Morphologic complexity of epithelial architecture for predicting invasive breast cancer survival

Nuclear Deformability of Breast Cells Analyzed from Patients with Malignant and Benign Breast Diseases

Breast cancer is a heterogeneous and dynamic disease, in which cancer cells are highly responsive to alterations in the microenvironment. Today, conventional methods of detecting cancer give a rather static image of the condition of the disease, so dynamic properties such as invasiveness and metastasis are difficult to capture. In this study, conventional molecular-level evaluations of the patients with breast adenocarcinoma were combined with in vitro methods on micropatterned poly(methyl methacrylate) (PMMA) biomaterial surfaces that deform cells. A correlation between deformability of the nuclei and cancer stemness, invasiveness, and metastasis was sought. Clinical patient samples were from regions of the breast with different proximities to the tumor. Responses at the single-cell level toward the micropatterned surfaces were studied using CD44/24, epithelial cell adhesion marker (EpCAM), MUC1, and PCK. Results showed that molecular markers and shape descriptors can discriminate the cells from different proximities to the tumor center and from different patients. The cells with the most metastatic and invasive properties showed both the highest deformability and the highest level of metastatic markers. In conclusion, by using a combination of molecular markers together with nuclear deformation, it is possible to improve detection and separation of subpopulations in heterogenous breast cancer specimens at the single-cell level.

Centrosome Aberrations as Drivers of Chromosomal Instability in Breast Cancer

Chromosomal instability (CIN), or the dynamic change in chromosome number and composition, has been observed in cancer for decades. Recently, this phenomenon has been implicated as facilitating the acquisition of cancer hallmarks and enabling the formation of aggressive disease. Hence, CIN has the potential to serve as a therapeutic target for a wide range of cancers. CIN in cancer often occurs as a result of disrupting key regulators of mitotic fidelity and faithful chromosome segregation. As a consequence of their essential roles in mitosis, dysfunctional centrosomes can induce and maintain CIN. Centrosome defects are common in breast cancer, a heterogeneous disease characterized by high CIN. These defects include amplification, structural defects, and loss of primary cilium nucleation. Recent studies have begun to illuminate the ability of centrosome aberrations to instigate genomic flux in breast cancer cells and the tumor evolution associated with aggressive disease and poor patient outcomes. Here, we review the role of CIN in breast cancer, the processes by which centrosome defects contribute to CIN in this disease, and the emerging therapeutic approaches that are being developed to capitalize upon such aberrations.

Different spatial distribution of inflammatory cells in the tumor microenvironment of ABC and GBC subgroups of diffuse large B cell lymphoma

Diffuse Large B-Cell Lymphoma (DLBCL) presents a high clinical and biological heterogeneity, and the tumor microenvironment chracteristics are important in its  progression. The aim of this study was to evaluate tumor T, B cells, macrophages and mast cells distribution in GBC and ABC DLBCL subgroups through a set of morphometric parameters allowing to provide a quantitative evaluation of the morphological features of the spatial patterns generated by these inflammatory cells.   Histological ABC and GCB samples were immunostained for CD4, CD8, CD68, CD 163, and tryptase in order to determine both percentage and position of positive cells in the tissue characterizing their spatial distribution. The results evidenced that cell patterns generated by CD4-, CD8-, CD68-, CD163- and tryptase-positive cell profiles exhibited a significantly higher uniformity index in ABC than in GCB subgroup. The positive-cell distributions appeared clustered in tissues from GCB, while in tissues from ABC such a feature was lower or absent. The combinations of spatial statistics-derived parameters can lead to better predictions of tumor cell infiltration than any classical morphometric method providing a more accurate description of the functional status of the tumor, useful for patient prognosis.

Spatial distribution of B cells and lymphocyte clusters as a predictor of triple-negative breast cancer outcome

While tumor infiltration by CD8+ T cells is now widely accepted to predict outcomes, the clinical significance of intratumoral B cells is less clear. We hypothesized that spatial distribution rather than density of B cells within tumors may provide prognostic significance. We developed statistical techniques (fractal dimension differences and a box-counting method 'occupancy') to analyze the spatial distribution of tumor-infiltrating lymphocytes (TILs) in human triple-negative breast cancer (TNBC). Our results indicate that B cells in good outcome tumors (no recurrence within 5 years) are spatially dispersed, while B cells in poor outcome tumors (recurrence within 3 years) are more confined. While most TILs are located within the stroma, increased numbers of spatially dispersed lymphocytes within cancer cell islands are associated with a good prognosis. B cells and T cells often form lymphocyte clusters (LCs) identified via density-based clustering. LCs consist either of T cells only or heterotypic mixtures of B and T cells. Pure B cell LCs were negligible in number. Compared to tertiary lymphoid structures (TLS), LCs have fewer lymphocytes at lower densities. Both types of LCs are more abundant and more spatially dispersed in good outcomes compared to poor outcome tumors. Heterotypic LCs in good outcome tumors are smaller and more numerous compared to poor outcome. Heterotypic LCs are also closer to cancer islands in a good outcome, with LC size decreasing as they get closer to cancer cell islands. These results illuminate the significance of the spatial distribution of B cells and LCs within tumors.

Fractal dimension analysis as an easy computational approach to improve breast cancer histopathological diagnosis

Histopathology is a well-established standard diagnosis employed for the majority of malignancies, including breast cancer. Nevertheless, despite training and standardization, it is considered operator-dependent and errors are still a concern. Fractal dimension analysis is a computational image processing technique that allows assessing the degree of complexity in patterns. We aimed here at providing a robust and easily attainable method for introducing computer-assisted techniques to histopathology laboratories. Slides from two databases were used: A) Breast Cancer Histopathological; and B) Grand Challenge on Breast Cancer Histology. Set A contained 2480 images from 24 patients with benign alterations, and 5429 images from 58 patients with breast cancer. Set B comprised 100 images of each type: normal tissue, benign alterations, in situ carcinoma, and invasive carcinoma. All images were analyzed with the FracLac algorithm in the ImageJ computational environment to yield the box count fractal dimension (Db) results. Images on set A on 40x magnification were statistically different (p = 0.0003), whereas images on 400x did not present differences in their means. On set B, the mean Db values presented promissing statistical differences when comparing. Normal and/or benign images to in situ and/or invasive carcinoma (all p < 0.0001). Interestingly, there was no difference when comparing normal tissue to benign alterations. These data corroborate with previous work in which fractal analysis allowed differentiating malignancies. Computer-aided diagnosis algorithms may beneficiate from using Db data; specific Db cut-off values may yield ~ 99% specificity in diagnosing breast cancer. Furthermore, the fact that it allows assessing tissue complexity, this tool may be used to understand the progression of the histological alterations in cancer.

Physics approaches to the spatial distribution of immune cells in tumors

The goal of immunotherapy is to mobilize the immune system to kill cancer cells. Immunotherapy is more effective and, in general, the prognosis is better, when more immune cells infiltrate the tumor. We explore the question of whether the spatial distribution rather than just the density of immune cells in the tumor is important in forecasting whether cancer recurs. After reviewing previous work on this issue, we introduce a novel application of maximum entropy to quantify the spatial distribution of discrete point-like objects. We apply our approach to B and T cells in images of tumor tissue taken from triple negative breast cancer patients. We find that the immune cells are more spatially dispersed in good clinical outcome (no recurrence of cancer within at least 5 years of diagnosis) compared to poor clinical outcome (recurrence within 3 years of diagnosis). Our results highlight the importance of spatial distribution of immune cells within tumors with regard to clinical outcome, and raise new questions on their role in cancer recurrence.

Occupancy and Fractal Dimension Analyses of the Spatial Distribution of Cytotoxic (CD8+) T Cells Infiltrating the Tumor Microenvironment in Triple Negative Breast Cancer

Favorable outcomes have been associated with high densities of tumor infiltrating lymphocytes (TILs) such as cytotoxic ([Formula: see text]) T cells. However, the clinical significance of the spatial distribution of TILs is less well understood. We have developed novel statistical techniques to characterize the spatial distribution of TILs at various length scales. These include a box counting method that we call “occupancy” and novel applications of fractal dimensions. We apply these techniques to the spatial distribution of [Formula: see text] T cells in the tumor microenvironment of tissue resected from 35 triple negative breast cancer patients. We find that there is a distinct difference in the spatial distribution of [Formula: see text] T cells between good clinical outcome (no recurrence within at least 5 years of diagnosis) and poor clinical outcome (recurrence within 3 years of diagnosis). The statistical significance of the difference between good and poor outcome in the occupancy, fractal dimension (FD), and FD difference of [Formula: see text] T cells is comparable to that of the [Formula: see text] T cell density. Even when we randomly exclude some of the cells so that the images have the same cell density, we still find that the fractal dimension at short length scales is correlated with cancer recurrence, implying that the actual spatial distribution of [Formula: see text] cells, and not just the [Formula: see text] cell density, is associated with clinical outcome. The occupancy and FD difference indicate that the [Formula: see text] T cells are more spatially dispersed in good outcome and more aggregated in poor outcome. We discuss possible interpretations.

Analysis of Spatial Distribution and Prognostic Value of Different Pan Cytokeratin Immunostaining Intensities in Breast Tumor Tissue Sections

Cancer risk prognosis could improve patient survival through early personalized treatment decisions. This is the first systematic analysis of the spatial and prognostic distribution of different pan cytokeratin immunostaining intensities in breast tumors. The prognostic model included 102 breast carcinoma patients, with distant metastasis occurrence as the endpoint. We segmented the full intensity range (0–255) of pan cytokeratin digitized immunostaining into seven discrete narrow grey level ranges: 0–130, 130–160, 160–180, 180–200, 200–220, 220–240, and 240–255. These images were subsequently examined by 33 major (GLCM), fractal and first-order statistics computational analysis features. Interestingly, while moderate intensities were strongly associated with metastasis outcome, high intensities of pan cytokeratin immunostaining provided no prognostic value even after an exhaustive computational analysis. The intense pan cytokeratin immunostaining was also relatively rare, suggesting the low differentiation state of epithelial cells. The observed variability in immunostaining intensities highlighted the intratumoral heterogeneity of the malignant cells and its association with a poor disease outcome. The prognostic importance of the moderate intensity range established by complex computational morphology analyses was supported by simple measurements of its immunostaining area which was associated with favorable disease outcome. This study reveals intratumoral heterogeneity of the pan cytokeratin immunostaining together with the prognostic evaluation and spatial distribution of its discrete intensities.

The topology of vitronectin: A complementary feature for neuroblastoma risk classification based on computer-aided detection

Tumors are complex networks of constantly interacting elements: tumor cells, stromal cells, immune and stem cells, blood/lympathic vessels, nerve fibers and extracellular matrix components. These elements can influence their microenvironment through mechanical and physical signals to promote tumor cell growth. To get a better understanding of tumor biology, cooperation between multidisciplinary fields is needed. Diverse mathematic computations and algorithms have been designed to find prognostic targets and enhance diagnostic assessment. In this work, we use computational digital tools to study the topology of vitronectin, a glycoprotein of the extracellular matrix. Vitronectin is linked to angiogenesis and migration, two processes closely related to tumor cell spread. Here, we investigate whether the distribution of this molecule in the tumor stroma may confer mechanical properties affecting neuroblastoma aggressiveness. Combining image analysis and graph theory, we analyze different topological features that capture the organizational cues of vitronectin in histopathological images taken from human samples. We find that the Euler number and the branching of territorial vitronectin, two topological features, could allow for a more precise pretreatment risk stratification to guide treatment strategies in neuroblastoma patients. A large amount of recently synthesized VN would create migration tracks, pinpointed by both topological features, for malignant neuroblasts, so that dramatic change in the extracellular matrix would increase tumor aggressiveness and worsen patient outcomes.

Size and Shape Filtering of Malignant Cell Clusters within Breast Tumors Identifies Scattered Individual Epithelial Cells as the Most Valuable Histomorphological Clue in the Prognosis of Distant Metastasis Risk

Survival and life quality of breast cancer patients could be improved by more aggressive chemotherapy for those at high metastasis risk and less intense treatments for low-risk patients. Such personalized treatment cannot be currently achieved due to the insufficient reliability of metastasis risk prognosis. The purpose of this study was therefore, to identify novel histopathological prognostic markers of metastasis risk through exhaustive computational image analysis of 80 size and shape subsets of epithelial clusters in breast tumors. The group of 102 patients had a follow-up median of 12.3 years, without lymph node spread and systemic treatments. Epithelial cells were stained by the AE1/AE3 pan-cytokeratin antibody cocktail. The size and shape subsets of the stained epithelial cell clusters were defined in each image by use of the circularity and size filters and analyzed for prognostic performance. Epithelial areas with the optimal prognostic performance were uniformly small and round and could be recognized as individual epithelial cells scattered in tumor stroma. Their count achieved an area under the receiver operating characteristic curve (AUC) of 0.82, total area (AUC = 0.77), average size (AUC = 0.63), and circularity (AUC = 0.62). In conclusion, by use of computational image analysis as a hypothesis-free discovery tool, this study reveals the histomorphological marker with a high prognostic value that is simple and therefore easy to quantify by visual microscopy.

Nuclear shape and orientation features from H&E images predict survival in early-stage estrogen receptor-positive breast cancers

Early-stage estrogen receptor-positive (ER+) breast cancer (BCa) is the most common type of BCa in the United States. One critical question with these tumors is identifying which patients will receive added benefit from adjuvant chemotherapy. Nuclear pleomorphism (variance in nuclear shape and morphology) is an important constituent of breast grading schemes, and in ER+ cases, the grade is highly correlated with disease outcome. This study aimed to investigate whether quantitative computer-extracted image features of nuclear shape and orientation on digitized images of hematoxylin-stained and eosin-stained tissue of lymph node-negative (LN-), ER+ BCa could help stratify patients into discrete (<10 years short-term vs. >10 years long-term survival) outcome groups independent of standard clinical and pathological parameters. We considered a tissue microarray (TMA) cohort of 276 ER+, LN- patients comprising 150 patients with long-term and 126 patients with short-term overall survival, wherein 177 randomly chosen cases formed the modeling set, and 99 remaining cases the test set. Segmentation of individual nuclei was performed using multiresolution watershed; subsequently, 615 features relating to nuclear shape/texture and orientation disorder were extracted from each TMA spot. The Wilcoxon's rank-sum test identified the 15 most prognostic quantitative histomorphometric features within the modeling set. These features were then subsequently combined via a linear discriminant analysis classifier and evaluated on the test set to assign a probability of long-term vs. short-term disease-specific survival. In univariate survival analysis, patients identified by the image classifier as high risk had significantly poorer survival outcome: hazard ratio (95% confident interval) = 2.91(1.23-6.92), p = 0.02786. Multivariate analysis controlling for T-stage, histology grade, and nuclear grade showed the classifier to be independently predictive of poorer survival: hazard ratio (95% confident interval) = 3.17(0.33-30.46), p = 0.01039. Our results suggest that quantitative histomorphometric features of nuclear shape and orientation are strongly and independently predictive of patient survival in ER+, LN- BCa.

The Pan-Cytokeratin Staining Intensity and Fractal Computational Analysis of Breast Tumor Malignant Growth Patterns Prognosticate the Occurrence of Distant Metastasis

Improved prognosis of breast cancer outcome could prolong patient survival by reliable identification of patients at high risk of metastasis occurrence which could benefit from more aggressive treatments. Based on such clinical need, we prognostically evaluated the malignant cells in breast tumors, as the obvious potential source of unexploited prognostic information. The patient group was homogeneous, without any systemic treatments or lymph node spread, with smaller tumor size (pT1/2) and a long follow-up. Epithelial cells were labeled with AE1/AE3 pan-cytokeratin antibody cocktail and comprehensively analyzed. Monofractal and multifractal analyses were applied for quantification of distribution, shape, complexity and texture of malignant cell clusters, while mean pixel intensity and total area were measures of the pan-cytokeratin immunostaining intensity. The results surprisingly indicate that simple binary images and monofractal analysis provided better prognostic information then grayscale images and multifractal analysis. The key findings were that shapes and distribution of malignant cell clusters (by binary fractal dimension; AUC = 0.29), their contour shapes (by outline fractal dimension; AUC = 0.31) and intensity of the pan-cytokeratin immunostaining (by mean pixel intensity; AUC = 0.30) offered significant performance in metastasis risk prognostication. The results reveal an association between the lower pan-cytokeratin staining intensity and the high metastasis risk. Another interesting result was that multivariate analysis could confirm the prognostic independence only for fractal but not for immunostaining intensity features. The obtained results reveal several novel and unexpected findings highlighting the independent prognostic efficacy of malignant cell cluster distribution and contour shapes in breast tumors.

Role of the Interplay Between the Internal and External Conditions in Invasive Behavior of Tumors

Tumor growth, which plays a central role in cancer evolution, depends on both the internal features of the cells, such as their ability for unlimited duplication, and the external conditions, e.g., supply of nutrients, as well as the dynamic interactions between the two. A stem cell theory of cancer has recently been developed that suggests the existence of a subpopulation of self-renewing tumor cells to be responsible for tumorigenesis, and is able to initiate metastatic spreading. The question of abundance of the cancer stem cells (CSCs) and its relation to tumor malignancy has, however, remained an unsolved problem and has been a subject of recent debates. In this paper we propose a novel model beyond the standard stochastic models of tumor development, in order to explore the effect of the density of the CSCs and oxygen on the tumor's invasive behavior. The model identifies natural selection as the underlying process for complex morphology of tumors, which has been observed experimentally, and indicates that their invasive behavior depends on both the number of the CSCs and the oxygen density in the microenvironment. The interplay between the external and internal conditions may pave the way for a new cancer therapy.

The non-receptor tyrosine kinase TNK2/ACK1 is a novel therapeutic target in triple negative breast cancer

Breast cancer is the most prevalent cancer in women worldwide. About 15-20% of all breast cancers do not express estrogen receptor, progesterone receptor or HER2 receptor and hence are collectively classified as triple negative breast cancer (TNBC). These tumors are often relatively aggressive when compared to other types of breast cancer, and this issue is compounded by the lack of effective targeted therapy. In our previous phosphoproteomic profiling effort, we identified the non-receptor tyrosine kinase TNK2 as activated in a majority of aggressive TNBC cell lines. In the current study, we show that high expression of TNK2 in breast cancer cell lines correlates with high proliferation, invasion and colony forming ability. We demonstrate that knockdown of TNK2 expression can substantially suppress the invasiveness and proliferation advantage of TNBC cells in vitro and tumor formation in xenograft mouse models. Moreover, inhibition of TNK2 with small molecule inhibitor (R)-9bMS significantly compromised TNBC proliferation.

Finally, we find that high levels of TNK2 expression in high-grade basal-like breast cancers correlates significantly with poorer patient outcome. Taken together, our study suggests that TNK2 is a novel potential therapeutic target for the treatment of TNBC.

Morphometric study of adipocytes on breast cancer by means of photonic microscopy and image analysis

Worldwide, breast cancer (BrCa) is currently the leading cause of deaths associated to malignant lesions in adult women. Given that some studies have mentioned that peritumoral adipocytes may contribute to breast carcinogenesis, present work sought to quantitative evaluate the morphometry of these cells in a group of adult women. Three thousand six hundred sixty four breast adipocytes, that came from biopsies of a group of adult females with different types of breast carcinomas (ductal, lobular, and mixed) and one with normal tissues, were evaluated through an image analysis (IA) process regarding six morphometric descriptors: area (A), perimeter (P), Feret diameter (F_D ), aspect ratio (AR), roundness factor (RF), and fractal dimension of cellular contour (FD_C ). Data showed that the adipocytes of the normal tissues group were bigger (A: 3398 ± 2331 µm² , P: 239 ± 83 µm, and F_D : 79.9 ± 24.5 µm) than those from BrCa samples (A: 2860 ± 1933 µm² , P: 214 ± 66 µm, and F_D : 73.2 ± 22.5 µm), and presented a more irregular contour (FD_C of 1.370 ± 0.037 for normal group and of 1.335 ± 0.049 for the oncologic one). Moreover, it could be accounted that adipocytes of mixed carcinomas were largest (F_D : 75.1 ± 22.4 µm) than those of lobular lesions (F_D : 61.6 ± 22.6 µm), while the adipocytes of ductal carcinomas were the most oval (AR: 1.421 ± 0.524) and roughest (FD_C : 1.324 ± 0.050) cells. IA results suggest that BrCa lesions can be categorized through a quantitative morphometric evaluation of peritumoral adipocytes. These findings could let the development of an analytical tool to help the Pathologist to enhance the accuracy of the oncologic diagnose.

Automatic prediction of tumour malignancy in breast cancer with fractal dimension

Breast cancer is one of the most prevalent types of cancer today in women. The main avenue of diagnosis is through manual examination of histopathology tissue slides. Such a process is often subjective and error-ridden, suffering from both inter- and intraobserver variability. Our objective is to develop an automatic algorithm for analysing histopathology slides free of human subjectivity. Here, we calculate the fractal dimension of images of numerous breast cancer slides, at magnifications of 40×, 100×, 200× and 400×. Using machine learning, specifically, the support vector machine (SVM) method, the F1 score for classification accuracy of the 40× slides was found to be 0.979. Multiclass classification on the 40× slides yielded an accuracy of 0.556. A reduction of the size and scope of the SVM training set gave an average F1 score of 0.964. Taken together, these results show great promise in the use of fractal dimension to predict tumour malignancy.

Image analysis-derived metrics of histomorphological complexity predicts prognosis and treatment response in stage II-III colon cancer

The complexity of tumor histomorphology reflects underlying tumor biology impacting on natural course and response to treatment. This study presents a method of computer-aided analysis of tissue sections, relying on multifractal (MF) analyses, of cytokeratin-stained tumor sections which quantitatively evaluates of the morphological complexity of the tumor-stroma interface. This approach was applied to colon cancer collection, from an adjuvant treatment randomized study. Metrics obtained with the method acted as independent markers for natural course of the disease, and for benefit of adjuvant treatment. Comparative analyses demonstrated that MF metrics out-performed standard histomorphological features such as tumor grade, budding and configuration of invasive front. Notably, the MF analyses-derived "α_max" -metric constitutes the first response-predictive biomarker in stage II-III colon cancer showing significant interactions with treatment in analyses using a randomized trial-derived study population. Based on these results the method appears as an attractive and easy-to-implement tool for biomarker identification.

Prognostic biomarker value of binary and grayscale breast tumor histopathology images

AIM

Breast cancer prognosis is in the spotlight owing to its potentially major clinical importance in effective therapeutic management. Following our recent prognostic establishment of the fractal features calculated on binary breast tumor histopathology images, this study aimed to accomplish the first optimization of this methodology by direct comparison of monofractal, multifractal and co-occurrence algorithms in analysis of binary versus grayscale image formats.

PATIENTS & METHODS

The study included 93 patients with invasive breast cancer, without systemic treatment and a long median follow-up of 150 months.

RESULTS

Grayscale images provided a better prognostic source in comparison to binary, while monofractal, multifractal and co-occurrence image analysis algorithms exerted a comparable performance.

CONCLUSION

The critical prognostic importance of the grayscale texture is revealed.

Automated Tubule Nuclei Quantification and Correlation with Oncotype DX risk categories in ER+ Breast Cancer Whole Slide Images

Early stage estrogen receptor positive (ER+) breast cancer (BCa) treatment is based on the presumed aggressiveness and likelihood of cancer recurrence. Oncotype DX (ODX) and other gene expression tests have allowed for distinguishing the more aggressive ER+ BCa requiring adjuvant chemotherapy from the less aggressive cancers benefiting from hormonal therapy alone. However these tests are expensive, tissue destructive and require specialized facilities. Interestingly BCa grade has been shown to be correlated with the ODX risk score. Unfortunately Bloom-Richardson (BR) grade determined by pathologists can be variable. A constituent category in BR grading is tubule formation. This study aims to develop a deep learning classifier to automatically identify tubule nuclei from whole slide images (WSI) of ER+ BCa, the hypothesis being that the ratio of tubule nuclei to overall number of nuclei (a tubule formation indicator - TFI) correlates with the corresponding ODX risk categories. This correlation was assessed in 7513 fields extracted from 174 WSI. The results suggests that low ODX/BR cases have a larger TFI than high ODX/BR cases (p < 0.01). The low ODX/BR cases also presented a larger TFI than that obtained for the rest of cases (p < 0.05). Finally, the high ODX/BR cases have a significantly smaller TFI than that obtained for the rest of cases (p < 0.01).

Multifractal analysis of tumour microscopic images in the prediction of breast cancer chemotherapy response

Due to the individual heterogeneity, highly accurate predictors of chemotherapy response in invasive breast cancer are needed for effective chemotherapeutic management. However, predictive molecular determinants for conventional chemotherapy are only emerging and still incorporate a high degree of predictive variability. Based on such pressing need for predictive performance improvement, we explored the value of pre-therapy tumour histology image analysis to predict chemotherapy response. Fractal analysis was applied to hematoxylin/eosin stained archival tissue of diagnostic biopsies derived from 106 patients diagnosed with invasive breast cancer. The tissue was obtained prior to neoadjuvant anthracycline-based chemotherapy and patients were subsequently divided into three groups according to their actual chemotherapy response: partial pathological response (pPR), pathological complete response (pCR) and progressive/stable disease (PD/SD). It was shown that multifractal analysis of breast tumour tissue prior to chemotherapy indeed has the capacity to distinguish between histological images of the different chemotherapy responder groups with accuracies of 91.4% for pPR, 82.9% for pCR and 82.1% for PD/SD. F(α)max was identified as the most important predictive parameter. It represents the maximum of multifractal spectrum f(α), where α is the Hölder's exponent. This is the first study investigating the predictive value of multifractal analysis as a simple and cost-effective tool to predict the chemotherapy response. Improvements in chemotherapy prediction provide clinical benefit by enabling more optimal chemotherapy decisions, thus directly affecting the quality of life and survival.

Quantification of morphology of canine circumanal gland tumors: a fractal based study

Circumanal gland tumors are very common neoplasms of dogs. Their classification relies on microscopic examination and is further supported by a few immunohistochemical markers that help indicate their prognosis. However, new additional tests would be highly useful. The purpose of this study was to develop such a test using fractal analysis which is increasingly being applied in science, especially in the field of biomedicine. A total of 53 circumanal gland tumors were chosen from our department archives. After a precise histological classification according to the World Health Organization classification, the number of de novo classified samples was as follows: 15 adenomas, 11 epitheliomas, 21 well differentiated carcinomas, 6 poorly differentiated carcinomas. Ten samples of normal circumanal gland were also included as control. All samples were immunohistochemicaly stained with vimentin. All immunohistochemical reactions were photographed at two different magnifications -100X and 400X- and converted to 1 bit in black and white (bitmap) images, thus enhancing the positive vimentin reactions. These images were used for the assessment of fractal dimension applying the box counting method and computer software Fractalyse. To determine the significance of results, conventional statistics were performed using Statistica software.

The overall vimentin stain score was significantly higher in epitheliomas and carcinomas than in normal circumanal glands (CG) or adenomas. Mean values of fractal dimension estimated at magnification 100X and 400X were as follows: normal CG 1.318 and 1.372, CG adenomas 1.384 and 1.408, CG epitheliomas 1.547 and 1.597, CG well differentiated carcinomas 1.569 and 1.607, CG poorly differentiated carcinomas 1.679 and 1.723. Significant differences (at level of 5%) of these values were observed between individual groups of CG adenomas or normal CG, and epitheliomas or carcinomas.

The above results indicate vimentin immunohistochemistry staining and assessment of fractal dimension as an ancillary diagnostic method of choice when discerning between benign and malignant tumors of circumanal glands. Additional development of the method of fractal dimension assessment may yield a possibility for this tool to successfully discern between all of the types of CG tumors.

Fractal dimension and lacunarity of tumor microscopic images as prognostic indicators of clinical outcome in early breast cancer

AIM

Research in the field of breast cancer outcome prognosis has been focused on molecular biomarkers, while neglecting the discovery of novel tumor histology structural clues. We thus aimed to improve breast cancer prognosis by fractal analysis of tumor histomorphology.

PATIENTS & METHODS

This retrospective study included 92 breast cancer patients without systemic treatment.

RESULTS

Fractal dimension and lacunarity of the breast tumor microscopic histology possess prognostic value comparable to the major clinicopathological prognostic parameters.

CONCLUSION

Fractal analysis was performed for the first time on routinely produced archived pan-tissue stained primary breast tumor sections, indicating its potential for clinical use as a simple and cost-effective prognostic indicator of distant metastasis risk to complement the molecular approaches for cancer risk prognosis.

Fractal analysis of heart graft acute rejection microscopic images

BACKGROUND

Endomyocardial biopsy to evaluate rejection in the transplanted heart is accepted at the "gold standard." The complexity of microscopic images suggested using digital methods for precise evaluating of acute rejection episodes with numerical representation. The aim of the present was study to characterize digitally acute rejection of the transplanted heart using complexity/fractal image analysis.

MATERIAL AND METHODS

Biopsy samples harvested form 40 adult recipients after orthotropic heart transplantation were collected and rejection grade was evaluated according to the International Society for Heart and Lung Transplantation (0, 1a, 1b, or 3a) at transverse and longitudinal sections. Fifteen representative digital microscope images from each grade were collected and analyzed after Sobel edge detection and binarization.

RESULTS

Only mean fractal dimension showed a progressive and significant increase and correlation based on rejection grade using longitudinal sections. Lacunarity and number of foreground pixels showed unequivocal results.

CONCLUSION

Mean fractal diameter could serve as auxiliary digital parameter for grading of acute rejection in the transplanted heart.

Gray-Level Co-Occurrence Matrix Texture Analysis of Breast Tumor Images in Prognosis of Distant Metastasis Risk

Owing to exceptional heterogeneity in the outcome of invasive breast cancer it is essential to develop highly accurate prognostic tools for effective therapeutic management. Based on this pressing need, we aimed to improve breast cancer prognosis by exploring the prognostic value of tumor histology image analysis. Patient group (n=78) selection was based on invasive breast cancer diagnosis without systemic treatment with a median follow-up of 147 months. Gray-level co-occurrence matrix texture analysis was performed retrospectively on primary tumor tissue section digital images stained either nonspecifically with hematoxylin and eosin or specifically with a pan-cytokeratin antibody cocktail for epithelial malignant cells. Univariate analysis revealed stronger association with metastasis risk by texture analysis when compared with clinicopathological parameters. The combination of individual clinicopathological and texture variables into composite scores resulted in further powerful enhancement of prognostic performance, with an accuracy of up to 90%, discrimination efficiency by the area under the curve [95% confidence interval (CI)] of 0.94 (0.87-0.99) and hazard ratio (95% CI) of 20.1 (7.5-109.4). Internal validation was successfully performed by bootstrap and split-sample cross-validation, suggesting that the models are generalizable. Whereas further validation is needed on an external set of patients, this preliminary study indicates the potential use of primary breast tumor histology texture as a highly accurate, simple, and cost-effective prognostic indicator of distant metastasis risk.

New breast cancer prognostic factors identified by computer-aided image analysis of HE stained histopathology images

Computer-aided image analysis (CAI) can help objectively quantify morphologic features of hematoxylin-eosin (HE) histopathology images and provide potentially useful prognostic information on breast cancer. We performed a CAI workflow on 1,150 HE images from 230 patients with invasive ductal carcinoma (IDC) of the breast. We used a pixel-wise support vector machine classifier for tumor nests (TNs)-stroma segmentation, and a marker-controlled watershed algorithm for nuclei segmentation. 730 morphologic parameters were extracted after segmentation, and 12 parameters identified by Kaplan-Meier analysis were significantly associated with 8-year disease free survival (P < 0.05 for all). Moreover, four image features including TNs feature (HR 1.327, 95%CI [1.001 - 1.759], P = 0.049), TNs cell nuclei feature (HR 0.729, 95%CI [0.537 - 0.989], P = 0.042), TNs cell density (HR 1.625, 95%CI [1.177 - 2.244], P = 0.003), and stromal cell structure feature (HR 1.596, 95%CI [1.142 - 2.229], P = 0.006) were identified by multivariate Cox proportional hazards model to be new independent prognostic factors. The results indicated that CAI can assist the pathologist in extracting prognostic information from HE histopathology images for IDC. The TNs feature, TNs cell nuclei feature, TNs cell density, and stromal cell structure feature could be new prognostic factors.

Fractal analysis of nuclear histology integrates tumor and stromal features into a single prognostic factor of the oral cancer microenvironment

Background

The lack of prognostic biomarkers in oral squamous cell carcinoma (OSCC) has hampered treatment decision making and survival in OSCC remains poor. Histopathological features are used for prognostication in OSCC and, although useful for predicting risk, manual assessment of histopathology is subjective and labour intensive. In this study, we propose a method that integrates multiple histopathological features of the tumor microenvironment into a single, digital pathology-based biomarker using nuclear fractal dimension (nFD) analysis.

Methods

One hundred and seven consecutive OSCC patients diagnosed between 1998 and 2006 in Calgary, Canada were included in the study. nFD scores were generated from DAPI-stained images of tissue microarray (TMA) cores. Ki67 protein expression was measured in the tumor using fluorescence immunohistochemistry (IHC) and automated quantitative analysis (AQUA®). Lymphocytic infiltration (LI) was measured in the stroma from haematoxylin-eosin (H&E)-stained TMA slides by a pathologist.

Results

Twenty-five (23.4%) and 82 (76.6%) patients were classified as high and low nFD, respectively. nFD was significantly associated with pathological tumor-stage (pT-stage; P = 0.01) and radiation treatment (RT; P = 0.01). High nFD of the total tumor microenvironment (stroma plus tumor) was significantly associated with improved disease-specific survival (DSS; P = 0.002). No association with DSS was observed when nFD of either the tumor or the stroma was measured separately. pT-stage (P = 0.01), pathological node status (pN-status; P = 0.02) and RT (P = 0.03) were also significantly associated with DSS. In multivariate analysis, nFD remained significantly associated with DSS [HR 0.12 (95% CI 0.02-0.89, P = 0.04)] in a model adjusted for pT-stage, pN-status and RT. We also found that high nFD was significantly associated with high tumor proliferation (P < 0.0001) and high LI (P < 0.0001), factors that we and others have shown to be associated with improved survival in OSCC.

Conclusions

We provide evidence that nFD analysis integrates known prognostic factors from the tumor microenvironment, such as proliferation and immune infiltration, into a single digital pathology-based biomarker. Prospective validation of our results could establish nFD as a valuable tool for clinical decision making in OSCC.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-015-1380-0) contains supplementary material, which is available to authorized users.

Quantum dots-based quantitative and in situ multiple imaging on ki67 and cytokeratin to improve ki67 assessment in breast cancer

Background

As a marker for tumor cell proliferation, Ki67 has important impacts on breast cancer (BC) prognosis. Although immunohistochemical staining is the current standard method, variations in analytical practice make it difficult for pathologists to manually measure Ki67 index. This study was to develop a fluorescent spectrum-based quantitative analysis of Ki67 expression by quantum-dots (QDs) multiple imaging technique.

Methods

A QDs-based in situ multiple fluorescent imaging method was developed, which stained nuclear Ki67 as red signal and cytoplasmic cytokeratin (CK) as green signal. Both Ki67 and CK signals were automatically separated and quantified by professional spectrum analysis software. This technique was applied to tissue microarrays from 240 BC patients. Both Ki67 and CK values, and Ki67/CK ratio were obtained for each patient, and their prognostic value on 5-year disease free survival was assessed.

Results

This method simultaneously stains nuclear Ki67 and cytoplasmic CK with clear signal contrast, making it easy for signal separation and quantification. The total fluorescent signal intensities of both Ki67 sum and CK sum were obtained, and Ki67/CK ratio calculated. Ki67 sum and Ki67/CK ratio were each attributed into two grades by X-tile software based on the best P value principle. Multivariate analysis showed Ki67 grade (P = 0.047) and Ki67/CK grade (P = 0.004) were independent prognostic factors. Furthermore, area under curve (AUC) of ROC analysis for Ki67/CK grade (AUC: 0.683, 95%CI: 0.613–0.752) was higher than Ki67 grade (AUC: 0.665, 95%CI: 0.596–0.734) and HER-2 gene (AUC: 0.586, 95%CI: 0.510–0.661), but lower than N stage (AUC: 0.760, 95%CI: 0.696–0.823) and histological grade (AUC: 0.756, 95%CI: 0.692–0.820) on predicting the risk for recurrence.

Conclusions

A QDs-based quantitative and in situ multiple imaging on Ki67 and CK was developed to improve Ki67 assessment in BC, and Ki67/CK grade had better performance than Ki67 grade in predicting prognosis.

Practical Applications of Digital Pathology

Background

Virtual microscopy and advances in machine learning have paved the way for the ever-expanding field of digital pathology. Multiple image-based computing environments capable of performing automated quantitative and morphological analyses are the foundation on which digital pathology is built.

Methods

The applications for digital pathology in the clinical setting are numerous and are explored along with the digital software environments themselves, as well as the different analytical modalities specific to digital pathology. Prospective studies, case-control analyses, meta-analyses, and detailed descriptions of software environments were explored that pertained to digital pathology and its use in the clinical setting.

Results

Many different software environments have advanced platforms capable of improving digital pathology and potentially influencing clinical decisions.

Conclusions

The potential of digital pathology is vast, particularly with the introduction of numerous software environments available for use. With all the digital pathology tools available as well as those in development, the field will continue to advance, particularly in the era of personalized medicine, providing health care professionals with more precise prognostic information as well as helping them guide treatment decisions.

Breast cancer histopathology image analysis: a review

This paper presents an overview of methods that have been proposed for the analysis of breast cancer histopathology images. This research area has become particularly relevant with the advent of whole slide imaging (WSI) scanners, which can perform cost-effective and high-throughput histopathology slide digitization, and which aim at replacing the optical microscope as the primary tool used by pathologist. Breast cancer is the most prevalent form of cancers among women, and image analysis methods that target this disease have a huge potential to reduce the workload in a typical pathology lab and to improve the quality of the interpretation. This paper is meant as an introduction for nonexperts. It starts with an overview of the tissue preparation, staining and slide digitization processes followed by a discussion of the different image processing techniques and applications, ranging from analysis of tissue staining to computer-aided diagnosis, and prognosis of breast cancer patients.

Nuclear fractal dimension in oral squamous cell carcinoma: a novel method for the evaluation of grading, staging, and survival

Fractal dimension (FD) in tissue specimens from patients with oral squamous cell carcinoma (OSCC) was evaluated. FD values in different stages of OSCC, and the correlations with clinicopathological variables and patient survival were investigated. Histological sections from OSCC and control non-neoplastic mucosa specimens were stained with hematoxylin-eosin for pathological analysis and with Feulgen for nuclear evaluation. FD in OSCC groups vs. controls revealed statistically significant differences (P < 0.001). In addition, a progressive increase of FD from stage I and II lesions and stage III and IV lesions was observed, with statistically significant differences (P = 0.003). Moreover, different degrees of tumor differentiation showed a significant difference in the average nuclear FD values (P = 0.001). A relationship between FD and patients' survival was also detected with lower FD values associated to longer survival time and higher FD values with shorter survival time (P = 0.034). These data showed that FD significantly increased during OSCC progression. Thus, FD could represent a novel prognostic tool for OSCC, as FD values significantly correlated with patient survival. Fractal geometry could give insights into tumor morphology and could become an useful tool for analyzing irregular tumor growth patterns.

Cdc42 overexpression induces hyperbranching in the developing mammary gland by enhancing cell migration

Introduction

The Rho GTPase Cdc42 is overexpressed and hyperactivated in breast tumors compared to normal breast tissue. Cdc42 regulates key processes that are critical for mammary gland morphogenesis and become disrupted during the development, progression, and metastasis of breast cancer. However, the contribution of Cdc42 to normal and neoplastic mammary gland development in vivo remains poorly understood. We were therefore interested in investigating the effects of Cdc42 overexpression on mammary gland morphogenesis as a first step toward understanding how its overexpression may contribute to mammary tumorigenesis.

Methods

We developed a tetracycline-regulatable Cdc42 overexpression mouse model in which Cdc42 can be inducibly overexpressed in the developing mammary gland. The effects of Cdc42 overexpression during postnatal mammary gland development were investigated using in vivo and in vitro approaches, including morphometric analysis of wholemounted mammary glands, quantification of histological markers, and primary mammary epithelial cell (MEC) functional and biochemical assays.

Results

Analysis of Cdc42-overexpressing mammary glands revealed abnormal terminal end bud (TEB) morphologies, characterized by hyperbudding and trifurcation, and increased side branching within the ductal tree. Quantification of markers of proliferation and apoptosis suggested that these phenotypes were not due to increased cell proliferation or survival. Rather, Cdc42 overexpressing MECs were more migratory and contractile and formed dysmorphic, invasive acini in three-dimensional cultures. Cdc42 and RhoA activities, phosphorylated myosin light chain, and MAPK signaling, which contribute to migration and invasion, were markedly elevated in Cdc42 overexpressing MECs. Interestingly, Cdc42 overexpressing mammary glands displayed several features associated with altered epithelial-stromal interactions, which are known to regulate branching morphogenesis. These included increased stromal thickness and collagen deposition, and stromal cells isolated from Cdc42 overexpressing mammary glands exhibited elevated mRNA expression of extracellular matrix proteins and remodeling enzymes.

Conclusions

These data suggest that Cdc42 overexpression disrupts mammary gland branching morphogenesis by altering Rho GTPase and MAPK signaling, leading to increased MEC contractility and migration in association with stromal alterations. Our studies provide insight into how aberrant Cdc42 expression may contribute to mammary tumorigenesis.

Histomorphometric parameters and fractal complexity of the equine placenta from healthy and sick foals

Computer-based digital image analysis of tissue samples shows promise both to reduce the subjectivity of traditional manual tissue assessments and potentially to shorten the time required to analyze each sample. The present study used digital image analysis to investigate the histomorphometric parameters and fractal complexity of the equine placenta from healthy and sick foals. We hypothesized that the placentas of sick foals could have a different growth pattern and complexity that could be objectively estimated by their fractal dimension (FD). Fourteen placentas from 30 mares were selected in the 2013 breeding season and divided into two groups: seven mares with normal pregnancy, eutocic delivery, and healthy foals (group 1) and seven mares with normal or high-risk pregnancy, eutocic delivery and sick foals (group 2). Four mares in group 2 were classified as having a high-risk pregnancy on the basis of anamnesis and/or ultrasound findings. Clinical diagnosis of group 2 foals included perinatal asphyxia syndrome (n = 4), prematurity/dysmaturity (n = 2), and both diagnoses (n = 1). Seven out of fourteen placentas showed diffuse gross abnormalities. Grossly abnormal placentas were observed in one out of seven (14.28%) animals in group 1 and in six out of seven (85.72%) animals in group 2. Digital image analysis proved to be reliable and efficient in segmentation, calculation, outline extraction of villi as also resulted in sampled test images. The placentas of group 1 foals displayed a uniform and homogeneous villi development, as revealed by geometric parameters and FD. These results can be interpreted as a harmonic growth pattern of microcotyledons throughout the placenta in healthy foals. By contrast, the placentas of group 2 foals showed a nonuniform growth pattern and complexity with more villi and more developed villi in pregnant horn (PH) and nonpregnant horn (NPH) compared with body (B) and higher FD in NPH than in the other areas. This finding can be interpreted as a compensatory growth with increased complexity. Our results show that morphometric analysis, particularly FD measurement, can be proposed as an ancillary histological tool for equine placenta evaluation. Chorionic villi tend to have greater branching and complexity in sick than in healthy foals, particularly in the NPH. This could represent an attempt to increase the exchange area between fetal and maternal compartments of the equine placenta, and merits further investigation.

Digital differentiation of non-small cell carcinomas of the lung by the fractal dimension of their epithelial architecture

INTRODUCTION

In recent years, differences have emerged in the treatment of squamous and non-squamous non-small cell lung carcinomas (NSCLCs). This highlights the importance of accurate histopathologic classification. However, there remains inter-observer disagreement when making diagnoses based on histology. Fractal dimension (FD) is a mathematical measure of irregularity and complexity of shape. We hypothesize that the FD of carcinoma epithelial architecture can assist in differentiating adenocarcinoma (ADC) from squamous cell carcinoma (SCC) of the lung.

METHODS

134 resected (88 ADC and 46 SCC) cases of resected early-stage NSCLC were analyzed. Tissue micro arrays were generated from formalin-fixed paraffin-embedded tissue, stained with pan-cytokeratin, and digitally imaged and the FD of the epithelial structure calculated. Mean FD of ADC and SCC were compared using the independent t-test, partial correlations, and receiver operating characteristic (ROC) analyses.

RESULTS

A statistically significant difference (p<0.001) between the mean FD of ADC (M=1.70, SD=0.07) and SCC (M=1.78, SD=0.07) was found. Significance remained (p<0.001) when controlling for several possible confounders. ROC analysis demonstrated an area-under-the-curve of 0.81 (p<0.001).

CONCLUSIONS

The epithelial structure FD of NSCLC has potential as a reproducible and automated measure to help subtype NSCLCs into ADC and SCC. With further image analysis algorithm improvements, fractal analysis may be a component in computerized histomorphological assessments of lung cancer and may provide an adjunct test in differentiating NSCLCs.

Computer-based image studies on tumor nests mathematical features of breast cancer and their clinical prognostic value

Background

The expending and invasive features of tumor nests could reflect the malignant biological behaviors of breast invasive ductal carcinoma. Useful information on cancer invasiveness hidden within tumor nests could be extracted and analyzed by computer image processing and big data analysis.

Methods

Tissue microarrays from invasive ductal carcinoma (n = 202) were first stained with cytokeratin by immunohistochemical method to clearly demarcate the tumor nests. Then an expert-aided computer analysis system was developed to study the mathematical and geometrical features of the tumor nests. Computer recognition system and imaging analysis software extracted tumor nests information, and mathematical features of tumor nests were calculated. The relationship between tumor nests mathematical parameters and patients' 5-year disease free survival was studied.

Results

There were 8 mathematical parameters extracted by expert-aided computer analysis system. Three mathematical parameters (number, circularity and total perimeter) with area under curve >0.5 and 4 mathematical parameters (average area, average perimeter, total area/total perimeter, average (area/perimeter)) with area under curve <0.5 in ROC analysis were combined into integrated parameter 1 and integrated parameter 2, respectively. Multivariate analysis showed that integrated parameter 1 (P = 0.040) was independent prognostic factor of patients' 5-year disease free survival. The hazard risk ratio of integrated parameter 1 was 1.454 (HR 95% CI [1.017–2.078]), higher than that of N stage (HR 1.396, 95% CI [1.125–1.733]) and hormone receptor status (HR 0.575, 95% CI [0.353–0.936]), but lower than that of histological grading (HR 3.370, 95% CI [1.125–5.364]) and T stage (HR 1.610, 95% CI [1.026 –2.527]).

Conclusions

This study indicated integrated parameter 1 of mathematical features (number, circularity and total perimeter) of tumor nests could be a useful parameter to predict the prognosis of early stage breast invasive ductal carcinoma.

Scale-specific multifractal medical image analysis

Fractal geometry has been applied widely in the analysis of medical images to characterize the irregular complex tissue structures that do not lend themselves to straightforward analysis with traditional Euclidean geometry. In this study, we treat the nonfractal behaviour of medical images over large-scale ranges by considering their box-counting fractal dimension as a scale-dependent parameter rather than a single number. We describe this approach in the context of the more generalized Rényi entropy, in which we can also compute the information and correlation dimensions of images. In addition, we describe and validate a computational improvement to box-counting fractal analysis. This improvement is based on integral images, which allows the speedup of any box-counting or similar fractal analysis algorithm, including estimation of scale-dependent dimensions. Finally, we applied our technique to images of invasive breast cancer tissue from 157 patients to show a relationship between the fractal analysis of these images over certain scale ranges and pathologic tumour grade (a standard prognosticator for breast cancer). Our approach is general and can be applied to any medical imaging application in which the complexity of pathological image structures may have clinical value.

Multi-field-of-view framework for distinguishing tumor grade in ER+ breast cancer from entire histopathology slides

Modified Bloom-Richardson (mBR) grading is known to have prognostic value in breast cancer (BCa), yet its use in clinical practice has been limited by intra- and interobserver variability. The development of a computerized system to distinguish mBR grade from entire estrogen receptor-positive (ER+) BCa histopathology slides will help clinicians identify grading discrepancies and improve overall confidence in the diagnostic result. In this paper, we isolate salient image features characterizing tumor morphology and texture to differentiate entire hematoxylin and eosin (H and E) stained histopathology slides based on mBR grade. The features are used in conjunction with a novel multi-field-of-view (multi-FOV) classifier--a whole-slide classifier that extracts features from a multitude of FOVs of varying sizes--to identify important image features at different FOV sizes. Image features utilized include those related to the spatial arrangement of cancer nuclei (i.e., nuclear architecture) and the textural patterns within nuclei (i.e., nuclear texture). Using slides from 126 ER+ patients (46 low, 60 intermediate, and 20 high mBR grade), our grading system was able to distinguish low versus high, low versus intermediate, and intermediate versus high grade patients with area under curve values of 0.93, 0.72, and 0.74, respectively. Our results suggest that the multi-FOV classifier is able to 1) successfully discriminate low, medium, and high mBR grade and 2) identify specific image features at different FOV sizes that are important for distinguishing mBR grade in H and E stained ER+ BCa histology slides.

Morphologic features do not influence response to trastuzumab-containing neoadjuvant chemotherapy in HER2-positive breast cancer

Trastuzumab-containing neoadjuvant chemotherapy in patients with HER2-positive breast cancer is highly effective in reducing tumor volume and enables more patients to have breast-conserving surgery. The tumor hormone receptor level has been shown to significantly influence response to trastuzumab-containing neoadjuvant chemotherapy. In this study, we comprehensively evaluated various morphologic features and proliferative activity in 50 HER2-positive invasive breast carcinomas treated with trastuzumab-containing neoadjuvant chemotherapy to determine whether any of these features have the same predictive value as tumor hormone receptor content. The tumor proliferation activity as measured by Ki-67 and various morphologic parameters were compared between tumors that achieved >50% tumor volume reduction including pathologic complete response (pCR) and tumors that showed ≤50% tumor volume reduction. Thirty-seven cases (74%) showed >50% tumor volume reduction including 18 cases with pCR. Thirteen cases (26%) showed ≤50% tumor volume reduction. Neither morphologic variables nor Ki-67 labeling index were predictive of >50% tumor volume reduction. In contrast, hormone receptor status and semiquantitative H scores for hormone receptors were predictive of>50% tumor volume reduction. The mean estrogen and progesterone receptor H scores for tumors that showed >50% tumor volume reduction (including pCR) were 77 (median 10) and 31 (median 0), respectively, compared with 168 (median 200) and 79 (median 75) for cases that showed ≤50% tumor volume reduction. Semiquantitative scoring for hormone receptors provides useful information in terms of therapeutic response in HER2-positive tumors.

Longitudinal study of mammary epithelial and fibroblast co-cultures using optical coherence tomography reveals morphological hallmarks of pre-malignancy

The human mammary gland is a complex and heterogeneous organ, where the interactions between mammary epithelial cells (MEC) and stromal fibroblasts are known to regulate normal biology and tumorigenesis. We aimed to longitudinally evaluate morphology and size of organoids in 3D co-cultures of normal (MCF10A) or pre-malignant (MCF10DCIS.com) MEC and hTERT-immortalized fibroblasts from reduction mammoplasty (RMF). This co-culture model, based on an isogenic panel of cell lines, can yield insights to understand breast cancer progression. However, 3D cultures pose challenges for quantitative assessment and imaging, especially when the goal is to measure the same organoid structures over time. Using optical coherence tomography (OCT) as a non-invasive method to longitudinally quantify morphological changes, we found that OCT provides excellent visualization of MEC-fibroblast co-cultures as they form ductal acini and remodel over time. Different concentrations of fibroblasts and MEC reflecting reported physiological ratios [1] were evaluated, and we found that larger, hollower, and more aspherical acini were formed only by pre-malignant MEC (MCF10DCIS.com) in the presence of fibroblasts, whereas in comparable conditions, normal MEC (MCF10A) acini remained smaller and less aspherical. The ratio of fibroblast to MEC was also influential in determining organoid phenotypes, with higher concentrations of fibroblasts producing more aspherical structures in MCF10DCIS.com. These findings suggest that stromal-epithelial interactions between fibroblasts and MEC can be modeled in vitro, with OCT imaging as a convenient means of assaying time dependent changes, with the potential for yielding important biological insights about the differences between benign and pre-malignant cells.

Fractal dimension and Shannon's entropy analyses of the architectural complexity caused by the inflammatory reactions induced by highly crystalline poly(vinyl alcohol) microspheres implanted in subcutaneous tissues of the Wistar rats

The results of the histopathological analyses after the implantation of highly crystalline PVA microspheres in subcutaneous tissues of Wistar rats are here in reported. Three different groups of PVA microparticles were systematically studied: highly crystalline, amorphous, and commercial ones. In addition to these experiments, complementary analyses of architectural complexity were performed using fractal dimension (FD), and Shannon's entropy (SE) concepts. The highly crystalline microspheres induced inflammatory reactions similar to the ones observed for the commercial ones, while the inflammatory reactions caused by the amorphous ones were less intense. Statistical analyses of the subcutaneous tissues of Wistar rats implanted with the highly crystalline microspheres resulted in FD and SE values significantly higher than the statistical parameters observed for the amorphous ones. The FD and SE parameters obtained for the subcutaneous tissues of Wistar rats implanted with crystalline and commercial microparticles were statistically similar. Briefly, the results indicated that the new highly crystalline microspheres had biocompatible behavior comparable to the commercial ones. In addition, statistical tools such as FD and SE analyses when combined with histopathological analyses can be useful tools to investigate the architectural complexity tissues caused by complex inflammatory reactions.