Folkert Michael R, Setton Jeremy, Apte Aditya P, Grkovski Milan, Young Robert J, Schöder Heiko, Thorstad Wade L, Lee Nancy Y, Deasy Joseph O, Oh Jung Hun
Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States of America.
Phys Med Biol. 2017 Jul 7;62(13):5327-5343. doi: 10.1088/1361-6560/aa73cc. Epub 2017 Jun 12.
In this study, we investigate the use of imaging feature-based outcomes research ('radiomics') combined with machine learning techniques to develop robust predictive models for the risk of all-cause mortality (ACM), local failure (LF), and distant metastasis (DM) following definitive chemoradiation therapy (CRT). One hundred seventy four patients with stage III-IV oropharyngeal cancer (OC) treated at our institution with CRT with retrievable pre- and post-treatment 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) scans were identified. From pre-treatment PET scans, 24 representative imaging features of FDG-avid disease regions were extracted. Using machine learning-based feature selection methods, multiparameter logistic regression models were built incorporating clinical factors and imaging features. All model building methods were tested by cross validation to avoid overfitting, and final outcome models were validated on an independent dataset from a collaborating institution. Multiparameter models were statistically significant on 5 fold cross validation with the area under the receiver operating characteristic curve (AUC) = 0.65 (p = 0.004), 0.73 (p = 0.026), and 0.66 (p = 0.015) for ACM, LF, and DM, respectively. The model for LF retained significance on the independent validation cohort with AUC = 0.68 (p = 0.029) whereas the models for ACM and DM did not reach statistical significance, but resulted in comparable predictive power to the 5 fold cross validation with AUC = 0.60 (p = 0.092) and 0.65 (p = 0.062), respectively. In the largest study of its kind to date, predictive features including increasing metabolic tumor volume, increasing image heterogeneity, and increasing tumor surface irregularity significantly correlated to mortality, LF, and DM on 5 fold cross validation in a relatively uniform single-institution cohort. The LF model also retained significance in an independent population.
在本研究中，我们调查了基于影像特征的结局研究（“放射组学”）与机器学习技术的结合使用，以开发用于明确放化疗（CRT）后全因死亡率（ACM）、局部失败（LF）和远处转移（DM）风险的稳健预测模型。确定了174例在我们机构接受CRT治疗的III-IV期口咽癌（OC）患者，这些患者有可检索的治疗前和治疗后18F-氟脱氧葡萄糖正电子发射断层扫描（FDG-PET）。从治疗前PET扫描中，提取了FDG摄取疾病区域的24个代表性影像特征。使用基于机器学习的特征选择方法，构建了纳入临床因素和影像特征的多参数逻辑回归模型。所有模型构建方法均通过交叉验证进行测试以避免过拟合，最终结局模型在来自合作机构的独立数据集上进行验证。多参数模型在5折交叉验证中具有统计学意义，ACM、LF和DM的受试者工作特征曲线下面积（AUC）分别为0.65（p = 0.004）、0.73（p = 0.026）和0.66（p = 0.015）。LF模型在独立验证队列中仍具有显著性，AUC = 0.68（p = 0.029），而ACM和DM模型未达到统计学意义，但与5折交叉验证相比具有相当的预测能力，AUC分别为0.60（p = 0.092）和0.65（p = 0.062）。在迄今为止同类最大规模的研究中，在相对统一的单机构队列中，5折交叉验证显示预测特征包括代谢肿瘤体积增加、图像异质性增加和肿瘤表面不规则性增加与死亡率、LF和DM显著相关。LF模型在独立人群中也具有显著性。
