Wu Xiaoqian, Guo Yu, Sa Yu, Song Yipeng, Li Xinghua, Lv Yongbin, Xing Dong, Sun Yan, Cong Yizi, Yu Hui, Jiang Wei
Department of Biomedical Engineering, School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, China.
Department of Radiotherapy, Yantai Yuhuangding Hospital, Yantai, China.
Front Oncol. 2022 May 6;12:823897. doi: 10.3389/fonc.2022.823897. eCollection 2022.
To establish and evaluate non-invasive models for estimating the risk of non-sentinel lymph node (NSLN) metastasis and axillary tumor burden among breast cancer patients with 1-2 positive sentinel lymph nodes (SLNs).
Breast cancer patients with 1-2 positive SLNs who underwent axillary lymph node dissection (ALND) and contrast-enhanced spectral mammography (CESM) examination were enrolled between 2018 and 2021. CESM-based radiomics and deep learning features of tumors were extracted. The correlation analysis, least absolute shrinkage and selection operator (LASSO), and analysis of variance (ANOVA) were used for further feature selection. Models based on the selected features and clinical risk factors were constructed with multivariate logistic regression. Finally, two radiomics nomograms were proposed for predicting NSLN metastasis and the probability of high axillary tumor burden.
A total of 182 patients [53.13 years ± 10.03 (standard deviation)] were included. For predicting the NSLN metastasis status, the radiomics nomogram built by 5 selected radiomics features and 3 clinical risk factors including the number of positive SLNs, ratio of positive SLNs, and lymphovascular invasion (LVI), achieved the area under the receiver operating characteristic curve (AUC) of 0.85 [95% confidence interval (CI): 0.71-0.99] in the testing set and 0.82 (95% CI: 0.67-0.97) in the temporal validation cohort. For predicting the high axillary tumor burden, the AUC values of the developed radiomics nomogram are 0.82 (95% CI: 0.66-0.97) in the testing set and 0.77 (95% CI: 0.62-0.93) in the temporal validation cohort.
CESM images contain useful information for predicting NSLN metastasis and axillary tumor burden of breast cancer patients. Radiomics can inspire the potential of CESM images to identify lymph node metastasis and improve predictive performance.
建立并评估用于估计1 - 2枚前哨淋巴结（SLN）阳性的乳腺癌患者非前哨淋巴结（NSLN）转移风险及腋窝肿瘤负荷的非侵入性模型。
纳入2018年至2021年间接受腋窝淋巴结清扫（ALND）及对比增强光谱乳腺摄影（CESM）检查且有1 - 2枚SLN阳性的乳腺癌患者。提取基于CESM的肿瘤放射组学和深度学习特征。采用相关性分析、最小绝对收缩和选择算子（LASSO）以及方差分析（ANOVA）进行进一步特征选择。基于所选特征和临床危险因素，通过多变量逻辑回归构建模型。最后，提出两个放射组学列线图用于预测NSLN转移及高腋窝肿瘤负荷的概率。
共纳入182例患者[53.13岁±10.03（标准差）]。对于预测NSLN转移状态，由5个选定的放射组学特征和3个临床危险因素（包括阳性SLN数量、阳性SLN比例和淋巴管浸润（LVI））构建的放射组学列线图，在测试集中受试者操作特征曲线（AUC）下面积为0.85[95%置信区间（CI）：0.71 - 0.99]，在时间验证队列中为0.82（95% CI：0.67 - 0.97）。对于预测高腋窝肿瘤负荷，所构建的放射组学列线图在测试集中AUC值为0.82（95% CI：0.66 - 0.97），在时间验证队列中为0.77（95% CI：0.62 - 0.93）。
CESM图像包含预测乳腺癌患者NSLN转移和腋窝肿瘤负荷的有用信息。放射组学可激发CESM图像识别淋巴结转移的潜力并提高预测性能。
