Qiu Xiaoming, Fu Yufei, Ye Yu, Wang Zhen, Cao Changjian
Department of Radiology, Huangshi Central Hospital, Edong Healthcare Group, Affiliated Hospital of Hubei Polytechnic University, Huangshi, China.
Front Oncol. 2022 Mar 15;12:790076. doi: 10.3389/fonc.2022.790076. eCollection 2022.
The aim of this study was to explore the feasibility and efficacy of a non-invasive quantitative imaging evaluation model to assess the lymphatic metastasis of breast cancer based on a radiomics signature constructed using conventional T1-weighted image (T1WI) enhanced MRI and molecular biomarkers.
Patients with breast cancer diagnosed lymph biopsies between June 2015 and June 2019 were selected for the study. All patients underwent T1WI contrast-enhancement before treatment; lymph biopsy after surgery; and simultaneous Ki-67, COX-2, PR, Her2 and proliferating cell nuclear antigen detection. All images were imported into ITK-SNAP for whole tumor delineation, and AK software was used for radiomics feature extraction. Next, the radiomics signature Rad-score was constructed after reduction of specific radiomic features. A multiple regression logistic model was built by combining the Rad-score and molecular biomarkers based on the minimum AIC.
In all, 100 patients were enrolled in this study, including 45 with non-lymph node (LN) metastasis and 55 with LN metastasis. A total of 1,051 texture feature parameters were extracted, and LASSO was used to reduce the dimensionality of the radiomics features. The log(λ) was set to 0.002786, and 19 parameters were retained for the construction of the radiomics tag Rad-score. ROC was used to evaluate the diagnostic efficiency of Rad-score: the area under the ROC curve (AUC) of the Rad-score for identifying non-lymphatic and lymphatic metastases was 0.891 in the training cohort and 0.744 in the validation cohort. With the incorporation of tumor molecular markers, the AUCs of the training cohort and validation cohort of the nomogram were 0.936 and 0.793, respectively, which were notably higher than the AUCs of the clinical parameters in the training and validation cohorts (0.719 and 0.588, respectively).
The combined model constructed using the Rad-score and molecular biomarkers can be used as an effective non-invasive method to assess LN metastasis of breast cancer. Furthermore, it can be used to quantitatively evaluate the risk of breast cancer LN metastasis before surgery.
本研究旨在探讨一种基于使用常规T1加权成像(T1WI)增强MRI构建的影像组学特征和分子生物标志物的非侵入性定量成像评估模型,以评估乳腺癌的淋巴结转移情况。
选取2015年6月至2019年6月间经淋巴结活检确诊为乳腺癌的患者进行研究。所有患者在治疗前均接受T1WI增强检查;术后进行淋巴结活检;同时检测Ki-67、COX-2、PR、Her2和增殖细胞核抗原。所有图像均导入ITK-SNAP进行全肿瘤勾画,并使用AK软件进行影像组学特征提取。接下来,在对特定影像组学特征进行降维后构建影像组学特征Rad-score。基于最小AIC,通过结合Rad-score和分子生物标志物建立多元回归逻辑模型。
本研究共纳入100例患者,其中45例无淋巴结(LN)转移,55例有LN转移。共提取1051个纹理特征参数,并使用LASSO对影像组学特征进行降维。将log(λ)设置为0.002786,保留19个参数用于构建影像组学标签Rad-score。使用ROC评估Rad-score的诊断效率:在训练队列中,Rad-score识别非淋巴结和淋巴结转移的ROC曲线下面积(AUC)为0.891,在验证队列中为0.744。纳入肿瘤分子标志物后,列线图训练队列和验证队列的AUC分别为0.936和0.793,显著高于训练和验证队列中临床参数的AUC(分别为0.719和0.588)。
使用Rad-score和分子生物标志物构建的联合模型可作为评估乳腺癌LN转移的有效非侵入性方法。此外,它可用于术前定量评估乳腺癌LN转移风险。