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Abstract：
Breast cancer is a prevalent global health concern, necessitating accurate diagnostic tools for effective management. Diagnostic imaging plays a pivotal role in breast cancer diagnosis, staging, treatment planning, and outcome evaluation. Radiomics is an emerging field of study in medical imaging that contains a broad set of computational methods to extract quantitative features from radiographic images. This can be utilized to guide diagnosis, treatment response, and prognosis in clinical settings. A systematic review was performed in concordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy. Quality was assessed using the radiomics quality score. Diagnostic sensitivity and specificity of radiomics analysis, with 95% confidence intervals (CIs), were included for meta-analysis. The area under the curve analysis was recorded. An extensive statistical analysis was performed following the Cochrane guidelines. Statistical significance was determined if p-values were less than 0.05. Statistical analyses were conducted using Review Manager (RevMan), Version 5.4.1. A total of 31 manuscripts involving 8,773 patients were included, with 17 contributing to the meta-analysis. The cohort comprised 56.2% malignant breast cancers and 43.8% benign breast lesions. MRI demonstrated a sensitivity of 0.91 (95% CI: 0.89-0.92) and a specificity of 0.84 (95% CI: 0.82-0.86) in differentiating between benign and malignant breast cancers. Mammography-based radiomic features predicted breast cancer subtype with a sensitivity of 0.79 (95% CI: 0.76-0.82) and a specificity of 0.81 (95% CI: 0.79-0.84). Ultrasound-based analysis yielded a sensitivity of 0.92 (95% CI: 0.90-0.94) and a specificity of 0.85 (95% CI: 0.83-0.88). Only one study reported the results of radiomic evaluation from CT, which had a sensitivity of 0.95 (95% CI: 0.88-0.99) and a specificity of 0.56 (95% CI: 0.45-0.67). Across different imaging modalities, radiomics exhibited robust diagnostic accuracy in differentiating benign and malignant breast lesions. The results underscore the potential of radiomic assessment as a minimally invasive alternative or adjunctive diagnostic tool for breast cancer. This is pioneering data that reports on a novel diagnostic approach that is understudied and underreported. However, due to study limitations, the complexity of this technology, and the need for future development, biopsy still remains the current gold standard method of determining breast cancer type.
摘要：
乳腺癌是一个普遍的全球健康问题，需要准确的诊断工具来进行有效的管理。诊断成像在乳腺癌诊断中起着举足轻重的作用,分期,治疗计划,和结果评估。影像组学是医学成像中的新兴研究领域，其中包含一组广泛的计算方法来从放射线图像中提取定量特征。这可以用来指导诊断，治疗反应,和临床环境中的预后。根据系统审查和荟萃分析（PRISMA）指南的首选报告项目和Cochrane诊断测试准确性系统审查手册进行了系统审查。使用影像组学质量评分评估质量。影像组学分析的诊断敏感性和特异性，95%置信区间(CI)，纳入荟萃分析。记录曲线分析下的面积。遵循Cochrane指南进行了广泛的统计分析。如果p值小于0.05,则确定统计学显著性。使用审查经理(RevMan)进行统计分析，版本5.4.1.共包括31份手稿，涉及8,773名患者，17人参与了荟萃分析。该队列包括56.2％的恶性乳腺癌和43.8％的良性乳腺病变。MRI在区分良性和恶性乳腺癌方面显示出0.91（95％CI：0.89-0.92）的敏感性和0.84（95％CI：0.82-0.86）的特异性。基于乳房X线摄影的影像特征预测乳腺癌亚型的敏感性为0.79（95％CI：0.76-0.82），特异性为0.81（95％CI：0.79-0.84）。超声分析的灵敏度为0.92（95%CI：0.90-0.94），特异性为0.85（95%CI：0.83-0.88）。只有一项研究报告了CT的影像学评估结果，其敏感性为0.95（95%CI：0.88-0.99），特异性为0.56（95%CI：0.45-0.67）。在不同的成像模式中，影像组学在鉴别乳腺良恶性病变方面具有良好的诊断准确性.结果强调了放射学评估作为乳腺癌微创替代或辅助诊断工具的潜力。这是开创性的数据，报告了一种新的诊断方法，该方法被研究和报道不足。然而,由于研究的局限性，这项技术的复杂性，以及未来发展的需要，活检仍是目前确定乳腺癌类型的金标准方法。
