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Managing patients with kidney allografts largely depends on biopsy diagnosis which is based on semiquantitative assessments of rejection features and extent of acute and chronic changes within the renal parenchyma. Current methods lack reproducibility while digital image data-driven computational models enable comprehensive and quantitative assays. In this study we aimed to develop a computational method for automated assessment of histopathology transformations within the tubulointerstitial compartment of the renal cortex. Whole slide images of modified Picrosirius red-stained biopsy slides were used for the training (n = 852) and both internal (n = 172) and external (n = 94) tests datasets. The pipeline utilizes deep learning segmentations of renal tubules, interstitium, and peritubular capillaries from which morphometry features were extracted. Seven indicators were selected for exploring the intrinsic spatial interactions within the tubulointerstitial compartment. A principal component analysis revealed two independent factors which can be interpreted as representing chronic and acute tubulointerstitial injury. A K-means clustering classified biopsies according to potential phenotypes of combined acute and chronic transformations of various degrees. We conclude that multivariate analyses of tubulointerstitial morphometry transformations enable extraction of and quantification of acute and chronic components of injury. The method is developed for renal allograft biopsies; however, the principle can be applied more broadly for kidney pathology assessment.

Our study included 41 patients fulfilling the Milan criteria preoperatively and aimed to identify individuals at high risk of post-resection HCC relapse, which occurred in 18 out of 41 patients (43.9%), retrospectively. We analyzed whole slide images of CD8 immunohistochemistry with automated segmentation of tissue classes and detection of CD8+ lymphocytes. The image analysis outputs were subsampled using a hexagonal grid-based method to assess spatial distribution of CD8+ lymphocytes with regards to the epithelial edges. The CD8+ lymphocyte density indicators, along with clinical, radiological, post-surgical and pathological variables, were tested to predict HCC relapse. Low standard deviation of CD8+ density along the tumor edge and R1 resection emerged as independent predictors of shorter recurrence-free survival (RFS). In particular, patients presenting with both adverse predictors exhibited 100% risk of relapse within 200 days. Our results highlight the potential utility of integrating CD8+ density variability and surgical margin to identify a high relapse-risk group among Milan criteria-fulfilling HCC patients. Validation in cohorts with core biopsy could provide CD8+ distribution data preoperatively and guide preoperative decisions, potentially prioritizing liver transplantation for patients at risk of incomplete resection (R1) and thereby improving overall treatment outcomes significantly.

A recurring issue with microstructure studies is specimen lighting. In particular, microscope lighting must be deployed in such a way as to highlight biological elements without enhancing caustic effects and diffraction. We describe here a high frequency technique due to address this lighting issue. First, an extensive study is undertaken concerning asymptotic equations in order to identify the most promising algorithm for 3D microstructure analysis. Ultimately, models based on virtual light rays are discarded in favor of a model that considers the joint computation of phase and irradiance. This paper maintains the essential goal of the study concerning biological microstructures but offers several supplementary notes on computational details which provide perspectives on analyses of the arrangements of numerous objects in biological tissues.

Ki67 has potential clinical importance in breast cancer but has yet to see broad acceptance due to inter-laboratory variability. Here we tested an open source and calibrated automated digital image analysis (DIA) platform to: (i) investigate the comparability of Ki67 measurement across corresponding core biopsy and resection specimen cases, and (ii) assess section to section differences in Ki67 scoring. Two sets of 60 previously stained slides containing 30 core-cut biopsy and 30 corresponding resection specimens from 30 estrogen receptor-positive breast cancer patients were sent to 17 participating labs for automated assessment of average Ki67 expression. The blocks were centrally cut and immunohistochemically (IHC) stained for Ki67 (MIB-1 antibody). The QuPath platform was used to evaluate tumoral Ki67 expression. Calibration of the DIA method was performed as in published studies. A guideline for building an automated Ki67 scoring algorithm was sent to participating labs. Very high correlation and no systematic error (p = 0.08) was found between consecutive Ki67 IHC sections. Ki67 scores were higher for core biopsy slides compared to paired whole sections from resections (p ≤ 0.001; median difference: 5.31%). The systematic discrepancy between core biopsy and corresponding whole sections was likely due to pre-analytical factors (tissue handling, fixation). Therefore, Ki67 IHC should be tested on core biopsy samples to best reflect the biological status of the tumor.

Assessment of intratumoral heterogeneity and tumor-host interaction within the tumor microenvironment is becoming increasingly important for innovative cancer therapy decisions because of the unique information it can generate about the state of the disease. However, its assessment and quantification are limited by ambiguous definitions of the tumor-host interface and by human cognitive capacity in current pathology practice. Advances in machine learning and artificial intelligence have opened the field of digital pathology to novel tissue image analytics and feature extraction for generation of high-capacity computational disease management models. A particular benefit is expected from machine-learning applications that can perform extraction and quantification of subvisual features of both intratumoral heterogeneity and tumor microenvironment aspects. These methods generate information about cancer cell subpopulation heterogeneity, potential tumor-host interactions, and tissue microarchitecture, derived from morphologically resolved content using both explicit and implicit features. Several studies have achieved promising diagnostic, prognostic, and predictive artificial intelligence models that often outperform current clinical and pathology criteria. However, further effort is needed for clinical adoption of such methods through development of standardizable high-capacity workflows and proper validation studies.

Intranuclear birefringent inclusions (IBI) found in various cell types in paraffin-embedded tissue sections have long been considered to be a tissue processing artifact, although an association with biological processes has been suggested. We applied polychromatic polarization microscopy to image their spatial organization. Our study provides evidence that IBI are caused by liquid paraffin-macromolecular crystals formed during paraffin-embedding procedures within cells and potentially reflect an active transcriptional status.

IntroductionBreast cancer (BC) presents diverse malignancies with varying biological and clinical behaviors, driven by an interplay between cancer cells and tumor microenvironment. Deciphering these interactions is crucial for personalized diagnostics and treatment. This study explores the prognostic impact of tumor proliferation and immune response patterns, assessed by computational pathology indicators, on breast cancer-specific survival (BCSS) models in estrogen receptor-positive HER2-negative (ER+HER2-) and triple-negative BC (TNBC) patients.Materials and methodsWhole-slide images of tumor surgical excision samples from 252 ER+HER2- patients and 63 TNBC patients stained for estrogen and progesterone receptors, Ki67, HER2, and CD8 were analyzed. Digital image analysis (DIA) was performed for tumor tissue segmentation and quantification of immunohistochemistry (IHC) markers; the DIA outputs were subsampled by hexagonal grids to assess the spatial distributions of Ki67-positive tumor cells and CD8-positive (CD8+) cell infiltrates, expressed as Ki67-entropy and CD8-immunogradient indicators, respectively. Prognostic models for BCSS were generated using multivariable Cox regression analysis, integrating clinicopathological and computational IHC indicators.ResultsIn the ER+HER2- BC, multivariable Cox regression revealed that high CD8+ density within the tumor interface zone (IZ) (HR: 0.26, p = 0.0056), low immunodrop indicator of CD8+ density (HR: 2.93, p = 0.0051), and low Ki67-entropy (HR: 5.95, p = 0.0.0061) were independent predictors of better BCSS, while lymph node involvement predicted worse BCSS (HR: 3.30, p = 0.0013). In TNBC, increased CD8+ density in the IZ stroma (HR: 0.19, p = 0.0119) and Ki67-entropy (HR: 3.31, p = 0.0250) were independent predictors of worse BCSS. Combining these independent indicators enhanced prognostic stratification in both BC subtypes.ConclusionsComputational biomarkers, representing spatial properties of the tumor proliferation and immune cell infiltrates, provided independent prognostic information beyond conventional IHC markers in BC. Integrating Ki67-entropy and CD8-immunogradient indicators into prognostic models can improve patient stratification with regard to BCSS.

Abstract A recurring issue with microstructure studies is specimen lighting. In particular, microscope lighting must be deployed in such a way as to highlight biological elements without enhancing caustic effects and diffraction. We describe here a high frequency technique due to address this lighting issue. First, an extensive study is undertaken concerning asymptotic equations in order to identify the most promising algorithm for 3D microstructure analysis. Ultimately, models based on virtual light rays are discarded in favor of a model that considers the joint computation of phase and irradiance. This paper maintains the essential goal of the study concerning biological microstructures but offers several supplementary notes on computational details which provide perspectives on analyses of the arrangements of numerous objects in biological tissues.

The limited reproducibility of the grading of non-muscle invasive papillary urothelial carcinoma (NMIPUC) necessitates the search for more robust image-based predictive factors. In a cohort of 157 NMIPUC patients treated with Bacille Calmette–Guérin (BCG) immunotherapy, we explored the multiple instance learning (MIL)-based classification approach for the prediction of 2-year and 5-year relapse-free survival and the multiple instance survival learning (MISL) framework for survival regression. We used features extracted from image patches sampled from whole slide images of hematoxylin–eosin-stained transurethral resection (TUR) NPMIPUC specimens and tested several patch sampling and feature extraction network variations to optimize the model performance. We selected the model showing the best patient survival stratification for further testing in the context of clinical and pathological variables. MISL with the multiresolution patch sampling technique achieved the best patient risk stratification (concordance index = 0.574, p = 0.010), followed by a 2-year MIL classification. The best-selected model revealed an independent prognostic value in the context of other clinical and pathologic variables (tumor stage, grade, and presence of tumor on the repeated TUR) with statistically significant patient risk stratification. Our findings suggest that MISL-based predictions can improve NMIPUC patient risk stratification, while validation studies are needed to test the generalizability of our models.

IntroductionCharacterization of the tumour immune infiltrate (notably CD8+ T-cells) has strong predictive survival value for cancer patients. Quantification of CD8 T-cells alone cannot determine antigenic experience, as not all infiltrating T-cells recognize tumour antigens. Activated tumour-specific tissue resident memory CD8 T-cells (TRM) can be defined by the co-express of CD103, CD39 and CD8. We investigated the hypothesis that the abundance and localization of TRM provides a higher-resolution route to patient stratification.MethodsA comprehensive series of 1000 colorectal cancer (CRC) were arrayed on a tissue microarray, with representative cores from three tumour locations and the adjacent normal mucosa. Using multiplex immunohistochemistry we quantified and determined the localization of TRM.ResultsAcross all patients, activated TRM were an independent predictor of survival, and superior to CD8 alone. Patients with the best survival had immune-hot tumours heavily infiltrated throughout with activated TRM. Interestingly, differences between right- and left-sided tumours were apparent. In left-sided CRC, only the presence of activated TRM (and not CD8 alone) was prognostically significant. Patients with low numbers of activated TRM cells had a poor prognosis even with high CD8 T-cell infiltration. In contrast, in right-sided CRC, high CD8 T-cell infiltration with low numbers of activated TRM was a good prognosis.ConclusionThe presence of high intra-tumoural CD8 T-cells alone is not a predictor of survival in left-sided CRC and potentially risks under treatment of patients. Measuring both high tumour-associated TRM and total CD8 T-cells in left-sided disease has the potential to minimize current under-treatment of patients. The challenge will be to design immunotherapies, for left-sided CRC patients with high CD8 T-cells and low activate TRM,that result in effective immune responses and thereby improve patient survival.

Introduction The diagnosis of glomerular diseases is primarily based on visual assessment of histologic patterns. Semi-quantitative scoring of active and chronic lesions is often required to assess individual characteristics of the disease. Reproducibility of the visual scoring systems remains debatable, while digital and machine-learning technologies present opportunities to detect, classify and quantify glomerular lesions, also considering their inter- and intraglomerular heterogeneity. Materials and methods: We performed a cross-validated comparison of three modifications of a convolutional neural network (CNN)-based approach for recognition and intraglomerular quantification of nine main glomerular patterns of injury. Reference values provided by two nephropathologists were used for validation. For each glomerular image, visual attention heatmaps were generated with a probability of class attribution for further intraglomerular quantification. The quality of classifier-produced heatmaps was evaluated by intersection over union metrics (IoU) between predicted and ground truth localization heatmaps. Results: A proposed spatially guided modification of the CNN classifier achieved the highest glomerular pattern classification accuracies, with area under curve (AUC) values up to 0.981. With regards to heatmap overlap area and intraglomerular pattern quantification, the spatially guided classifier achieved a significantly higher generalized mean IoU value compared to single-multiclass and multiple-binary classifiers. Conclusions: We propose a spatially guided CNN classifier that in our experiments reveals the potential to achieve high accuracy for the localization of intraglomerular patterns.

Abstract Within the tumor microenvironment, specifically aligned collagen has been shown to stimulate tumor progression by directing the migration of metastatic cells along its structural framework. Tumor-associated collagen signatures (TACS) have been linked to breast cancer patient outcome. Robust and affordable methods for assessing biological information contained in collagen architecture need to be developed. We have developed a novel artificial neural network (ANN) based approach for tumor collagen segmentation from bright-field histology images and have tested it on a set of tissue microarray sections from early hormone receptor-positive invasive ductal breast carcinoma stained with Sirius Red (1 core per patient, n = 92). We designed and trained ANNs on sets of differently annotated image patches to segment collagen fibers and extracted 37 features of collagen fiber morphometry, density, orientation, texture, and fractal characteristics in the entire cohort. Independent instances of ANN models trained on highly differing annotations produced reasonably concordant collagen segmentation masks and allowed reliable prognostic Cox regression models (with likelihood ratios 14.11–22.99, at p-value

Introduction: Breast cancer (BC) presents diverse malignancies with varying biological and clinical behaviors, driven by an interplay between cancer cells and tumor microenvironment. Deciphering these interactions is crucial for personalized diagnostics and treatment. This study explores the prognostic impact of tumor proliferation and immune response patterns, assessed by computational pathology indicators, on breast cancer-specific survival (BCSS) models in estrogen receptor-positive HER2-negative (ER+HER2–) and triple-negative BC (TNBC) patients. Materials and methods: Whole-slide images of tumor surgical excision samples from 252 ER+HER2– patients and 63 TNBC patients stained for estrogen and progesterone receptors, Ki67, HER2, and CD8 were analyzed. Digital image analysis (DIA) was performed for tumor tissue segmentation and quantification of immunohistochemistry (IHC) markers; the DIA outputs were subsampled by hexagonal grids to assess the spatial distributions of Ki67-positive tumor cells and CD8-positive (CD8+) cell infiltrates, expressed as Ki67-entropy and CD8-immunogradient indicators, respectively. Prognostic models for BCSS were generated using multivariable Cox regression analysis, integrating clinicopathological and computational IHC indicators. Results: In the ER+HER2– BC, multivariable Cox regression revealed that high CD8+ density within the tumor interface zone (IZ) (HR: 0.26, p = 0.0056), low immunodrop indicator of CD8+ density (HR: 2.93, p = 0.0051), and low Ki67-entropy (HR: 5.95, p = 0.0.0061) were independent predictors of better BCSS, while lymph node involvement predicted worse BCSS (HR: 3.30, p = 0.0013). In TNBC, increased CD8+ density in the IZ stroma (HR: 0.19, p = 0.0119) and Ki67-entropy (HR: 3.31, p = 0.0250) were independent predictors of worse BCSS. Combining these independent indicators enhanced prognostic stratification in both BC subtypes. Conclusions: Computational biomarkers, representing spatial properties of the tumor proliferation and immune cell infiltrates, provided independent prognostic information beyond conventional IHC markers in BC. Integrating Ki67-entropy and CD8-immunogradient indicators into prognostic models can improve patient stratification with regard to BCSS. [ABSTRACT FROM AUTHOR]

What will be the next disruptive technology that will change pathology's routine practice again? In this editorial we make a case for the need of more complex biomarkers in oncology diagnostics, to match the inherent complexity of cancer biology. This complexity will be achieved by the validation of technology able to generate more meaningful biological datapoints (epitomized in tissue pathology by technologies such as multiplex immunofluorescence) and, more important, by the systematic analysis of multimodal technology outputs with artificial intelligence tools, which is the essence of integrated diagnostics. While describing these processes, the authors highlight the pivotal role that histopathology will play, once again, in yet another transformation in diagnostics. [ABSTRACT FROM AUTHOR]

Acute kidney injury (AKI) is widely recognized as a precursor to the onset or rapid progression of chronic kidney disease (CKD). However, there is currently no effective treatment available for AKI, underscoring the urgent need for the development of new strategies to improve kidney function. Human placental mesenchymal stromal cells (hpMSCs) were isolated from donor placentas, cultured, and characterized with regard to yield, viability, flow cytometry, and potency. To mimic AKI and its progression to CKD in a rat model, a dedicated sensitive non-clinical bilateral kidney ischemia-reperfusion injury (IRI) model was utilized. The experimental group received 3 × 105 hpMSCs into each kidney, while the control group received IRI and saline and the untreated group received IRI only. Urine, serum, and kidney tissue samples were collected over a period of 28 days. The hpMSCs exhibited consistent yields, viability, and expression of mesenchymal lineage markers, and were also shown to suppress T cell proliferation in a dose-dependent manner. To ensure optimal donor selection, manufacturing optimization, and rigorous quality control, the rigorous Good Manufacturing Practice (GMP) conditions were utilized. The results indicated that hpMSCs increased rat survival rates and improved kidney function by decreasing serum creatinine, urea, potassium, and fractionated potassium levels. Furthermore, the study demonstrated that hpMSCs can prevent the initial stages of kidney structural fibrosis and improve kidney function in the early stages by mitigating late interstitial fibrosis and tubular atrophy. Additionally, a robust manufacturing process with consistent technical parameters was established. [ABSTRACT FROM AUTHOR]

Our study included 41 patients fulfilling the Milan criteria preoperatively and aimed to identify individuals at high risk of post-resection HCC relapse, which occurred in 18 out of 41 patients (43.9%), retrospectively. We analyzed whole slide images of CD8 immunohistochemistry with automated segmentation of tissue classes and detection of CD8+ lymphocytes. The image analysis outputs were subsampled using a hexagonal grid-based method to assess spatial distribution of CD8+ lymphocytes with regards to the epithelial edges. The CD8+ lymphocyte density indicators, along with clinical, radiological, post-surgical and pathological variables, were tested to predict HCC relapse. Low standard deviation of CD8+ density along the tumor edge and R1 resection emerged as independent predictors of shorter recurrence-free survival (RFS). In particular, patients presenting with both adverse predictors exhibited 100% risk of relapse within 200 days. Our results highlight the potential utility of integrating CD8+ density variability and surgical margin to identify a high relapse-risk group among Milan criteria-fulfilling HCC patients. Validation in cohorts with core biopsy could provide CD8+ distribution data preoperatively and guide preoperative decisions, potentially prioritizing liver transplantation for patients at risk of incomplete resection (R1) and thereby improving overall treatment outcomes significantly. [ABSTRACT FROM AUTHOR]

Abstract Intranuclear birefringent inclusions (IBI) found in various cell types in paraffin-embedded tissue sections have long been considered to be a tissue processing artifact, although an association with biological processes has been suggested. We applied polychromatic polarization microscopy to image their spatial organization. Our study provides evidence that IBI are caused by liquid paraffin-macromolecular crystals formed during paraffin-embedding procedures within cells and potentially reflect an active transcriptional status.

Acute interstitial nephritis (AIN) has been recently recognized as one of the infrequent kidney involvement phenotypes among adult patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Although SARS-CoV-2 associated intrinsic kidney disease has been scarcely reported in children, only one case of AIN temporally associated with the infection has been described in the pediatric population so far. We presented a case of a 12-year old boy who presented with fatigue, anorexia, and polydipsia following an RT-PCR that confirmed SARS-CoV-2 infection seven weeks prior to admission. Initial workup revealed increased serum creatinine (235 μmol/L), glucosuria, low-molecular-weight proteinuria, mild leukocyturia, and microhematuria with hyaline and granular casts on microscopy. Antibodies against the SARS-CoV-2 S protein receptor-binding domain confirmed prior infection with high titers. Kidney biopsy showed diffuse active interstitial nephritis with negative immunofluorescence and positive immunohistochemistry for SARS-CoV-2 in the inflammatory cells within the interstitium. Electron microscopy revealed several SARS-CoV-2-like particles. Kidney function continued to deteriorate despite several days of supportive therapy only (peak serum creatinine 272 μmol/L); thus, treatment with methylprednisolone pulse-dose therapy was initiated and was followed by oral prednisolone with gradual tapering. Kidney function completely recovered after 3 weeks post-discharge and remained normal after 11 weeks of follow-up (last estimated glomerular filtration rate 106 ml/min/1.73 m2) with only residual microhematuria. Our case adds to the emerging evidence of SARS-CoV-2 as a potential etiological agent of AIN in children and also suggests that interstitial kidney injury may result from secondary inflammatory damage. Epidemiological history, serologic testing, and SARS-CoV-2 detection in biopsy should be considered in the work-up of children with AIN of unknown etiology.

Introduction:Alport syndrome (AS) is an inherited disorder characterized by hematuria, proteinuria, and kidney function impairment, and frequently associated with extrarenal manifestations. Pathogenic variants in COL4A5 usually cause X-linked Alport syndrome (XLAS), whereas those in the COL4A3 or COL4A4 genes are associated with autosomal dominant (AD) or recessive (AR) inheritance. To date, more than 3000 different disease-causing variants in COL4A5, COL4A3, and COL4A4 have been identified. The aim of this study was to evaluate the clinical and genetic spectrum of individuals with novel, pathogenic or likely pathogenic variants in the COL4A3-A5 genes in a previously unstudied cohort.MethodsIn this study molecular analysis by next generation sequencing (NGS) was performed on individuals from a Lithuanian cohort, with suspected AS. The presence of AS was assessed by reviewing clinical evidence of hematuria, proteinuria, chronic kidney disease (CKD), kidney failure (KF), a family history of AS or persistent hematuria, and specific histological lesions in the kidney biopsy such as thinning or lamellation of the glomerular basement membrane (GBM). Clinical, genetic, laboratory, and pathology data were reviewed. The novelty of the COL4A3-A5 variants was confirmed in the genetic variant databases (Centogene, Franklin, ClinVar, Varsome, InterVar). Only undescribed variants were included in this study.ResultsMolecular testing of 171 suspected individuals led to the detection of 99 individuals with 44 disease causing variants including 27, previously undescribed changes, with the frequency of 9/27 (33,3%) in genes COL4A5, COL4A3 and COL4A4 equally. Three individuals were determined as having digenic AS causing variants: one in COL4A3 and COL4A4, two in COL4A4 and COL4A5. The most prevalent alterations in genes COL4A3-5 were missense variants (n = 19), while splice site, frameshift, unknown variant and stop codon changes were detected more in genes COL4A4 and COL4A5 and accounted for 3, 3, 1 and 1 of all novel variants, respectively.ConclusionGenotype-phenotype correlation analysis suggested that some variants demonstrated intra-familial phenotypic variability. These novel variants represented more than half of all the variants found in a cohort of 171 individuals from 109 unrelated families who underwent testing. Our study expands the knowledge of the genetic and phenotypic spectrum for AS.

Resumo Fundamento A razão neutrófilo-linfócito (RNL) tem sido proposta como um marcador inflamatório possivelmente associado a aterosclerose coronariana, embora a maioria dos dados atuais seja restrita à fase aguda. Além disso, a associação entre a RNL e a aterosclerose extracoronariana ainda não está clara. Objetivo Analisar a associação entre a RNL e aterosclerose da aorta abdominal (AtAA). Métodos Foram incluídos pacientes assintomáticos submetidos a um programa de rastreamento. A AtAA foi avaliada através de ultrassom. Os números absolutos de leucócitos e linfócitos foram utilizados para calcular a RNL. Foi estabelecido um nível de significância estatística de 0,05. Resultados De 36.985 indivíduos (idade: 42±10 anos, 72% homens), foi identificada a presença de AtAA em 7%. Aqueles com AtAA eram mais velhos e tinham maior propensão a serem homens e diabéticos. A presença de AtAA foi associada a RNL aumentada (odds ratio [OR] 1,17; intervalo de confiança de 95% [IC95%] 1,13-1,21). No entanto, a associação deixou de ser significativa quando a análise foi ajustada para os fatores de risco (OR 1,02; IC95% 0,97-1,06), principalmente devido à inclusão da idade no modelo. Quando os neutrófilos e linfócitos foram analisados separadamente, a associação negativa entre os linfócitos e a RNL foi invertida com a inclusão da idade, o que sugere um forte efeito confundidor da idade na relação entre linfócitos e aterosclerose. Por fim, a associação entre os neutrófilos e a AtAA deixou de ser significativa após o ajuste adicional para os fatores de risco tradicionais, mas não apenas para a idade. Conclusão Embora a RNL tenha se associado a AtAA, foi principalmente devido ao efeito confundidor da idade. No geral, os resultados sugerem um papel limitado da contagem de leucócitos como biomarcador de AtAA.

Breast cancer (BC) categorized as human epidermal growth factor receptor 2 (HER2) borderline [2+ by immunohistochemistry (IHC 2+)] presents challenges for the testing, frequently obscured by intratumoral heterogeneity (ITH). This leads to difficulties in therapy decisions. We aimed to establish prognostic models of overall survival (OS) of these patients, which take into account spatial aspects of ITH and tumor microenvironment by using hexagonal tiling analytics of digital image analysis (DIA). In particular, we assessed the prognostic value of Immunogradient indicators at the tumor–stroma interface zone (IZ) as a feature of antitumor immune response. Surgical excision samples stained for estrogen receptor (ER), progesterone receptor (PR), Ki67, HER2, and CD8 from 275 patients with HER2 IHC 2+ invasive ductal BC were used in the study. DIA outputs were subsampled by HexT for ITH quantification and tumor microenvironment extraction for Immunogradient indicators. Multiple Cox regression revealed HER2 membrane completeness (HER2 MC) (HR: 0.18, p = 0.0007), its spatial entropy (HR: 0.37, p = 0.0341), and ER contrast (HR: 0.21, p = 0.0449) as independent predictors of better OS, with worse OS predicted by pT status (HR: 6.04, p = 0.0014) in the HER2 non-amplified patients. In the HER2-amplified patients, HER2 MC contrast (HR: 0.35, p = 0.0367) and CEP17 copy number (HR: 0.19, p = 0.0035) were independent predictors of better OS along with worse OS predicted by pN status (HR: 4.75, p = 0.0018). In the non-amplified tumors, three Immunogradient indicators provided the independent prognostic value: CD8 density in the tumor aspect of the IZ and CD8 center of mass were associated with better OS (HR: 0.23, p = 0.0079 and 0.14, p = 0.0014, respectively), and CD8 density variance along the tumor edge predicted worse OS (HR: 9.45, p = 0.0002). Combining these three computational indicators of the CD8 cell spatial distribution within the tumor microenvironment augmented prognostic stratification of the patients. In the HER2-amplified group, CD8 cell density in the tumor aspect of the IZ was the only independent immune response feature to predict better OS (HR: 0.22, p = 0.0047). In conclusion, we present novel prognostic models, based on computational ITH and Immunogradient indicators of the IHC biomarkers, in HER2 IHC 2+ BC patients.

Immunohistochemistry (IHC) for ER, PR, HER2, and Ki67 is used to predict outcome and therapy response in breast cancer patients. The current IHC assessment, visual or digital, is based mostly on global biomarker expression levels in the tissue sample. In our study, we explored the prognostic value of digital image analysis of conventional breast cancer IHC biomarkers supplemented with their intratumoral heterogeneity and tissue immune response indicators. Surgically excised tumor samples from 101 female patients with hormone receptor-positive breast cancer (HRBC) were stained for ER, PR, HER2, Ki67, SATB1, CD8, and scanned at 20x. Digital image analysis was performed using the HALO™ platform. Subsequently, hexagonal tiling was used to compute intratumoral heterogeneity indicators for ER, PR and Ki67 expression. Multiple Cox regression analysis revealed three independent predictors of the patient's overall survival: Haralick's texture entropy of PR (HR = 0.19, p = 0.0005), Ki67 Ashman's D bimodality (HR = 3.0, p = 0.01), and CD8+SATB1+ cell density in tumor tissue (HR = 0.32, p = 0.02). Remarkably, the PR and Ki67 intratumoral heterogeneity indicators were prognostically more informative than the rates of their expression. In particular, a distinct non-linear relationship between the rate of PR expression and its intratumoral heterogeneity was observed and revealed a non-linear prognostic effect of PR expression. The independent prognostic significance of CD8+SATB1+ cells infiltrating the tumor could indicate their role in anti-tumor immunity. In conclusion, we suggest that prognostic modeling, based entirely on the computational image-based IHC biomarkers, is possible in HRBC patients. The intratumoral heterogeneity and immune response indicators outperformed both conventional breast cancer IHC and clinicopathological variables while markedly increasing the power of the model.

The data presented in this article is part of the whole slide imaging (WSI) datasets generated in European project AIDPATH 2 This data is also related to the research paper entitle “Glomerulosclerosis Identification in Whole Slide Images using Semantic Segmentation”, published in Computer Methods and Programs in Biomedicine Journal [1]. In that article, different methods based on deep learning for glomeruli segmentation and their classification into normal and sclerotic glomerulous are presented and discussed. The raw data used is described and provided here. In addition, the detected glomeruli are also provided as individual image files. These data will encourage research on artificial intelligence (AI) methods, create and compare fresh algorithms, and measure their usability in quantitative nephropathology. Keywords: Glomeruli identification, Normal glomerulus, Global sclerotic glomerulus, Whole slide image, Digital pathology

ABSTRACT Objective We sought to investigate the impact of self-reported fasting duration times on the lipid profile results and its impact on the cardiovascular risk stratification and metabolic syndrome diagnosis. Subjects and methods We analyzed data from all consecutive individuals evaluated in a comprehensive health examination at the Hospital Israelita Albert Einstein from January to December 2015. We divided these patients in three groups, according to the fasting duration recalled (< 8h, 8-12h and > 12h). We calculated the global cardiovascular risk and diagnosed metabolic syndrome according to the current criteria and estimated their change according to fasting duration. Results A total of 12,196 (42.3 ± 9.2 years-old, 30.2% females) patients were evaluated. The distribution of cardiovascular risk was not different among groups defined by fasting duration in both men and women (p = 0.547 for women and p = 0.329 for men). Similarly, the prevalence of metabolic syndrome was not influenced by the fasting duration (p = 0.431 for women and p = 0.166 for men). Conclusion Self-reported fasting duration had no significant impact on the lipid profile results, including triglyceride levels. Consequently, no changes on the cardiovascular risk stratification using the Framingham risk score nor changes on the prevalence of metabolic syndrome were noted.