Patel Yogita S, Gatti Anthony A, Farrokhyar Forough, Xie Feng, Hanna Waël C
Division of Thoracic Surgery, Department of Surgery, McMaster University, Hamilton, Ontario, Canada.
Department of Radiology, Stanford University, Stanford, Calif.
JTCVS Tech. 2024 Jul 24;27:158-166. doi: 10.1016/j.xjtc.2024.06.024. eCollection 2024 Oct.
Endobronchial ultrasound elastography produces a color map of mediastinal lymph nodes, with the color blue (level 60) indicating stiffness. Our pilot study demonstrated that predominantly blue lymph nodes, with a stiffness area ratio greater than 0.496, are likely malignant. This large-scale study aims to validate this stiffness area ratio compared with pathology.
This is a single-center prospective clinical trial where B-mode ultrasound and endobronchial ultrasound elastography lymph node images were collected from patients undergoing endobronchial ultrasound transbronchial needle aspiration for suspected or diagnosed non-small cell lung cancer. Images were fed to a trained deep neural network algorithm (NeuralSeg), which segmented the lymph nodes, identified the percent of lymph node area above the color blue threshold of level 60, and assigned a malignant label to lymph nodes with a stiffness area ratio above 0.496. Diagnostic statistics and receiver operating characteristic analyses were conducted. NeuralSeg predictions were compared with pathology.
B-mode ultrasound and endobronchial ultrasound elastography lymph node images (n = 210) were collected from 124 enrolled patients. Only lymph nodes with conclusive pathology results (n = 187) were analyzed. NeuralSeg was able to predict 98 of 143 true negatives and 34 of 44 true positives, resulting in an overall accuracy of 70.59% (95% CI, 63.50-77.01), sensitivity of 43.04% (95% CI, 31.94-54.67), specificity of 90.74% (95% CI, 83.63-95.47), positive predictive value of 77.27% (95% CI, 64.13-86.60), negative predictive value of 68.53% (95% CI, 64.05-72.70), and area under the curve of 0.820 (95% CI, 0.758-0.883).
NeuralSeg was able to predict nodal malignancy based on endobronchial ultrasound elastography lymph node images with high area under the receiver operating characteristic curve and specificity. This technology should be refined further by testing its validity and applicability through a larger dataset in a multicenter trial.
支气管内超声弹性成像可生成纵隔淋巴结的彩色图谱，蓝色（60级）表示硬度。我们的初步研究表明，硬度面积比大于0.496且主要为蓝色的淋巴结可能为恶性。这项大规模研究旨在将该硬度面积比与病理结果进行验证。
这是一项单中心前瞻性临床试验，从接受支气管内超声引导经支气管针吸活检以诊断疑似或确诊非小细胞肺癌的患者中收集B型超声和支气管内超声弹性成像淋巴结图像。将图像输入经过训练的深度神经网络算法（NeuralSeg），该算法对淋巴结进行分割，识别出高于60级蓝色阈值的淋巴结面积百分比，并为硬度面积比高于0.496的淋巴结赋予恶性标签。进行诊断统计和受试者操作特征分析。将NeuralSeg的预测结果与病理结果进行比较。
从124名入组患者中收集了B型超声和支气管内超声弹性成像淋巴结图像（n = 210）。仅分析病理结果明确的淋巴结（n = 187）。NeuralSeg能够预测143个真阴性中的98个和44个真阳性中的34个，总体准确率为70.59%（95%CI，63.50 - 77.01），敏感性为43.04%（95%CI，31.94 - 54.67），特异性为90.74%（95%CI，83.63 - 95.47），阳性预测值为77.27%（95%CI，64.13 - 86.60），阴性预测值为68.53%（95%CI，64.05 - 72.70），曲线下面积为0.820（95%CI，0.758 - 0.883）。
NeuralSeg能够根据支气管内超声弹性成像淋巴结图像预测淋巴结恶性情况，受试者操作特征曲线下面积和特异性较高。应通过多中心试验中的更大数据集测试其有效性和适用性，进一步完善该技术。
