Nomogram model combining macro and micro tumor-associated collagen signatures obtained from multiphoton images to predict the histologic grade in breast cancer.

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; P < 0.001; 4.245, 2.876-6.264, P < 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.