Purpose

We aimed at assessing the predictive ability of ultrasound-based radiomics combined with clinical characteristics for axillary lymph node (ALN) status in early-stage breast cancer patients and to compare performance in different peritumoral regions.

Materials and methods

A total of 755 patients (527 in the primary cohort and 228 in the external validation cohort) were enrolled in this study. Ultrasound images for all patients were acquired and radiomics analysis performed for intratumoral and different peritumoral regions. The MRMR and LASSO regression analyses were performed on extracted features from the primary cohort to construct a radiomics signature formula combined with clinical characteristics. Pearson’s coefficient and the variance inflation factor (VIF) were performed to check the correlation and the multicollinearity among the final predictors. The best performing model was selected to develop a nomogram, which was established by performing binary logistic regression and acquiring cut-off values based on the corresponding nomogram scores of the masses.

Results

Among all the radiomics models, the “Mass + Margin3mm” model exhibited the best performance. The areas under the curves (AUC) of the nomogram in the primary and external validation cohorts were 0.906 (95% confidence intervals [CI] 0.882–0.930) and 0.922 (95% CI 0.894–0.960), respectively. They both showed good calibrations. The nomogram exhibited a good ability to discriminate between positive and negative lymph nodes (AUC: 0.853 (95% CI 0.816–0.889) in primary cohort, 0.870 (95% CI 0.818–0.922) in validation cohort), and between low-volume and high-volume lymph nodes (AUC: 0.832 (95% CI 0.781–0.884) in primary cohort, 0.911 (95% CI 0.858–0.964) in validation cohort).

Conclusions

The established nomogram is a prospective clinical prediction tool for non-invasive assessment of ALN status. It has the ability to enhance the accuracy of early-stage breast cancer treatment.

中文翻译：

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基于超声的放射组学列线图预测早期乳腺癌腋窝淋巴结转移

目的

我们的目的是评估基于超声的放射组学结合临床特征对早期乳腺癌患者腋窝淋巴结（ALN）状态的预测能力，并比较不同瘤周区域的表现。

材料和方法

共有 755 名患者（527 名患者属于主要队列，228 名患者属于外部验证队列）参与了本研究。获取所有患者的超声图像，并对瘤内和不同瘤周区域进行放射组学分析。对主要队列中提取的特征进行 MRMR 和 LASSO 回归分析，以构建结合临床特征的放射组学特征公式。使用皮尔逊系数和方差膨胀因子 (VIF) 检查最终预测变量之间的相关性和多重共线性。选择性能最佳的模型来开发列线图，该列线图是通过执行二元逻辑回归并根据质量的相应列线图分数获取截止值来建立的。

结果

在所有放射组学模型中，“Mass + Margin3mm”模型表现出最好的性能。主要和外部验证队列中列线图的曲线下面积 (AUC) 分别为 0.906（95% 置信区间 [CI] 0.882–0.930）和 0.922（95% CI 0.894–0.960）。它们都显示出良好的校准效果。列线图表现出良好的区分阳性和阴性淋巴结的能力（AUC：在主要队列中为 0.853 (95% CI 0.816–0.889)，在验证队列中为 0.870 (95% CI 0.818–0.922)），以及区分低容量和阴性淋巴结。高容量淋巴结（AUC：主要队列中为 0.832（95% CI 0.781–0.884），验证队列中为 0.911（95% CI 0.858–0.964））。

结论

所建立的列线图是一种用于非侵入性评估 ALN 状态的前瞻性临床预测工具。它能够提高早期乳腺癌治疗的准确性。