Radiomics model based on shear-wave elastography in the assessment of axillary lymph node status in early-stage breast cancer
Radiomics model based on shear-wave elastography in the assessment of axillary lymph node status in early-stage breast cancer
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DOI：
10.1007/s00330-021-08330-w
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发表时间：
2021-10-20
影响因子：
5.9
通讯作者：
Dietrich, Christoph F.
中科院分区：
文献类型：
--
作者：
Jiang, Meng;Li, Chang-Li;Dietrich, Christoph F.
目的建立并验证超声弹性成像放射组学正常图用于早期乳腺癌腋窝淋巴结(ALN)负荷的术前评估。方法收集2016年6月至2019年5月期间第一医院(培训队列)303例和第二医院(外部验证队列)130例患者的临床资料。从剪切波弹性成像(SWE)和相应的B型超声(BMUS)图像中提取放射组学特征。使用最小冗余度、最大相关性和最小绝对收缩和选择算子算法来选择与ALN状态相关的特征。使用放射组学签名和临床数据进行比例优势序贯Logistic回归，并随后绘制序数诺模图。我们使用C指数和定标对其性能进行了评估。结果SWE信号、US报道的LN状态和分子亚型是ALN状态的独立危险因素。基于这些变量的诺模图在训练(总体C指数：0.842；95%CI，0.773-0.879)和验证集(总体C指数：0.822；95%CI，0.765-0.838)中显示出良好的区分性。为了区分无瘤腋窝(N0)和任何腋窝转移(N+)(>；=1)，训练队列的C指数为0.845(95%CI，0.777-0.914)，验证队列的C指数为0.817(95%CI，0.769-0.865)。该工具还可以区分低(N+(1-2))和重转移性(N+(>；=3))，C指数在训练队列中为0.827(95%CI，0.742-0.913)，在验证队列中为0.810(95%CI，0.755-0.864)。结论放射组学模型对早期乳腺癌患者的ALN分期具有良好的预测能力，可为决策提供增量信息。
Objectives To develop and validate an ultrasound elastography radiomics nomogram for preoperative evaluation of the axillary lymph node (ALN) burden in early-stage breast cancer. Methods Data of 303 patients from hospital #1 (training cohort) and 130 cases from hospital #2 (external validation cohort) between Jun 2016 and May 2019 were enrolled. Radiomics features were extracted from shear-wave elastography (SWE) and corresponding B-mode ultrasound (BMUS) images. The minimum redundancy maximum relevance and least absolute shrinkage and selection operator algorithms were used to select ALN status-related features. Proportional odds ordinal logistic regression was performed using the radiomics signature together with clinical data, and an ordinal nomogram was subsequently developed. We evaluated its performance using C-index and calibration. Results SWE signature, US-reported LN status, and molecular subtype were independent risk factors associated with ALN status. The nomogram based on these variables showed good discrimination in the training (overall C-index: 0.842; 95%CI, 0.773-0.879) and the validation set (overall C-index: 0.822; 95%CI, 0.765-0.838). For discriminating between disease-free axilla (N0) and any axillary metastasis (N + (>= 1)), it achieved a C-index of 0.845 (95%CI, 0.777-0.914) for the training cohort and 0.817 (95%CI, 0.769-0.865) for the validation cohort. The tool could also discriminate between low (N + (1-2)) and heavy metastatic ALN burden (N + (>= 3)), with a C-index of 0.827 (95%CI, 0.742-0.913) in the training cohort and 0.810 (95%CI, 0.755-0.864) in the validation cohort. Conclusion The radiomics model shows favourable predictive ability for ALN staging in patients with early-stage breast cancer, which could provide incremental information for decision-making.