The notion of personalized medicine demands proper prognostic biomarkers to guide the optimal therapy for an invasive breast cancer patient. However, various risk prediction models based on conventional clinicopathological factors and emergent molecular assays have been frequently limited by either a low strength of prognosis or restricted applicability to specific types of patients. Therefore, there is a critical need to develop a strong and general prognosticator. Methods: We observed five large-scale tumor-associated collagen signatures (TACS4-8) obtained by multiphoton microscopy at the invasion front of the breast primary tumor, which contrasted with the three tumor-associated collagen signatures (TACS1-3) discovered by Keely and coworkers at a smaller scale. Highly concordant TACS1-8 classifications were obtained by three independent observers. Using the ridge regression analysis, we obtained a TACS-score for each patient based on the combined TACS1-8 and established a risk prediction model based on the TACS-score. In a blind fashion, consistent retrospective prognosis was obtained from 995 breast cancer patients in both a training cohort ( n = 431) and an internal validation cohort ( n = 300) collected from one clinical center, and in an external validation cohort ( n = 264) collected from a different clinical center. Results: TACS1-8 model alone competed favorably with all reported models in predicting disease-free survival (AUC: 0.838, [0.800-0.872]; 0.827, [0.779-0.868]; 0.807, [0.754-0.853] in the three cohorts) and stratifying low- and high-risk patients (HR 7.032, [4.869-10.158]; 6.846, [4.370-10.726], 4.423, [2.917-6.708]). The combination of these factors with the TACS-score into a nomogram model further improved the prognosis (AUC: 0.865, [0.829-0.896]; 0.861, [0.816-0.898]; 0.854, [0.805-0.894]; HR 7.882, [5.487-11.323]; 9.176, [5.683-14.816], and 5.548, [3.705-8.307]). The nomogram identified 72 of 357 (~20%) patients with unsuccessful 5-year disease-free survival that might have been undertreated postoperatively. Conclusions: The risk prediction model based on TACS1-8 considerably outperforms the contextual clinical model and may thus convince pathologists to pursue a TACS-based breast cancer prognosis. Our methodology identifies a significant portion of patients susceptible to undertreatment (high-risk patients), in contrast to the multigene assays that often strive to mitigate overtreatment. The compatibility of our methodology with standard histology using traditional (non-tissue-microarray) formalin-fixed paraffin-embedded (FFPE) tissue sections could simplify subsequent clinical translation.
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.
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.
Dirty necrosis within glandular lumina is often considered as a characteristic of colorectal carcinomas (CRCs) that is a diagnostically useful feature of CRCs with DNA microsatellite instability (MSI). Multiphoton microscopy (MPM), which is based on the second-harmonic generation and two-photon excited fluorescence signals, was used to identify dirty necrosis. Our results demonstrated that MPM has the ability to exhibit the microstructure of dirty necrosis and the signal intensity as well as an emission spectrum that can help to differentiate dirty necrosis from cancer cells. These findings indicate that MPM may be helpful in distinguishing MSI colorectal carcinoma via the identification of dirty necrosis.
Without sophisticated data inversion algorithms, nonlinear optical microscopy can acquire images at subcellular resolution and relatively large depth, with plausible endogenous contrasts indicative of authentic biological and pathological states. Although independent contrasts have been derived by sequentially imaging the same sample plane or volume under different and often optimized excitation conditions, new laser source engineering with inputs from key biomolecules surprisingly enable real-time simultaneous acquisition of multiple endogenous molecular contrasts to segment a rich set of cellular and extracellular components. Since this development allows simple single-beam single-shot excitation and simultaneous multicontrast epidirected signal detection, the resulting platform avoids perturbative sample pretreatments such as fluorescent labeling, mechanical sectioning, scarce or interdependent contrast generation, constraints to the sample or imaging geometry, and intraimaging motion artifacts that have limited in vivo nonlinear optical molecular imaging.
Label-free imaging techniques are gaining acceptance within the medical imaging field, including brain imaging, because they have the potential to be applied to intraoperative in situ identifications of pathological conditions. In this paper, we describe the use of twophoton excited fluorescence (TPEF) and second harmonic generation (SHG) microscopy in combination for the label-free detection of brain and brain tumor specimens; gliomas. Two independently detecting channels were chosen to subsequently collect TPEF/SHG signals from the specimen to increase TPEF/SHG image contrasts. Our results indicate that the combined TPEF/SHG microscopic techniques can provide similar rat brain structural information and produce a similar resolution like conventional H&E staining in neuropathology; including meninges, cerebral cortex, white-matter structure corpus callosum, choroid plexus, hippocampus, striatum, and cerebellar cortex. It can simultaneously detect infiltrating human brain tumor cells, the extracellular matrix collagen fiber of connective stroma within brain vessels and collagen depostion in tumor microenvironments. The nuclear-to-cytoplasmic ratio and collagen content can be extracted as quantitative indicators for differentiating brain gliomas from healthy brain tissues. With the development of twophoton fiberscopes and microendoscope probes and their clinical applications, the combined TPEF and SHG microcopy may become an important multimodal, nonlinear optical imaging approach for real-time intraoperative histological diagnostics of residual brain tumors. These occur in various brain regions during ongoing surgeries through the method of simultaneously identifying tumor cells, and the change of tumor microenvironments, without the need for the removal biopsies and without the need for tissue labelling or fluorescent markers.
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.
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