Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Huanhuxi Road, Hexi Distinct, Tianjin City, 300060, People's Republic of China.
National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for China, Tianjin, People's Republic of China.
Eur Radiol. 2021 Jun;31(6):3983-3992. doi: 10.1007/s00330-020-07466-5. Epub 2020 Nov 17.
The purpose of this study was to develop a classification method based on support vector machine (SVM) to improve the diagnostic performance of F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) to detect the lymph node (LN) metastasis in non-small cell lung cancer (NSCLC).
Two hundred nineteen lymph nodes (37 metastatic) from 71 patients were evaluated in this study. SVM models were developed with 7 LN features. The area under the curve (AUC) and accuracy of 9 models were compared to select the best model. The best SVM model was simplified on the basis of the feature weights and value distribution to further suit the clinical application.
The maximum, minimum, and mean accuracy of the best model was 91.89% (68/74, 95% CI 83.1196.54%), 66.22% (49/74, 95% CI 54.8575.98%), and 80.09% (59,266/74,000, 95% CI 70.27~89.19%), respectively, with an AUC of 0.94, 0.66, and 0.81, respectively. The best SVM model was finally simplified into a score rule: LNs with scores more than 3.0 were considered as malignant ones, whereas LNs with scores less than 1.5 tended to be benign ones. For the LNs with scores within a range of 1.5-3.0, metastasis was suspected.
An SVM model based on F-FDG PET/CT images was able to predict the metastatic LNs for patients with NSCLC. The ratio of the maximum of standard uptake value of LNs to aortic arch played a major role in the model. After simplification, the model could be transferred into a scoring method which may partly help clinicians determine the clinical staging of patients with NSCLC relatively easier.
• The SVM model based on F-FDG PET/CT features may help clinicians to make a decision for metastatic mediastinal lymph nodes in patients with NSCLC. • The SUR plays a major role in the SVM model. • The score rule based on the SVM model simplified the complexity of the model and may partly help clinicians determine the clinical staging of patients with NSCLC relatively easier.
本研究旨在开发一种基于支持向量机（SVM）的分类方法，以提高 F-氟脱氧葡萄糖（FDG）正电子发射断层扫描/计算机断层扫描（PET/CT）检测非小细胞肺癌（NSCLC）淋巴结（LN）转移的诊断性能。
本研究共评估了 71 例患者的 219 个淋巴结（37 个转移性）。使用 7 个 LN 特征建立 SVM 模型。比较了 9 个模型的曲线下面积（AUC）和准确性，以选择最佳模型。基于特征权重和值分布，对最佳 SVM 模型进行简化，以进一步适应临床应用。
最佳模型的最大、最小和准确率分别为 91.89%（68/74，95%CI83.1196.54%）、66.22%（49/74，95%CI54.8575.98%）和 80.09%（59/74，95%CI70.27~89.19%），AUC 分别为 0.94、0.66 和 0.81。最佳 SVM 模型最终简化为评分规则：评分大于 3.0 的 LN 被认为是恶性的，而评分小于 1.5 的 LN 倾向于良性。评分在 1.5-3.0 范围内的 LN，怀疑有转移。
基于 F-FDG PET/CT 图像的 SVM 模型能够预测 NSCLC 患者的转移性 LN。LN 标准摄取值与主动脉弓的比值在模型中起主要作用。简化后，该模型可转化为评分方法，可能有助于临床医生相对更容易地确定 NSCLC 患者的临床分期。
•基于 F-FDG PET/CT 特征的 SVM 模型可能有助于临床医生对 NSCLC 患者的纵隔转移性淋巴结做出决策。•SUR 在 SVM 模型中起主要作用。•基于 SVM 模型的评分规则简化了模型的复杂性，可能有助于临床医生相对更容易地确定 NSCLC 患者的临床分期。
