Zhang Lu, Gao Qiuru, Dou Yincong, Cheng Tianming, Xia Yuwei, Li Hailiang, Gao Song
Department of Medical Imaging, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Department of Radiology, Fuwai Central China Cardiovascular Hospital, Zhengzhou, Henan, China.
Front Oncol. 2024 Mar 21;14:1345576. doi: 10.3389/fonc.2024.1345576. eCollection 2024.
To evaluate the value of a nomogram combined MRI Diffusion Weighted Imaging (DWI) and clinical features to predict the treatment response of Neoadjuvant Chemotherapy (NAC) in patients with osteosarcoma.
A retrospective analysis was conducted on 209 osteosarcoma patients admitted into two bone cancer treatment centers (133 males, 76females; mean age 16.31 ± 11.42 years) from January 2016 to January 2022. Patients were classified as pathological good responders (pGRs) if postoperative histopathological examination revealed ≥90% tumor necrosis, and non-pGRs if <90%. Their clinical features were subjected to univariate and multivariate analysis, and features with statistically significance were utilized to construct a clinical signature using machine learning algorithms. Apparent diffusion coefficient (ADC) values pre-NAC (ADC 0) and post two chemotherapy cycles (ADC 1) were recorded. Regions of interest (ROIs) were delineated from pre-treatment DWI images (b=1000 s/mm²) for radiomic features extraction. Variance thresholding, SelectKBest, and LASSO regression were used to select features with strong relevance, and three machine learning models (Logistic Regression, RandomForest and XGBoost) were used to construct radiomics signatures for predicting treatment response. Finally, the clinical and radiomics signatures were integrated to establish a comprehensive nomogram model. Predictive performance was assessed using ROC curve analysis, with model clinical utility appraised through AUC and decision curve analysis (DCA).
Of the 209patients, 51 (24.4%) were pGRs, while 158 (75.6%) were non-pGRs. No significant ADC1 difference was observed between groups (P>0.05), but pGRs had a higher ADC 0 (P<0.01). ROC analysis indicated an AUC of 0.681 (95% CI: 0.482-0.862) for ADC 0 at the threshold of ≥1.37×10 mm²/s, achieving 74.7% sensitivity and 75.7% specificity. The clinical and radiomics models reached AUCs of 0.669 (95% CI: 0.401-0.826) and 0.768 (95% CI: 0.681-0.922) respectively in the test set. The combined nomogram displayed superior discrimination with an AUC of 0.848 (95% CI: 0.668-0.951) and 75.8% accuracy. The DCA suggested the clinical utility of the nomogram.
The nomogram based on combined radiomics and clinical features outperformed standalone clinical or radiomics model, offering enhanced accuracy in evaluating NAC response in osteosarcoma. It held significant promise for clinical applications.
评估列线图联合磁共振扩散加权成像（DWI）及临床特征预测骨肉瘤患者新辅助化疗（NAC）治疗反应的价值。
对2016年1月至2022年1月在两个骨癌治疗中心收治的209例骨肉瘤患者进行回顾性分析（男性133例，女性76例；平均年龄16.31±11.42岁）。若术后组织病理学检查显示肿瘤坏死≥90%，则患者被分类为病理良好反应者（pGRs）；若<90%，则为非pGRs。对其临床特征进行单因素和多因素分析，利用具有统计学意义的特征，采用机器学习算法构建临床特征。记录新辅助化疗前（ADC 0）及两个化疗周期后（ADC 1）的表观扩散系数（ADC）值。从治疗前DWI图像（b=1000 s/mm²）中勾勒出感兴趣区域（ROIs）以提取放射组学特征。采用方差阈值法、SelectKBest和LASSO回归选择相关性强的特征，并使用三种机器学习模型（逻辑回归、随机森林和XGBoost）构建预测治疗反应的放射组学特征。最后，将临床和放射组学特征整合以建立综合列线图模型。采用ROC曲线分析评估预测性能，并通过AUC和决策曲线分析（DCA）评估模型的临床实用性。
209例患者中，51例（24.4%）为pGRs，158例（75.6%）为非pGRs。两组间ADC1差异无统计学意义（P>0.05），但pGRs的ADC 0较高（P<0.01）。ROC分析显示，在阈值≥1.37×10 mm²/s时，ADC 0的AUC为0.681（95%CI：0.482-0.862），敏感性为74.7%，特异性为75.7%。临床和放射组学模型在测试集中的AUC分别为0.669（95%CI：0.401-0.826）和0.768（95%CI：0.681-0.922）。联合列线图显示出更好的辨别力，AUC为0.848（95%CI：0.668-0.951），准确率为75.8%。DCA表明列线图具有临床实用性。
基于放射组学和临床特征联合的列线图优于单独的临床或放射组学模型，在评估骨肉瘤新辅助化疗反应方面具有更高的准确性。它在临床应用中具有重要前景。
