Table_2_18F-FDG PET/CT Radiomics for Preoperative Prediction of Lymph Node Metastases and Nodal Staging in Gastric Cancer.docx

ObjectivesThe accurate assessment of lymph node metastases (LNMs) and the preoperative nodal (N) stage are critical for the precise treatment of patients with gastric cancer (GC). The diagnostic performance, however, of current imaging procedures used for this assessment is sub-optimal. Our aim was to investigate the value of preoperative 18F-FDG PET/CT radiomic features to predict LNMs and the N stage.
MethodsWe retrospectively collected clinical and 18F-FDG PET/CT imaging data of 185 patients with GC who underwent total or partial radical gastrectomy. Patients were allocated to training and validation sets using the stratified method at a fixed ratio (8:2). There were 2,100 radiomic features extracted from the 18F-FDG PET/CT scans. After selecting radiomic features by the random forest, relevancy-based, and sequential forward selection methods, the BalancedBagging ensemble classifier was established for the preoperative prediction of LNMs, and the OneVsRest classifier for the N stage. The performance of the models was primarily evaluated by the AUC and accuracy, and validated by the independent validation methods. Analysis of the feature importance and the correlation were also conducted. We also compared the predictive performance of our radiomic models to that with the contrast-enhanced CT (CECT) and 18F-FDG PET/CT.
ResultsThere were 185 patients—127 men, 58 women, with the median age of 62, and an age range of 22–86 years. One CT feature and one PET feature were selected to predict LNMs and achieved the best performance (AUC: 82.2%, accuracy: 85.2%). This radiomic model also detected some LNMs that were missed in CECT (19.6%) and 18F-FDG PET/CT (35.7%). For predicting the N stage, four CT features and one PET feature were selected (AUC: 73.7%, accuracy: 62.3%). Of note, a proportion of patients in the validation set whose LNMs were incorrectly staged by CECT (57.4%) and 18F-FDG PET/CT (55%) were diagnosed correctly by our radiomic model.
ConclusionWe developed and validated two machine learning models based on the preoperative 18F-FDG PET/CT images that have a predictive value for LNMs and the N stage in GC. These predictive models show a promise to offer a potentially useful adjunct to current staging approaches for patients with GC.

研究目的：准确评估淋巴结转移（lymph node metastases, LNMs）及术前淋巴结（N）分期对于胃癌（gastric cancer, GC）患者的精准治疗至关重要。然而，当前用于此类评估的影像学检查手段诊断效能尚不理想。本研究旨在探讨术前18F-氟代脱氧葡萄糖正电子发射断层显像/计算机断层成像（18F-FDG PET/CT）放射组学特征在预测胃癌淋巴结转移及N分期中的应用价值。

研究方法：本研究回顾性收集了185例行根治性全胃或部分胃切除术的胃癌患者的临床资料及18F-FDG PET/CT影像学数据。采用分层抽样法按8:2的固定比例将患者划分为训练集与验证集。从所有患者的18F-FDG PET/CT扫描图像中共提取出2100个放射组学特征。通过随机森林、相关性筛选及序列前向选择三种方法进行放射组学特征筛选后，分别构建平衡装袋（BalancedBagging）集成分类器用于术前预测淋巴结转移，以及一对多（OneVsRest）分类器用于预测N分期。模型性能主要以受试者工作特征曲线下面积（AUC）及准确率进行评估，并通过独立验证方法进行验证。此外，本研究还开展了特征重要性及相关性分析，并将本研究构建的放射组学模型的预测性能与增强计算机断层成像（contrast-enhanced CT, CECT）及常规18F-FDG PET/CT的预测性能进行了对比。

研究结果：本研究共纳入185例患者，其中男性127例，女性58例，中位年龄62岁，年龄范围22~86岁。针对淋巴结转移预测任务，筛选出1个CT特征与1个PET特征，该模型实现了最优性能（AUC：82.2%，准确率：85.2%）。本放射组学模型还检出了部分在CECT（19.6%）及常规18F-FDG PET/CT（35.7%）中被漏诊的淋巴结转移病例。针对N分期预测任务，共筛选出4个CT特征与1个PET特征，模型性能为AUC 73.7%，准确率62.3%。值得注意的是，在验证集中，有部分患者的淋巴结转移分期被CECT（57.4%）及常规18F-FDG PET/CT（55%）误判，而本放射组学模型对该类患者均做出了正确诊断。

研究结论：本研究基于术前18F-FDG PET/CT影像构建并验证了两款机器学习模型，二者可用于预测胃癌患者的淋巴结转移及N分期。上述预测模型有望为胃癌患者的现有分期诊疗方案提供潜在的实用辅助手段。