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Integrated strategy combining endobronchial ultrasound with positron emission tomography to diagnose peripheral pulmonary lesions.

综合策略联合支气管内超声和正电子发射断层扫描诊断周围性肺部病变。

  • 影响因子:2.17
  • DOI:10.1111/1759-7714.13484
  • 作者列表:"Wu X","An Z","Zhao K","Yang S","Lin X","Dai X","Radisky D","Hu J
  • 发表时间:2020-06-16
Abstract

BACKGROUND:Endobronchial ultrasound-guided transbronchial lung biopsy (EBUS-TBLB) and fluorodeoxyglucose positron emission tomography (FDG-PET) have been widely used in the diagnosis of peripheral pulmonary lesions (PPLs). This study was conducted to determine the diagnostic value of EBUS-TBLB combined with FDG-PET in the assessment of PPLs. METHODS:The clinical data of 76 patients with PPLs who received both FDG-PET and EBUS-TBLB from January 2016 to February 2018 were retrospectively evaluated. Further subgroup analysis was performed according to lesion diameter (≤20 mm or >20 mm). Related diagnostic indices were calculated and compared between groups. RESULTS:When combining EBUS-TBLB with FDG-PET, the diagnostic accuracy rate, sensitivity, specificity, Youden's index, positive predictive value, and negative predictive value for PPLs were 86.8%, 90.2%, 73.3%, 63.5%, 93.2%, and 64.7%, respectively. In addition, the diagnostic accuracy rate of the combined approach was significantly higher than the single EBUS-TBLB and FDG-PET (P < 0.01 and P < 0.05, respectively), and its Youden's index was also at a higher level. When stratified by lesion diameter, the combined approach showed a significantly higher diagnostic accuracy rate (P < 0.05) and a higher Youden's index for PPLs >20 mm than PPLs ≤20 mm. In addition, we found that positive bronchus sign and probe within the probe were two important factors conducing to enhancing the diagnostic accuracy rate for EBUS-TBLB. CONCLUSIONS:An integrated approach combining EBUS-TBLB with FDG-PET is particularly useful for diagnosing PPLs, and the improved diagnostic yields were especially evident for PPLs >20 mm.

摘要

背景: 支气管内超声引导下经支气管肺活检 (EBUS-TBLB) 和氟脱氧葡萄糖正电子发射断层扫描 (FDG-PET) 已广泛应用于周围型肺部病变 (PPLs) 的诊断。本研究旨在确定 EBUS-TBLB 联合 FDG-PET 在 PPLs 评估中的诊断价值。 方法: 回顾性分析 2016 年 1 月至 2018 年 2 月接受 FDG-PET 和 EBUS-TBLB 治疗的 76 例 PPLs 患者的临床资料。根据病灶直径 (≤ 20mm 或> 20mm) 进行进一步的亚组分析。计算相关诊断指标,并进行组间比较。 结果: EBUS-TBLB 联合 FDG-PET 诊断 PPLs 的准确率、敏感性、特异性、 Youden 指数、阳性预测值、阴性预测值分别为 86.8% 、 90.2% 、分别为 73.3% 、 63.5% 、 93.2% 和 64.7%。此外,联合入路的诊断准确率明显高于单一 EBUS-TBLB 和 FDG-PET (分别为 p <0.01 和 p <0.05),其 Youden 指数也处于较高水平。按病变直径分层时,联合入路诊断准确率明显高于 PPLs ≤ 20mm (p <0.05),且 PPLs >20mm 的 Youden 指数高于 PPLs ≤ 20mm。此外,我们发现支气管征阳性和探头内探头是提高 EBUS-TBLB 诊断准确率的两个重要因素。 结论: 将 EBUS-TBLB 与 FDG-PET 相结合的综合方法对诊断 PPLs 特别有用,对于> 20mm 的 PPLs,诊断率的提高尤为明显。

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影响因子:3.31
发表时间:2020-01-02
DOI:10.1007/s10916-019-1481-4
作者列表:["Matava, Clyde","Pankiv, Evelina","Raisbeck, Sam","Caldeira, Monica","Alam, Fahad"]

METHODS:Background The use of artificial intelligence, including machine learning, is increasing in medicine. Use of machine learning is rising in the prediction of patient outcomes. Machine learning may also be able to enhance and augment anesthesia clinical procedures such as airway management. In this study, we sought to develop a machine learning algorithm that could classify vocal cords and tracheal airway anatomy real-time during video laryngoscopy or bronchoscopy as well as compare the performance of three novel convolutional networks for detecting vocal cords and tracheal rings. Methods Following institutional approval, a clinical dataset of 775 video laryngoscopy and bronchoscopy videos was used. The dataset was divided into two categories for use for training and testing. We used three convolutional neural networks (CNNs): ResNet, Inception and MobileNet. Backpropagation and a mean squared error loss function were used to assess accuracy as well as minimize bias and variance. Following training, we assessed transferability using the generalization error of the CNN, sensitivity and specificity, average confidence error, outliers, overall confidence percentage, and frames per second for live video feeds. After the training was complete, 22 models using 0 to 25,000 steps were generated and compared. Results The overall confidence of classification for the vocal cords and tracheal rings for ResNet, Inception and MobileNet CNNs were as follows: 0.84, 0.78, and 0.64 for vocal cords, respectively, and 0.69, 0.72, 0.54 for tracheal rings, respectively. Transfer learning following additional training resulted in improved accuracy of ResNet and Inception for identifying the vocal cords (with a confidence of 0.96 and 0.93 respectively). The two best performing CNNs, ResNet and Inception, achieved a specificity of 0.985 and 0.971, respectively, and a sensitivity of 0.865 and 0.892, respectively. Inception was able to process the live video feeds at 10 FPS while ResNet processed at 5 FPS. Both were able to pass a feasibility test of identifying vocal cords and tracheal rings in a video feed. Conclusions We report the development and evaluation of a CNN that can identify and classify airway anatomy in real time. This neural network demonstrates high performance. The availability of artificial intelligence may improve airway management and bronchoscopy by helping to identify key anatomy real time. Thus, potentially improving performance and outcomes during these procedures. Further, this technology may theoretically be extended to the settings of airway pathology or airway management in the hands of experienced providers. The researchers in this study are exploring the performance of this neural network in clinical trials.

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影响因子:3.84
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DOI:10.1016/j.chest.2019.06.018
作者列表:["Dhooria S","Chaudhary S","Ram B","Sehgal IS","Muthu V","Prasad KT","Aggarwal AN","Agarwal R"]

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