Quantitative second harmonic generation microscopy for the structural characterization of capsular collagen in thyroid neoplasms

Multitask machine learning-based tumor-associated collagen signatures predict peritoneal recurrence and disease-free survival in gastric cancer

BACKGROUND

Accurate prediction of peritoneal recurrence for gastric cancer (GC) is crucial in clinic. The collagen alterations in tumor microenvironment affect the migration and treatment response of cancer cells. Herein, we proposed multitask machine learning-based tumor-associated collagen signatures (TACS), which are composed of quantitative collagen features derived from multiphoton imaging, to simultaneously predict peritoneal recurrence (TACSPR) and disease-free survival (TACSDFS).

METHODS

Among 713 consecutive patients, with 275 in training cohort, 222 patients in internal validation cohort, and 216 patients in external validation cohort, we developed and validated a multitask machine learning model for simultaneously predicting peritoneal recurrence (TACSPR) and disease-free survival (TACSDFS). The accuracy of the model for prediction of peritoneal recurrence and prognosis as well as its association with adjuvant chemotherapy were evaluated.

RESULTS

The TACSPR and TACSDFS were independently associated with peritoneal recurrence and disease-free survival in three cohorts, respectively (all P < 0.001). The TACSPR demonstrated a favorable performance for peritoneal recurrence in all three cohorts. In addition, the TACSDFS also showed a satisfactory accuracy for disease-free survival among included patients. For stage II and III diseases, adjuvant chemotherapy improved the survival of patients with low TACSPR and low TACSDFS, or high TACSPR and low TACSDFS, or low TACSPR and high TACSDFS, but had no impact on patients with high TACSPR and high TACSDFS.

CONCLUSIONS

The multitask machine learning model allows accurate prediction of peritoneal recurrence and survival for GC and could distinguish patients who might benefit from adjuvant chemotherapy.

Machine learning-based diagnostics of capsular invasion in thyroid nodules with wide-field second harmonic generation microscopy

Papillary thyroid carcinoma (PTC) is one of the most common, well-differentiated carcinomas of the thyroid gland. PTC nodules are often surrounded by a collagen capsule that prevents the spread of cancer cells. However, as the malignant tumor progresses, the integrity of this protective barrier is compromised, and cancer cells invade the surroundings. The detection of capsular invasion is, therefore, crucial for the diagnosis and the choice of treatment and the development of new approaches aimed at the increase of diagnostic performance are of great importance. In the present study, we exploited the wide-field second harmonic generation (SHG) microscopy in combination with texture analysis and unsupervised machine learning (ML) to explore the possibility of quantitative characterization of collagen structure in the capsule and designation of different capsule areas as either intact, disrupted by invasion, or apt to invasion. Two-step k-means clustering showed that the collagen capsules in all analyzed tissue sections were highly heterogeneous and exhibited distinct segments described by characteristic ML parameter sets. The latter allowed a structural interpretation of the collagen fibers at the sites of overt invasion as fragmented and curled fibers with rarely formed distributed networks. Clustering analysis also distinguished areas in the PTC capsule that were not categorized as invasion sites by the initial histopathological analysis but could be recognized as prospective micro-invasions after additional inspection. The characteristic features of suspicious and invasive sites identified by the proposed unsupervised ML approach can become a reliable complement to existing methods for diagnosing encapsulated PTC, increase the reliability of diagnosis, simplify decision making, and prevent human-related diagnostic errors. In addition, the proposed automated ML-based selection of collagen capsule images and exclusion of non-informative regions can greatly accelerate and simplify the development of reliable methods for fully automated ML diagnosis that can be integrated into clinical practice.

Widefield polarization-resolved second harmonic generation imaging of entire thyroid nodule sections for the detection of capsular invasion

The identification of tumor capsular invasion as a sign of malignancy is currently employed in traditional histopathology routines for thyroid nodules. However, its limitations are associated with the assessment criteria for invasion, which often lead to disagreements among observers. The aim of this paper is to introduce a widefield imaging technique combined with quantitative collagen analysis to identify areas of capsular invasion in thyroid neoplasms. In this study, we introduce the application of widefield polarization-resolved second harmonic generation microscopy for imaging entire thyroid nodule sections on histological slides. We employ a cylindrical collagen model to extract parameters associated with the ultrastructure and orientation of collagen within the entire capsule of the thyroid nodule. We showcase the effectiveness of these parameters in distinguishing between areas of nodule capsule invasion and unaffected regions of the capsule through statistical analysis of individual parameters and employing a machine learning technique that involves generating maps via cluster analysis. Our results suggest that quantitative analysis facilitated by polarization-resolved widefield second harmonic generation microscopy could prove beneficial for the automated evaluation of capsular invasion sites in thyroid pathology.

Structural changes in the crystalline lens as a function of the postmortem interval assessed with two-photon imaging microscopy

The properties and structure of the crystalline lens change as time after death passes. Some experiments have suggested that these might be used to estimate the postmortem interval (PMI). In this study, the organization and texture of the rabbit lens were objectively evaluated as a function of the PMI using two-photon excitation fluorescence (TPEF) imaging microscopy. Between 24 h and 72 h, the lens presented a highly organized structure, although the fiber delineation was progressively vanishing. At 96 h, this turned into a homogeneous pattern where fibers were hardly observed. This behaviour was similar for parameters providing information on tissue texture. On the other hand, the fiber density of the lens is linearly reduced with the PMI. On average, density at 24 h was approximately two-fold when compared to 96 h after death. The present results show that TPEF microscopy combined with different quantitative tools can be used to objectively monitor temporal changes in the lens fiber organization after death. This might help to estimate the PMI, which is one of the most complex problems in forensic science.

Imaging of colorectal adenomas with pseudoinvasion and malignant polyps using two-photon excitation microscopy

Introduction

Although the incidence and mortality rates of colorectal cancer exhibit significant variability, it remains one of the most prevalent cancers worldwide. Endeavors to prevent colorectal cancer development focus on detecting precursor lesions during colonoscopy. The diagnosis of endoscopically resected polyps relies on hematoxylin and eosin staining examination. For challenging cases like adenomatous polyps with epithelial misplacement, additional diagnostic methods could prove beneficial.

Methods

This paper aims to underscore stromal changes observed in malignant polyps and polyps with pseudoinvasion, leveraging two-photon excitation microscopy (TPEM), a technique extensively employed in the medical field in recent years.

Results and discussions

Both the subjective and quantitative analysis of TPEM images revealed distinct distributions and densities of collagen at the invasion front in malignant polyps compared to areas of pseudoinvasion. TPEM holds potential in discerning true invasion in malignant polyps from pseudoinvasion, offering enhanced visualization of local stromal changes.

Investigating cartilage-related diseases by polarization-resolved second harmonic generation (P-SHG) imaging

Establishing quantitative parameters for differentiating between healthy and diseased cartilage tissues by examining collagen fibril degradation patterns facilitates the understanding of tissue characteristics during disease progression. These findings could also complement existing clinical methods used to diagnose cartilage-related diseases. In this study, cartilage samples from normal, osteoarthritis (OA), and rheumatoid arthritis (RA) tissues were prepared and analyzed using polarization-resolved second harmonic generation (P-SHG) imaging and quantitative image texture analysis. The enhanced molecular contrast obtained from this approach is expected to aid in distinguishing between healthy and diseased cartilage tissues. P-SHG image analysis revealed distinct parameters in the cartilage samples, reflecting variations in collagen fibril arrangement and organization across different pathological states. Normal tissues exhibited distinct χ33/χ31 values compared with those of OA and RA, indicating collagen type transition and cartilage erosion with chondrocyte swelling, respectively. Compared with those of normal tissues, OA samples demonstrated a higher degree of linear polarization, suggesting increased tissue birefringence due to the deposition of type-I collagen in the extracellular matrix. The distribution of the planar orientation of collagen fibrils revealed a more directional orientation in the OA samples, associated with increased type-I collagen, while the RA samples exhibited a heterogeneous molecular orientation. This study revealed that the imaging technique, the quantitative analysis of the images, and the derived parameters presented in this study could be used as a reference for disease diagnostics, providing a clear understanding of collagen fibril degradation in cartilage.

Compact portable higher harmonic generation microscopy for the real time assessment of unprocessed thyroid tissue

During thyroid surgery fast and reliable intra-operative pathological feedback has the potential to avoid a two-stage procedure and significantly reduce health care costs in patients undergoing a diagnostic hemithyroidectomy (HT). We explored higher harmonic generation (HHG) microscopy, which combines second harmonic generation (SHG), third harmonic generation (THG), and multiphoton excited autofluorescence (MPEF) for this purpose. With a compact, portable HHG microscope, images of freshly excised healthy tissue, benign nodules (follicular adenoma) and malignant tissue (papillary carcinoma, follicular carcinoma and spindle cell carcinoma) were recorded. The images were generated on unprocessed tissue within minutes and show relevant morphological thyroid structures in good accordance with the histology images. The thyroid follicle architecture, cells, cell nuclei (THG), collagen organization (SHG) and the distribution of thyroglobulin and/or thyroid hormones T3 or T4 (MPEF) could be visualized. We conclude that SHG/THG/MPEF imaging is a promising tool for clinical intraoperative assessment of thyroid tissue.

Comparative analysis of dermal collagen and lipids in cereblon ablated mice using a multimodal nonlinear optical system

By utilizing a multimodal nonlinear optical system that combines coherent anti-Stokes Raman scattering and second harmonic generation to investigate biological characteristics of dermal tissues ex vivo, we demonstrate the potential feasibility of using this optical approach as a powerful new investigative tool for future biomedical research. For this study, our optical system was utilized for the first time to analyze lipid and collagen profiles in cereblon knockout (KO) mouse skin, and we were able to discover significant alterations in the number of carbon-carbon double bonds (wild-type vs. cereblon KO; N_CC : 0.75 vs. 0.85) of skin fatty acids in triacylglycerides as well as changes in dermal collagen fibers (25% reduction in cereblon KO). By adopting our optical system to biological studies, we provide researchers with another diagnostic approach to validate their experimental results, which will significantly advance the state of biomedical research.

Differential diagnosis of thyroid nodule capsules using random forest guided selection of image features

Microscopic evaluation of tissue sections stained with hematoxylin and eosin is the current gold standard for diagnosing thyroid pathology. Digital pathology is gaining momentum providing the pathologist with additional cues to traditional routes when placing a diagnosis, therefore it is extremely important to develop new image analysis methods that can extract image features with diagnostic potential. In this work, we use histogram and texture analysis to extract features from microscopic images acquired on thin thyroid nodule capsules sections and demonstrate how they enable the differential diagnosis of thyroid nodules. Targeted thyroid nodules are benign (i.e., follicular adenoma) and malignant (i.e., papillary thyroid carcinoma and its sub-type arising within a follicular adenoma). Our results show that the considered image features can enable the quantitative characterization of the collagen capsule surrounding thyroid nodules and provide an accurate classification of the latter's type using random forest.

Identification of the collagen family as prognostic biomarkers in papillary thyroid carcinoma

OBJECTIVE

The aim of this study was to construct a collagen-related prognostic model for thyroid cancer and to investigate prognostic value of collagen family genes for thyroid cancer.

METHODS

A LASSO Cox regression model for thyroid cancer was developed based on the expression profiles of collagen-related genes. Kaplan-Meier survival analysis was performed for high and low risk groups. The ROC method was used to assess its predictive performance. Predictive independence was verified by multivariate Cox regression analysis. The relationship between this feature and immune cell infiltration was analyzed by tumor microenvironment. COL18A1 was validated by immunohistochemistry and RT-PCR in thyroid cancer tissues. The effect of COL18A1 on cell proliferation, migration and invasion ability of tumor cells were further valuated by CCK-8 assay and transwell assay. The effect of COL18A1 on the immune escape ability of tumor cells was further valuated by cytotoxicity assays.

RESULTS

A model including 4 collagen family genes was developed to predict thyroid cancer prognosis. Patients with high-risk score had a poorer prognosis than those with low-risk scores for 1-, 2-, 3-, and 5- year survival. The model independently predicted prognosis after adjusting for other prognostic factors. A nomogram combining risk score and age was constructed with high sensitivity and specificity. This feature was significantly associated with immune cell infiltration. COL18A1 was aberrantly over-expressed in thyroid cancer compared with control tissues and significantly increased proliferative capacity, migration capacity, invasion capacity, and immune escape ability of tumor cells.

CONCLUSION

Our findings establish a signature associated with collagen family genes that can be a promising tool to predict the prognosis of thyroid cancer. High COL18A1 expression significantly correlates with the poor prognosis of patients and enhances the immune escape ability of tumor cells.

Kinetics and Mechanisms of Saccharomyces boulardii Release from Optimized Whey Protein-Agavin-Alginate Beads under Simulated Gastrointestinal Conditions

Encapsulation is a process in which a base material is encapsulated in a wall material that can protect it against external factors and/or improve its bioavailability. Among the different encapsulation techniques, ionic gelation stands out as being useful for thermolabile compounds. The aim of this work was to encapsulate Saccharomyces boulardii by ionic gelation using agavins (A) and whey protein (WP) as wall materials and to evaluate the morphostructural changes that occur during in vitro gastrointestinal digestion. Encapsulations at different levels of A and WP were analyzed using microscopic, spectroscopic and thermal techniques. Encapsulation efficiency and cell viability were evaluated. S. boulardii encapsulated at 5% A: 3.75% WP (AWB6) showed 88.5% cell survival after the simulated gastrointestinal digestion; the bead showed a significantly different microstructure from the controls. The mixture of A and WP increased in the survival of S. boulardii respect to those encapsulated with alginate, A or WP alone. The binary material mixture simultaneously allowed a controlled release of S. boulardii by mostly diffusive Fickian mechanisms and swelling. The cell-release time was found to control the increment of the Damköhler number when A and WP were substrates for S. boulardii, in this way allowing greater protection against gastrointestinal conditions.

Spatial patterning of PCL µ-scaffolds directs 3D vascularized bio-construct morphogenesis in vitro

Modular tissue engineering (mTE) strategies aim to build three-dimensional tissue analogues in vitro by the sapient combination of cells, micro-scaffolds (μ-scaffs) and bioreactors. The translation of these newly engineered tissues into current clinical approaches is, among other things, dependent on implant-to-host microvasculature integration, a critical issue for cells and tissue survival in vivo. In this work we reported, for the first time, a computer-aided modular approach suitable to build fully vascularized hybrid (biological/synthetic) constructs (bio-constructs) with micro-metric size scale control of blood vessels growth and orientation. The approach consists of four main steps, starting with the fabrication of polycaprolactone μ-scaffs by fluidic emulsion technique, which exhibit biomimetic porosity features. In the second step, layers of μ-scaffs following two different patterns, namely ordered and disordered, were obtained by a soft lithography-based process. Then, the as obtained μ-scaff patterns were used as template for human dermal fibroblasts and human umbilical vein endothelial cells co-culture, aiming to promote and guide the biosynthesis of collagenous extracellular matrix and the growth of new blood vessels within the mono-layered bio-constructs. Finally, bi-layered bio-constructs were built by the alignment, stacking and fusion of two vascularized mono-layered samples featuring ordered patterns. Our results demonstrated that, if compared to the disordered pattern, the ordered one provided better control over bio-constructs shape and vasculature architecture, while minor effect was observed with respect to cell colonization and new tissue growth. Furthermore, by assembling two mono-layered bio-constructs it was possible to build 1-mm thick fully vascularized viable bio-constructs and to study tissue morphogenesis during 1 week of in vitro culture. In conclusion, our results highlighted the synergic role of μ-scaff architectural features and spatial patterning on cells colonization and biosynthesis, and pay the way for the possibility to create in silico designed vasculatures within modularly engineered bio-constructs.

PSHG-TISS: A collection of polarization-resolved second harmonic generation microscopy images of fixed tissues

Second harmonic generation (SHG) microscopy is acknowledged as an established imaging technique capable to provide information on the collagen architecture in tissues that is highly valuable for the diagnostics of various pathologies. The polarization-resolved extension of SHG (PSHG) microscopy, together with associated image processing methods, retrieves extensive image sets under different input polarization settings, which are not fully exploited in clinical settings. To facilitate this, we introduce PSHG-TISS, a collection of PSHG images, accompanied by additional computationally generated images which can be used to complement the subjective qualitative analysis of SHG images. These latter have been calculated using the single-axis molecule model for collagen and provide 2D representations of different specific PSHG parameters known to account for the collagen structure and distribution. PSHG-TISS can aid refining existing PSHG image analysis methods, while also supporting the development of novel image processing and analysis methods capable to extract meaningful quantitative data from the raw PSHG image sets. PSHG-TISS can facilitate the breadth and widespread of PSHG applications in tissue analysis and diagnostics.

Measurement(s)	Type I Collagen
Technology Type(s)	multi-photon laser scanning microscopy
Factor Type(s)	second order susceptibility tensor elements
Sample Characteristic - Organism	Homo sapiens
Sample Characteristic - Environment	laboratory environment
Sample Characteristic - Location	Romania

Assessment of Ultra-Early-Stage Liver Fibrosis in Human Non-Alcoholic Fatty Liver Disease by Second-Harmonic Generation Microscopy

Non-alcoholic fatty liver disease (NAFLD) is associated with the chronic progression of fibrosis. In general, the progression of liver fibrosis is determined by a histopathological assessment with a collagen-stained section; however, the ultra-early stage of liver fibrosis is challenging to identify because of the low sensitivity in the collagen-selective staining method. In the present study, we demonstrate the feasibility of second-harmonic generation (SHG) microscopy in the histopathological diagnosis of the liver of NAFLD patients for the quantitative assessment of the ultra-early stage of fibrosis. We investigated four representative NAFLD patients with early stages of fibrosis. SHG microscopy visualised well-matured fibrotic structures and early fibrosis diffusely involving liver tissues, whereas early fibrosis is challenging to be identified by conventional histopathological methods. Furthermore, the SHG emission directionality analysis revealed the maturation of each collagen fibre of each patient. As a result, SHG microscopy is feasible for assessing liver fibrosis on NAFLD patients, including the ultra-early stage of liver fibrosis that is difficult to diagnose by the conventional histopathological method. The assessment method of the ultra-early fibrosis by using SHG microscopy may serve as a crucial means for pathological, clinical, and prognostic diagnosis of NAFLD patients.

Assessment of Extramammary Paget Disease by Two-Photon Microscopy

Two-photon microscopy techniques are non-linear optical imaging methods which are gaining momentum in the investigation of fixed tissue sections, fresh tissue or even for in vivo experiments. Two-photon excited fluorescence and second harmonic generation are two non-linear optical contrast mechanisms which can be simultaneously used for offering complementary information on the tissue architecture. While the former can originate from endogenous autofluorescence sources (e.g., NADH, FAD, elastin, keratin, lipofuscins, or melanin), or exogenous eosin, the latter is generated in fibrillar structures within living organisms (e.g., collagen and myosin). Here we test the ability of both these contrast mechanisms to highlight features of the extramammary Paget disease on fixed tissue sections prepared for standard histological examination using immunohistochemical markers and hematoxylin and eosin staining. We also demonstrate the label-free abilities of both imaging techniques to highlight histological features on unstained fixed tissue sections. The study demonstrated that two-photon microscopy can detect specific cellular features of the extramammary Paget disease in good correlation with histopathological results.

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.

Association of Tumor-Associated Collagen Signature With Prognosis and Adjuvant Chemotherapy Benefits in Patients With Gastric Cancer

IMPORTANCE

The current TNM staging system provides limited information for prognosis prediction and adjuvant chemotherapy benefits for patients with gastric cancer (GC).

OBJECTIVE

To develop a tumor-associated collagen signature of GC (TACSGC) in the tumor microenvironment to predict prognosis and adjuvant chemotherapy benefits in patients with GC.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study included a training cohort of 294 consecutive patients treated between January 1, 2012, and December 31, 2013, from Nanfang Hospital, Southern Medical University, People's Republic of China, and a validation cohort of 225 consecutive patients treated between October 1, 2010, and December 31, 2012, from Fujian Provincial Cancer Hospital, Fujian Medical University, People's Republic of China. In total, 146 collagen features in the tumor microenvironment were extracted with multiphoton imaging. A TACSGC was then constructed using the least absolute shrinkage and selection operator Cox proportional hazards regression model in the training cohort. Data analysis was conducted from October 1, 2020, to April 30, 2021.

MAIN OUTCOMES AND MEASURES

The association of TACSGC with disease-free survival (DFS) and overall survival (OS) was assessed. An independent external cohort was included to validate the results. Interactions between TACSGC and adjuvant chemotherapy were calculated.

RESULTS

This study included 519 patients (median age, 57 years [IQR, 49-65 years]; 360 [69.4%] male). A 9 feature-based TACSGC was built. A higher TACSGC level was significantly associated with worse DFS and OS in both the training (DFS: hazard ratio [HR], 3.57 [95% CI, 2.45-5.20]; OS: HR, 3.54 [95% CI, 2.41-5.20]) and validation (DFS: HR, 3.10 [95% CI, 2.26-4.27]; OS: HR, 3.24 [95% CI, 2.33-4.50]) cohorts (continuous variable, P < .001 for all comparisons). Multivariable analyses found that carbohydrate antigen 19-9, depth of invasion, lymph node metastasis, distant metastasis, and TACSGC were independent prognostic predictors of GC, and 2 integrated nomograms that included the 5 predictors were established for predicting DFS and OS. Compared with clinicopathological models that included only the 4 clinicopathological predictors, the integrated nomograms yielded an improved discrimination for prognosis prediction in a C index comparison (training cohort: DFS, 0.80 [95% CI, 0.73-0.88] vs 0.78 [95% CI, 0.71-0.85], P = .03; OS, 0.81 [95% CI, 0.75-0.88] vs 0.80 [95% CI, 0.73-0.86], P = .03; validation cohort: DFS, 0.78 [95% CI, 0.70-0.87] vs 0.76 [95% CI, 0.67-0.84], P = .006; OS, 0.78 [95% CI, 0.69-0.86] vs 0.75 [95% CI, 0.67-0.84], P = .002). Patients with stage II and III GC and low TACSGC levels rather than high TACSGC levels had a favorable response to adjuvant chemotherapy (DFS: HR, 0.65 [95% CI, 0.43-0.96]; P = .03; OS: HR, 0.55 [95% CI, 0.36-0.82]; P = .004; dichotomized variable, P < .001 for interaction for both comparisons).

CONCLUSIONS AND RELEVANCE

The findings suggest that TACSGC provides additional prognostic information for patients with GC and may distinguish patients with stage II and III disease who are more likely to derive benefits from adjuvant chemotherapy.

Nomogram model combining macro and micro tumor-associated collagen signatures obtained from multiphoton images to predict the histologic grade in breast cancer

The purpose of this study is to develop and validate a new nomogram model combining macro and micro tumor-associated collagen signatures obtained from multiphoton images to differentiate tumor grade in patients with invasive breast cancer. A total of 543 patients were included in this study. We used computer-generated random numbers to assign 328 of these patients to the training cohort and 215 patients to the validation cohort. Macroscopic tumor-associated collagen signatures (TACS1-8) were obtained by multiphoton microscopy at the invasion front and inside of the breast primary tumor. TACS corresponding microscopic features (TCMF) including morphology and texture features were extracted from the segmented regions of interest using Matlab 2016b. Using ridge regression analysis, we obtained a TACS-score for each patient based on the combined TACS1-8, and the least absolute shrinkage and selection operator (LASSO) regression was applied to select the most robust TCMF features to build a TCMF-score. Univariate logistic regression analysis demonstrates that the TACS-score and TCMF-score are significantly associated with histologic grade (odds ratio, 2.994; 95% CI, 2.013-4.452; P < 0.001; 4.245, 2.876-6.264, P < 0.001 in the training cohort). The nomogram (collagen) model combining the TACS-score and TCMF-score could stratify patients into Grade1 and Grade2/3 groups with the AUC of 0.859 and 0.863 in the training and validation cohorts. The predictive performance can be further improved by combining the clinical factors, achieving the AUC of 0.874 in both data cohorts. The nomogram model combining the TACS-score and TCMF-score can be useful in differentiating breast tumor patients with Grade1 and Grade2/3.

Influence of hematoxylin and eosin staining on the quantitative analysis of second harmonic generation imaging of fixed tissue sections

Second harmonic generation (SHG) microscopy has emerged over the past two decades as a powerful tool for tissue characterization and diagnostics. Its main applications in medicine are related to mapping the collagen architecture of in-vivo, ex-vivo and fixed tissues based on endogenous contrast. In this work we present how H&E staining of excised and fixed tissues influences the extraction and use of image parameters specific to polarization-resolved SHG (PSHG) microscopy, which are known to provide quantitative information on the collagen structure and organization. We employ a theoretical collagen model for fitting the experimental PSHG datasets to obtain the second order susceptibility tensor elements ratios and the fitting efficiency. Furthermore, the second harmonic intensity acquired under circular polarization is investigated. The evolution of these parameters in both forward- and backward-collected SHG are computed for both H&E-stained and unstained tissue sections. Consistent modifications are observed between the two cases in terms of the fitting efficiency and the second harmonic intensity. This suggests that similar quantitative analysis workflows applied to PSHG images collected on stained and unstained tissues could yield different results, and hence affect the diagnostic accuracy.

Tumor extracellular matrix: lessons from the second-harmonic generation microscopy

Extracellular matrix (ECM) represents more than a mere intercellular cement. It is physiologically active in cell communication, adhesion and proliferation. Collagen is the most abundant protein, making up to 90% of ECM, and 30% of total protein weight in humans. Second-harmonic generation (SHG) microscopy represents an important tool to study collagen organization of ECM in freshly unfixed tissues and paraffin-embedded tissue samples. This manuscript aims to review some of the applications of SHG microscopy in Oncologic Pathology, mainly in the study of ECM of epithelial tumors. It is shown how collagen parameters measured by this technique can aid in the differential diagnosis and in prognostic stratification. There is a tendency to associate higher amount, lower organization and higher linearity of collagen fibers with tumor progression and metastasizing. These represent complex processes, in which matrix remodeling plays a central role, together with cancer cell genetic modifications. Integration of studies on cancer cell biology and ECM are highly advantageous to give us a more complete picture of these processes. As microscopic techniques provide topographic information allied with biologic characteristics of tissue components, they represent important tools for a more complete understanding of cancer progression. In this context, SHG has provided significant insights in human tumor specimens, readily available for Pathologists.

Quantitative evaluation of the human vocal fold extracellular matrix using multiphoton microscopy and optical coherence tomography

Identifying distinct normal extracellular matrix (ECM) features from pathology is of the upmost clinical importance for laryngeal diagnostics and therapy. Despite remarkable histological contributions, our understanding of the vocal fold (VF) physiology remains murky. The emerging field of non-invasive 3D optical imaging may be well-suited to unravel the complexity of the VF microanatomy. This study focused on characterizing the entire VF ECM in length and depth with optical imaging. A quantitative morphometric evaluation of the human vocal fold lamina propria using two-photon excitation fluorescence (TPEF), second harmonic generation (SHG), and optical coherence tomography (OCT) was investigated. Fibrillar morphological features, such as fiber diameter, orientation, anisotropy, waviness and second-order statistics features were evaluated and compared according to their spatial distribution. The evidence acquired in this study suggests that the VF ECM is not a strict discrete three-layer structure as traditionally described but instead a continuous assembly of different fibrillar arrangement anchored by predominant collagen transitions zones. We demonstrated that the ECM composition is distinct and markedly thinned in the anterior one-third of itself, which may play a role in the development of some laryngeal diseases. We further examined and extracted the relationship between OCT and multiphoton imaging, promoting correspondences that could lead to accurate 3D mapping of the VF architecture in real-time during phonosurgeries. As miniaturization of optical probes is consistently improving, a clinical translation of OCT imaging and multiphoton imaging, with valuable qualitative and quantitative features, may have significant implications for treating voice disorders.

Predicting postoperative peritoneal metastasis in gastric cancer with serosal invasion using a collagen nomogram

Accurate prediction of peritoneal metastasis for gastric cancer (GC) with serosal invasion is crucial in clinic. The presence of collagen in the tumour microenvironment affects the metastasis of cancer cells. Herein, we propose a collagen signature, which is composed of multiple collagen features in the tumour microenvironment of the serosa derived from multiphoton imaging, to describe the extent of collagen alterations. We find that a high collagen signature is significantly associated with a high risk of peritoneal metastasis (P < 0.001). A competing-risk nomogram including the collagen signature, tumour size, tumour differentiation status and lymph node metastasis is constructed. The nomogram demonstrates satisfactory discrimination and calibration. Thus, the collagen signature in the tumour microenvironment of the gastric serosa is associated with peritoneal metastasis in GC with serosal invasion, and the nomogram can be conveniently used to individually predict the risk of peritoneal metastasis in GC with serosal invasion after radical surgery.

Pixel-level angular quantification of capsular collagen in second harmonic generation microscopy images of encapsulated thyroid nodules

Polarization-resolved second harmonic generation microscopy is used to provide pixel-level angular distribution of collagen in thyroid nodule capsules. The pixel-level angular distribution is combined with textural analysis to quantify the collagen distribution in follicular adenoma (benign) and papillary thyroid carcinoma (malignant). Three second order nonlinear susceptibility tensor elements ratios, the collagen angular distribution and two parameters accounting for the collagen angular dispersion in different sized areas are extracted and corresponding images are computed in a pixel-by-pixel fashion. Subsequently, we show that texture analysis can be performed on these images to detect significant differences between the considered benign and malignant nodule capsules.

Characterization of pathological thyroid tissue using polarization-sensitive second harmonic generation microscopy

Polarization-sensitive second harmonic generation (SHG) microscopy is an established imaging technique able to provide information related to specific molecular structures including collagen. In this investigation, polarization-sensitive SHG microscopy was used to investigate changes in the collagen ultrastructure between histopathology slides of normal and diseased human thyroid tissues including follicular nodular disease, Grave's disease, follicular variant of papillary thyroid carcinoma, classical papillary thyroid carcinoma, insular or poorly differentiated carcinoma, and anaplastic or undifferentiated carcinoma ex vivo. The second-order nonlinear optical susceptibility tensor component ratios, χ⁽²⁾_zzz'/χ⁽²⁾_zxx' and χ⁽²⁾_xyz'/χ⁽²⁾_zxx', were obtained, where χ⁽²⁾_zzz'/χ⁽²⁾_zxx' is a structural parameter and χ⁽²⁾_xyz'/χ⁽²⁾_zxx' is a measure of the chirality of the collagen fibers. Furthermore, the degree of linear polarization (DOLP) of the SHG signal was measured. A statistically significant increase in χ⁽²⁾_zzz'/χ⁽²⁾_zxx' values for all the diseased tissues except insular carcinoma and a statistically significant decrease in DOLP for all the diseased tissues were observed compared to normal thyroid. This finding indicates a higher ultrastructural disorder in diseased collagen and provides an innovative approach to discriminate between normal and diseased thyroid tissues that is complementary to standard histopathology.

Objective analysis of collagen organization in thyroid nodule capsules using second harmonic generation microscopy images and the Hough transform

Papillary carcinoma is the most prevalent type of thyroid cancer. Its diagnosis requires accurate and subjective analyses from expert pathologists. Here we propose a method based on the Hough transform (HT) to detect and objectively quantify local structural differences in collagen thyroid nodule capsules. Second harmonic generation (SHG) microscopy images were acquired on non-stained histological sections of capsule fragments surrounding the healthy thyroid gland and benign and tumoral/malignant nodules. The HT was applied to each SHG image to extract numerical information on the organization of the collagen architecture in the tissues under analysis. Results show that control thyroid capsule samples present a non-organized structure composed of wavy collagen distribution with local orientations. On the opposite, in capsules surrounding malignant nodules, a remodeling of the collagen network takes place and local undulations disappear, resulting in an aligned pattern with a global preferential orientation. The HT procedure was able to quantitatively differentiate thyroid capsules from capsules surrounding papillary thyroid carcinoma (PTC) nodules. Moreover, the algorithm also reveals that the collagen arrangement of the capsules surrounding benign nodules significantly differs from both the thyroid control and PTC nodule capsules. Combining SHG imaging with the HT results thus in an automatic and objective tool to discriminate between the pathological modifications that affect the capsules of thyroid nodules across the progressions of PTC, with potential to be used in clinical settings to complement current state-of-the-art diagnostic methods.

HISTOBREAST, a collection of brightfield microscopy images of Haematoxylin and Eosin stained breast tissue

Modern histopathology workflows rely on the digitization of histology slides. The quality of the resulting digital representations, in the form of histology slide image mosaics, depends on various specific acquisition conditions and on the image processing steps that underlie the generation of the final mosaic, e.g. registration and blending of the contained image tiles. We introduce HISTOBREAST, an extensive collection of brightfield microscopy images that we collected in a principled manner under different acquisition conditions on Haematoxylin - Eosin (H&E) stained breast tissue. HISTOBREAST is comprised of neighbour image tiles and ensemble of mosaics composed from different combinations of the available image tiles, exhibiting progressively degraded quality levels. HISTOBREAST can be used to benchmark image processing and computer vision techniques with respect to their robustness to image modifications specific to brightfield microscopy of H&E stained tissues. Furthermore, HISTOBREAST can serve in the development of new image processing methods, with the purpose of ensuring robustness to typical image artefacts that raise interpretation problems for expert histopathologists and affect the results of computerized image analysis.

Intrinsic Abnormalities of Cystic Fibrosis Airway Connective Tissue Revealed by an In Vitro 3D Stromal Model

Cystic fibrosis is characterized by lung dysfunction involving mucus hypersecretion, bacterial infections, and inflammatory response. Inflammation triggers pro-fibrotic signals that compromise lung structure and function. At present, several in vitro cystic fibrosis models have been developed to study epithelial dysfunction but none of these focuses on stromal alterations. Here we show a new cystic fibrosis 3D stromal lung model made up of primary fibroblasts embedded in their own extracellular matrix and investigate its morphological and transcriptomic features. Cystic fibrosis fibroblasts showed a high proliferation rate and produced an abundant and chaotic matrix with increased protein content and elastic modulus. More interesting, they had enhanced pro-fibrotic markers and genes involved in epithelial function and inflammatory response. In conclusion, our study reveals that cystic fibrosis fibroblasts maintain in vitro an activated pro-fibrotic state. This abnormality may play in vivo a role in the modulation of epithelial and inflammatory cell behavior and lung remodeling. We argue that the proposed bioengineered model may provide new insights on epithelial/stromal/inflammatory cells crosstalk in cystic fibrosis, paving the way for novel therapeutic strategies.

Analysis of Collagen Spatial Structure Using Multiphoton Microscopy and Machine Learning Methods

Pathogenesis of many diseases is associated with changes in the collagen spatial structure. Traditionally, the 3D structure of collagen in biological tissues is analyzed using histochemistry, immunohistochemistry, magnetic resonance imaging, and X-radiography. At present, multiphoton microscopy (MPM) is commonly used to study the structure of biological tissues. MPM has a high spatial resolution comparable to histological analysis and can be used for direct visualization of collagen spatial structure. Because of a large volume of data accumulated due to the high spatial resolution of MPM, special analytical methods should be used for identification of informative features in the images and quantitative evaluation of relationship between these features and pathological processes resulting in the destruction of collagen structure. Here, we describe current approaches and achievements in the identification of informative features in the MPM images of collagen in biological tissues, as well as the development on this basis of algorithms for computer-aided classification of collagen structures using machine learning as a type of artificial intelligence methods.