Lu Di, Peng Jinxing, Wang Zhongju, Sun Ying, Zhai Jianxue, Wang Zhizhi, Chen Zhiming, Matsumoto Yuji, Wang Long, Xin Sherman Xuegang, Cai Kaican
Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China.
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China.
Transl Lung Cancer Res. 2022 Mar;11(3):342-356. doi: 10.21037/tlcr-22-92.
One of the important criteria for thoracic surgeons in making surgical strategies is whether the thoracic lymph nodes (LNs) are metastatic. Frozen section (FS) is widely used as an intraoperative diagnostic method, which is time-consuming and expensive. The dielectric property, including permittivity and conductivity, varies with different tissues. The extreme gradient boosting (XGBoost) is a powerful classifier and widely used. Thus, this study aims to develop the rapid differentiation method combining dielectric property and XGBoost, and assess its efficacy on the thoracic LNs in patients with non-small cell lung cancer (NSCLC).
This was a single center self-control clinical trial with paraffin pathology section (PPS) results as gold diagnosis. The LNs from the pathologically diagnosed patients with NSCLC were recruited, which were measured by open-ended coaxial probe for the dielectric property within 1-4,000 MHz after removal from the patients and then were sent to perform FS and PPS diagnosis. The XGBoost combining with dielectric property was developed to differentiate malignant LNs from benign LNs. The classified efficacy was determined using the receiver operator characteristic (ROC) curve and area under the curve (AUC).
A total of 204 LNs from 67 NSCLC patients were analyzed. The mean values of the two parameters differed significantly (P<0.001) between benign and malignant LNs. The AUC for permittivity and conductivity were 0.850 [95% confidence interval (CI): 0.786 to 0.915; P<0.001] and 0.887 (95% CI: 0.828 to 0.946; P<0.001), respectively. The AUC was 0.893 (95% CI: 0.834 to 0.951; P<0.001) when the two parameters were combined. After the application of the XGBoost, the AUC was 0.968 (95% CI: 0.918 to 1.000; P<0.001), and the accuracy was 87.80%. Its sensitivity was 58.33% and the specificity was 100%. When the Synthetic Minority Oversampling Technique (SMOTE) algorithm was used, the AUC was 0.954 (95% CI: 0.883 to 1.000; P<0.001) and the accuracy was 92.68%. Its sensitivity was 83.33% and the specificity was 96.55%.
This method might be useful for thoracic surgeons during surgery, for its relatively high efficacy in rapid differentiation of LNs for patients with NSCLC.
胸外科医生制定手术策略的重要标准之一是胸段淋巴结（LN）是否发生转移。冰冻切片（FS）作为一种术中诊断方法被广泛应用，但它既耗时又昂贵。包括介电常数和电导率在内的介电特性会因不同组织而有所变化。极端梯度提升（XGBoost）是一种强大的分类器且应用广泛。因此，本研究旨在开发一种结合介电特性和XGBoost的快速鉴别方法，并评估其对非小细胞肺癌（NSCLC）患者胸段LN的鉴别效果。
这是一项以石蜡病理切片（PPS）结果作为金标准诊断的单中心自身对照临床试验。招募经病理诊断为NSCLC患者的LN，在从患者体内取出后，用开放式同轴探头在1 - 4000 MHz范围内测量其介电特性，然后送去进行FS和PPS诊断。开发了结合介电特性的XGBoost来区分恶性LN和良性LN。使用受试者工作特征（ROC）曲线和曲线下面积（AUC）来确定分类效果。
共分析了67例NSCLC患者的204个LN。良性和恶性LN的这两个参数的平均值差异显著（P<0.001）。介电常数和电导率的AUC分别为0.850 [95%置信区间（CI）：0.786至0.915；P<0.001]和0.887（95%CI：0.828至0.946；P<0.001）。当两个参数结合时，AUC为0.893（95%CI：0.834至0.951；P<0.
