Department of Ultrasound, First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China.
Department of Thyroid and Breast Surgery, First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China.
Ultrasound Med Biol. 2023 Dec;49(12):2565-2572. doi: 10.1016/j.ultrasmedbio.2023.08.023. Epub 2023 Sep 24.
Accurately predicting nipple-areola complex (NAC) involvement in breast cancer is essential for identifying eligible patients for a nipple-sparing mastectomy. This study was aimed at developing a pre-operative nomogram for NAC involvement in breast cancer using conventional ultrasound (US) and contrast-enhanced ultrasound (CEUS).
All patients with primary breast cancer confirmed by pre-operative biopsy underwent US and CEUS examinations. Post-operative pathology was used as the gold standard in assessing NAC involvement. Lasso regression was used to select the predictors most associated with NAC involvement. A nomogram was constructed to calculate the diagnostic efficacy. The data were internally verified with 500 bootstrapped replications, and a calibration curve was generated to validate the predictive capability.
Seventy-six patients with primary breast cancer were included in this study, which included 16 patients (21.1%) with NAC involvement and 60 patients (78.9%) without NAC involvement. Among the 23 features of US and CEUS, Lasso regression selected one US feature and two CEUS features, namely, ductal echo extending from the lesion, ductal enhancement extending to the nipple and focal nipple enhancement. A nomogram was constructed, and the results revealed that the area under the curve, sensitivity, specificity and accuracy were 0.891, 81.3%, 86.7% and 85.5%, respectively. The calibration curve exhibited good consistency between the predicted probability and the actual probability.
The nomogram developed based on US and CEUS had good performance in predicting NAC involvement in breast cancer before surgery, which may facilitate the selection of suitable patients for NAC preservation with greater oncological safety.
准确预测乳腺癌乳头乳晕复合体（NAC）受累情况对于识别有资格接受保乳乳头切除术的患者至关重要。本研究旨在使用常规超声（US）和超声造影（CEUS）建立术前预测乳腺癌 NAC 受累的列线图。
所有经术前活检证实为原发性乳腺癌的患者均接受 US 和 CEUS 检查。术后病理作为评估 NAC 受累的金标准。使用 Lasso 回归选择与 NAC 受累最相关的预测因素。构建列线图以计算诊断效能。对数据进行 500 次 bootstrap 复制进行内部验证，并生成校准曲线以验证预测能力。
本研究共纳入 76 例原发性乳腺癌患者，其中 16 例（21.1%）患者存在 NAC 受累，60 例（78.9%）患者无 NAC 受累。在 US 和 CEUS 的 23 个特征中，Lasso 回归选择了 1 个 US 特征和 2 个 CEUS 特征，即病灶延伸的导管回声、延伸至乳头的导管增强以及局灶性乳头增强。构建了列线图，结果显示曲线下面积、敏感度、特异度和准确率分别为 0.891、81.3%、86.7%和 85.5%。校准曲线显示预测概率与实际概率之间具有良好的一致性。
基于 US 和 CEUS 建立的列线图在术前预测乳腺癌 NAC 受累方面具有良好的性能，这可能有助于选择更具肿瘤安全性的适合 NAC 保留的患者。
