disease occurred in 27% of patients, 59% of patients showed adequate therapy response, 25% experienced relapse after therapy, and 30% died from OS. Classification models based on bone/soft tissue segmentation generally performed the best, with certain clinical features improving performance, especially for therapy response and mortality. The top performing classifier in each outcome achieved 0.94–1.0 validation ROC AUC and 0.63–1.0 testing ROC AUC, while those without radiomic features (RFs) generally performed suboptimally.Conclusions: This study demonstrates the strong predictive capabilities of MRI-based radiomics and multi-region segmentations for predicting clinical outcomes in pediatric OS.
背景/目的：骨肉瘤是儿童及青少年中最常见的恶性骨肿瘤，其生存率低至24%。由于肿瘤异质性及儿科病例的复杂性，准确预测临床结局仍具挑战。本研究旨在利用基于磁共振成像的影像组学与机器学习方法，提升对儿童骨肉瘤疾病进展、治疗反应、复发及生存情况的预测能力。 方法：研究收集了63例儿童骨肉瘤患者治疗前的增强冠状位T1加权磁共振扫描数据，并额外纳入9例外部队列病例用于验证。针对基于磁共振的影像组学分析，采用了三种靶区分割策略（全肿瘤分割、肿瘤取样分割、骨/软组织分割），并结合临床特征共同预测骨肉瘤的临床结局。 结果：骨肉瘤患者的平均年龄为11.8±3.5岁。肿瘤最常见于股骨（65%）。组织学分类以成骨细胞型最为常见（79%）。多数患者（79%）在诊断时未见转移证据。27%的患者出现疾病进展，59%的患者治疗反应良好，25%的患者治疗后复发，30%的患者因骨肉瘤死亡。基于骨/软组织分割的分类模型总体表现最佳，特定临床特征的加入进一步提升了模型性能，尤其在治疗反应和死亡率预测方面。各临床结局预测中表现最优的分类器在验证集上获得0.94–1.0的ROC曲线下面积，测试集上获得0.63–1.0的ROC曲线下面积，而未纳入影像组学特征的模型普遍表现欠佳。 结论：本研究证实基于磁共振成像的影像组学结合多区域分割策略对儿童骨肉瘤临床结局具有强大的预测能力。
MRI-Based Radiomics for Outcome Stratification in Pediatric Osteosarcoma
肿瘤(癌症)患者之家