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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. [ABSTRACT FROM AUTHOR]

The tumor microenvironment (TME) is now recognized as an important participant of tumor progression. As the most abundant extracellular matrix component in TME, collagen plays an important role in tumor development. The imaging study of collagen morphological feature in TME is of great significance for understanding the state of tumor. Multiphoton microscopy (MPM), based on second harmonic generation (SHG) and two-photon excitation fluorescence (TPEF), can be used to monitor the morphological changes of biological tissues without labeling. In this study, we used MPM for large-scale imaging of early invasive breast cancer from the tumor center to normal tissues far from the tumor. We found that there were significant differences in collagen morphology between breast cancer tumor boundary, near tumor transition region and normal tissues far from the tumor. Furthermore, the morphological feature of eight collagen fibers was extracted to quantify the variation trend of collagen in three regions. These results may provide a new perspective for the optimal negative margin width of breast-conserving surgery and the understanding of tumor metastasis. [ABSTRACT FROM AUTHOR]

Worldwide, the most common malignancy in women is breast cancer. At present, the gold standard for breast cancer diagnosis, namely pathological analysis, cannot reach the subcellular level, and is unable to provide more physiological information. Multiphoton microscopy (MPM) has its unique advantages, such as label‐free detection, high resolution, low damage, deep imaging depth, low photobleaching and phototoxicity, and so on, which make up for the deficiences of traditional methods. This review mainly introduces the principle of MPM and its applications in breast diseases, especially in monitoring the occurrence, progression, metastasis, and therapy response of tumor. At the same time, the performance and progress of MPM combined with nanomaterials in breast cancer treatment are summarized. Finally, we propose the clinical challenges and future research directions of MPM. [ABSTRACT FROM AUTHOR]

Neoadjuvant chemotherapy has become a standard treatment for breast cancer as it has been shown to increase the rate of breast preservation and to improve outcome in patients. However, how to accurately detect residual tumors is still a challenge. In this work, we tried to use multiphoton imaging to look for residual breast tumors after preoperative therapy. Imaging results demonstrate that multiphoton microscopy can identify remaining tumor tissues and can even detect rarely residual tumor cells, which would be helpful for surgeons to accurately assess the surgical margin in real time to confirm negative margins during operation. We also performed a quantification analysis of the nuclear area of tumor cells before and after treatment with neoadjuvant chemotherapy. The measurement data show that the tumor cell nuclei after chemotherapy are significantly larger than those without treatment, and there is a statistically significant difference in the nuclear areas between the pre-treatment and post-treatment mammary carcinoma. Our pilot study indicates the potential utility of multiphoton imaging for detecting residual breast carcinoma tissues in fresh, ex vivo specimens without the use of exogenous contrast agents. We foresee real-time intraoperative applications of multiphoton microscopy in evaluating therapy response, and thereby helping clinicians develop individualized treatment plans. [ABSTRACT FROM AUTHOR]