列线图
医学
乳腺癌
放射科
超声波
乳房切除术
活检
癌症
肿瘤科
内科学
作者
Jinghui Fang,Qiongxia Deng,Jingwen Zhang,Yuqin Ma,Chunchun Jin,Jianghao Lu,Yanli Hao,Yuanyuan Ma,Weizong Liu,Zhengyi Li,Guowen Liu,Yongpan Mo,Yu Xiao,Chang Zheng,Yajie Yang,Tingting Wu,Chao Zhao,Xin Zhou,Peng Zhou
标识
DOI:10.1016/j.ultrasmedbio.2023.08.023
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI