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).

Exploration of the relationship between tumor-infiltrating lymphocyte score and histological grade in breast cancer.

BACKGROUND: The histological grade is an important factor in the prognosis of invasive breast cancer and is vital to accurately identify the histological grade and reclassify of Grade2 status in breast cancer patients. METHODS: In this study, data were collected from 556 invasive breast cancer patients, and then randomly divided into training cohort (n = 335) and validation cohort (n = 221). All patients were divided into actual low risk group (Grade1) and high risk group (Grade2/3) based on traditional histological grade, and tumor-infiltrating lymphocyte score (TILs-score) obtained from multiphoton images, and the TILs assessment method proposed by International Immuno-Oncology Biomarker Working Group (TILs-WG) were also used to differentiate between high risk group and low risk group of histological grade in patients with invasive breast cancer. Furthermore, TILs-score was used to reclassify Grade2 (G2) into G2 /Low risk and G2/High risk. The coefficients for each TILs in the training cohort were retrieved using ridge regression and TILs-score was created based on the coefficients of the three kinds of TILs. RESULTS: Statistical analysis shows that TILs-score is significantly correlated with histological grade, and is an independent predictor of histological grade (odds ratio [OR], 2.548; 95%CI, 1.648-3.941; P < 0.0001), but TILs-WG is not an independent predictive factor for grade (P > 0.05 in the univariate analysis). Moreover, the risk of G2/High risk group is higher than that of G2/Low risk group, and the survival rate of patients with G2/Low risk is similar to that of Grade1, while the survival rate of patients with G2/High risk is even worse than that of patients with G3. CONCLUSION: Our results suggest that TILs-score can be used to predict the histological grade of breast cancer and potentially to guide the therapeutic management of breast cancer patients.

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.

Prognostic value of tumor necrosis based on the evaluation of frequency in invasive breast cancer.

BACKGROUND: Tumor necrosis (TN) was associated with poor prognosis. However, the traditional classification of TN ignored spatial intratumor heterogeneity, which may be associated with important prognosis. The purpose of this study was to propose a new method to reveal the hidden prognostic value of spatial heterogeneity of TN in invasive breast cancer (IBC). METHODS: Multiphoton microscopy (MPM) was used to obtain multiphoton images from 471 patients. According to the relative spatial positions of TN, tumor cells, collagen fibers and myoepithelium, four spatial heterogeneities of TN (TN1-4) were defined. Based on the frequency of individual TN, TN-score was obtained to investigate the prognostic value of TN. RESULTS: Patients with high-risk TN had worse 5-year disease-free survival (DFS) than patients with no necrosis (32.5% vs. 64.7%; P < 0.0001 in training set; 45.8% vs. 70.8%; P = 0.017 in validation set), while patients with low-risk TN had a 5-year DFS comparable to patients with no necrosis (60.0% vs. 64.7%; P = 0.497 in training set; 59.8% vs. 70.8%; P = 0.121 in validation set). Furthermore, high-risk TN "up-staged" the patients with IBC. Patients with high-risk TN and stage I tumors had a 5-year DFS comparable to patients with stage II tumors (55.6% vs. 62.0%; P = 0.565 in training set; 62.5% vs. 66.3%; P = 0.856 in validation set), as well as patients with high-risk TN and stage II tumors had a 5-year DFS comparable to patients with stage III tumors (33.3% vs. 24.6%; P = 0.271 in training set; 44.4% vs. 39.3%; P = 0.519 in validation set). CONCLUSIONS: TN-score was an independent prognostic factor for 5-year DFS. Only high-risk TN was associated with poor prognosis. High-risk TN "up-staged" the patients with IBC. Incorporating TN-score into staging category could improve its performance to stratify patients.

Computer-assisted quantification of tumor-associated collagen signatures to improve the prognosis prediction of breast cancer.

BACKGROUND: Collagen fibers play an important role in tumor initiation, progression, and invasion. Our previous research has already shown that large-scale tumor-associated collagen signatures (TACS) are powerful prognostic biomarkers independent of clinicopathological factors in invasive breast cancer. However, they are observed on a macroscale and are more suitable for identifying high-risk patients. It is necessary to investigate the effect of the corresponding microscopic features of TACS so as to more accurately and comprehensively predict the prognosis of breast cancer patients. METHODS: In this retrospective and multicenter study, we included 942 invasive breast cancer patients in both a training cohort (n = 355) and an internal validation cohort (n = 334) from one clinical center and in an external validation cohort (n = 253) from a different clinical center. TACS corresponding microscopic features (TCMFs) were firstly extracted from multiphoton images for each patient, and then least absolute shrinkage and selection operator (LASSO) regression was applied to select the most robust features to build a TCMF-score. Finally, the Cox proportional hazard regression analysis was used to evaluate the association of TCMF-score with disease-free survival (DFS). RESULTS: TCMF-score is significantly associated with DFS in univariate Cox proportional hazard regression analysis. After adjusting for clinical variables by multivariate Cox regression analysis, the TCMF-score remains an independent prognostic indicator. Remarkably, the TCMF model performs better than the clinical (CLI) model in the three cohorts and is particularly outstanding in the ER-positive and lower-risk subgroups. By contrast, the TACS model is more suitable for the ER-negative and higher-risk subgroups. When the TACS and TCMF are combined, they could complement each other and perform well in all patients. As expected, the full model (CLI+TCMF+TACS) achieves the best performance (AUC 0.905, [0.873-0.938]; 0.896, [0.860-0.931]; 0.882, [0.840-0.925] in the three cohorts). CONCLUSION: These results demonstrate that the TCMF-score is an independent prognostic factor for breast cancer, and the increased prognostic performance (TCMF+TACS-score) may help us develop more appropriate treatment protocols.

Visualization of lymphatic vascular invasion in breast cancer by multiphoton microscopy.

Lymphatic vascular invasion (LVI) is regarded as one of the independent factors which affect the prognosis of breast cancer. Once LVI is formed, it indicates the tumor has metastasized or has the possibility of metastasis. In this work, multiphoton microscopy (MPM), which relies on the two-photon excited fluorescence (TPEF) and second harmonic generation (SHG), was applied to identify the typical morphology of LVI and also visualize the histological features of LVI. Furthermore, the pixel density of collagen fibers was extracted as a quantitative parameter to differentiate LVI from the ductal carcinoma in situ (DCIS). By comparing with the corresponding H&E-stained images, it was confirmed that MPM can be used as an auxiliary tool for pathologists to diagnose LVI, and has a possibility for the application in clinical examination.

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.

Nonlinear optical microscopy for label-free detection of gastrointestinal neuroendocrine tumors.

Neuroendocrine tumors (NETs), which are rare and slow-growing neoplasms, pose a diagnostic challenge as they are clinically silent at the time of presentation. Here, gastrointestinal neuroendocrine tumors were researched by nonlinear microscopy, and results demonstrate that this technique has the capability to identify neuroendocrine tumors in the absence of labels and can, in particular, detect rare neuroendocrine tumor cells, vascular invasion, desmoplastic reaction, and fibroelastosis induced by neuroendocrine tumors. These conclusions highlight the possibility of nonlinear optical microscopy as a diagnostic tool for label-freely differentiating neuroendocrine tumors by these histopathologic features.

Tripchlorolide ameliorates experimental autoimmune encephalomyelitis by down-regulating ERK1/2-NF-κB and JAK/STAT signaling pathways.

Tripchlorolide (T4), an extract of the natural herb Tripterygium wilfordii Hook F, has been found to possess anti-inflammatory and immunosuppressive actions. In the current study, these actions were evaluated in experimental autoimmune encephalomyelitis (EAE), an animal model of multiple sclerosis by scoring the clinical signs, observing the infiltration of inflammatory cells and myelin sheath in the lumbar spinal cord of EAE mice. The results demonstrated that T4 (at a dose of 40 µg/kg) significantly reduced the severity of EAE and slowed down the ongoing EAE. Further analysis showed that T4 suppressed the mRNA and protein levels of the transcription factors T-bet and RoRrt and mRNA levels of IFN-γ and IL-17 in the spinal cords. Furthermore, T4 down-regulated the ERK1/2-NF-κB and JAK/STAT signaling pathways. At 40 µg/kg, T4 did not induce side effects on hematological parameters. These findings suggest that T4 ameliorates EAE by immunosuppression, providing a new insight into T4 application in multiple sclerosis treatment.

Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy.

Diagnostic pathology, historically dependent on visual scrutiny by experts, is essential for disease detection. Advances in digital pathology and developments in computer vision technology have led to the application of artificial intelligence (AI) in this field. Despite these advancements, the variability in pathologists' subjective interpretations of diagnostic criteria can lead to inconsistent outcomes. To meet the need for precision in cancer therapies, there is an increasing demand for accurate pathological diagnoses. Consequently, traditional diagnostic pathology is evolving towards "next-generation diagnostic pathology", prioritizing on the development of a multi-dimensional, intelligent diagnostic approach. Using nonlinear optical effects arising from the interaction of light with biological tissues, multiphoton microscopy (MPM) enables high-resolution label-free imaging of multiple intrinsic components across various human pathological tissues. AI-empowered MPM further improves the accuracy and efficiency of diagnosis, holding promise for providing auxiliary pathology diagnostic methods based on multiphoton diagnostic criteria. In this review, we systematically outline the applications of MPM in pathological diagnosis across various human diseases, and summarize common multiphoton diagnostic features. Moreover, we examine the significant role of AI in enhancing multiphoton pathological diagnosis, including aspects such as image preprocessing, refined differential diagnosis, and the prognostication of outcomes. We also discuss the challenges and perspectives faced by the integration of MPM and AI, encompassing equipment, datasets, analytical models, and integration into the existing clinical pathways. Finally, the review explores the synergy between AI and label-free MPM to forge novel diagnostic frameworks, aiming to accelerate the adoption and implementation of intelligent multiphoton pathology systems in clinical settings.

The clinicopathologic and molecular features, and treatment outcome of fumarate hydratase-deficient renal cell carcinoma: a retrospective comparison with type 2 papillary renal cell carcinoma.

BACKGROUND: To compare clinicopathologic, molecular features, and treatment outcome between fumarate hydratase-deficient renal cell carcinoma (FH-dRCC) and type 2 papillary renal cell carcinoma (T2 pRCC). METHODS: Data of T2 pRCC patients and FH-dRCC patients with additional next-generation sequencing information were retrospectively analyzed. The cancer-specific survival (CSS) and disease-free survival (DFS) were primary endpoint. RESULTS: A combination of FH and 2-succino-cysteine (2-SC) increased the rate of negative predictive value of FH-dRCC. Compared with T2 pRCC cases, FH-dRCC cases displayed a greater prevalence in young patients, a higher frequency of radical nephrectomy. Seven FH-dRCC and two T2 pRCC cases received systemic therapy. The VEGF treatment was prescribed most frequently, with an objective response rate (ORR) of 22.2% and a disease control rate (DCR) of 30%. A combined therapy with VEGF and checkpoint inhibitor reported an ORR of 40% and a DCR of 100%. FH-dRCC cases showed a shortened CSS (P = 0.042) and DFS (P < 0.001). The genomic sequencing revealed 9 novel mutations. CONCLUSIONS: Coupled with genetic detection, immunohistochemical biomarkers (FH and 2-SC) can distinguish the aggressive FH-dRCC from T2 pRCC. Future research is awaited to illuminate the association between the novel mutations and the clinical phenotypes of FH-dRCC in the disease progression.

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.

Two-photon imaging reveals histopathological changes in the gastric tumor microenvironment induced by neoadjuvant treatment.

There is a close association between tumor response and survival in gastric cancer patients after receiving neoadjuvant treatment. An accurate and rapid assessment of therapeutic efficacy would be helpful for subsequent treatments and individual prognosis. At present, pathological examination is the gold standard for evaluating treatment response, however, it requires additional staining and the process is tedious, labor-intensive, as well as time-consuming. Here, we introduce a label-free imaging technique, two-photon imaging, to evaluate histopathological changes induced by pre-operative therapy, with a focus on assessing tumor regression as well as stromal response. Imaging data show that two-photon imaging allows label-free, rapid visualization of various aspects of pathological alterations in tumor microenvironment such as fibrotic reaction, inflammatory cell infiltration, mucinous response, isolated residual tumor cells. Moreover, a semi-automatic image processing approach is developed to extract the collagen morphological features, and statistical results show that there are significant differences in collagen area, length, width, cross-link space between the gastric cancer tissues with and without treatment. With the advent of a portable, miniaturized two-photon imaging device, we have enough reason to believe that this technique will become as an important auxiliary diagnostic tool in assessing neoadjuvant treatment response and thereby tailoring the most appropriate therapy strategies for the patients.

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.

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.

A deep learning-based stripe self-correction method for stitched microscopic images.

Stitched fluorescence microscope images inevitably exist in various types of stripes or artifacts caused by uncertain factors such as optical devices or specimens, which severely affects the image quality and downstream quantitative analysis. Here, we present a deep learning-based Stripe Self-Correction method, so-called SSCOR. Specifically, we propose a proximity sampling scheme and adversarial reciprocal self-training paradigm that enable SSCOR to utilize stripe-free patches sampled from the stitched microscope image itself to correct their adjacent stripe patches. Comparing to off-the-shelf approaches, SSCOR can not only adaptively correct non-uniform, oblique, and grid stripes, but also remove scanning, bubble, and out-of-focus artifacts, achieving the state-of-the-art performance across different imaging conditions and modalities. Moreover, SSCOR does not require any physical parameter estimation, patch-wise manual annotation, or raw stitched information in the correction process. This provides an intelligent prior-free image restoration solution for microscopists or even microscope companies, thus ensuring more precise biomedical applications for researchers.

Combining the guidelines and multiphoton imaging methods to improve the prognostic value of tumor-infiltrating lymphocytes in breast cancer.

Multiphoton microscopy (MPM) was introduced to label-freely obtain tumor-infiltrating lymphocytes (TILs) images from a total of 611 patients, and the prognostic value of TILs in breast cancer was assessed by the MPM method (TILs-MPM) and guidelines method proposed by the International Immuno-Oncology Biomarker Working Group (TILs-WG), respectively. Moreover, the clinical (CLI) model, TILs-WG + TILs-MPM model, and full model (CLI + TILs-WG + TILs-MPM) were developed to investigate the prognostic value of TILs. The results show that TILs-WG performs better in estrogen receptor (ER)-negative subgroup, and TILs-MPM is comparable with TILs-WG in the ER-negative subgroup, but has the best performance in the ER-positive subgroup. Furthermore, the TILs-WG + TILs-MPM model can significantly improve the prognostic power compared with the TILs-WG model, and the full model has excellent performance, with high area under the curve (AUC) and hazard ratio (HR) in both ER-positive, ER-negative subgroups, and the complete cohort. Our results suggest that the combination of TILs-WG with TILs-MPM model can greatly improve the prognostic value of TILs.

Collagen signature as a novel biomarker to predict axillary lymph node metastasis in breast cancer using multiphoton microscopy.

Accurate identification of axillary lymph node (ALN) status is crucial for tumor staging procedure and decision making. This retrospective study of 898 participants from two institutions was conducted. The aim of this study is to evaluate the diagnostic performance of clinical parameters combined with collagen signatures (tumor-associated collagen signatures [TACS] and the TACS corresponding microscopic features [TCMF]) in predicting the probability of ALN metastasis in patients with breast cancer. These findings suggest that TACS and TCMF in the breast tumor microenvironment are both novel and independent biomarkers for the estimation of ALN metastasis. The nomogram based on independent clinical parameters combined with TACS and TCMF yields good diagnostic performance in predicting ALN status.

Intratumor graph neural network recovers hidden prognostic value of multi-biomarker spatial heterogeneity.

Biomarkers are indispensable for precision medicine. However, focused single-biomarker development using human tissue has been complicated by sample spatial heterogeneity. To address this challenge, we tested a representation of primary tumor that synergistically integrated multiple in situ biomarkers of extracellular matrix from multiple sampling regions into an intratumor graph neural network. Surprisingly, the differential prognostic value of this computational model over its conventional non-graph counterpart approximated that of combined routine prognostic biomarkers (tumor size, nodal status, histologic grade, molecular subtype, etc.) for 995 breast cancer patients under a retrospective study. This large prognostic value, originated from implicit but interpretable regional interactions among the graphically integrated in situ biomarkers, would otherwise be lost if they were separately developed into single conventional (spatially homogenized) biomarkers. Our study demonstrates an alternative route to cancer prognosis by taping the regional interactions among existing biomarkers rather than developing novel biomarkers.