Prognostic significance of collagen signatures at breast tumor boundary obtained by combining multiphoton imaging and imaging analysis

• Published in: Cellular oncology (Dordrecht) 2024-02, Vol.47 (1), p.69-80
• Main Authors: Huang, Xingxin, Fu, Fangmeng, Guo, Wenhui, Kang, Deyong, Han, Xiahui, Zheng, Liqin, Zhan, Zhenlin, Wang, Chuan, Zhang, Qingyuan, Wang, Shu, Xu, Shunwu, Ma, Jianli, Qiu, Lida, Chen, Jianxin, Li, Lianhuang
• Format: Article
• Language: English
• Subjects: Biomedical and Life Sciences; Biomedicine; Breast cancer; Cancer Research; Collagen; Decision making; Deep learning; Medical prognosis; Metastases; Oncology; Pathology; Patients; Prognosis; Regression analysis; Survival analysis; Tumor microenvironment; Tumors
• ISSN: 2211-3428; 2211-3436
• DOI: 10.1007/s13402-023-00851-4

Purpose Collagen features in breast tumor microenvironment is closely associated with the prognosis of patients. We aim to explore the prognostic significance of collagen features at breast tumor border by combining multiphoton imaging and imaging analysis. Methods We used multiphoton microscopy (MPM) to label-freely image human breast tumor samples and then constructed an automatic classification model based on deep learning to identify collagen signatures from multiphoton images. We recognized three kinds of collagen signatures at tumor boundary (CSTB I-III) in a small-scale, and furthermore obtained a CSTB score for each patient based on the combined CSTB I-III by using the ridge regression analysis. The prognostic performance of CSTB score is assessed by the area under the receiver operating characteristic curve (AUC), Cox proportional hazard regression analysis, as well as Kaplan-Meier survival analysis. Results As an independent prognostic factor, statistical results reveal that the prognostic performance of CSTB score is better than that of the clinical model combining three independent prognostic indicators, molecular subtype, tumor size, and lymph nodal metastasis (AUC, Training dataset: 0.773 vs. 0.749; External validation: 0.753 vs. 0.724; HR, Training dataset: 4.18 vs. 3.92; External validation: 4.98 vs. 4.16), and as an auxiliary indicator, it can greatly improve the accuracy of prognostic prediction. And furthermore, a nomogram combining the CSTB score with the clinical model is established for prognosis prediction and clinical decision making. Conclusion This standardized and automated imaging prognosticator may convince pathologists to adopt it as a prognostic factor, thereby customizing more effective treatment plans for patients.