Dong Fei, Li Jie, Wang Junbo, Yang Xiaohui
Department of Medical Imaging, Yuncheng Central Hospital Affiliated to Shanxi Medical University, Yuncheng, Shanxi Province, China.
Department of Anesthesiology, Yuncheng Central Hospital Affiliated to Shanxi Medical University, Yuncheng, Shanxi Province, China.
PLoS One. 2024 Dec 3;19(12):e0314653. doi: 10.1371/journal.pone.0314653. eCollection 2024.
Radiomics offers a novel strategy for the differential diagnosis, prognosis evaluation, and prediction of treatment responses in breast cancer. Studies have explored radiomic signatures from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for predicting axillary lymph node metastasis (ALNM) and sentinel lymph node metastasis (SLNM), but the diagnostic accuracy varies widely. To evaluate this performance, we conducted a meta-analysis performing a comprehensive literature search across databases including PubMed, EMBASE, SCOPUS, Web of Science (WOS), Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang Data, and the Chinese BioMedical Literature Database (CBM) until March 31, 2024. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and the area under the receiver operating characteristic curve (AUC) were calculated. Twenty-four eligible studies encompassing 5588 breast cancer patients were included in the meta-analysis. The meta-analysis yielded a pooled sensitivity of 0.81 (95% confidence interval [CI]: 0.77-0.84), specificity of 0.85 (95%CI: 0.81-0.87), PLR of 5.24 (95%CI: 4.32-6.34), NLR of 0.23 (95%CI: 0.19-0.27), DOR of 23.16 (95%CI: 17.20-31.19), and AUC of 0.90 (95%CI: 0.87-0.92), indicating good diagnostic performance. Significant heterogeneity was observed in analyses of sensitivity (I2 = 74.64%) and specificity (I2 = 83.18%). Spearman's correlation coefficient suggested no significant threshold effect (P = 0.538). Meta-regression and subgroup analyses identified several potential heterogeneity sources, including data source, integration of clinical factors and peritumor features, MRI equipment, magnetic field strength, lesion segmentation, and modeling methods. In conclusion, DCE-MRI radiomic models exhibit good diagnostic performance in predicting ALNM and SLNM in breast cancer. This non-invasive and effective tool holds potential for the preoperative diagnosis of lymph node metastasis in breast cancer patients.
放射组学为乳腺癌的鉴别诊断、预后评估及治疗反应预测提供了一种新策略。已有研究探索了动态对比增强磁共振成像(DCE-MRI)的放射组学特征用于预测腋窝淋巴结转移(ALNM)和前哨淋巴结转移(SLNM),但其诊断准确性差异很大。为评估其性能,我们进行了一项荟萃分析,对包括PubMed、EMBASE、SCOPUS、科学网(WOS)、Cochrane图书馆、中国知网(CNKI)、万方数据和中国生物医学文献数据库(CBM)在内的多个数据库进行了全面的文献检索,检索截至2024年3月31日。计算了合并敏感度、特异度、阳性似然比(PLR)、阴性似然比(NLR)、诊断比值比(DOR)及受试者工作特征曲线下面积(AUC)。荟萃分析纳入了24项符合条件的研究,共5588例乳腺癌患者。荟萃分析得出的合并敏感度为0.81(95%置信区间[CI]:0.77 - 0.84),特异度为0.85(95%CI:0.81 - 0.87),PLR为5.24(95%CI:4.32 - 6.34),NLR为0.23(95%CI:0.19 - 0.27),DOR为23.16(95%CI:17.20 - 31.19),AUC为0.90(95%CI:0.87 - 0.92),表明诊断性能良好。在敏感度分析(I2 = 74.64%)和特异度分析(I2 = 83.18%)中观察到显著的异质性。Spearman相关系数表明无显著的阈值效应(P = 0.538)。Meta回归和亚组分析确定了几个潜在的异质性来源,包括数据来源、临床因素和肿瘤周围特征的整合、MRI设备、磁场强度、病变分割及建模方法。总之,DCE-MRI放射组学模型在预测乳腺癌的ALNM和SLNM方面表现出良好的诊断性能。这种无创且有效的工具在乳腺癌患者淋巴结转移的术前诊断中具有潜力。