Department of General Surgery, The Second Xiangya Hospital, Central South University.
Clinical Research Center For Breast Disease In Hunan Province, Changsha.
Int J Surg. 2024 Sep 1;110(9):5363-5373. doi: 10.1097/JS9.0000000000001778.
The accuracy of traditional clinical methods for assessing the metastatic status of axillary lymph nodes (ALNs) is unsatisfactory. In this study, the authors propose the use of radiomic technology and three-dimensional (3D) visualization technology to develop an unsupervised learning model for predicting axillary lymph node metastasis in patients with breast cancer (BC), aiming to provide a new method for clinical axillary lymph node assessment in patients with this disease.
In this study, we retrospectively analyzed the data of 350 patients with invasive BC who underwent lung-enhanced computed tomography (CT) and axillary lymph node dissection surgery at the Department of Breast Surgery of the Second Xiangya Hospital of Central South University. The authors used 3D visualization technology to create a 3D atlas of ALNs and identified the region of interest for the lymph nodes. Radiomic features were subsequently extracted and selected, and a prediction model for ALNs was constructed using the K-means unsupervised algorithm. To validate the model, the authors prospectively collected data from 128 BC patients who were clinically evaluated as negative at our center.
Using 3D visualization technology, we extracted and selected a total of 36 CT radiomics features. The unsupervised learning model categorized 1737 unlabeled lymph nodes into two groups, and the analysis of the radiomic features between these groups indicated potential differences in lymph node status. Further validation with 1397 labeled lymph nodes demonstrated that the model had good predictive ability for axillary lymph node status, with an area under the curve of 0.847 (0.825-0.869). Additionally, the model's excellent predictive performance was confirmed in the 128 axillary clinical assessment negative cohort (cN0) and the 350 clinical assessment positive (cN+) cohort, for which the correct classification rates were 86.72 and 87.43%, respectively, which were significantly greater than those of clinical assessment methods.
The authors created an unsupervised learning model that accurately predicts the status of ALNs. This approach offers a novel solution for the precise assessment of ALNs in patients with BC.
传统的临床方法评估腋窝淋巴结（ALN）转移状态的准确性并不令人满意。在这项研究中，作者提出利用放射组学技术和三维（3D）可视化技术，开发一种用于预测乳腺癌（BC）患者腋窝淋巴结转移的无监督学习模型，旨在为该疾病患者的临床腋窝淋巴结评估提供一种新方法。
本研究回顾性分析了 350 例在中南大学湘雅二医院乳腺外科行肺部增强 CT 和腋窝淋巴结清扫术的浸润性 BC 患者的数据。作者使用 3D 可视化技术构建了 ALN 的 3D 图谱，并确定了淋巴结的感兴趣区域。随后提取并选择放射组学特征，并使用 K-均值无监督算法构建 ALN 预测模型。为了验证该模型，作者前瞻性地收集了来自本中心临床评估为阴性的 128 例 BC 患者的数据。
使用 3D 可视化技术，共提取并选择了 36 个 CT 放射组学特征。无监督学习模型将 1737 个未标记的淋巴结分为两组，对这些组之间的放射组学特征进行分析表明，淋巴结状态存在潜在差异。对 1397 个标记的淋巴结进一步验证表明，该模型对腋窝淋巴结状态具有良好的预测能力，曲线下面积为 0.847（0.825-0.869）。此外，该模型在 128 例临床评估阴性（cN0）和 350 例临床评估阳性（cN+）队列中表现出良好的预测性能，其正确分类率分别为 86.72%和 87.43%，明显高于临床评估方法。
作者创建了一种能够准确预测 ALN 状态的无监督学习模型。该方法为 BC 患者的 ALN 精确评估提供了一种新的解决方案。
