| 基于病理和超声图像特征的列线图模型预测乳腺癌腋窝淋巴结转移的临床价值 |
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| 引用本文： | 阮彦,查国芬,郑雨欣,张雅娇,方程钰,胡丹琦,刘俊平. 基于病理和超声图像特征的列线图模型预测乳腺癌腋窝淋巴结转移的临床价值[J]. 临床超声医学杂志, 2024, 26(7) |
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| 作者姓名： | 阮彦 查国芬 郑雨欣 张雅娇 方程钰 胡丹琦 刘俊平 |
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| 作者单位： | 浙江中医药大学研究生院,衢州市柯城区人民医院超声科,浙江中医药大学研究生院,浙江中医药大学研究生院,浙江省肿瘤医院超声科,浙江省肿瘤医院超声科,浙江省肿瘤医院超声科 |
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| 摘 要： | 目的 探讨浸润性乳腺癌原发灶的临床病理和超声特征对腋窝淋巴结转移的预测价值并构建列线图模型。方法 回顾性分析2022年6月至2023年10月于浙江省肿瘤医院经病理证实的浸润性乳腺癌女性患者原发灶的临床病理和超声特征,以7:3比例将其随机分为训练集和验证集。对训练集数据进行单变量和多变量logistic回归分析,构建浸润性乳腺癌是否发生腋窝淋巴结转移的预测模型,并绘制列线图。在验证集中,评价模型的区分度和校准度。结果 训练集共纳入258例患者,其中116例（45%）有腋窝淋巴结转移,验证集共纳入111例患者,其中61例（55%）有腋窝淋巴结转移。基于训练集数据分析,位置（内下象限OR=0.064,P=0.002）、高回声晕环（OR=13.278,P＜0.001）、最大径（OR=1.049,P=0.001）、组织学分级（OR=9.277,P=0.014）是腋窝淋巴结转移的独立预测因素。在验证集中,预测模型的ROC曲线下面积为0.823,说明区分度良好,校准曲线与理想曲线高度吻合,说明校准度良好。结论 基于浸润性乳腺癌原发灶超声特征（位置、高回声晕环、最大径）和病理特征（组织学分级）的列线图模型可有效预测腋窝淋巴结转移风险,为精准诊疗提供参考。
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| 关 键 词： | 超声检查 临床病理 浸润性乳腺癌 腋窝淋巴结转移 列线图 |
| 收稿时间： | 2024-01-21 |
| 修稿时间： | 2024-05-31 |
Prediction of axillary lymph node metastasis of breast cancer based on clinicopathological and ultrasound features |
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| Ruan Yan,Cha Guo-fen,Zheng Yu-xin,Zhang Ya-jiao,Fang Cheng-yu,Hu Dan-qi and Liu Jun-ping. Prediction of axillary lymph node metastasis of breast cancer based on clinicopathological and ultrasound features[J]. Journal of Ultrasound in Clinical Medicine, 2024, 26(7) |
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| Authors: | Ruan Yan Cha Guo-fen Zheng Yu-xin Zhang Ya-jiao Fang Cheng-yu Hu Dan-qi Liu Jun-ping |
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| Affiliation: | Graduate School of Zhejiang Chinese Medicine University,,,,,,Department of Ultrasound,Zhejiang Cancer Hospital |
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| Abstract: | Objective To investigate the predictive value of clinicopathological and ultrasonic features for axillary lymph node metastasis in patients with invasive breast cancer and construct a nomogram to provide reference for individual diagnosis and treatment. Method The clinicopathologic and ultrasonic features of female patients with pathologically confirmed invasive breast cancer from June 2022 to October 2023 in Zhejiang Cancer Hospital were retrospectively analyzed, and randomly divided into the training set and the validation set at a ratio of 7:3. Univariate and multivariate logistic regression analysis was performed on the training set data to build a prediction model for axillary lymph node metastasis of invasive breast cancer and draw a nomogram. In the verification set, the differentiation and calibration degree of the model are evaluated. Result A total of 258 patients were included in the training set, of whom 116 (45%) had axillary lymph node metastasis. A total of 111 patients were included in the validation set, of whom 61 (55%) had axillary lymph node metastasis. Based on training set data analysis, high echo halo (OR, 15.783; P< 0.00 1), maximum diameter of primary focus (OR, 1.039; P= 0.011), tumor histological grade (OR, 12.469; P=0.012), the lesion was located in the inner lower breast quadrant (OR, 0.046; P<0.001) was an independent predictor of axillary lymph node metastasis. In the verification set, the area under the ROC curve of the model was: 0.823, the differentiation was good, the calibration curve was highly consistent with the ideal curve, and the calibration was good. Conclusion The nomogram based on ultrasonic characteristics (halo, maximum diameter and location of primary lesion) and pathological characteristics (tumor histological grade) can effectively predict the risk of axillary lymph node metastasis, and provide reference for accurate diagnosis and treatment. |
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