PDF(Pubmed)
Abstract：
UNASSIGNED: The performance of artificial intelligence (AI) in the prediction of lymph node (LN) metastasis in patients with oral squamous cell carcinoma (OSCC) has not been quantitatively evaluated. The purpose of this study was to conduct a systematic review and meta-analysis of published data on the diagnostic performance of CT and MRI based on AI algorithms for predicting LN metastases in patients with OSCC.
UNASSIGNED: We searched the Embase, PubMed (Medline), Web of Science, and Cochrane databases for studies on the use of AI in predicting LN metastasis in OSCC. Binary diagnostic accuracy data were extracted to obtain the outcomes of interest, namely, the area under the curve (AUC), sensitivity, and specificity, and compared the diagnostic performance of AI with that of radiologists. Subgroup analyses were performed with regard to different types of AI algorithms and imaging modalities.
UNASSIGNED: Fourteen eligible studies were included in the meta-analysis. The AUC, sensitivity, and specificity of the AI models for the diagnosis of LN metastases were 0.92 (95% CI 0.89-0.94), 0.79 (95% CI 0.72-0.85), and 0.90 (95% CI 0.86-0.93), respectively. Promising diagnostic performance was observed in the subgroup analyses based on algorithm types [machine learning (ML) or deep learning (DL)] and imaging modalities (CT vs. MRI). The pooled diagnostic performance of AI was significantly better than that of experienced radiologists.
UNASSIGNED: In conclusion, AI based on CT and MRI imaging has good diagnostic accuracy in predicting LN metastasis in patients with OSCC and thus has the potential for clinical application.
UNASSIGNED: https://www.crd.york.ac.uk/PROSPERO/#recordDetails, PROSPERO (No. CRD42024506159).
UNASSIGNED: We searched the Embase, PubMed (Medline), Web of Science, and Cochrane databases for studies on the use of AI in predicting LN metastasis in OSCC. Binary diagnostic accuracy data were extracted to obtain the outcomes of interest, namely, the area under the curve (AUC), sensitivity, and specificity, and compared the diagnostic performance of AI with that of radiologists. Subgroup analyses were performed with regard to different types of AI algorithms and imaging modalities.
UNASSIGNED: Fourteen eligible studies were included in the meta-analysis. The AUC, sensitivity, and specificity of the AI models for the diagnosis of LN metastases were 0.92 (95% CI 0.89-0.94), 0.79 (95% CI 0.72-0.85), and 0.90 (95% CI 0.86-0.93), respectively. Promising diagnostic performance was observed in the subgroup analyses based on algorithm types [machine learning (ML) or deep learning (DL)] and imaging modalities (CT vs. MRI). The pooled diagnostic performance of AI was significantly better than that of experienced radiologists.
UNASSIGNED: In conclusion, AI based on CT and MRI imaging has good diagnostic accuracy in predicting LN metastasis in patients with OSCC and thus has the potential for clinical application.
UNASSIGNED: https://www.crd.york.ac.uk/PROSPERO/#recordDetails, PROSPERO (No. CRD42024506159).
摘要：
■尚未定量评估人工智能（AI）在预测口腔鳞状细胞癌（OSCC）患者淋巴结（LN）转移中的性能。这项研究的目的是对基于AI算法预测OSCC患者LN转移的CT和MRI诊断性能的已发表数据进行系统评价和荟萃分析。
■我们搜索了Embase,PubMed(Medline),WebofScience,和Cochrane数据库，用于研究AI在预测OSCC中LN转移中的应用。提取二元诊断准确性数据以获得感兴趣的结果，即,曲线下面积（AUC），灵敏度,和特异性，并将AI的诊断性能与放射科医生的诊断性能进行了比较。针对不同类型的AI算法和成像模式进行亚组分析。
■14项符合条件的研究纳入荟萃分析。AUC,灵敏度,诊断LN转移的AI模型的特异性为0.92（95％CI0.89-0.94），0.79（95%CI0.72-0.85），和0.90（95%CI0.86-0.93），分别。在基于算法类型[机器学习（ML）或深度学习（DL）]和成像模式（CT与MRI）。AI的合并诊断性能明显优于有经验的放射科医生。
■总而言之，基于CT和MRI成像的AI在预测OSCC患者LN转移方面具有良好的诊断准确性，具有临床应用潜力。
■https://www.crd.约克。AC.uk/PROSPERO/#recordDetails,PROSPERO（编号CRD42024506159)。
■我们搜索了Embase,PubMed(Medline),WebofScience,和Cochrane数据库，用于研究AI在预测OSCC中LN转移中的应用。提取二元诊断准确性数据以获得感兴趣的结果，即,曲线下面积（AUC），灵敏度,和特异性，并将AI的诊断性能与放射科医生的诊断性能进行了比较。针对不同类型的AI算法和成像模式进行亚组分析。
■14项符合条件的研究纳入荟萃分析。AUC,灵敏度,诊断LN转移的AI模型的特异性为0.92（95％CI0.89-0.94），0.79（95%CI0.72-0.85），和0.90（95%CI0.86-0.93），分别。在基于算法类型[机器学习（ML）或深度学习（DL）]和成像模式（CT与MRI）。AI的合并诊断性能明显优于有经验的放射科医生。
■总而言之，基于CT和MRI成像的AI在预测OSCC患者LN转移方面具有良好的诊断准确性，具有临床应用潜力。
■https://www.crd.约克。AC.uk/PROSPERO/#recordDetails,PROSPERO（编号CRD42024506159)。
