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Increased stiffness in the extracellular matrix (ECM) contributes to tumor progression and metastasis. Therefore, stromal modulating therapies and accompanying biomarkers are being developed to target ECM stiffness. Magnetic resonance (MR) elastography can noninvasively and quantitatively map the viscoelastic properties of tumors in vivo and thus has clear clinical applications. Herein, we used MR elastography, coupled with computational histopathology, to interrogate the contribution of collagen to the tumor biomechanical phenotype and to evaluate its sensitivity to collagenase-induced stromal modulation. Elasticity (Gd) and viscosity (Gl) were significantly greater for orthotopic BT-474 (Gd = 5.9 ± 0.2 kPa, Gl = 4.7 ± 0.2 kPa, n = 7) and luc-MDA-MB-231-LM2-4 (Gd = 7.9 ± 0.4 kPa, Gl = 6.0 ± 0.2 kPa, n = 6) breast cancer xenografts, and luc-PANC1 (Gd = 6.9 ± 0.3 kPa, Gl = 6.2 ± 0.2 kPa, n = 7) pancreatic cancer xenografts, compared with tumors associated with the nervous system, including GTML/Trp53KI/KI medulloblastoma (Gd = 3.5 ± 0.2 kPa, Gl = 2.3 ± 0.2 kPa, n = 7), orthotopic luc-D-212-MG (Gd = 3.5 ± 0.2 kPa, Gl = 2.3 ± 0.2 kPa, n = 7), luc-RG2 (Gd = 3.5 ± 0.2 kPa, Gl = 2.3 ± 0.2 kPa, n = 5), and luc-U-87-MG (Gd = 3.5 ± 0.2 kPa, Gl = 2.3 ± 0.2 kPa, n = 8) glioblastoma xenografts, intracranially propagated luc-MDA-MB-231-LM2-4 (Gd = 3.7 ± 0.2 kPa, Gl = 2.2 ± 0.1 kPa, n = 7) breast cancer xenografts, and Th-MYCN neuroblastomas (Gd = 3.5 ± 0.2 kPa, Gl = 2.3 ± 0.2 kPa, n = 5). Positive correlations between both elasticity (r = 0.72, P < 0.0001) and viscosity (r = 0.78, P < 0.0001) were determined with collagen fraction, but not with cellular or vascular density. Treatment with collagenase significantly reduced Gd (P = 0.002) and Gl (P = 0.0006) in orthotopic breast tumors. Texture analysis of extracted images of picrosirius red staining revealed significant negative correlations of entropy with Gd (r = −0.69, P < 0.0001) and Gl (r = −0.76, P < 0.0001), and positive correlations of fractal dimension with Gd (r = 0.75, P < 0.0001) and Gl (r = 0.78, P < 0.0001). MR elastography can thus provide sensitive imaging biomarkers of tumor collagen deposition and its therapeutic modulation.Significance:MR elastography enables noninvasive detection of tumor stiffness and will aid in the development of ECM-targeting therapies.

Contains Supplementary Methods and Table S1 and S2 which summarize the origin, characteristics, provenance and culture conditions of the cell lines used in this study

Background: Despite treatment advances, there remains a significant risk of recurrence in ovarian cancer, at which stage it is usually incurable. Consequently, there is a clear need for improved patient stratification. However, at present clinical prognosticators remain largely unchanged due to the lack of reproducible methods to identify high-risk patients. Methods: In high-grade serous ovarian cancer patients with advanced disease, we spatially define a tumour ecological balance of stromal resource and immune hazard using high-throughput image and spatial analysis of routine histology slides. On this basis an EcoScore is developed to classify tumours by a shift in this balance towards cancer-favouring or inhibiting conditions. Findings: The EcoScore provides prognostic value stronger than, and independent of, known risk factors. Crucially, the clinical relevance of mutational burden and genomic instability differ under different stromal resource conditions, suggesting that the selective advantage of these cancer hallmarks is dependent on the context of stromal spatial structure. Under a high resource condition defined by a high level of geographical intermixing of cancer and stromal cells, selection appears to be driven by point mutations; whereas, in low resource tumours featured with high hypoxia and low cancer-immune co-localization, selection is fuelled by aneuploidy. Interpretation: Our study offers empirical evidence that cancer fitness depends on tumour spatial constraints, and presents a biological basis for developing better assessments of tumour adaptive strategies in overcoming ecological constraints including immune surveillance and hypoxia. Keywords: Tumour ecology, Tumour spatial heterogeneity, Cancer evolution, Ovarian cancer, Histological image analysis

ImportanceScreening mammography with two human readers increases cancer detection and lowers recall rates, but workforce shortages make double reading unsustainable in many countries. Artificial intelligence (AI) as an independent reader in double reading may support screening performance while improving cost-effectiveness. The clinical validation of AI requires large-scale, multi-vendor studies on unenriched cohorts.ObjectiveTo evaluate the performance of the Mia® AI system on data that the AI system would process in real-world deployments.DesignA retrospective study simulating the impact of AI on an unenriched screening sample.SettingSeven European breast screening sites representing four centers: three from the UK and one in Hungary (HU), between 2009 and 2019.ParticipantsThe sample included 275,900 cases (177,882 participants) from seven screening sites, involving two countries and four hardware vendors from 2009 to 2019.InterventionSimulation of double reading using AI as an independent reader in breast cancer screening on historical data.Main Outcomes and MeasuresPerformance was determined for standalone AI compared to the historical single reader and for simulated double reading with AI compared to historical double reading, assessing non-inferiority and superiority on relevant screening metrics using a non-inferiority margin of 10% relative difference and a one-sided alpha of 2.5% for both tests.ResultsStandalone AI detected 29.8% of missed interval cancers. When compared with historical double reading, double reading with AI showed non-inferiority for sensitivity and superiority for recall rate, specificity and positive predictive value. AI as an independent reader reduced the workload for the second human reader but increased the arbitration rate from 3.3% to 12.3%. Applying the AI system could have reduced the human reading time required by up to 44.8% and reduced the recall rate by a relative 7.7% (from 5.2% to 4.8%).Conclusions and RelevanceUsing the AI system as an independent reader maintains or improves the double reading standard of care, while substantially reducing the workload. Thus, it has the potential to provide operational and economic benefits.Trial RegistrationRegistered on ISRCTN, study ID: ISRCTN18056078

PURPOSE: To explore whether generative adversarial networks (GANs) can enable synthesis of realistic medical images that are indiscernible from real images, even by domain experts. MATERIALS AND METHODS: In this retrospective study, progressive growing GANs were used to synthesize mammograms at a resolution of 1280 × 1024 pixels by using images from 90 000 patients (average age, 56 years ± 9) collected between 2009 and 2019. To evaluate the results, a method to assess distributional alignment for ultra–high-dimensional pixel distributions was used, which was based on moment plots. This method was able to reveal potential sources of misalignment. A total of 117 volunteer participants (55 radiologists and 62 nonradiologists) took part in a study to assess the realism of synthetic images from GANs. RESULTS: A quantitative evaluation of distributional alignment shows 60%–78% mutual-information score between the real and synthetic image distributions, and 80%–91% overlap in their support, which are strong indications against mode collapse. It also reveals shape misalignment as the main difference between the two distributions. Obvious artifacts were found by an untrained observer in 13.6% and 6.4% of the synthetic mediolateral oblique and craniocaudal images, respectively. A reader study demonstrated that real and synthetic images are perceptually inseparable by the majority of participants, even by trained breast radiologists. Only one out of the 117 participants was able to reliably distinguish real from synthetic images, and this study discusses the cues they used to do so. CONCLUSION: On the basis of these findings, it appears possible to generate realistic synthetic full-field digital mammograms by using a progressive GAN architecture up to a resolution of 1280 × 1024 pixels. Supplemental material is available for this article. © RSNA, 2020

Despite increasing evidence supporting the clinical utility of immune infiltration in the estrogen receptor-negative (ER-) subtype, the prognostic value of immune infiltration for ER+ disease is not well defined.Quantitative immune scores of cell abundance and spatial heterogeneity were computed using a fully automated hematoxylin and eosin-stained image analysis algorithm and spatial statistics for 1178 postmenopausal patients with ER+ breast cancer treated with five years' tamoxifen or anastrozole. The prognostic significance of immune scores was compared with Oncotype DX 21-gene recurrence score (RS), PAM50 risk of recurrence (ROR) score, IHC4, and clinical treatment score, available for 963 patients. Statistical tests were two-sided.Scores of immune cell abundance were not associated with recurrence-free survival. In contrast, high immune spatial scores indicating increased cell spatial clustering were associated with poor 10-year, early (0-5 years), and late (5-10 years) recurrence-free survival (Immune Hotspot: LR-χ2 = 14.06, P.001, for 0-10 years; LR-χ2 = 6.24, P = .01, for 0-5 years; LR-χ2 = 7.89, P = .005, for 5-10 years). The prognostic value of spatial scores for late recurrence was similar to that of IHC4 and RS in both populations, but was not as strong as other tests in comparison for recurrence across 10 years.These results provide a missing link between tumor immunity and disease outcome in ER+ disease by examining tumor spatial architecture. The association between spatial scores and late recurrence suggests a lasting memory of protumor immunity that may impact disease progression and evolution of endocrine treatment resistance, which may be exploited for therapeutic advances.

Increased stiffness in the extracellular matrix (ECM) contributes to tumor progression and metastasis. Therefore, stromal modulating therapies and accompanying biomarkers are being developed to target ECM stiffness. Magnetic resonance (MR) elastography can noninvasively and quantitatively map the viscoelastic properties of tumors

Traditional computer aided detection (CAD) systems for breast cancer screening relied on machine learning with human-coded feature-engineering. They have largely failed to fulfill the promise of improving screening accuracy and workflow efficiency, and are often associated with increased recall rates and avoidable screening costs due to high instances of false positive markings. Advances in machine learning (such as deep learning) are on the cusp of providing more effective, more efficient, and even more patient-centric breast cancer screening support than ever before. By leveraging the consistent high sensitivity and specificity performance of autonomous systems, in combination with expert human oversight, the potential for efficient single-reader software-supported screening programs with low recall rates is on the horizon.

In routine breast cancer clinical practice, multiple serial sections are stained for guiding therapy. These include the most widely used hematoxylin & eosin (H&E) as well as established immunohistochemical biomarkers such as estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and increasingly so, Ki67, collectively known as the IHC4. Analysis of these sections involves visual assessment which is labor intensive and inefficient. More importantly, it ignores the wealth of spatial information in histological samples that can be used to better understand clonal heterogeneity, which may potentially be responsible for mixed response to treatment and drug resistance. Material and Methods: We have developed an automated image registration algorithm that aligns H&E and IHC4 serial section images from the same tumor by employing a combination of rigid and non-rigid registration. This automated image registration algorithm subsequently enables us to develop SpEeCH (spatial expression of clonal heterogeneity), a novel method to identify spatial clusters with differential expression. At the core of SpEeCH is a clustering method that exploits spatial cellular characteristics of tumor regions to identify spatial clonal clusters. Gaussian mixture Model (GMM) is used in combination with the Bayesian information criterion and resampling to identify the optimal number of stable clusters. As GMM ignores local spatial dependency which is an important tumor characteristic, we are also comparing GMM with conditional random fields that are undirected graphical models to account for local dependencies instead of full joint distribution. To discover spatial clusters, we applied SpEeCH to a set of 13 high-grade surgically resected breast tumors with serial sections of H&E and IHC4. Tumor regions were defined as 5 μm x 5 μm squares, resulting in >10,000 regions for clustering in each tumor. Clinical implications of the spatial clusters were verified in an independent dataset (METABRIC) that consists of gene expression whole-tumor profiles and disease-specific survival data of 2,000 breast cancer tumors. Results: By employing SpEeCH, we identified five stable spatial clusters based on histological section of H&E and IHC4 in the discovery set, with significantly different disease-specific survival validated in METABRIC gene expression data (p=0, log-rank test). This analysis revealed a group of poor prognosis ER-/HER2+/Ki67+ patients (p=0, hazard ratio = 4.4, 95% confidence interval = 3.0-6.3, log-rank test compared to a good prognosis group ER+/HER2-/Ki67-). Marked differences in prognosis among these groups highlight the significance of identified spatial clusters. For example, one of the tumors in the discovery set contains both ER-/HER2+/Ki67+ and ER+/HER2-/Ki67- clones. Therefore, such clonal heterogeneity, which is ignored during diagnosis, may help explain treatment resistance. To this end, our observation confirmed the clinical relevance of spatial clusters by leveraging a large-scale dataset under the assumption that such data represents signal from major clones. We propose that the number of clonal clusters and their spatial relationship within a tumor can be used as measures of intra-tumor heterogeneity. Their efficacy is currently being evaluated on independent clinical datasets. Conclusion: Large numbers of histology samples are available from clinical diagnostics and research laboratories, yet there is a lack of tools that facilitate objective analysis of tumor heterogeneity in these samples. Compared to high-cost multimarker immunofluorescence approaches, our automated image and statistical analysis pipeline enables efficient and effective analysis of available histology samples, which is critical for understanding cancers as highly heterogeneous diseases. Citation Format: Andreas Heindl, Adnan Mujahid Khan, Yinyin Yuan. SpEeCH: Quantifying Spatial Expression of Clonal Heterogeneity in breast cancer. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B2-51.

Purpose: The abundance of tumor-infiltrating lymphocytes has been associated with a favorable prognosis in estrogen receptor-negative breast cancer. However, a high degree of spatial heterogeneity in lymphocytic infiltration is often observed in histology samples and its clinical implications and underpinning molecular scaffold remain unclear. Materials and methods: To quantify spatial heterogeneity in the distribution of tumor infiltrating lymphocytes, we combined automated histological image processing with methods of spatial statistics used in ecological data analysis. Hematoxylin and eosin-stained sections from two cohorts of estrogen receptor-negative breast cancer patients (discovery: n=120; validation: n=125) were processed with our automated cell classification algorithm to identify the location of lymphocytes and cancer cells. Subsequently, hotspot analysis (Getis-Ord Gi*) was applied to identify statistically significant hotspots of cancer and immune cells, defined as tumor regions with a significantly high number of cancer or immune cells respectively. To identify molecular aberrations that explain tumor spatial heterogeneity, we integrated our image-based hotspots results with microarray gene expression and copy number data profiled for the same set of tumors. Molecular data were generated with tumor materials sandwiched between these sections, thereby maximizing the biological relevance of multiple data types being generated. Results: We found that the amount of colocalized cancer and immune hotspots weighted by tumor area, rather than number of cancer or immune hotspots, significantly correlates with a better prognosis in estrogen receptor-negative breast cancer in uni- and multivariate Cox analysis. Moreover, colocalization of cancer and immune hotspots further stratified patients with immune cell-rich tumors. Subsequently, we developed a bioinformatics tool, iMmune hOTspoT Omics (iMOTTO), to explain the hotspots as a clinically relevant phenotype using molecular profiling data. Our preliminary analysis revealed significant correlations between this phenotype and expression of immune-specific genes such as CD79, CCL19 and SLAMF1, as well as an immunotherapy target, CTLA4. By incorporating the expression of hotspots-associated genes and copy number alteration data into a multivariate regression model, we aim to define a minimal set of genes to explain the observed degree of cancer-immune hotspot colocalization. Conclusion: Taken together, our study demonstrates the importance of quantifying not only the abundance of lymphocytes but also their spatial variation in the tumor specimen for which methods from other disciplines such as spatial statistics can be successfully applied. Systematic integration of histology and omics data revealed key immune regulators as well as novel genes which warrant further investigation to help elucidate the biological processes underlying immune spatial heterogeneity with potentially important clinical implications. Furthermore, our computational approach can be adapted for studying other cancer types for which immunotherapy has been applied, such as melanoma and non-small cell lung cancer, where our hotspot measures can potentially serve as prognostic and predictive biomarkers. Citation Format: Sidra Nawaz, Andreas Heindl, Andrea Agostinelli, Yinyin Yuan. Critical role of immune spatial heterogeneity and the molecular scaffold in estrogen receptor-negative breast cancer. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B2-55.

The abundance of tumor-infiltrating lymphocytes has been associated with a favorable prognosis in estrogen receptor-negative breast cancer. However, a high degree of spatial heterogeneity in lymphocytic infiltration is often observed and its clinical implication remains unclear. Here we combine automated histological image processing with methods of spatial statistics used in ecological data analysis to quantify spatial heterogeneity in the distribution patterns of tumor-infiltrating lymphocytes. Hematoxylin and eosin-stained sections from two cohorts of estrogen receptor-negative breast cancer patients (discovery: n=120; validation: n=125) were processed with our automated cell classification algorithm to identify the location of lymphocytes and cancer cells. Subsequently, hotspot analysis (Getis-Ord Gi*) was applied to identify statistically significant hotspots of cancer and immune cells, defined as tumor regions with a significantly high number of cancer cells or immune cells, respectively. We found that the amount of co-localized cancer and immune hotspots weighted by tumor area, rather than number of cancer or immune hotspots, correlates with a better prognosis in estrogen receptor-negative breast cancer in univariate and multivariate analysis. Moreover, co-localization of cancer and immune hotspots further stratified patients with immune cell-rich tumors. Our study demonstrates the importance of quantifying not only the abundance of lymphocytes but also their spatial variation in the tumor specimen for which methods from other disciplines such as spatial statistics can be successfully applied.

In colorectal cancer (CRC), an increase in the stromal (S) area with the reduction of the epithelial (E) parts has been suggested as an indication of tumor progression. Therefore, an automated image method capable of discriminating E and S areas would allow an improved diagnosis. Immunofluorescence staining was performed on paraffin-embedded sections from colorectal tumors (16 samples from patients with liver metastasis and 18 without). Noncancerous tumor adjacent mucosa (n=5) and normal mucosa (n=4) were taken as controls. Epithelial cells were identified by an anti-keratin 8 (K8) antibody. Large tissue areas (5–63 mm2/slide) including tumor center, tumor front, and adjacent mucosa were scanned using an automated microscopy system (TissueFAXS). With our newly developed algorithms, we showed that there is more K8-immunoreactive E in the tumor center than in tumor adjacent and normal mucosa. Comparing patients with and without metastasis, the E/S ratio decreased by 20% in the tumor center and by 40% at tumor front in metastatic samples. The reduction of E might be due to a more aggressive phenotype in metastasis patients. The novel software allowed a detailed morphometric analysis of cancer tissue compartments as tools for objective quantitative measurements, reduced analysis time, and increased reproducibility of the data.

SummaryHigh-grade serous ovarian cancer exhibits extensive intratumoral heterogeneity coupled with widespread intraperitoneal disease. Despite this, metastatic spread of tumor clones is non-random, implying the existence of local microenvironmental factors that shape tumor progression. We interrogated the molecular interface between tumor-infiltrating lymphocytes (TIL) and cancer cells in 143 samples from 21 patients using whole-genome sequencing, immunohistochemistry, histologic image analysis, gene expression profiling, and T- and B-cell receptor sequencing. We identify 3 immunologic response categories, which frequently co-exist within individual patients. Furthermore, epithelial CD8+ TIL were inversely associated with malignant cell diversity, evidenced by subclonal neoepitope elimination and spatial tracking between tumor and T-cell clones. Intersecting mutational signatures and immune analysis showed that foldback inversion genomic aberrations lead to worse outcomes even in the presence of cytotoxic TIL (n=433). Thus, regional variation in immune contexture mirrors the pattern of intraperitoneal malignant spread, provoking new perspectives for treatment of this challenging disease.

High-grade serous ovarian cancer (HGSC) exhibits extensive malignant clonal diversity with widespread but non-random patterns of disease dissemination. We investigated whether local immune microenvironment factors shape tumor progression properties at the interface of tumor-infiltrating lymphocytes (TILs) and cancer cells. Through multi-region study of 212 samples from 38 patients with whole-genome sequencing, immunohistochemistry, histologic image analysis, gene expression profiling, and T and B cell receptor sequencing, we identified three immunologic subtypes across samples and extensive within-patient diversity. Epithelial CD8+ TILs negatively associated with malignant diversity, reflecting immunological pruning of tumor clones inferred by neoantigen depletion, HLA I loss of heterozygosity, and spatial tracking between T cell and tumor clones. In addition, combinatorial prognostic effects of mutational processes and immune properties were observed, illuminating how specific genomic aberration types associate with immune response and impact survival. We conclude that within-patient spatial immune microenvironment variation shapes intraperitoneal malignant spread, provoking new evolutionary perspectives on HGSC clonal dispersion.

How tumor microenvironmental forces shape plasticity of cancer cell morphology is poorly understood. Here, we conduct automated histology image and spatial statistical analyses in 514 high grade serous ovarian samples to define cancer morphological diversification within the spatial context of the microenvironment. Tumor spatial zones, where cancer cell nuclei diversify in shape, are mapped in each tumor. Integration of this spatially explicit analysis with omics and clinical data reveals a relationship between morphological diversification and the dysregulation of DNA repair, loss of nuclear integrity, and increased disease mortality. Within the Immunoreactive subtype, spatial analysis further reveals significantly lower lymphocytic infiltration within diversified zones compared with other tumor zones, suggesting that even immune-hot tumors contain cells capable of immune escape. Our findings support a model whereby a subpopulation of morphologically plastic cancer cells with dysregulated DNA repair promotes ovarian cancer progression through positive selection by immune evasion.

Automated microscopic image analysis of immunofluorescence-stained targets on tissue sections is challenged by autofluorescent elements such as erythrocytes, which might interfere with target segmentation and quantification. Therefore, we developed an automated system (Automated REcognition of Tissue-associated Erythrocytes; ARETE) for in silico exclusion of erythrocytes. To detect erythrocytes in transmission images, a cascade of boosted decision trees of Haar-like features was trained on 8,640/4,000 areas (15 × 15 pixels) with/without erythrocytes from images of placental sections (4 µm). Ground truth data were generated on 28 transmission images. At least two human experts labelled the area covered by erythrocytes. For validation, output masks of human experts and ARETE were compared pixel-wise against a mask obtained from majority voting of human experts. F1 score, specificity, and Cohen's κ coefficients were calculated. To study the influence of erythrocyte-derived autofluorescence, we investigated the expression levels of a protein (receptor for advanced glycated end products; RAGE) in placenta and number of Ki-67-positive/cytokeratin 8-positive epithelial cells in colon sections. ARETE exhibited high sensitivity (99.87%) and specificity (99.81%) on a training-subset and processed transmission images (1,392 × 1,024 pixels) within 4 sec. ARETE and human expert's F1-scores were 0.55 versus 0.76, specificities 0.85 versus 0.92 and Cohen's κ coefficients 0.41 versus 0.68. A ranking of Cohen's κ coefficient by the scale of Fleiss certified “good agreement” between ARETE and the human experts. Applying ARETE, we demonstrated 4–14% false-positive RAGE-expression in placenta, and 18% falsely detected proliferative epithelial cells in colon, caused by erythrocyte-autofluorescence. ARETE is a fast system for in silico reduction of erythrocytes, which improves automated image analysis in research and diagnostic pathology. © 2013 International Society for Advancement of Cytometry.

Concerted efforts in genomic studies examining RNA transcription and DNA methylation patterns have revealed profound insights in prognostic ovarian cancer subtypes. On the other hand, abundant histology slides have been generated to date, yet their uses remain very limited and largely qualitative. Our goal is to develop automated histology analysis as an alternative subtyping technology for ovarian cancer that is cost-efficient and does not rely on DNA quality. We developed an automated system for scoring primary tumour sections of 91 late-stage ovarian cancer to identify single cells. We demonstrated high accuracy of our system based on expert pathologists’ scores (cancer = 97.1%, stromal = 89.1%) as well as compared to immunohistochemistry scoring (correlation = 0.87). The percentage of stromal cells in all cells is significantly associated with poor overall survival after controlling for clinical parameters including debulking status and age (multivariate analysis p = 0.0021, HR = 2.54, CI = 1.40–4.60) and progression-free survival (multivariate analysis p = 0.022, HR = 1.75, CI = 1.09–2.82). We demonstrate how automated image analysis enables objective quantification of microenvironmental composition of ovarian tumours. Our analysis reveals a strong effect of the tumour microenvironment on ovarian cancer progression and highlights the potential of therapeutic interventions that target the stromal compartment or cancer-stroma signalling in the stroma-high, late-stage ovarian cancer subset.

To improve early diagnosis of prostate cancer to aid clinical decision-making. Diffusion-weighted magnetic resonance imaging (DW-MRI) is sensitive to water diffusion throughout tissues, which correlates with Gleason score, a histological measure of prostate cancer aggressiveness. In this study the ability of DW-MRI to detect prostate cancer onset and development was evaluated in transgenic adenocarcinoma of the mouse prostate (TRAMP) mice.T2 -weighted and DW-MRI were acquired using a 7T MR scanner, 200 mm bore diameter; 10 TRAMP and 6 C57BL/6 control mice were scanned every 4 weeks from 8 weeks of age until sacrifice at 28-30 weeks. After sacrifice, the genitourinary tract was excised and sectioned for histological analysis. Histology slides registered with DW-MR images allowed for validation of DW-MR images and the apparent diffusion coefficient (ADC) as tools for cancer detection and disease stratification. An automated early assessment tool based on ADC threshold values was developed to aid cancer detection and progression monitoring.The ADC differentiated between control prostate ((1.86 ± 0.20) × 10(-3) mm(2) /s) and normal TRAMP prostate ((1.38 ± 0.10) × 10(-3) mm(2) /s) (P = 0.0001), between TRAMP prostate and well-differentiated cancer ((0.93 ± 0.18) × 10(-3) mm(2) /s) (P = 0.0006), and between well-differentiated cancer and poorly differentiated cancer ((0.63 ± 0.06) × 10(-3) mm(2) /s) (P = 0.02).DW-MRI is a tool for early detection of cancer, and discrimination between cancer stages in the TRAMP model. The incorporation of DW-MRI-based prostate cancer stratification and monitoring could increase the accuracy of preclinical trials using TRAMP mice.

Sofern es die Datenverfugbarkeit zulasst, und wenn ein theoretisches Interesse an Zusammenhangen uber Raum und Zeit hinweg (also an der Querschnitts- ebenso wie an der Langsschnittsvariation) besteht, kommt auch die – zumindest vermeintliche – Konigsdisziplin der Makro-Regressionen in Betracht, die Analyse gepoolter Zeitreihen. Sie ist auch unter dem Kurzel TSCS (time-series cross-section) bekannt und wird zuweilen auch unter dem Rubrum ‚Panel-Analyse‘ abgehandelt. Im Folgenden stellen wir zunachst ihr Grundprinzip vor, diskutieren dann die Modellauswahl und stellen schlieslich Ideen dazu vor, wie diese eher anspruchsvolle und zeitaufwandige Methode fur eine Master-Arbeit realistischerweise in Frage kommt. Im Anschluss daran wird exemplarisch eine mogliche Anwendung der Makroregression uber Raum und Zeit im Erfahrungsbericht von Tobias Rommel vorgestellt, der diese Methode in seiner Masterarbeit verwendet hat. Zuvor sei aber festgehalten, dass bivariate Korrelationen sowie multivariate Regressionen (siehe Kap. 4) der einzelnen den Pool konstituierenden Quer- oder auch Langsschnitte eine ideale Vorstufe zu gepoolten Analysen darstellen, da man mithilfe Ersterer ein Gefuhl fur den Datensatz gewinnen und die Verteilung der Gesamtvariation und ihrer Erklarung auf die raumliche und die zeitliche Dimension besser einschatzen kann, was fur die gepoolten Analysen sehr hilfreich sein kann.

Die Event-History-Analyse (EHA) ist eine statistische Methode, welche auf den Wechsel von Untersuchungsobjekten von einem Zustand in einen anderen fokussiert. Ein solcher Zustandswechsel wird als Ereignis bezeichnet. Relevant ist zudem die Dauer, die ein Objekt in einem bestimmten Zustand verweilt, bis es ein Event erfahrt. In der Medizin will man beispielsweise nicht nur wissen, ob ein Patient uberlebt, sondern wie lange er uberlebt und ob eine bestimmte Therapieform seine Uberlebensdauer verlangert. Die abhangige Variable ist dort entsprechend die Dauer, die die Person uberlebt, bis ihr das Ereignis Tod, widerfahrt. Untersucht wird also der Zustandswechsel von „lebendig“ zu „tot“. Um Transitionsprozesse sinnvoll untersuchen zu konnen, mussen drei Bedingungen erfullt sein: erstens muss sich jedes Untersuchungsobjekt auf eine bestimmte Anzahl an moglichen Endzustanden zubewegen, zweitens mussen diese Zustandswechselereignisse generell zu jedem Zeitpunkt eintreten konnen und drittens geht man davon aus, dass es bestimmte zeitkonstante und/oder zeitvariable Faktoren gibt, die das Eintreten der Ereignisse beeinflussen.

Dieses Kapitel vermittelt Grundlagen der Sekundaranalyse von Umfragedaten. Wir orientieren uns dabei an den tatsachlichen Arbeitsschritten in der empirischen Forschung: Zur Vorbereitung der Datenanalyse (3.1) mussen die theoretischen Konstrukte operationalisiert werden, der Umgang mit fehlenden Werten muss geklart und gegebenenfalls mussen die Daten gewichtet werden. Die Operationalisierung der Konstrukte wird mithilfe einer Faktorenanalyse empirisch uberpruft (3.2), auf dieser Basis werden danach Indizes konstruiert (3.3). Den Abschluss des Kapitels bildet ein kurzer Einblick in die Varianzanalyse (3.4), die sich in Einzelfallen als Vorstufe zur Regressionsanalyse anbietet. Die Regressionsanalyse von Umfragedaten wird dann im folgenden Kap. 4.1 behandelt, zunachst diskutieren wir den Sinn von Sekundaranalysen.

Die tiefe Sehnsucht nach Erzahlungen, die die Gewordenheit der vorfindlichen Welt erhellen, kann zumindest von anthropologischen Laien als eine allgemein menschliche Basiseigenschaft angesehen werden. Davon zeugen vielfaltige Befunde der oral history oder Schopfungsgeschichten wie die beiden alttestamentarischen in 1. Mose 1 und 1. Mose 2 oder die nordische Gylfaginning. Sie geben mehr oder weniger befriedigende Antworten auf Fragen wie: Warum ist uberhaupt etwas und nicht Nichts? Welchen Sinn und welche Ursache hat das Bose, haben die Schattenseiten der conditio humana?

Das folgende Kapitel uber die lineare OLS-Regression setzt basale Kenntnisse voraus, die ublicherweise ein Gegenstand von Einfuhrungsbuchern sind, welche in Lehrveranstaltungen der Bachelor-Studiengange verwendet werden. Wir gehen deshalb nicht noch einmal auf die Darstellung der Regressionsgleichung und die Streuungszerlegung in einem bivariaten Streudiagramm ein und konzentrieren uns stattdessen auf die multiple Regression. Wer die Grundlagen noch einmal auffrischen will, findet eine kompakte Darstellung in Gehring und Weins.

The emergent field of digital pathology employing automated image analysis techniques is to revolutionize traditional pathology at the center of clinical diagnostics. Histological images provide important tumor features unavailable in molecular profiling or omics data- the spatial context of tumor and stromal cells at single-cell resolution. Methods to map the spatial and morphological patterns of cancer and normal cells can contribute to a more comprehensive understanding of the highly heterogeneous tumor microenvironment. This review focuses on methods that help expand our knowledge of intra-tumoral spatial heterogeneity of the tumor microenvironment and their potential synergies with molecular profiling technologies.

Multiple regulatory elements distant from their targets on the linear genome can influence the expression of a single gene through chromatin looping. Chromosome conformation capture implemented in Hi-C allows for genome-wide agnostic characterization of chromatin contacts. However, detection of functional enhancer–promoter interactions is precluded by its effective resolution that is determined by both restriction fragmentation and sensitivity of the experiment. Here we develop a capture Hi-C (cHi-C) approach to allow an agnostic characterization of these physical interactions on a genome-wide scale. Single-nucleotide polymorphisms associated with complex diseases often reside within regulatory elements and exert effects through long-range regulation of gene expression. Applying this cHi-C approach to 14 colorectal cancer risk loci allows us to identify key long-range chromatin interactions in cis and trans involving these loci., Multiple regulatory elements distant from their targets on the linear genome can influence gene expression through chromatin looping. Here, the authors report an improved chromosome conformation capture approach that can be used to identify long-range chromatin interactions in cancer risk loci.

Microscopes have been used for a long time to observe biological samples. However, measurements of tissue- and cell-related parameters were conducted by human observers and were consequently ad hoc, not reproducible and restricted to small sample numbers. Since computers have become vastly more powerful, life sciences now routinely take advantage of new opportunities to couple microscopy and in silico methods. Automated image segmentation and analysis of large numbers of digital images allow algorithmic recognition of cell and tissue structures and subsequent numeric measurements of cellular parameters. Nevertheless, these new methods also come with technical challenges concerning computational resources like processing capacity, memory and disk space, biological sensor limitations, as well as algorithm development.

Analyzing in-situ tissue structures with complex shapes and textures such as multinuclear cells or cells without nuclei is still a challenge for currently available imageprocessing software. This work aims to provide a versatile system to solve such tasks provided that the structures of interests were detected by immunofluorescence microscopy. Images were automatically acquired using slide-based microscopy. Human domain-experts manually marked up tissue samples to evaluate the performance of the computer generated masks. From precision and recall a balanced F-score was computed to measure the correlation between experts and algorithm output. Exhaustive parameter optimization was conducted to ensure that the optimal input parameters were applied during evaluation of the developed algorithm. This procedure significantly increased the performance compared to manually chosen input parameters. We present an approach that can handle huge tissue areas and does not rely on nuclei detection. Once a markup has been created, the algorithm can be parameter-optimized on ground-truth data for the chosen tissue sample. Thereafter, the resulting settings could be applied automatically to the respective stitched image. Concluding, we provide new insights in physiological and pathopysiological cellular mechanisms by automating the in-situ analysis of proteins in intact tissues.