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基于深度学习和组织病理图像的癌症分类研究进展 被引量:8
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作者 颜锐 陈丽萌 +1 位作者 李锦涛 任菲 《协和医学杂志》 CSCD 2021年第5期742-748,共7页
癌症的精确分类直接关系到患者治疗方案的选择和预后。病理诊断是癌症诊断的金标准,病理图像的数字化和深度学习的突破性进展使得计算机辅助癌症诊断和预后预测成为可能。本文通过简述病理图像分类常用的4种深度学习方法,总结基于深度... 癌症的精确分类直接关系到患者治疗方案的选择和预后。病理诊断是癌症诊断的金标准,病理图像的数字化和深度学习的突破性进展使得计算机辅助癌症诊断和预后预测成为可能。本文通过简述病理图像分类常用的4种深度学习方法,总结基于深度学习和组织病理图像的癌症分类最新研究进展,指出该领域研究中普遍存在的问题与挑战,并对未来可能的发展方向进行展望。 展开更多
关键词 病理图像 深度学习 癌症分类 癌症分级 计算机辅助诊断
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Staging accuracy of esophageal cancer by endoscopic ultrasound:A meta-analysis and systematic review 被引量:57
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作者 Srinivas R Puli Jyotsna BK Reddy +3 位作者 Matthew L Bechtold Daphne Antillon Jamal A Ibdah Mainor R Antillon 《World Journal of Gastroenterology》 SCIE CAS CSCD 2008年第10期1479-1490,共12页
AIM: To evaluate the accuracy of endoscopic ultrasound (EUS) in the staging of esophageal cancer. METHODS: Only EUS studies confirmed by surgery were selected. Articles were searched in Medline and Pubmed. Two reviewe... AIM: To evaluate the accuracy of endoscopic ultrasound (EUS) in the staging of esophageal cancer. METHODS: Only EUS studies confirmed by surgery were selected. Articles were searched in Medline and Pubmed. Two reviewers independently searched and extracted data. Meta-analysis of the accuracy of EUS was analyzed by calculating pooled estimates of sensitivity, specificity, likelihood ratios, and diagnostic odds ratio. Pooling was conducted by both the Mantel-Haenszel method (fixed effects model) and DerSimonian Laird method (random effects model). The heterogeneity of studies was tested using Cochran’s Q test based upon inverse variance weights. RESULTS: Forty-nine studies (n = 2558) which met the inclusion criteria were included in this analysis. Pooled sensitivity and specificity of EUS to diagnose T1 was 81.6% (95% CI: 77.8-84.9) and 99.4% (95% CI: 99.0-99.7), respectively. To diagnose T4, EUS had a pooled sensitivity of 92.4% (95% CI: 89.2-95.0) and specificity of 97.4% (95% CI: 96.6-98.0). With Fine Needle Aspiration (FNA), sensitivity of EUS to diagnose N stage improved from 84.7% (95% CI: 82.9-86.4) to 96.7% (95% CI: 92.4-98.9). The P value for the χ2 test of heterogeneity for all pooled estimates was > 0.10. CONCLUSION: EUS has excellent sensitivity and specificity in accurately diagnosing the TN stage of esophageal cancer. EUS performs better with advanced (T4) than early (T1) disease. FNA substantially improves the sensitivity and specificity of EUS in evaluating N stage disease. EUS should be strongly considered for staging esophageal cancer. 展开更多
关键词 Esophageal cancer Cancer staging Endoscopic ultrasound TNM staging Diagnostic accuracy
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