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Ductal carcinoma in situ with microinvasion (DCISM) is a challenging subtype of breast cancer with controversial invasiveness and prognosis. Accurate diagnosis of DCISM from ductal carcinoma in situ (DCIS) is crucial for optimal treatment and improved clinical outcomes. However, there are often some suspicious small cancer nests in DCIS, and it is difficult to diagnose the presence of intact myoepithelium by conventional hematoxylin and eosin (H&E) stained images. Although a variety of biomarkers are available for immunohistochemical (IHC) staining of myoepithelial cells, no single biomarker is consistently sensitive to all tumor lesions. Here, we introduced a new diagnostic method that provides rapid and accurate diagnosis of DCISM using multiphoton microscopy (MPM). Suspicious foci in H&E‐stained images were labeled as regions of interest (ROIs), and the nuclei within these ROIs were segmented using a deep learning model. MPM was used to capture images of the ROIs in H&E‐stained sections. The intensity of two‐photon excitation fluorescence (TPEF) in the myoepithelium was significantly different from that in tumor parenchyma and tumor stroma. Through the use of MPM, the myoepithelium and basement membrane can be easily observed via TPEF and second‐harmonic generation (SHG), respectively. By fusing the nuclei in H&E‐stained images with MPM images, DCISM can be differentiated from suspicious small cancer clusters in DCIS. The proposed method demonstrated good consistency with the cytokeratin 5/6 (CK5/6) myoepithelial staining method (kappa coefficient = 0.818). [ABSTRACT FROM AUTHOR]

Pancreatic intraepithelial neoplasia (PanIN) is the most common precursor lesion that has the potential to progress to invasive pancreatic cancer, and early and rapid detection may offer patients a chance for treatment before the development of invasive carcinoma. Therefore, the identification of PanIN holds significant clinical importance. In this study, we first used multiphoton microscopy (MPM) combining two‐photon excitation fluorescence and second‐harmonic generation imaging to label‐free detect PanIN and attempted to differentiate between normal pancreatic ducts and different grades of PanIN. Then, we also developed an automatic image processing strategy to extract eight morphological features of collagen fibers from MPM images to quantify the changes in collagen fibers surrounding the ducts. Experimental results demonstrate that the combination of MPM and quantitative information can accurately identify normal pancreatic ducts and different grades of PanIN. This study may contribute to the rapid diagnosis of pancreatic diseases and may lay the foundation for further clinical application of MPM. [ABSTRACT FROM AUTHOR]