دورية أكاديمية
A deep learning-based segmentation system for rapid onsite cytologic pathology evaluation of pancreatic masses: A retrospective, multicenter, diagnostic study
| العنوان: | A deep learning-based segmentation system for rapid onsite cytologic pathology evaluation of pancreatic masses: A retrospective, multicenter, diagnostic study |
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| المؤلفون: | Song Zhang, Yangfan Zhou, Dehua Tang, Muhan Ni, Jinyu Zheng, Guifang Xu, Chunyan Peng, Shanshan Shen, Qiang Zhan, Xiaoyun Wang, Duanmin Hu, Wu-Jun Li, Lei Wang, Ying Lv, Xiaoping Zou |
| المصدر: | EBioMedicine, Vol 80, Iss , Pp 104022- (2022) |
| بيانات النشر: | Elsevier, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Medicine LCC:Medicine (General) |
| مصطلحات موضوعية: | Rapid on-site cytopathology evaluation, EUS-FNA, Deep convolutional neural network, Pancreatic mass, Medicine, Medicine (General), R5-920 |
| الوصف: | Summary: Background: We aimed to develop a deep learning-based segmentation system for rapid on-site cytopathology evaluation (ROSE) to improve the diagnostic efficiency of endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) biopsy. Methods: A retrospective, multicenter, diagnostic study was conducted using 5345 cytopathological slide images from 194 patients who underwent EUS-FNA. These patients were from Nanjing Drum Tower Hospital (109 patients), Wuxi People's Hospital (30 patients), Wuxi Second People's Hospital (25 patients), and The Second Affiliated Hospital of Soochow University (30 patients). A deep convolutional neural network (DCNN) system was developed to segment cell clusters and identify cancer cell clusters with cytopathological slide images. Internal testing, external testing, subgroup analysis, and human–machine competition were used to evaluate the performance of the system. Findings: The DCNN system segmented stained cells from the background in cytopathological slides with an F1-score of 0·929 and 0·899–0·938 in internal and external testing, respectively. For cancer identification, the DCNN system identified images containing cancer clusters with AUCs of 0·958 and 0·948–0·976 in internal and external testing, respectively. The generalizable and robust performance of the DCNN system was validated in sensitivity analysis (AUC > 0·900) and was superior to that of trained endoscopists and comparable to cytopathologists on our testing datasets. Interpretation: The DCNN system is feasible and robust for identifying sample adequacy and pancreatic cancer cell clusters. Prospective studies are warranted to evaluate the clinical significance of the system. Funding: Jiangsu Natural Science Foundation; Nanjing Medical Science and Technology Development Funding; National Natural Science Foundation of China. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2352-3964 |
| العلاقة: | http://www.sciencedirect.com/science/article/pii/S2352396422002067Test; https://doaj.org/toc/2352-3964Test |
| DOI: | 10.1016/j.ebiom.2022.104022 |
| الوصول الحر: | https://doaj.org/article/5c5c98a2e91b46bf9202c4ad5802f0d6Test |
| رقم الانضمام: | edsdoj.5c5c98a2e91b46bf9202c4ad5802f0d6 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 23523964 |
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| DOI: | 10.1016/j.ebiom.2022.104022 |