Search

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]

Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal tumors arising in the digest tract. It brings a challenge to diagnosis because it is asymptomatic clinically. It is well known that tumor development is often accompanied by the changes in the morphology of collagen fibers. Nowadays, an emerging optical imaging technique, second-harmonic generation (SHG), can directly identify collagen fibers without staining due to its noncentrosymmetric properties. Therefore, in this study, we attempt to assess the feasibility of SHG imaging for detecting GISTs by monitoring the morphological changes of collagen fibers in tumor microenvironment. We found that collagen alterations occurred obviously in the GISTs by comparing with normal tissues, and furthermore, two morphological features from SHG images were extracted to quantitatively assess the morphological difference of collagen fibers between normal muscular layer and GISTs by means of automated image analysis. Quantitative analyses show a significant difference in the two collagen features. This study demonstrates the potential of SHG imaging as an adjunctive diagnostic tool for label-free identification of GISTs. [ABSTRACT FROM AUTHOR]