Prediction of pathological complete response of breast cancer patients who received neoadjuvant chemotherapy with a nomogram based on clinicopathologic variables, ultrasound, and MRI

列线图 医学 乳腺癌 接收机工作特性 逻辑回归 磁共振成像 单变量分析 优势比 超声波 肿瘤科 内科学 放射科 单变量 新辅助治疗 癌症 多元分析 多元统计 数学 统计
作者
Manqi Zhang,Xinpei Liu,Yu Du,Hailing Zha,Xiaoming Zha,Jue Wang,Xiaoan Liu,Shou-Ju Wang,Qigui Zou,Jiulou Zhang,Cuiying Li
出处
期刊:British Journal of Radiology [British Institute of Radiology]
卷期号:97 (1153): 228-236 被引量:2
标识
DOI:10.1093/bjr/tqad014
摘要

Abstract Objective To establish a nomogram for predicting the pathologic complete response (pCR) in breast cancer (BC) patients after NAC by applying magnetic resonance imaging (MRI) and ultrasound (US). Methods A total of 607 LABC women who underwent NAC before surgery between January 2016 and June 2022 were retrospectively enrolled, and then were randomly divided into the training (n = 425) and test set (n = 182) with the ratio of 7:3. MRI and US variables were collected before and after NAC, as well as the clinicopathologic features. Univariate and multivariate logistic regression analyses were applied to confirm the potentially associated predictors of pCR. Finally, a nomogram was developed in the training set with its performance evaluated by the area under the receiver operating characteristics curve (ROC) and validated in the test set. Results Of the 607 patients, 108 (25.4%) achieved pCR. Hormone receptor negativity (odds ratio [OR], 0.3; P < .001), human epidermal growth factor receptor 2 positivity (OR, 2.7; P = .001), small tumour size at post-NAC US (OR, 1.0; P = .031), tumour size reduction ≥50% at MRI (OR, 9.8; P < .001), absence of enhancement in the tumour bed at post-NAC MRI (OR, 8.1; P = .003), and the increase of ADC value after NAC (OR, 0.3; P = .035) were all significantly associated with pCR. Incorporating the above variables, the nomogram showed a satisfactory performance with an AUC of 0.884. Conclusion A nomogram including clinicopathologic variables and MRI and US characteristics shows preferable performance in predicting pCR. Advances in knowledge A nomogram incorporating MRI and US with clinicopathologic variables was developed to provide a brief and concise approach in predicting pCR to assist clinicians in making treatment decisions early.
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