Publications by authors named "Xinxin Zhi"

5 Publications

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Automatic Image Selection Model Based on Machine Learning for Endobronchial Ultrasound Strain Elastography Videos.

Front Oncol 2021 31;11:673775. Epub 2021 May 31.

School of Electronic Information & Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.

Background: Endoscopic ultrasound (EBUS) strain elastography can diagnose intrathoracic benign and malignant lymph nodes (LNs) by reflecting the relative stiffness of tissues. Due to strong subjectivity, it is difficult to give full play to the diagnostic efficiency of strain elastography. This study aims to use machine learning to automatically select high-quality and stable representative images from EBUS strain elastography videos.

Methods: LNs with qualified strain elastography videos from June 2019 to November 2019 were enrolled in the training and validation sets randomly at a quantity ratio of 3:1 to train an automatic image selection model using machine learning algorithm. The strain elastography videos in December 2019 were used as the test set, from which three representative images were selected for each LN by the model. Meanwhile, three experts and three trainees selected one representative image severally for each LN on the test set. Qualitative grading score and four quantitative methods were used to evaluate images above to assess the performance of the automatic image selection model.

Results: A total of 415 LNs were included in the training and validation sets and 91 LNs in the test set. Result of the qualitative grading score showed that there was no statistical difference between the three images selected by the machine learning model. Coefficient of variation (CV) values of the four quantitative methods in the machine learning group were all lower than the corresponding CV values in the expert and trainee groups, which demonstrated great stability of the machine learning model. Diagnostic performance analysis on the four quantitative methods showed that the diagnostic accuracies were range from 70.33% to 73.63% in the trainee group, 78.02% to 83.52% in the machine learning group, and 80.22% to 82.42% in the expert group. Moreover, there were no statistical differences in corresponding mean values of the four quantitative methods between the machine learning and expert groups (p >0.05).

Conclusion: The automatic image selection model established in this study can help select stable and high-quality representative images from EBUS strain elastography videos, which has great potential in the diagnosis of intrathoracic LNs.
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http://dx.doi.org/10.3389/fonc.2021.673775DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8201408PMC
May 2021

Endobronchial Ultrasound Multimodal Imaging for the Diagnosis of Intrathoracic Lymph Nodes.

Respiration 2021 Jun 2:1-11. Epub 2021 Jun 2.

Department of Respiratory Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.

Background: Endobronchial ultrasound (EBUS) imaging is valuable in diagnosing intrathoracic lymph nodes (LNs), but there has been little analysis of multimodal imaging. This study aimed to comprehensively compare the diagnostic performance of single and multimodal combinations of EBUS imaging in differentiating benign and malignant intrathoracic LNs.

Methods: Subjects from July 2018 to June 2019 were consecutively enrolled in the model group and July 2019 to August 2019 in the validation group. Sonographic features of three EBUS modes were analysed in the model group for the identification of malignant LNs from benign LNs. The validation group was used to verify the diagnostic efficiency of single and multimodal diagnostic methods built in the model group.

Results: 373 LNs (215 malignant and 158 benign) from 335 subjects and 138 LNs (79 malignant and 59 benign) from 116 subjects were analysed in the model and validation groups, respectively. For single mode, elastography had the best diagnostic value, followed by grayscale and Doppler. The corresponding accuracies in the validation group were 83.3%, 76.8%, and 71.0%, respectively. Grayscale with elastography had the best diagnostic efficiency of multimodal methods. When at least two of the three features (absence of central hilar structure, heterogeneity, and qualitative elastography score 4-5) were positive, the sensitivity, specificity, and accuracy in the validation group were 88.6%, 78.0%, and 84.1%, respectively.

Conclusions: In both model and validation groups, elastography performed the best in single EBUS modes, as well as grayscale combined with elastography in multimodal imaging. Elastography alone or combined with grayscale are feasible to help predict intrathoracic benign and malignant LNs.
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http://dx.doi.org/10.1159/000515664DOI Listing
June 2021

Deep learning with convex probe endobronchial ultrasound multimodal imaging: A validated tool for automated intrathoracic lymph nodes diagnosis.

Endosc Ultrasound 2021 Feb 9. Epub 2021 Feb 9.

School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.

Background And Objectives: Along with the rapid improvement of imaging technology, convex probe endobronchial ultrasound (CP-EBUS) sonographic features play an increasingly important role in the diagnosis of intrathoracic lymph nodes (LNs). Conventional qualitative and quantitative methods for EBUS multimodal imaging are time-consuming and rely heavily on the experience of endoscopists. With the development of deep-learning (DL) models, there is great promise in the diagnostic field of medical imaging.

Materials And Methods: We developed DL models to retrospectively analyze CP-EBUS images of 294 LNs from 267 patients collected between July 2018 and May 2019. The DL models were trained on 245 LNs to differentiate benign and malignant LNs using both unimodal and multimodal CP-EBUS images and independently evaluated on the remaining 49 LNs to validate their diagnostic efficiency. The human comparator group consisting of three experts and three trainees reviewed the same test set as the DL models.

Results: The multimodal DL framework achieves an accuracy of 88.57% (95% confidence interval [CI] [86.91%-90.24%]) and area under the curve (AUC) of 0.9547 (95% CI [0.9451-0.9643]) using the three modes of CP-EBUS imaging in comparison to the accuracy of 80.82% (95% CI [77.42%-84.21%]) and AUC of 0.8696 (95% CI [0.8369-0.9023]) by experts. Statistical comparison of their average receiver operating curves shows a statistically significant difference (P < 0.001). Moreover, the multimodal DL framework is more consistent than experts (kappa values 0.7605 vs. 0.5800).

Conclusions: The DL models based on CP-EBUS imaging demonstrated an accurate automated tool for diagnosis of the intrathoracic LNs with higher diagnostic efficiency and consistency compared with experts.
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http://dx.doi.org/10.4103/EUS-D-20-00207DOI Listing
February 2021

Scoring model of convex probe endobronchial ultrasound multimodal imaging in differentiating benign and malignant lung lesions.

J Thorac Dis 2020 Dec;12(12):7645-7655

Department of Respiratory Endoscopy, Shanghai Jiao Tong University, Shanghai, China.

Background: Convex probe endobronchial ultrasound images can reflect the morphology, blood flow status and stiffness of the lesions. Endobronchial ultrasound multimodal imaging has great value for the diagnosis of intrathoracic lymph nodes. This study aimed to analyze the application of endobronchial ultrasound multimodal imaging on lung lesions.

Methods: Patients undergoing endobronchial ultrasound-guided transbronchial needle aspiration in Shanghai Chest Hospital from July 2018 to December 2019 were retrospectively enrolled. Nine grayscale features (long and short axes, margin, shape, lobulation sign, echogenicity, necrosis, liquefaction, calcification, and air-bronchogram), blood flow volume and elastography five-score method were analyzed to explore the best diagnostic method. The gold standard for diagnosing lesions depends on the histological and cytopathological findings of endobronchial ultrasound-guided transbronchial needle aspiration, transthoracic biopsy, resected sample of lesions, microbiological examination or clinical follow-up of at least 6 months.

Results: Endobronchial ultrasound multimodal imaging of 97 malignant lung lesions and 19 benign lung lesions from 116 patients were analyzed. There were statistically significant differences in distinct margin, presence of lobulation sign, presence of necrosis, and elastography grading score 4-5 between malignant and benign lung lesions, among which presence of lobulation sign and elastography grading score 4-5 were independent predictors. A diagnostic scoring model was then constructed based on the above four features, and when two or more features were present, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy for malignant lung lesions prediction were 92.78%, 57.89%, 91.84%, 61.11% and 87.07%, respectively.

Conclusions: The combination of endobronchial ultrasound grayscale and elastography has potential value for malignant and benign lung lesions differentiation. The diagnostic scoring model established in this study needs further validation to guide the malignant and benign diagnosis of lung lesions.
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http://dx.doi.org/10.21037/jtd-2020-abpd-005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7797845PMC
December 2020

Diagnostic value of endobronchial ultrasound image features: A specialized review.

Endosc Ultrasound 2021 Jan-Feb;10(1):3-18

Department of Pneumology and Critical Care Medicine, Thoraxklinik, University of Heidelberg, Heidelberg, Germany.

Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) technology is important in the diagnosis of intrathoracic benign and malignant lymph nodes (LNs). With the development of EBUS imaging technology, its role in noninvasive diagnosis, as a supplement to pathology diagnosis, has been given increasing attention in recent years. Many studies have explored qualitative and quantitative methods for the three EBUS modes, as well as a variety of multimodal analysis methods, to find the optimal method for the noninvasive diagnosis using EBUS for LNs. Here, we review and comment on the research methods and predictive diagnostic value, discuss the existing problems, and look ahead to the future application of EBUS imaging.
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http://dx.doi.org/10.4103/eus.eus_43_20DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7980684PMC
July 2020
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