Clinical imaging · 2025
The diagnostic accuracy of artificial intelligence models in detecting lymph node metastases in lung cancer using endobronchial ultrasound (EBUS) images: A bivariate meta-analysis.
СтатьяМетаанализСистематический обзорНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Ohannesian Victor Arthur, Falcão Luciano, Ishizuka Bruno Marçola, Menezes Isabelle Rodrigues, Han Mariana Lee, Suruagy-Motta Ricardo F O, Maximiano Mariana Letícia B, Cordeiro Davi M H, Baptista João Marcelo, Mariussi Miriana, Taneja Atul Kumar, Francisco Neto Miguel José, Garcia Rodrigo Gobbo, Jacomelli Marcia
Аннотация
PURPOSE: This study systematically evaluated AI models for detecting lymph node metastases in lung cancer using EBUS images and assessed their role in thoracic oncology.
MATERIALS AND METHODS: A systematic search following PRISMA-DTA guidelines was conducted in PubMed, Embase, Scopus, and Web of Science. Studies using AI models with cytologic or histologic analysis as the reference standard were included (PROSPERO: CRD42025635581). A bivariate random-effects model pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). AI models were categorized as CNN-A (Convolutional Neural Networks), BASM (Biomedical Application-Specific Models), AFP (Automated Frameworks and Platforms), and G-DDN (Generic Deep Neural Networks).
RESULTS: Twenty-two studies were included. The pooled sensitivity was 0.87 (95 % CI: 0.68-0.95), specificity 0.90 (95 % CI: 0.83-0.94), AUC 0.94 (95 % CI: 0.92-0.96), and DOR 56 (95 % CI: 17-182). CNN-A showed the highest accuracy, with an AUC of 0.970 and a DOR of 182, while AFP had the lowest sensitivity (0.058) and DOR (5.125), suggesting limited clinical applicability. Likelihood ratios were LR+ 8.39 (95 % CI: 4.93-14.28) and LR- 0.15 (95 % CI: 0.06-0.39), corresponding to post-test probabilities of 74 % for positive and 5 % for negative results. Subgroup analyses highlighted performance variations, emphasizing the need for refinement and validation in diverse settings.
CONCLUSION: AI models demonstrate high diagnostic accuracy in detecting lymph node metastases in lung cancer using EBUS images, reinforcing their potential in clinical decision-making. Future studies should refine accuracy metrics and further evaluate CNN-A across disease contexts.
MATERIALS AND METHODS: A systematic search following PRISMA-DTA guidelines was conducted in PubMed, Embase, Scopus, and Web of Science. Studies using AI models with cytologic or histologic analysis as the reference standard were included (PROSPERO: CRD42025635581). A bivariate random-effects model pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). AI models were categorized as CNN-A (Convolutional Neural Networks), BASM (Biomedical Application-Specific Models), AFP (Automated Frameworks and Platforms), and G-DDN (Generic Deep Neural Networks).
RESULTS: Twenty-two studies were included. The pooled sensitivity was 0.87 (95 % CI: 0.68-0.95), specificity 0.90 (95 % CI: 0.83-0.94), AUC 0.94 (95 % CI: 0.92-0.96), and DOR 56 (95 % CI: 17-182). CNN-A showed the highest accuracy, with an AUC of 0.970 and a DOR of 182, while AFP had the lowest sensitivity (0.058) and DOR (5.125), suggesting limited clinical applicability. Likelihood ratios were LR+ 8.39 (95 % CI: 4.93-14.28) and LR- 0.15 (95 % CI: 0.06-0.39), corresponding to post-test probabilities of 74 % for positive and 5 % for negative results. Subgroup analyses highlighted performance variations, emphasizing the need for refinement and validation in diverse settings.
CONCLUSION: AI models demonstrate high diagnostic accuracy in detecting lymph node metastases in lung cancer using EBUS images, reinforcing their potential in clinical decision-making. Future studies should refine accuracy metrics and further evaluate CNN-A across disease contexts.