He Junmei, Liu Yurong, Li Jinzhu, Liu Shuang
The Fifth People's Hospital of Jinan, Jinan, Shandong, China.
Front Oncol. 2024 Jan 25;14:1334546. doi: 10.3389/fonc.2024.1334546. eCollection 2024.
With the increasing use of radiomics in cancer diagnosis and treatment, it has been applied by some researchers to the preoperative risk assessment of endometrial cancer (EC) patients. However, comprehensive and systematic evidence is needed to assess its clinical value. Therefore, this study aims to investigate the application value of radiomics in the diagnosis and treatment of EC.
Pubmed, Cochrane, Embase, and Web of Science databases were retrieved up to March 2023. Preoperative risk assessment of EC included high-grade EC, lymph node metastasis, deep myometrial invasion status, and lymphovascular space invasion status. The quality of the included studies was appraised utilizing the RQS scale.
A total of 33 primary studies were included in our systematic review, with an average RQS score of 7 (range: 5-12). ML models based on radiomics for the diagnosis of malignant lesions predominantly employed logistic regression. In the validation set, the pooled c-index of the ML models based on radiomics and clinical features for the preoperative diagnosis of endometrial malignancy, high-grade tumors, lymph node metastasis, lymphovascular space invasion, and deep myometrial invasion was 0.900 (95%CI: 0.871-0.929), 0.901 (95%CI: 0.877-0.926), 0.906 (95%CI: 0.882-0.929), 0.795 (95%CI: 0.693-0.897), and 0.819 (95%CI: 0.705-0.933), respectively.
Radiomics shows excellent accuracy in detecting endometrial malignancies and in identifying preoperative risk. However, the methodological diversity of radiomics results in significant heterogeneity among studies. Therefore, future research should establish guidelines for radiomics studies based on different imaging sources.
https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=364320 identifier CRD42022364320.
随着放射组学在癌症诊断和治疗中的应用日益增加,一些研究人员已将其应用于子宫内膜癌(EC)患者的术前风险评估。然而,需要全面而系统的证据来评估其临床价值。因此,本研究旨在探讨放射组学在EC诊断和治疗中的应用价值。
检索截至2023年3月的Pubmed、Cochrane、Embase和Web of Science数据库。EC的术前风险评估包括高级别EC、淋巴结转移、肌层深部浸润状态和淋巴血管间隙浸润状态。采用RQS量表评估纳入研究的质量。
我们的系统评价共纳入33项原始研究,平均RQS评分为7分(范围:5 - 12分)。基于放射组学的机器学习(ML)模型在诊断恶性病变时主要采用逻辑回归。在验证集中,基于放射组学和临床特征的ML模型用于术前诊断子宫内膜恶性肿瘤、高级别肿瘤、淋巴结转移、淋巴血管间隙浸润和肌层深部浸润的合并c指数分别为0.900(95%CI:0.871 - 0.929)、0.901(95%CI:0.877 - 0.926)、0.906(95%CI:0.882 - 0.929)、0.795(95%CI:0.693 - 0.897)和0.819(95%CI:0.705 - 0.933)。
放射组学在检测子宫内膜恶性肿瘤和识别术前风险方面显示出优异的准确性。然而,放射组学方法的多样性导致研究之间存在显著异质性。因此,未来的研究应基于不同的影像来源建立放射组学研究指南。
https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=364320标识符CRD42022364320。