Department of Diagnostic Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Breast Cancer Res Treat. 2021 Jan;185(2):479-494. doi: 10.1007/s10549-020-05947-1. Epub 2020 Oct 3.
To investigate the performance of an imaging and biopsy parameters-based multivariate model in decreasing unnecessary surgeries for high-risk breast lesions.
In an IRB-approved study, we retrospectively reviewed all high-risk lesions (HRL) identified at imaging-guided biopsy in our institution between July 1, 2014-July 1, 2017. Lesions were categorized high-risk-I (HR-I = atypical ductal hyperplasia, atypical lobular hyperplasia, lobular carcinoma in situ and atypical papillary lesion) and II (HR-II = Flat epithelial atypia, radial scar, benign papilloma). Patient risk factors, lesion features, detection and biopsy modality, excision and cancer upgrade rates were collected. Reference standard for upgrade was either excision or at least 2-year imaging follow-up. Multiple logistic regression analysis was performed to develop a multivariate model using HRL type, lesion and biopsy needle size for surgical cancer upgrade with performance assessed using ROC analysis.
Of 699 HRL in 652 patients, 525(75%) had reference standard available, and 48/525(9.1%) showed cancer at surgical excision. Excision (84.5% vs 51.1%) and upgrade (17.6%vs1.8%) rates were higher in HR-I compared to HR-II (p < 0.01). In HR-I, small needle size < 12G vs ≥ 12G [32.1% vs 13.2%, p < 0.01] and less cores [< 6 vs ≥ 6, 28.6%vs13.7%, p = 0.01] were significantly associated with higher cancer upgrades. Our multivariate model had an AUC = 0.87, saving 28.1% of benign surgeries with 100% sensitivity, based on HRL subtype, lesion size(mm, continuous), needle size (< 12G vs ≥ 12G) and biopsy modality (US vs MRI vs stereotactic) CONCLUSION: Our multivariate model using lesion size, needle size and patient age had a high diagnostic performance in decreasing unnecessary surgeries and shows promise as a decision support tool.
研究基于影像学和活检参数的多变量模型在减少高危乳腺病变不必要手术中的作用。
在一项机构审查委员会批准的研究中，我们回顾性分析了 2014 年 7 月 1 日至 2017 年 7 月 1 日期间在我院行影像学引导下活检的所有高危病变（HRL）。病变分为高危 I 型（HR-I=非典型导管增生、非典型小叶增生、原位小叶癌和非典型乳头状病变）和 II 型（HR-II=扁平上皮不典型性、放射状瘢痕、良性乳头状瘤）。收集患者危险因素、病变特征、检测和活检方式、切除和癌症升级率。升级的参考标准是切除或至少 2 年影像学随访。使用多变量逻辑回归分析，采用 HRL 类型、病变和活检针大小建立多变量模型，通过 ROC 分析评估模型的性能。
在 652 例患者的 699 例 HRL 中，525 例（75%）有参考标准，525 例中有 48 例（9.1%）在手术切除时发现癌症。HR-I 组的切除率（84.5%比 51.1%）和升级率（17.6%比 1.8%）明显高于 HR-II 组（p<0.01）。在 HR-I 中，小针径<12G 比≥12G[32.1%比 13.2%，p<0.01]和针道数<6 比≥6[28.6%比 13.7%，p=0.01]与更高的癌症升级率显著相关。我们的多变量模型 AUC 为 0.87，根据 HRL 亚型、病变大小（mm，连续）、针径（<12G 比≥12G）和活检方式（US 比 MRI 比立体定向），可节省 28.1%的良性手术，且具有 100%的敏感性。
我们的多变量模型使用病变大小、针径和患者年龄，在减少不必要的手术方面具有较高的诊断性能，有望成为一种决策支持工具。
