Motiei Mahsa, Mansouri Sahand Sadat, Tamimi Amirhossein, Farokhi Simin, Fakouri Arshia, Rassam Khoosheh, Sedighi-Pirsaraei Nasrin, Hassanzadeh-Rad Afagh
Pediatric Diseases Research Center, Pediatric Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran.
USERN Office, Lorestan University of Medical Sciences, Khorramabad, Iran.
Int J Surg. 2025 Jun 20. doi: 10.1097/JS9.0000000000002588.
Breast cancer is the most prevalent malignancy in women and a leading cause of mortality. Accurate assessment of axillary lymph node metastasis (LNM) is critical for breast cancer management. Exploring non-invasive methods such as radiomics for the detection of LNM is highly important.
We systematically searched Pubmed, Embase, Scopus, Web of Science and google scholar until 11 March 2024. To assess the risk of bias and quality of studies, we utilized the quality assessment of diagnostic accuracy studies (QUADAS) tool as well as the radiomics quality score (RQS). Area under the curve (AUC), sensitivity, specificity and accuracy were determined for each study to evaluate the diagnostic accuracy of radiomics in magnetic resonance imaging (MRI) for detecting LNM in patients with breast cancer.
This meta-analysis of 20 studies (5072 patients) demonstrated an overall AUC of 0.83 (95% confidence interval (CI): 0.80-0.86). Subgroup analysis revealed a trend towards higher specificity when radiomics was combined with clinical factors (0.83) compared to radiomics alone (0.79). Sensitivity analysis confirmed the robustness of the findings and publication bias was not evident. The radiomics models increased the likelihood of a positive LNM outcome from 37% to 73.2% when initial probability was positive and decreased the likelihood to 8% when initial probability was negative, highlighting their potential clinical utility.
Radiomics as a non-invasive method demonstrates strong potential for detecting LNM in breast cancer, offering clinical promise. However, further standardization and validation are needed in future studies.
乳腺癌是女性中最常见的恶性肿瘤，也是主要的死亡原因。准确评估腋窝淋巴结转移（LNM）对乳腺癌的治疗至关重要。探索如放射组学等非侵入性方法来检测LNM非常重要。
我们系统检索了截至2024年3月11日的PubMed、Embase、Scopus、Web of Science和谷歌学术。为评估研究的偏倚风险和质量，我们使用了诊断准确性研究质量评估（QUADAS）工具以及放射组学质量评分（RQS）。确定每项研究的曲线下面积（AUC）、敏感性、特异性和准确性，以评估放射组学在磁共振成像（MRI）中检测乳腺癌患者LNM的诊断准确性。
这项对20项研究（5072例患者）的荟萃分析显示总体AUC为0.83（95%置信区间（CI）：0.80 - 0.86）。亚组分析显示，与单独使用放射组学（0.79）相比，放射组学与临床因素联合使用时特异性有升高趋势（0.83）。敏感性分析证实了研究结果的稳健性，且未发现明显的发表偏倚。当初始概率为阳性时，放射组学模型将LNM阳性结果的可能性从37%提高到73.2%，当初始概率为阴性时，将可能性降低到8%，突出了其潜在的临床应用价值。
放射组学作为一种非侵入性方法，在检测乳腺癌LNM方面显示出强大潜力，具有临床应用前景。然而，未来研究需要进一步的标准化和验证。
