Department of Surgery, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-752, Republic of Korea.
Breast. 2013 Oct;22(5):869-73. doi: 10.1016/j.breast.2013.03.009. Epub 2013 Apr 17.
It is unnecessary to perform axillary staging in patients with ductal carcinoma in situ (DCIS) of the breast because of the low incidence of axillary metastasis. However, diagnosis of DCIS by core needle biopsy showed a high rate of underestimation of invasive cancer. Thus, it is necessary to predict invasiveness in DCIS patients on core before surgery. We analyzed 340 patients with DCIS diagnosed by needle biopsy. The cases were divided into training and validation sets. Logistic regression was performed to predict the presence of invasive cancer in the final pathology, and a nomogram was constructed from the training set using the presence of palpability, the presence of ultrasonographic calcification and mass, the biopsy tools, and the presence of microinvasion. The model was subsequently applied to the validation set. The nomogram for the training set was both accurate and discriminating, with an area under the receiver operating characteristic curve (AUC) of 0.75. When applied to the validation group, the model accurately predicted the likelihood of invasive cancer (AUC: 0.71). Our nomogram will allow surgeons to easily and accurately estimate the likelihood of invasive cancer in patients with DCIS as diagnosed by preoperative needle biopsy.
对于乳腺导管原位癌(DCIS)患者,由于腋窝转移的发生率较低,因此无需进行腋窝分期。然而,通过核心针活检诊断为 DCIS 存在高估浸润性癌的高发生率。因此,有必要在术前对 DCIS 患者的核心活检进行侵袭性预测。我们分析了 340 例经针活检诊断为 DCIS 的患者。这些病例分为训练集和验证集。使用逻辑回归预测最终病理学中存在浸润性癌,并使用训练集的可触诊性、超声钙化和肿块的存在、活检工具和微浸润的存在来构建列线图。该模型随后应用于验证集。训练集的列线图具有较高的准确性和判别力,受试者工作特征曲线下面积(AUC)为 0.75。当应用于验证组时,该模型准确预测了浸润性癌的可能性(AUC:0.71)。我们的列线图将使外科医生能够在术前通过针活检轻松准确地评估 DCIS 患者发生浸润性癌的可能性。