Label-free assessment of pathological changes in pancreatic intraepithelial neoplasia by biomedical multiphoton microscopy.

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.

Improving the diagnosis of ductal carcinoma in situ with microinvasion without immunohistochemistry: An innovative method with H&E-stained and multiphoton microscopy images.

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).

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

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.

Detection of fibrotic changes in the progression of liver diseases by label-free multiphoton imaging.

Collagen fibers play an important role in the progression of liver diseases. The formation and progression of liver fibrosis is a dynamic pathological process accompanied by morphological changes in collagen fibers. In this study, we used multiphoton microscopy for label-free imaging of liver tissues, allowing direct detection of various components including collagen fibers, tumors, blood vessels, and lymphocytes. Then, we developed a deep learning classification model to automatically identify tumor regions, and the accuracy reaches 0.998. We introduced an automated image processing method to extract eight collagen morphological features from various stages of liver diseases. Statistical analysis showed significant differences between them, indicating the potential use of these quantitative features for monitoring fibrotic changes during the progression of liver diseases. Therefore, multiphoton imaging combined with automatic image processing method would hold a promising future in rapid and label-free diagnosis of liver diseases.

Rapid and label-free detection of gastrointestinal stromal tumor via a combination of two-photon microscopy and imaging analysis.

BACKGROUND: Gastrointestinal stromal tumor (GIST) is currently regarded as a potentially malignant tumor, and early diagnosis is the best way to improve its prognosis. Therefore, it will be meaningful to develop a new method for auxiliary diagnosis of this disease. METHODS: Here we try out a new means to detect GIST by combining two-photon imaging with automatic image processing strategy. RESULTS: Experimental results show that two-photon microscopy has the ability to label-freely identify the structural characteristics of GIST such as tumor cells, desmoplastic reaction, which are entirely different from those from gastric adenocarcinoma. Moreover, an image processing approach is used to extract eight collagen morphological features from tumor microenvironment and normal muscularis, and statistical analysis demonstrates that there are significant differences in three features-fiber area, density and cross-link density. The three morphological characteristics may be considered as optical imaging biomarkers to differentiate between normal and abnormal tissues. CONCLUSION: With continued improvement and refinement of this technology, we believe that two-photon microscopy will be an efficient surveillance tool for GIST and lead to better management of this disease.

Label-free detection of invasive micropapillary carcinoma of the breast using multiphoton microscopy.

Invasive micropapillary carcinoma of the breast (IMPC) is a rare form of breast cancer with unique histological features, and is associated with high axillary lymph node metastasis and poor clinical prognosis. Thus, IMPC should be diagnosed in time to improve the treatment and management of patients. In this study, multiphoton microscopy (MPM) is used to label-free visualize the morphological features of IMPC. Our results demonstrate that MPM images are well in agreement with hematoxylin and eosin staining and epithelial membrane antigen staining, indicating MPM is comparable to traditional histological analysis in identifying the tissue structure and cell morphology. Statistical analysis shows significant differences in the circumference and area of the glandular lumen and cancer nest between the different IMPC cell clusters with complete glandular lumen morphology, and also shows difference in collagen length, width, and orientation, indicating the invasive ability of different morphologies of IMPC may be different.

Detection of pathological response of axillary lymph node metastasis after neoadjuvant chemotherapy in breast cancer using multiphoton microscopy.

Neoadjuvant treatment is often considered in breast cancer patients with axillary lymph node involvement, but most of patients do not have a pathologic complete response to therapy. The detection of residual nodal disease has a significant impact on adjuvant therapy recommendations which may improve survival. Here, we investigate whether multiphoton microscopy (MPM) could identify the pathological changes of axillary lymphatic metastasis after neoadjuvant chemotherapy in breast cancer. And furthermore, we find that there are obvious differences in seven collagen morphological features between normal node and residual axillary disease by combining with a semi-automatic image processing method, and also find that there are significant differences in four collagen features between the effective and no-response treatment groups. These research results indicate that MPM may help estimate axillary treatment response in the neoadjuvant setting and thereby tailor more appropriate and personalized adjuvant treatments for breast cancer patients.

Rapid identification of human ovarian cancer in second harmonic generation images using radiomics feature analyses and tree-based pipeline optimization tool.

Ovarian cancer is currently one of the most common cancers of the female reproductive organs, and its mortality rate is the highest among all types of gynecologic cancers. Rapid and accurate classification of ovarian cancer plays an important role in the determination of treatment plans and prognoses. Nevertheless, the most commonly used classification method is based on histopathological specimen examination, which is time-consuming and labor-intensive. Thus, in this study, we utilize radiomics feature extraction methods and the automated machine learning tree-based pipeline optimization tool (TOPT) for analysis of 3D, second harmonic generation images of benign, malignant and normal human ovarian tissues, to develop a high-efficiency computer-aided diagnostic model. Area under the receiver operating characteristic curve values of 0.98, 0.96 and 0.94 were obtained, respectively, for the classification of the three tissue types. Furthermore, this approach can be readily applied to other related tissues and diseases, and has great potential for improving the efficiency of medical diagnostic processes.

Label-free multiphoton imaging to assess neoadjuvant therapy responses in breast carcinoma.

Neoadjuvant chemotherapy has been used increasingly in patients with early-stage or locally advanced breast carcinoma, and has been recommended as a general approach in locally advanced-stage diseases. Assessing therapy response could offer prognostic information to help determine subsequent nursing plan; particularly it is essential to identify responders and non-responders for the sake of helping develop follow-up treatment strategies. However, at present, diagnostic accuracy of preoperative clinical examination are still not satisfactory. Here we presented an alternate approach to monitor tumor and stroma changes associated with neoadjuvant therapy responses in breast carcinoma, with a great potential for becoming a new diagnostic tool-multiphoton microscopy. Imaging results showed that multiphoton imaging techniques have the ability to label-freely visualize tumor response such as tumor necrosis, and stromal response including fibrosis, mucinous response, inflammatory response as well as vascular hyperplasia in situ at cellular and subcellular levels. Moreover, using automated image analysis and a set of scoring methods, we found significant differences in the area of cell nucleus and in the content of collagen fibers between the pre-treatment and post-treatment breast carcinoma tissues. In summary, this study was conducted to pathologically evaluate the response of breast carcinoma to preoperative chemotherapy as well as to assess the efficacy of multiphoton microscopy in detecting these pathological changes, and experimental results demonstrated that this microscope may be a promising tool for label-free, real-time assessment of treatment response without the use of any exogenous contrast agents.

Evaluation of breast carcinoma regression after preoperative chemotherapy by label-free multiphoton imaging and image analysis.

Neoadjuvant chemotherapy is increasingly being used in breast carcinoma as it significantly improves the prognosis and consistently leads to an increased rate of breast preservation. How to accurately assess tumor response after treatment is a crucial factor for developing reasonable therapeutic strategy. In this study, we were in an attempt to monitor tumor response by multimodal multiphoton imaging including two-photon excitation fluorescence and second-harmonic generation imaging. We found that multiphoton imaging can identify different degrees of tumor response such as a slight, significant, or complete response and can detect morphological alteration associated with extracellular matrix during the progression of breast carcinoma following preoperative chemotherapy. Two quantitative optical biomarkers including tumor cellularity and collagen content were extracted based on automatic image analysis to help monitor changes in tumor and its microenvironment. Furthermore, tumor regression grade diagnosis was tried to evaluate by multiphoton microscopy. These results may offer a basic framework for using multiphoton microscopic imaging techniques as a helpful diagnostic tool for assessing breast carcinoma response after presurgical treatment.

Multimodal multiphoton imaging for label-free monitoring of early gastric cancer.

BACKGROUND: Early gastric cancer is associated with a much better prognosis than advanced disease, and strategies to improve prognosis is strictly dependent on earlier detection and accurate diagnosis. Therefore, a label-free, non-invasive imaging technique that allows the precise identification of morphologic changes in early gastric cancer would be of considerable clinical interest. METHODS: In this study, multiphoton microscopy (MPM) using two-photon excited fluorescence combined with second-harmonic generation was used for the identification of early gastric cancer. RESULTS: This microscope was able to directly reveal improved cellular detail and stromal changes during the development of early gastric cancer. Furthermore, two features were quantified from MPM images to assess the cell change in size and stromal collagen change as gastric lesion developed from normal to early cancer. CONCLUSIONS: These results clearly show that multiphoton microscopy can be used to examine early gastric cancer at the cellular level without the need for exogenous contrast agents. This study would be helpful for early diagnosis and treatment of gastric cancer, and may provide the groundwork for further exploration into the application of multiphoton microscopy in clinical practice.

Label-free detection of residual breast cancer after neoadjuvant chemotherapy using biomedical multiphoton microscopy.

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.

Label-free classification of hepatocellular-carcinoma grading using second harmonic generation microscopy.

The clear and accurate understanding of the degree of hepatocellular-carcinoma (HCC) differentiation plays a key role in the determination of the patient prognosis and development of a treatment plan by the clinician. However, label-free and automated classification of the HCC grading is challenging. Here, we demonstrate second-harmonic generation (SHG) microscopy for label-free classification of HCC grading in paraffin-embedded specimens. A total of 217 images from 113 patients were obtained using SHG microscopy, and the SHG signals from the collagen within the tumor were analyzed using feature extraction and selection, the Mann-Whitney test, and the receiver operating characteristic curves. The results exhibit good correlation between the software analysis and the diagnosis by experienced pathologists. Combining the image features and clinical information, an adaptive quantification algorithm is generated for automatically determining the HCC grade. The results suggest that SHG microscopy might be a promising automated diagnostic method for clinical use, without requiring time for tissue processing and staining.

In vitro investigation on Ho:YAG laser-assisted bone ablation underwater.

Liquid-assisted hard tissue ablation by infrared lasers has extensive clinical application. However, detailed studies are still needed to explore the underlying mechanism. In the present study, the dynamic process of bubble evolution induced by Ho:YAG laser under water without and with bone tissue at different thickness layer were studied, as well as its effects on hard tissue ablation. The results showed that the Ho:YAG laser was capable of ablating hard bone tissue effectively in underwater conditions. The penetration of Ho:YAG laser can be significantly increased up to about 4 mm with the assistance of bubble. The hydrokinetic forces associated with the bubble not only contributed to reducing the thermal injury to peripheral tissue, but also enhanced the ablation efficiency and improve the ablation crater morphology. The data also presented some clues to optimal selection of irradiation parameters and provided additional knowledge of the bubble-assisted hard tissue ablation mechanism.

Influence of water layer thickness on hard tissue ablation with pulsed CO2 laser.

The theory of hard tissue ablation reported for IR lasers is based on a process of thermomechanical interaction, which is explained by the absorption of the radiation in the water component of the tissue. The microexplosion of the water is the cause of tissue fragments being blasted from hard tissue. The aim of this study is to evaluate the influence of the interdependence of water layer thickness and incident radiant exposure on ablation performance. A total of 282 specimens of bovine shank bone were irradiated with a pulse CO(2) laser. Irradiation was carried out in groups: without a water layer and with a static water layer of thickness ranging from 0.2 to 1.2 mm. Each group was subdivided into five subgroups for different radiant exposures ranging from 18 to 84 J/cm(2), respectively. The incision geometry, surface morphology, and microstructure of the cut walls as well as thermal injury were examined as a function of the water layer thickness at different radiant exposures. Our results demonstrate that the additional water layer is actually a mediator of laser-tissue interaction. There exists a critical thickness of water layer for a given radiant exposure, at which the additional water layer plays multiple roles, not only acting as a cleaner to produce a clean cut but also as a coolant to prevent bone heating and reduce thermal injury, but also helping to improve the regularity of the cut shape, smooth the cut surface, and enhance ablation rate and efficiency. The results suggest that desired ablation results depend on optimal selection of both water layer thickness and radiant exposure.