Abstract：
We performed this systematic review and meta-analysis to investigate the performance of ML in detecting genetic mutation status in NSCLC patients.
We conducted a systematic search of PubMed, Cochrane, Embase, and Web of Science up until July 2023. We discussed the genetic mutation status of EGFR, ALK, KRAS, and BRAF, as well as the mutation status at different sites of EGFR.
We included a total of 128 original studies, of which 114 constructed ML models based on radiomic features mainly extracted from CT, MRI, and PET-CT data. From a genetic mutation perspective, 121 studies focused on EGFR mutation status analysis. In the validation set, for the detection of EGFR mutation status, the aggregated c-index was 0.760 (95%CI: 0.706-0.814) for clinical feature-based models, 0.772 (95%CI: 0.753-0.791) for CT-based radiomics models, 0.816 (95%CI: 0.776-0.856) for MRI-based radiomics models, and 0.750 (95%CI: 0.712-0.789) for PET-CT-based radiomics models. When combined with clinical features, the aggregated c-index was 0.807 (95%CI: 0.781-0.832) for CT-based radiomics models, 0.806 (95%CI: 0.773-0.839) for MRI-based radiomics models, and 0.822 (95%CI: 0.789-0.854) for PET-CT-based radiomics models. In the validation set, the aggregated c-indexes for radiomics-based models to detect mutation status of ALK and KRAS, as well as the mutation status at different sites of EGFR were all greater than 0.7.
The use of radiomics-based methods for early discrimination of EGFR mutation status in NSCLC demonstrates relatively high accuracy. However, the influence of clinical variables cannot be overlooked in this process. In addition, future studies should also pay attention to the accuracy of radiomics in identifying mutation status of other genes in EGFR.
We conducted a systematic search of PubMed, Cochrane, Embase, and Web of Science up until July 2023. We discussed the genetic mutation status of EGFR, ALK, KRAS, and BRAF, as well as the mutation status at different sites of EGFR.
We included a total of 128 original studies, of which 114 constructed ML models based on radiomic features mainly extracted from CT, MRI, and PET-CT data. From a genetic mutation perspective, 121 studies focused on EGFR mutation status analysis. In the validation set, for the detection of EGFR mutation status, the aggregated c-index was 0.760 (95%CI: 0.706-0.814) for clinical feature-based models, 0.772 (95%CI: 0.753-0.791) for CT-based radiomics models, 0.816 (95%CI: 0.776-0.856) for MRI-based radiomics models, and 0.750 (95%CI: 0.712-0.789) for PET-CT-based radiomics models. When combined with clinical features, the aggregated c-index was 0.807 (95%CI: 0.781-0.832) for CT-based radiomics models, 0.806 (95%CI: 0.773-0.839) for MRI-based radiomics models, and 0.822 (95%CI: 0.789-0.854) for PET-CT-based radiomics models. In the validation set, the aggregated c-indexes for radiomics-based models to detect mutation status of ALK and KRAS, as well as the mutation status at different sites of EGFR were all greater than 0.7.
The use of radiomics-based methods for early discrimination of EGFR mutation status in NSCLC demonstrates relatively high accuracy. However, the influence of clinical variables cannot be overlooked in this process. In addition, future studies should also pay attention to the accuracy of radiomics in identifying mutation status of other genes in EGFR.
摘要：
目的：我们进行了系统评价和荟萃分析，以探讨ML在检测NSCLC患者基因突变状态方面的表现。
方法：我们对PubMed进行了系统搜索，科克伦,Embase,和WebofScience直到2023年7月。我们讨论了EGFR的基因突变状态,ALK,KRAS,而BRAF,以及EGFR不同位点的突变状态。
结果：我们共纳入了128项原始研究，其中114个主要基于CT提取的影像组学特征构建的ML模型，MRI,和PET-CT数据。从基因突变的角度来看,121项研究集中于EGFR突变状态分析。在验证集中，用于检测EGFR突变状态，基于临床特征的模型的总c指数为0.760(95CI：0.706-0.814)，0.772（95CI：0.753-0.791）用于基于CT的影像组学模型，0.816（95CI：0.776-0.856）用于基于MRI的影像组学模型，基于PET-CT的影像组学模型为0.750（95CI：0.712-0.789）。结合临床特征，基于CT的影像组学模型的总c指数为0.807(95CI:0.781-0.832)，0.806（95CI：0.773-0.839）用于基于MRI的影像组学模型，基于PET-CT的影像组学模型为0.822（95CI：0.789-0.854）。在验证集中，基于影像组学的模型的聚合c指数，用于检测ALK和KRAS的突变状态，以及EGFR不同位点的突变状态均大于0.7.
结论：使用基于影像组学的方法对NSCLC中EGFR突变状态的早期鉴别显示出相对较高的准确性。然而,在这个过程中，临床变量的影响不容忽视。此外,未来的研究还应该关注影像组学在识别EGFR中其他基因突变状态方面的准确性.
方法：我们对PubMed进行了系统搜索，科克伦,Embase,和WebofScience直到2023年7月。我们讨论了EGFR的基因突变状态,ALK,KRAS,而BRAF,以及EGFR不同位点的突变状态。
结果：我们共纳入了128项原始研究，其中114个主要基于CT提取的影像组学特征构建的ML模型，MRI,和PET-CT数据。从基因突变的角度来看,121项研究集中于EGFR突变状态分析。在验证集中，用于检测EGFR突变状态，基于临床特征的模型的总c指数为0.760(95CI：0.706-0.814)，0.772（95CI：0.753-0.791）用于基于CT的影像组学模型，0.816（95CI：0.776-0.856）用于基于MRI的影像组学模型，基于PET-CT的影像组学模型为0.750（95CI：0.712-0.789）。结合临床特征，基于CT的影像组学模型的总c指数为0.807(95CI:0.781-0.832)，0.806（95CI：0.773-0.839）用于基于MRI的影像组学模型，基于PET-CT的影像组学模型为0.822（95CI：0.789-0.854）。在验证集中，基于影像组学的模型的聚合c指数，用于检测ALK和KRAS的突变状态，以及EGFR不同位点的突变状态均大于0.7.
结论：使用基于影像组学的方法对NSCLC中EGFR突变状态的早期鉴别显示出相对较高的准确性。然而,在这个过程中，临床变量的影响不容忽视。此外,未来的研究还应该关注影像组学在识别EGFR中其他基因突变状态方面的准确性.
