Abstract：
OBJECTIVE: To conduct a systematic review with meta-analyses to assess the recent scientific literature addressing the application of deep learning radiomics in oral squamous cell carcinoma (OSCC).
METHODS: Electronic and manual literature retrieval was performed using PubMed, Web of Science, EMbase, Ovid-MEDLINE, and IEEE databases from 2012 to 2023. The ROBINS-I tool was used for quality evaluation; random-effects model was used; and results were reported according to the PRISMA statement.
RESULTS: A total of 26 studies involving 64,731 medical images were included in quantitative synthesis. The meta-analysis showed that, the pooled sensitivity and specificity were 0.88 (95 %CI: 0.87∼0.88) and 0.80 (95 %CI: 0.80∼0.81), respectively. Deeks\' asymmetry test revealed there existed slight publication bias (P = 0.03).
CONCLUSIONS: The advances in the application of radiomics combined with learning algorithm in OSCC were reviewed, including diagnosis and differential diagnosis of OSCC, efficacy assessment and prognosis prediction. The demerits of deep learning radiomics at the current stage and its future development direction aimed at medical imaging diagnosis were also summarized and analyzed at the end of the article.
METHODS: Electronic and manual literature retrieval was performed using PubMed, Web of Science, EMbase, Ovid-MEDLINE, and IEEE databases from 2012 to 2023. The ROBINS-I tool was used for quality evaluation; random-effects model was used; and results were reported according to the PRISMA statement.
RESULTS: A total of 26 studies involving 64,731 medical images were included in quantitative synthesis. The meta-analysis showed that, the pooled sensitivity and specificity were 0.88 (95 %CI: 0.87∼0.88) and 0.80 (95 %CI: 0.80∼0.81), respectively. Deeks\' asymmetry test revealed there existed slight publication bias (P = 0.03).
CONCLUSIONS: The advances in the application of radiomics combined with learning algorithm in OSCC were reviewed, including diagnosis and differential diagnosis of OSCC, efficacy assessment and prognosis prediction. The demerits of deep learning radiomics at the current stage and its future development direction aimed at medical imaging diagnosis were also summarized and analyzed at the end of the article.
摘要：
目的：通过荟萃分析进行系统综述，以评估有关深度学习影像组学在口腔鳞状细胞癌（OSCC）中应用的最新科学文献。
方法：使用PubMed进行电子和手动文献检索，WebofScience,EMBase,Ovid-MEDLINE,和IEEE数据库从2012年到2023年。使用ROBINS-I工具进行质量评估；使用随机效应模型；并根据PRISMA声明报告结果。
结果：共26项研究，涉及64,731张医学图像，被纳入定量综合。荟萃分析表明,合并的敏感性和特异性分别为0.88(95CI:0.87~0.88)和0.80(95CI:0.80~0.81)，分别。Deeks\'不对称检验显示存在轻微的发表偏倚(P=0.03)。
结论：综述了影像组学结合学习算法在OSCC中的应用进展。包括OSCC的诊断和鉴别诊断，疗效评估和预后预测。文章最后还对深度学习影像组学现阶段存在的问题以及未来针对医学影像诊断的发展方向进行了总结和分析。
方法：使用PubMed进行电子和手动文献检索，WebofScience,EMBase,Ovid-MEDLINE,和IEEE数据库从2012年到2023年。使用ROBINS-I工具进行质量评估；使用随机效应模型；并根据PRISMA声明报告结果。
结果：共26项研究，涉及64,731张医学图像，被纳入定量综合。荟萃分析表明,合并的敏感性和特异性分别为0.88(95CI:0.87~0.88)和0.80(95CI:0.80~0.81)，分别。Deeks\'不对称检验显示存在轻微的发表偏倚(P=0.03)。
结论：综述了影像组学结合学习算法在OSCC中的应用进展。包括OSCC的诊断和鉴别诊断，疗效评估和预后预测。文章最后还对深度学习影像组学现阶段存在的问题以及未来针对医学影像诊断的发展方向进行了总结和分析。
