深度学习和放射组学在肺癌分期中的诊断准确性：系统评价和荟萃分析。

Diagnostic Accuracy of Deep Learning and Radiomics in Lung Cancer Staging: A Systematic Review and Meta-Analysis.

机构信息

Department of Thoracic Surgery, The 3rd Affiliated Hospital of Chengdu Medical College, Pidu District People's Hospital, Chengdu, China.

School of Electronic Engineering, Chengdu University of Technology, Chengdu, China.

出版信息

Front Public Health. 2022 Jul 18;10:938113. doi: 10.3389/fpubh.2022.938113. eCollection 2022.

BACKGROUND

Artificial intelligence has far surpassed previous related technologies in image recognition and is increasingly used in medical image analysis. We aimed to explore the diagnostic accuracy of the models based on deep learning or radiomics for lung cancer staging.

METHODS

Studies were systematically reviewed using literature searches from PubMed, EMBASE, Web of Science, and Wanfang Database, according to PRISMA guidelines. Studies about the diagnostic accuracy of radiomics and deep learning, including the identifications of lung cancer, tumor types, malignant lung nodules and lymph node metastase, were included. After identifying the articles, the methodological quality was assessed using the QUADAS-2 checklist. We extracted the characteristic of each study; the sensitivity, specificity, and AUROC for lung cancer diagnosis were summarized for subgroup analysis.

RESULTS

The systematic review identified 19 eligible studies, of which 14 used radiomics models and 5 used deep learning models. The pooled AUROC of 7 studies to determine whether patients had lung cancer was 0.83 (95% CI 0.78-0.88). The pooled AUROC of 9 studies to determine whether patients had NSCLC was 0.78 (95% CI 0.73-0.83). The pooled AUROC of the 6 studies that determined patients had malignant lung nodules was 0.79 (95% CI 0.77-0.82). The pooled AUROC of the other 6 studies that determined whether patients had lymph node metastases was 0.74 (95% CI 0.66-0.82).

CONCLUSION

The models based on deep learning or radiomics have the potential to improve diagnostic accuracy for lung cancer staging.

SYSTEMATIC REVIEW REGISTRATION

https://inplasy.com/inplasy-2022-3-0167/, identifier: INPLASY202230167.

背景

人工智能在图像识别方面已经远远超过了以前的相关技术，并且越来越多地应用于医学图像分析。我们旨在探索基于深度学习或放射组学的模型在肺癌分期中的诊断准确性。

方法

根据 PRISMA 指南，从 PubMed、EMBASE、Web of Science 和万方数据库进行系统文献检索，以系统综述的方式进行研究。包括识别肺癌、肿瘤类型、恶性肺结节和淋巴结转移的放射组学和深度学习的诊断准确性研究均包括在内。在确定文章后，使用 QUADAS-2 清单评估方法学质量。我们提取了每个研究的特征；总结了肺癌诊断的敏感性、特异性和 AUROC，用于亚组分析。

结果

系统综述确定了 19 项符合条件的研究，其中 14 项使用了放射组学模型，5 项使用了深度学习模型。7 项研究确定患者是否患有肺癌的汇总 AUROC 为 0.83（95%CI 0.78-0.88）。9 项研究确定患者是否患有 NSCLC 的汇总 AUROC 为 0.78（95%CI 0.73-0.83）。6 项研究确定患者是否患有恶性肺结节的汇总 AUROC 为 0.79（95%CI 0.77-0.82）。另外 6 项研究确定患者是否发生淋巴结转移的汇总 AUROC 为 0.74（95%CI 0.66-0.82）。