Refining Radial Ebus: Characterizing Imaging Interpretation and Proposal for a Standardized Grading System

Abbasi, Mohammed; Moulton, Nathaniel; Ahmad, Danish; Burks, Allen Cole; Chenna, Praveen; Haas, Kevin; Loiselle, Andrea; Mekhaiel, Essam; Pilli, Suchitra; Sadoughi, Ali; Patel, Tej; Chen, Alexander

doi:10.1016/j.chest.2020.08.1676

Cited by 1 publication

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“…Chao et al conducted a study using various image features to distinguish between neoplastic and non-neoplastic lesions, such as the margin outside the lesion, homogeneity among internal echoes, hyperechoic dots and concentric circles along the echo probe. Some of these image features were identified as diagnostic markers; however, the interpretation of images remains highly subjective 25. There is a pressing need for an objective method to interpret rEBUS images.…”

Section: Discussionmentioning

confidence: 99%

“…Some of these image features were identified as diagnostic markers; however, the interpretation of images remains highly subjective. 25 There is a pressing need for an objective method to interpret rEBUS images. This study demonstrated the efficacy of the proposed CNN prediction model in differentiating between malignant and benign lesions during bronchoscopic biopsy.…”

Section: Discussionmentioning

confidence: 99%

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Deep learning with test-time augmentation for radial endobronchial ultrasound image differentiation: a multicentre verification study

Tseng

Yang

et al. 2023

BMJ Open Resp Res

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PurposeDespite the importance of radial endobronchial ultrasound (rEBUS) in transbronchial biopsy, researchers have yet to apply artificial intelligence to the analysis of rEBUS images.Materials and methodsThis study developed a convolutional neural network (CNN) to differentiate between malignant and benign tumours in rEBUS images. This study retrospectively collected rEBUS images from medical centres in Taiwan, including 769 from National Taiwan University Hospital Hsin-Chu Branch, Hsinchu Hospital for model training (615 images) and internal validation (154 images) as well as 300 from National Taiwan University Hospital (NTUH-TPE) and 92 images were obtained from National Taiwan University Hospital Hsin-Chu Branch, Biomedical Park Hospital (NTUH-BIO) for external validation. Further assessments of the model were performed using image augmentation in the training phase and test-time augmentation (TTA).ResultsUsing the internal validation dataset, the results were as follows: area under the curve (AUC) (0.88 (95% CI 0.83 to 0.92)), sensitivity (0.80 (95% CI 0.73 to 0.88)), specificity (0.75 (95% CI 0.66 to 0.83)). Using the NTUH-TPE external validation dataset, the results were as follows: AUC (0.76 (95% CI 0.71 to 0.80)), sensitivity (0.58 (95% CI 0.50 to 0.65)), specificity (0.92 (95% CI 0.88 to 0.97)). Using the NTUH-BIO external validation dataset, the results were as follows: AUC (0.72 (95% CI 0.64 to 0.82)), sensitivity (0.71 (95% CI 0.55 to 0.86)), specificity (0.76 (95% CI 0.64 to 0.87)). After fine-tuning, the AUC values for the external validation cohorts were as follows: NTUH-TPE (0.78) and NTUH-BIO (0.82). Our findings also demonstrated the feasibility of the model in differentiating between lung cancer subtypes, as indicated by the following AUC values: adenocarcinoma (0.70; 95% CI 0.64 to 0.76), squamous cell carcinoma (0.64; 95% CI 0.54 to 0.74) and small cell lung cancer (0.52; 95% CI 0.32 to 0.72).ConclusionsOur results demonstrate the feasibility of the proposed CNN-based algorithm in differentiating between malignant and benign lesions in rEBUS images.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%