Department of Cancer Center, The Second Affiliated Hospital of Chongqing Medical University, No. 288 Tianwen Road, Nan'an District, Chongqing, 400010, China.
Department of Oncology and Hematology, The First People's Hospital of Longquanyi District, Chengdu, 610100, China.
BMC Cancer. 2024 Nov 5;24(1):1355. doi: 10.1186/s12885-024-13098-5.
To evaluate the diagnostic accuracy of machine learning models incorporating multimodal features for predicting radiation pneumonitis in lung cancer through a systematic review and meta-analysis.
Relevant studies were identified through a systematic search of PubMed, Web of Science, Embase, and the Cochrane Library from October 2003 to December 2023. Additional studies were located by reviewing bibliographies and relevant websites. Two independent researchers screened titles, abstracts, and full-text articles according to predefined inclusion and exclusion criteria. Data extraction was performed using standardized forms, and study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The primary outcomes, including combined sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC), were calculated using STATA MP-64 software(Stata Corporation LLC, College Station, USA) with a random-effects model. Meta-analysis was conducted to synthesize diagnostic accuracy measures, and analyses of heterogeneity and publication bias were performed.
A total of 1,406 patients with primary lung cancer were included in this systematic review, drawing data from 9 studies. The pooled analysis revealed a sensitivity of 0.74 [0.58-0.85] and a specificity of 0.91 [0.87-0.95] for machine learning models in diagnosing radiation pneumonitis. The positive likelihood ratio (PLR) was 8.69 [5.21-14.50], the negative likelihood ratio (NLR) was 0.28 [0.16-0.49], and the diagnostic odds ratio (DOR) was 30.73 [11.96-78.97]. The area under the curve (AUC) was 0.93 [0.90-0.95], indicating excellent diagnostic performance. Meta-regression analysis identified that the number of machine learning models, year of publication, and study design contributed to heterogeneity among studies. No evidence of publication bias was found. Overall, machine learning models incorporating multimodal characteristics demonstrated 75% accuracy in predicting moderate to severe radiation pneumonitis.
In conclusion, by integrating the current machine learning (ML) algorithm's ability in big data mining, a predictive model can be constructed by combining multi-modal features such as genetics, imaging, and cell factors. By selecting multiple machine learning algorithm frameworks and competing for the best combination model based on research goals, the reliability and accuracy of the radiation pneumonitis prediction model can be greatly improved.
PROSPERO (CRD42024497599).
通过系统评价和荟萃分析评估机器学习模型整合多模态特征预测肺癌放射性肺炎的诊断准确性。
从 2003 年 10 月至 2023 年 12 月，通过系统检索 PubMed、Web of Science、Embase 和 Cochrane Library 来确定相关研究。通过查阅文献和相关网站来查找其他研究。两名独立的研究人员根据预先设定的纳入和排除标准筛选标题、摘要和全文文章。使用标准化表格进行数据提取，并使用诊断准确性研究质量评估工具 2 版（Quality Assessment of Diagnostic Accuracy Studies-2 tool）评估研究质量。使用 STATA MP-64 软件（美国德克萨斯州立大学）以随机效应模型计算主要结局，包括合并敏感度、特异度、阳性似然比（PLR）、阴性似然比（NLR）、诊断比值比（DOR）和曲线下面积（AUC）。对诊断准确性测量进行荟萃分析，并进行异质性和发表偏倚分析。
本系统评价共纳入 1406 名原发性肺癌患者，来自 9 项研究的数据。汇总分析显示，机器学习模型诊断放射性肺炎的敏感度为 0.74[0.58-0.85]，特异度为 0.91[0.87-0.95]。阳性似然比（PLR）为 8.69[5.21-14.50]，阴性似然比（NLR）为 0.28[0.16-0.49]，诊断比值比（DOR）为 30.73[11.96-78.97]。曲线下面积（AUC）为 0.93[0.90-0.95]，表明诊断性能良好。元回归分析发现，机器学习模型数量、发表年份和研究设计是造成研究间异质性的因素。未发现发表偏倚的证据。总的来说，整合多模态特征的机器学习模型在预测中重度放射性肺炎方面具有 75%的准确率。
总之，通过整合当前机器学习（ML）算法在大数据挖掘方面的能力，可以通过结合遗传学、影像学和细胞因子等多模态特征构建预测模型。通过选择多个机器学习算法框架，并根据研究目标竞争最佳组合模型，可以大大提高放射性肺炎预测模型的可靠性和准确性。
PROSPERO（CRD42024497599）。
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