使用基于人工智能的成像技术预测非小细胞肺癌的淋巴结转移:系统综述和荟萃分析。

IF 2.9 2区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Quantitative Imaging in Medicine and Surgery Pub Date : 2024-10-01 Epub Date: 2024-09-26 DOI:10.21037/qims-24-664
Lujiao Chen, Bo Chen, Zhenhua Zhao, Liyijing Shen
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引用次数: 0

摘要

背景:肺癌,尤其是非小细胞肺癌(NSCLC),是全球致死率最高的恶性肿瘤之一。与许多原发性恶性肿瘤相比,肺癌的 5 年生存率更低。因此,肺癌的早期检测和预后预测至关重要。随着人工智能(AI)技术的广泛应用,肺癌的早期检测和预后预测得到了改善。这项荟萃分析研究了基于人工智能的模型利用影像数据预测NSCLC患者淋巴结转移(LNM)的准确性和有效性。我们的研究结果可帮助临床医生预测患者预后并选择替代疗法:我们在 PubMed、Web of Science、Cochrane Library 和 Embase 数据库中检索了截至 2024 年 1 月 31 日发表的相关文章。两名审稿人分别对所有检索到的文章进行了评估,以确定其是否符合纳入荟萃分析的条件。系统评估和荟萃分析包括符合纳入标准(如随机或非随机试验和观察性研究)和排除标准(如非英文发表的文章)的文章,并为定量综合提供数据。纳入文章的质量采用诊断准确性研究质量评估-2(QUADAS-2)进行评估。汇总的敏感性、特异性和曲线下面积(AUC)用于评估基于人工智能的成像模型预测NSCLC患者LNM的能力。采用元回归法研究了异质性的来源。在亚组分析中研究了包括国家、样本大小、成像方式、模型验证技术和模型算法在内的协变量:最终的荟萃分析包括11项回顾性研究,涉及6088名NSCLC患者,其中1483名患者患有LNM。基于 AI 的成像模型预测 NSCLC 患者 LNM 的汇总灵敏度、特异性和 AUC 分别为 0.87 [95% 置信区间 (CI):0.80-0.91]、0.85 (95% CI:0.78-0.89) 和 0.92 (95% CI:0.90-0.94)。根据 QUADAS-2 的结果,所纳入文章在患者选择和诊断测试方面存在偏倚风险。不过,所纳入文章的质量总体上是可以接受的。汇总的敏感性和特异性存在差异(I2>75%)。元回归和亚组分析表明,成像模式(计算机断层扫描(CT)或正电子发射断层扫描(PET)/CT)和神经网络方法模型设计对本研究的异质性有显著影响。与其他技术相比,采用单一中心样本量数据和最小绝对收缩与选择算子(LASSO)方法的模型具有更高的灵敏度。使用迪克漏斗图,没有发现发表偏倚。敏感性分析结果表明,逐一删除每篇文章并不会改变研究结果:基于人工智能算法的成像数据模型在预测NSCLC患者的LNM方面具有良好的诊断准确性,可应用于临床。
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Using artificial intelligence based imaging to predict lymph node metastasis in non-small cell lung cancer: a systematic review and meta-analysis.

Background: Lung cancer, especially non-small cell lung cancer (NSCLC), is one of the most-deadly malignancies worldwide. Lung cancer has a worse 5-year survival rate than many primary malignancies. Thus, the early detection and prognosis prediction of lung cancer are crucial. The early detection and prognosis prediction of lung cancer have improved with the widespread use of artificial intelligence (AI) technologies. This meta-analysis examined the accuracy and efficacy of AI-based models in predicting lymph node metastasis (LNM) in NSCLC patients using imaging data. Our findings could help clinicians predict patient prognosis and select alternative therapies.

Methods: We searched the PubMed, Web of Science, Cochrane Library, and Embase databases for relevant articles published up to January 31, 2024. Two reviewers individually evaluated all the retrieved articles to assess their eligibility for inclusion in the meta-analysis. The systematic assessment and meta-analysis comprised articles that satisfied the inclusion criteria (e.g., randomized or non-randomized trials, and observational studies) and exclusion criteria (e.g., articles not published in English), and provided data for the quantitative synthesis. The quality of the included articles was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). The pooled sensitivity, specificity, and area under the curve (AUC) were used to evaluate the ability of AI-based imaging models to predict LNM in NSCLC patients. Sources of heterogeneity were investigated using meta-regression. Covariates, including country, sample size, imaging modality, model validation technique, and model algorithm, were examined in the subgroup analysis.

Results: The final meta-analysis comprised 11 retrospective studies of 6,088 NSCLC patients, of whom 1,483 had LNM. The pooled sensitivity, specificity, and AUC of the AI-based imaging model for predicting LNM in NSCLC patients were 0.87 [95% confidence interval (CI): 0.80-0.91], 0.85 (95% CI: 0.78-0.89), and 0.92 (95% CI: 0.90-0.94). Based on the QUADAS-2 results, a risk of bias was detected in the patient selection and diagnostic tests of the included articles. However, the quality of the included articles was generally acceptable. The pooled sensitivity and specificity were heterogeneous (I2>75%). The meta-regression and subgroup analyses showed that imaging modality [computed tomography (CT) or positron emission tomography (PET)/CT], and the neural network method model design significantly affected heterogeneity of this study. Models employing sample size data from a single center and the least absolute shrinkage and selection operator (LASSO) method had greater sensitivity than other techniques. Using the Deek' s funnel plot, no publishing bias was found. The results of the sensitivity analysis showed that deleting each article one by one did not change the findings.

Conclusions: Imaging data models based on AI algorithms have good diagnostic accuracy in predicting LNM in patients with NSCLC and could be applied in clinical settings.

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来源期刊
Quantitative Imaging in Medicine and Surgery
Quantitative Imaging in Medicine and Surgery Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
4.20
自引率
17.90%
发文量
252
期刊介绍: Information not localized
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