Xi Gangqin, He Jiajia, Kang Deyong, Xu Shuoyu, Guo Wenhui, Fu Fangmeng, Liu Yulan, Zheng Liqin, Qiu Lida, Li Lianhuang, Wang Chuan, Chen Jianxin
Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou 350007, China.
These authors contributed equally to this work.
Biomed Opt Express. 2021 Sep 27;12(10):6558-6570. doi: 10.1364/BOE.433281. eCollection 2021 Oct 1.
The purpose of this study is to develop and validate a new nomogram model combining macro and micro tumor-associated collagen signatures obtained from multiphoton images to differentiate tumor grade in patients with invasive breast cancer. A total of 543 patients were included in this study. We used computer-generated random numbers to assign 328 of these patients to the training cohort and 215 patients to the validation cohort. Macroscopic tumor-associated collagen signatures (TACS1-8) were obtained by multiphoton microscopy at the invasion front and inside of the breast primary tumor. TACS corresponding microscopic features (TCMF) including morphology and texture features were extracted from the segmented regions of interest using Matlab 2016b. Using ridge regression analysis, we obtained a TACS-score for each patient based on the combined TACS1-8, and the least absolute shrinkage and selection operator (LASSO) regression was applied to select the most robust TCMF features to build a TCMF-score. Univariate logistic regression analysis demonstrates that the TACS-score and TCMF-score are significantly associated with histologic grade (odds ratio, 2.994; 95% CI, 2.013-4.452; < 0.001; 4.245, 2.876-6.264, < 0.001 in the training cohort). The nomogram (collagen) model combining the TACS-score and TCMF-score could stratify patients into Grade1 and Grade2/3 groups with the AUC of 0.859 and 0.863 in the training and validation cohorts. The predictive performance can be further improved by combining the clinical factors, achieving the AUC of 0.874 in both data cohorts. The nomogram model combining the TACS-score and TCMF-score can be useful in differentiating breast tumor patients with Grade1 and Grade2/3.
本研究的目的是开发并验证一种新的列线图模型，该模型结合从多光子图像中获得的宏观和微观肿瘤相关胶原特征，以区分浸润性乳腺癌患者的肿瘤分级。本研究共纳入543例患者。我们使用计算机生成的随机数将其中328例患者分配到训练队列，215例患者分配到验证队列。通过多光子显微镜在乳腺原发性肿瘤的侵袭前沿和内部获得宏观肿瘤相关胶原特征（TACS1-8）。使用Matlab 2016b从分割的感兴趣区域中提取与TACS对应的微观特征（TCMF），包括形态和纹理特征。使用岭回归分析，我们基于组合的TACS1-8为每位患者获得一个TACS评分，并应用最小绝对收缩和选择算子（LASSO）回归来选择最稳健的TCMF特征以构建一个TCMF评分。单因素逻辑回归分析表明，TACS评分和TCMF评分与组织学分级显著相关（优势比，2.994；95%CI，2.013-4.452；P<0.001；4.245，2.876-6.264，P<0.001，训练队列）。结合TACS评分和TCMF评分的列线图（胶原）模型可以将患者分层为1级和2/3级组，在训练和验证队列中的AUC分别为0.859和0.863。通过结合临床因素可以进一步提高预测性能，在两个数据队列中均达到0.874的AUC。结合TACS评分和TCMF评分的列线图模型可用于区分1级和2/3级乳腺肿瘤患者。
