Prognostic Impact of EBUS TBNA for Lung Adenocarcinoma Patients with Postoperative Recurrences

Introduction: EBUS-TBNA for lung cancer staging is operator-dependent, resulting in high rates of non-diagnostic lymph node (LN) samples. We hypothesized that an artificial intelligence (AI) algorithm can consistently and reliably predict nodal metastases from the ultrasound images of LNs, when compared to pathology. Methods: In this analysis of prospectively-recorded B-mode images of mediastinal LNs during EBUS-TBNA, we used transfer learning to build an end-to-end ensemble of three deep neural networks (ResNet152V2, InceptionV3, and DenseNet201). Model hyperparameters were tuned, and the optimal version(s) of each model trained using 80% of the images. A learned ensemble (multi-layer perceptron) of the optimal versions were applied to the remaining 20% of the images (Test Set). All predictions were compared to final pathology from nodal biopsies and/or surgical specimen. Results: A total of 2,569 LN images from 773 patients were used. The Training Set included 2,048 LNs, of which 70.02% were benign and 29.98% were malignant on pathology. The Testing Set included 521 LNs, of which 70.06% were benign and 29.94% were malignant on pathology. The final ensemble model had an overall accuracy of 80.63% [95%Confidence Interval (CI):76.93%-83.97%], 43.23% sensitivity (95%CI:35.30%-51.41%), 96.91% specificity (95%CI:94.54%-98.45%), 85.90% positive predictive value (95%CI:76.81%-91.80%), 79.68% negative predictive value (95%CI:77.34%-81.83%), and AUC of 0.701 (95%CI:0.646-0.755) for malignancy. Conclusion: There now exists an AI algorithm which can identify nodal metastases based only on ultrasound images with good overall accuracy, specificity, and positive predictive value. Further optimization with larger sample sizes would be beneficial prior to clinical application.

show abstract

Clinicopathological Features and Significance of Epidermal Growth Factor Receptor Mutation in Surgically Resected Early-Stage Lung Adenocarcinoma

Lin

Chiang

et al. 2023

Diagnostics

View full text Add to dashboard Cite

The clinicopathological presentation of early-stage lung adenocarcinoma patients with epidermal growth factor receptor (EGFR) mutations has been seldom studied. Our study enrolled patients with stage I and II lung adenocarcinoma between January 2014 and December 2017 at the National Taiwan University Hospital. Clinicopathological features and prognosis were retrospectively reviewed and analyzed depending on EGFR mutation status. EGFR mutations were detected in 622 (60%) out of 1034 patients. Compared to the group without EGFR mutations, the group with EGFR mutations had more patients above 65 years of age (p < 0.001), more non-lepidic histological subtypes (p < 0.001), higher CEA levels (p = 0.044), higher grade of pleural (p = 0.02) and lymphovascular (p = 0.001) invasion, higher histological grade (p < 0.001), and a more advanced pathological stage (p = 0.022). In multivariate analysis, there was no significant difference in PFS or OS between the EGFR mutant and wild-type groups. In subtype analysis, the tumors with an L858R mutation had a more lepidic predominant histological type (p = 0.019) and less lymphovascular invasion (p = 0.011). No significant differences in PFS or OS were detected between the exon 19 deletion and L858R mutation groups. In early-stage lung adenocarcinoma, EGFR mutation may be considered as a treatment response predictor for tyrosine kinase inhibitors, instead of a predictor of clinical prognosis.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Prognostic Impact of EBUS TBNA for Lung Adenocarcinoma Patients with Postoperative Recurrences

Cited by 3 publications

References 44 publications

Clinical utility of artificial intelligence–augmented endobronchial ultrasound elastography in lymph node staging for lung cancer

Clinical utility of artificial intelligence–augmented endobronchial ultrasound elastography in lymph node staging for lung cancer

Artificial Intelligence Algorithm Can Predict Lymph Node Malignancy from Endobronchial Ultrasound Transbronchial Needle Aspiration Images for Non-Small Cell Lung Cancer

Clinicopathological Features and Significance of Epidermal Growth Factor Receptor Mutation in Surgically Resected Early-Stage Lung Adenocarcinoma

Contact Info

Product

Resources

About