Digital Image Analysis Method Research Articles

Effect of Calcium Carbide Residue on Shrinkage Mechanism of Clayey Subgrade Soil

ABSTRACT Calcium carbide residue (CCR) has been identified as an effective replacement for traditional lime for the stabilization of expansive soils. The shrinkage of expansive clay severely threatens the foundation layers’ long-term stability. The efficacy of CCR stabilization in reducing compressibility and enhancing the strength and stiffness of clay has been widely reported; however, the calcium-based additive stabilized soil tends to lose its engineered integrity under field-exposed seasonal moisture variation, specifically, shrinkage induced by prolonged drying. In this study, the shrinkage characteristic of CCR-treated clay (6%, 9%, and 12%) slurry was investigated by digital image analysis and mercury displacement method to quantify volumetric shrinkage strain. The results were compared with those of 4% and 6% lime-treated clay, which revealed the higher efficacy of CCR in reducing shrinkage strain by 47–57%. A characteristic shrinkage curve that depicted the significant variation of shrinkage phases in CCR-treated clay samples is developed. The dominance of the proportional shrinkage phase is observed in both untreated and treated clay samples. The shrinkage caused by evaporation, hydration, and decalcification is postulated in treated clays. This study is expected to contribute to the understanding of shrinkage mechanisms of CCR-treated clay and assist in the future design of stabilized embankment sections or subgrade for unpaved roads.

A novel intelligent stereo vision approach for blast-induced fragmentation size distribution: Case study at Golgohar open-pit mine, Iran

Rock fragmentation assessment is a critical aspect of evaluating blasting operations in the mining industry, offering insights for optimising procedures and subsequent mining processes. Traditional methods, such as sieving, while accurate, are costly and time-consuming. Digital image analysis methods have emerged as alternatives, yet they face challenges in accurately handling fragments of varying sizes. Recent advancements in computer vision, particularly deep learning techniques, offer promising solutions to automate and refine fragmentation analysis. This paper introduces a novel intelligent stereo vision approach utilising YOLOv8, an advanced deep learning model, for blast-induced fragmentation size distribution assessment at the Golgohar open-pit mine in Iran. By training YOLOv8 with an automated self-labelled dataset and evaluating its performance against real mine site data, this research aims to enhance the accuracy and efficiency of blast fragmentation analysis. The proposed method leverages stereo images and machine learning to provide precise measurements, leading to improved optimisation of blasting parameters and potential economic benefits compared to traditional methods. Through rigorous validation processes and case studies across diverse scenarios, the proposed approach demonstrates robustness and reliability in assessing blast fragmentation, offering a practical and efficient solution for the mining industry’s evolving needs.

The Ki67 dilemma: investigating prognostic cut-offs and reproducibility for automated Ki67 scoring in breast cancer

PurposeQuantification of Ki67 in breast cancer is a well-established prognostic and predictive marker, but inter-laboratory variability has hampered its clinical usefulness. This study compares the prognostic value and reproducibility of Ki67 scoring using four automated, digital image analysis (DIA) methods and two manual methods.MethodsThe study cohort consisted of 367 patients diagnosed between 1990 and 2004, with hormone receptor positive, HER2 negative, lymph node negative breast cancer. Manual scoring of Ki67 was performed using predefined criteria. DIA Ki67 scoring was performed using QuPath and Visiopharm® platforms. Reproducibility was assessed by the intraclass correlation coefficient (ICC). ROC curve survival analysis identified optimal cutoff values in addition to recommendations by the International Ki67 Working Group and Norwegian Guidelines. Kaplan–Meier curves, log-rank test and Cox regression analysis assessed the association between Ki67 scoring and distant metastasis (DM) free survival.ResultsThe manual hotspot and global scoring methods showed good agreement when compared to their counterpart DIA methods (ICC > 0.780), and good to excellent agreement between different DIA hotspot scoring platforms (ICC 0.781–0.906). Different Ki67 cutoffs demonstrate significant DM-free survival (p < 0.05). DIA scoring had greater prognostic value for DM-free survival using a 14% cutoff (HR 3.054–4.077) than manual scoring (HR 2.012–2.056). The use of a single cutoff for all scoring methods affected the distribution of prediction outcomes (e.g. false positives and negatives).ConclusionThis study demonstrates that DIA scoring of Ki67 is superior to manual methods, but further study is required to standardize automated, DIA scoring and definition of a clinical cut-off.

Multiphase electroextraction of malachite green from surface water and its determination using digital imaging and chemometric tools.

This study introduces a novel method for the quantification of malachite green (MG), a pervasive cationic dye, in surface water by synergizing multiphase electroextraction (MPEE) with digital image analysis (DIA) and partial least square discriminant analysis. Aimed at addressing the limitations of conventional DIA methods in terms of quantitation limits and selectivity, this study achieves a significant breakthrough in the preconcentration of MG using magnesium silicate as a novel sorbent. Demonstrating exceptional processing efficiency, the method allows for the analysis of 10 samples within 20min, exhibiting remarkable sensitivity and specificity (over 0.95 and 0.90, respectively) across 156 samples in both training and test sets. Notably, the method detects MG at low concentrations (0.2µgL-1) in complex matrices, highlighting its potential for broader application in environmental monitoring. This approach not only underscores the method's cost-effectiveness and simplicity but also its precision, making it a valuable tool for the preliminary testing of MG in surface waters. This study underscores the synergy among MPEE, DIA, and chemometric tools, presenting a cost-efficient and reliable alternative for the sensitive detection of water contaminants.

Intratumoral heterogeneity of Ki67 proliferation index outperforms conventional immunohistochemistry prognostic factors in estrogen receptor-positive HER2-negative breast cancer.

In breast cancer (BC), pathologists visually score ER, PR, HER2, and Ki67 biomarkers to assess tumor properties and predict patient outcomes. This does not systematically account for intratumoral heterogeneity (ITH) which has been reported to provide prognostic value. This study utilized digital image analysis (DIA) and computational pathology methods to investigate the prognostic value of ITH indicators in ER-positive (ER+) HER2-negative (HER2-) BC patients. Whole slide images (WSIs) of surgically excised specimens stained for ER, PR, Ki67, and HER2 from 254 patients were used. DIA with tumor tissue segmentation and detection of biomarker-positive cells was performed. The DIA-generated data were subsampled by a hexagonal grid to compute Haralick's texture indicators for ER, PR, and Ki67. Cox regression analyses were performed to assess the prognostic significance of the immunohistochemistry (IHC) and ITH indicators in the context of clinicopathologic variables. In multivariable analysis, the ITH of Ki67-positive cells, measured by Haralick's texture entropy, emerged as an independent predictor of worse BC-specific survival (BCSS) (hazard ratio (HR) = 2.64, p-value = 0.0049), along with lymph node involvement (HR = 2.26, p-value = 0.0195). Remarkably, the entropy representing the spatial disarrangement of tumor proliferation outperformed the proliferation rate per se established either by pathology reports or DIA. We conclude that the Ki67 entropy indicator enables a more comprehensive risk assessment with regard to BCSS, especially in cases with borderline Ki67 proliferation rates. The study further demonstrates the benefits of high-capacity DIA-generated data for quantifying the essentially subvisual ITH properties.

Comparative Study of Ki-67 Labeling Index Quantification by Eye-rolling, Manual Count, and Digital Image Analysis; An Approach with Caution.

An accurate Ki-67 labeling index assessment is critical for managing a few tumors, like breast carcinomas and neuroendocrine tumors. We aimed to determine the degree of agreement between digital image analysis (DIA) and eye-rolling assessment (EE) and DIA and manual count (MC) for Ki-67 LI scoring. A total of 120 cases (both tru-cut biopsies and resected specimens) were selected during the study period from the institutional database, wherein the Ki-67 labeling index was performed. The selected cases were divided into two groups, i.e., breast neoplasms and other neoplasms. The correlation between DIA and EE and DIA and MC for Ki-67 LI scoring was calculated in both groups. A total of 113 cases were analyzed for Ki-67 LI by three different methods (EE, MC, and DIA); 7 cases were rejected due to poor image quality. Ki-67 LI scoring by DIA and EE was highly correlated in both study groups with a Spearman's rank correlation coefficient of 0.809 (P=0.01) and 0.904 (P=0.01), respectively. Correlation between DIA and MC methods was also found to be almost perfect in both study groups with a Spearman's rank correlation coefficient of 0.974 (P=0.01) and 0.955 (P=0.01), respectively. ImmunoRatio is a free web-based digital image analysis application that can be used for Ki-67 LI assessment with considerable reliability and reproducibility. Yet, it carries a few limitations and demands a careful approach and final confirmation by an expert.

Overall Survival Prediction by Tumor Microenvironment Lymphocyte Distribution in Hepatocellular Carcinoma After Liver Transplantation

IntroductionOur previous research demonstrated that CD8+ cell density profiling using a hexagonal grid-based digital image analysis method provides predictors of patient outcomes after liver resection due to hepatocellular carcinoma (HCC). Continuing our study, we have further investigated the applicability of the methodology to patients receiving a liver transplant for HCC. MethodsThe retrospective study enrolled patients with HCC who underwent liver transplantation (LT) at the Vilnius University Hospital Santaros Clinics between 2007 and 2020. We determined the density profiles of CD8+ lymphocytes at the interface between HCC and stroma and the interface between the perineoplastic liver parenchyma and stroma. Both digital image analysis and the hexagonal grid-based immunogradient method were applied to CD8+ immunohistochemistry images. Survival statistics based on clinicopathological, peripheral blood analysis, and surgical data determined the prognostic value of these indicators. ResultsUnivariate clinicopathological predictors of worse OS after LT included: patient's age at the time of the transplantation, a higher number of HCC nodules, lower platelet count, longer activated thromboplastin time, lower serum albumin, higher serum total bilirubin, and lower serum creatinine levels. The two independent predictors of overall survival were mean CD8+ cell density at the epithelial edge of the explanted liver parenchyma-stroma interface and peripheral blood platelet count. ConclusionsOur model discloses that preoperative peripheral blood platelet count and mean CD8+ cell density at the epithelial edge of nonmalignant interface in the explanted liver parenchyma are independent predictors of OS for HCC after LT.

Novel Approach in Fracture Characterization of Soft Adhesive Materials Using Spiral Cracking Patterns

A novel approach for the fracture characterization of soft adhesive materials using spiral cracking patterns is presented in this study. This research particularly focuses on hydrocarbon polymeric materials, such as asphalt binders. Ten different asphalt materials with distinct fracture characteristics were investigated. An innovative integrated experimental–computational framework coupling acoustic emissions (AE) approach in conjunction with a machine learning-based Digital Image Analysis (DIA) method was employed to precisely determine the crack geometry and characterize the material fracture behavior. Cylindrical-shaped samples (25 mm in diameter and 20 mm in height) bonded to a rigid substrate were employed as the testing specimens. A cooling rate of −1 °C/min was applied to produce the spiral cracks. Various image processing techniques and machine learning algorithms such as Convolutional Neural Networks (CNNs) and regression were utilized in the DIA to automatically analyze the spiral patterns. A new parameter, “Spiral Cracking Energy (ESpiral)”, was introduced to assess the fracture performance of soft adhesives. The compact tension (CT) test was conducted at −20 °C with a loading rate of 0.2 mm/min to determine the material’s fracture energy (Gf). The embrittlement temperature (TEMB) of the material was measured by performing an AE test. This study explored the relationship between the spiral tightness parameter (“b”), ESpiral, Gf, and TEMB of the material. The findings of this study showed a strong positive correlation between the ESpiral and fracture energies of the asphalt materials. Furthermore, the results indicated that both the spiral tightness parameter (“b”) and the embrittlement temperature (TEMB) were negatively correlated with the ESpiral and Gf parameters.

Enhancing Density Prediction of Agricultural Land Soil through Void Area Curve Analysis

Thresholding is a digital image analysis method used to distinguish objects from the background in images and it is mainly used for void and density analysis in soil. It is important to select an appropriate thresholding method because the accuracy of void analysis can vary significantly depending on the threshold value; however, there is currently no standard for soil density analysis. Therefore, this study proposes an image analysis method for soil density prediction. The experimental process involved collecting soil samples from agricultural lands, encompassing various soil textures including sandy loam, loam, silt loam, and silty clay loam. These samples were then meticulously prepared under controlled conditions, ensuring a comprehensive range of dry densities and water content levels. Digital images of the soil samples were acquired using a Canon EOS100d camera, employing a high-resolution setup that provided precise imaging capabilities. The porosity of the soil image is calculated by various thresholding methods. Based on the analysis results, we propose a void area curve, a new approach that can be applied to the soil density prediction. The void area curve is the relationship curve between the threshold value and porosity of the soil image. The standard deviation of the void area curve showed a high correlation with the dry density of the soil. The standard deviation of the void area curve was used to predict the dry density under various soil texture and water content conditions, and the RMSE was 0.037 t/m3. The method of estimating soil density with the standard deviation of the void area curve can be used more generally than the existing analysis method because there is no need to select a specific threshold value.

A Simple Low-cost Method for Accurate Canopy Density Evaluation of Citrus

The use of canopy density for crop production is a useful tool for evaluating management practices for informed decision-making and predicting crop yields. Traditional methods for analyzing canopy density include expensive equipment that requires advanced software and costly repairs, such as leaf area index analyzers, or equipment that can only be used during optimal weather conditions, such as light meters, that quantify photosynthetically active radiation (PAR), thus making it difficult for researchers to maximize the time needed for field research evaluations and data collection. Digital image analyses using technologically advanced cameras, such as smartphone cameras, have allowed new ways of collecting data without the need to purchase specialized instruments. Using a combination of smartphone cameras and ImageJ software, canopy density can be measured in any weather conditions for a much lower cost than that of traditional equipment. This low-cost, digital image analysis method was compared with traditional PAR measurements for ‘Valencia’ sweet orange [Citrus sinensis (L.) Osb.] trees with varying levels of canopy density. A strong positive correlation between the digital image analysis method and standard canopy density measurement method using PAR measurements (r = 0.79; P&lt;0.0001) was found, indicating that this method can be used as an alternative to the PAR method. The digital image analysis method was also consistent when used during different weather conditions, whereas the PAR method was highly variable when quantifying the canopy when clouds were present in comparison with clear sky conditions. This novel method provides researchers and growers with an easy, flexible, consistent, and low-cost option for analyzing canopy density.

Development of a grain-size determination method based on digital images of muddy sediments in coastal areas

The grain-size distribution is a fundamental parameter for assessment of coastal environments. In coastal areas, grain-size analysis of sediments is hampered by arduous collection of sediments from the seafloor at each sampling site and long analysis time. Although rapid determination methods using digital imaging have been developed over the last two decades, these methods cannot be applied to muddy sediments in coastal areas. In the present study, a digital image analysis method applicable to muddy sediments was developed based on sediment color analysis combined with an autocorrelation algorithm introduced by Rubin (2004). For digital image collection in the laboratory, a waterproof digital camera (16 megapixels) was used; it was placed 9 cm from the sediment surface. The imaging equipment consisted of the digital camera, an acrylic plate, and a housing filled with tap water, which was used to avoid effects of suspended sediment in the seawater. Model sediments were prepared and images of the sediment surfaces were taken. The mud and coarse fraction of the sediment were successfully estimated through binarization of the image based on brightness values and the grain-size distribution of the coarse fraction was determined using the autocorrelation algorithm introduced by Rubin (2004). In the field, the imaging apparatus consisting of a stainless-steel cage, lighting equipment, a housing filled with tap water, and a wide acrylic plate attached to the bottom of the housing provided a sturdy structure for obtaining clear digital images from uneven field sediments with suspended particles in the bottom water. The ship was moved several hundred meters at a time; the imaging apparatus was allowed to settle on the sediments and images were then taken continuously at set intervals. From these images, grain-size distributions of sediments containing >15% mud were successfully estimated. Thus, the digital image analysis method developed here will be suitable for measuring the fraction of surface area covered by mud and the size of the visible coarse fraction, and will be useful for monitoring of various sediment types, including muddy coastal sediments, demonstrating great promise for elucidating benthic ecosystem structures and functions in coastal areas.

Woven geotextile permeability under uniaxial and laterally constrained conditions

Woven slit-film geotextiles are often subjected to in-plane tensile loads in engineering applications, which may alter relevant permeability properties. The fractal model of the permeability coefficient in woven geotextiles is extended to predict the permeability coefficient of geotextiles subjected to uniaxial and laterally constrained uniaxial tensile strains. Based on the observation and summary of the variation of the pore size distribution pattern with tensile strain, the pore unit model is introduced. The model is expressed as the functions of the fractal dimension, pore size characteristics, physical parameters and weft strain. A clamping device capable of applying uniaxial tension and laterally constrained uniaxial tension to geotextiles is invented. The validation of the model is verified using the vertical permeability coefficient test and the digital image analysis method on two selected woven geotextile samples. It is shown that the permeability coefficient increased with increasing uniaxial tensile strain. Furthermore, the experimental values tended to change more significantly under laterally constrained uniaxial strain conditions for thinner geotextiles approaching breaking strain and thicker geotextiles. The improved model can accurately predict the values and increasing rate of the permeability coefficient of woven geotextiles subjected to uniaxial and laterally constrained uniaxial tensile strains.

Prognostic Value of CD8+ Lymphocytes in Hepatocellular Carcinoma and Perineoplastic Parenchyma Assessed by Interface Density Profiles in Liver Resection Samples.

Hepatocellular carcinoma (HCC) often emerges in the setting of long-standing inflammatory liver disease. CD8 lymphocytes are involved in both the antitumoral response and hepatocyte damage in the remaining parenchyma. We investigated the dual role of CD8 lymphocytes by assessing density profiles at the interfaces of both HCC and perineoplastic liver parenchyma with surrounding stroma in whole-slide immunohistochemistry images of surgical resection samples. We applied a hexagonal grid-based digital image analysis method to sample the interface zones and compute the CD8 density profiles within them. The prognostic value of the indicators was explored in the context of clinicopathological, peripheral blood testing, and surgery data. Independent predictors of worse OS were a low standard deviation of CD8+ density along the tumor edge, high mean CD8+ density within the epithelial aspect of the perineoplastic liver-stroma interface, longer duration of surgery, a higher level of aspartate transaminase (AST), and a higher basophil count in the peripheral blood. A combined score, derived from these five independent predictors, enabled risk stratification of the patients into three prognostic categories with a 5-year OS probability of 76%, 40%, and 8%. Independent predictors of longer RFS were stage pT1, shorter duration of surgery, larger tumor size, wider tumor-free margin, and higher mean CD8+ density in the epithelial aspect of the tumor-stroma interface. We conclude that (1) our computational models reveal independent and opposite prognostic impacts of CD8+ cell densities at the interfaces of the malignant and non-malignant epithelium interfaces with the surrounding stroma; and (2) together with pathology, surgery, and laboratory data, comprehensive prognostic models can be constructed to predict patient outcomes after liver resection due to HCC.

Digital Image Analysis of Ki67 Heterogeneity Improves the Diagnosis and Prognosis of Gastroenteropancreatic Neuroendocrine Neoplasms

Ki67 is a reliable grading and prognostic biomarker of gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs). The intratumor heterogeneity of Ki67, correlated with tumor progression, is a valuable factor that requires image analysis. The application of digital image analysis (DIA) enables new approaches for the assessment of Ki67 heterogeneity distribution. We investigated the diagnostic utility of Ki67 heterogeneity parameters in the classification and grading of GEP-NENs and explored their clinical values with regard to their prognostic relevance. The DIA algorithm was performed on whole-slide images of 102 resection samples with Ki67 staining. Good agreement was observed between the manual and DIA methods in the hotspot evaluation (R2 = 0.94, P < .01). Using the grid-based region of interest approach, score-based heat maps provided a distinctive overview of the intratumoral distribution of Ki67 between neuroendocrine carcinomas and neuroendocrine tumors. The computation of heterogeneity parameters related to DIA-determined Ki67 showed that the coefficient of variation and Morisita-Horn index were directly related to the classification and grading of GEP-NENs and provided insights into distinguishing high-grade neuroendocrine neoplasms (grade 3 neuroendocrine tumor vs neuroendocrine carcinoma, P < .01). Our study showed that a high Morisita-Horn index correlated with poor disease-free survival (multivariate analysis: hazard ratio, 56.69), which was found to be the only independent predictor of disease-free survival in patients with GEP-NEN. These spatial biomarkers have an impact on the classification and grading of tumors and highlight the prognostic associations of tumor heterogeneity. Digitization of Ki67 variations provides a direct and objective measurement of tumor heterogeneity and better predicts the biological behavior of GEP-NENs.

Validation of automated positive cell and region detection of immunohistochemically stained laryngeal tumor tissue using digital image analysis

ObjectivesThis study aimed to validate a digital image analysis (DIA) workflow for automatic positive cell detection and positive region delineation for immunohistochemical hypoxia markers with a nuclear (hypoxia-inducible factor 1α [HIF-1α]) and a cytoplasmic (pimonidazole [PIMO]) staining pattern. Materials and methods101 tissue fragments from 44 laryngeal tumor biopsies were immunohistochemically stained for HIF-1α and PIMO. QuPath was used to determine the percentage of positive cells and to delineate positive regions automatically. For HIF-1α, only cells with strong staining were considered positive. Three dedicated head and neck pathologists scored the percentage of positive cells using three categories (0: <1%; 1: 1%–33%; 2: >33%;). The pathologists also delineated the positive regions on 14 corresponding PIMO and HIF-1α-stained fragments. The consensus between observers was used as the reference standard and was compared to the automatic delineation. ResultsAgreement between categorical positivity scores was 76.2% and 65.4% for PIMO and HIF-1α, respectively. In all cases of disagreement in HIF-1α fragments, the DIA underestimated the percentage of positive cells. As for the region detection, the DIA correctly detected most positive regions on PIMO fragments (false positive area=3.1%, false negative area=0.7%). In HIF-1α, the DIA missed some positive regions (false positive area=1.3%, false negative area=9.7%). ConclusionsPositive cell and region detection on biopsy material is feasible, but further optimization is needed before unsupervised use. Validation at varying DAB staining intensities is hampered by lack of reliability of the gold standard (i.e., visual human interpretation). Nevertheless, the DIA method has the potential to be used as a tool to assist pathologists in the analysis of IHC staining.

Enhancing the accuracy of mid-infrared spectroscopy-based liver steatosis quantification using digital image analysis as a reference.

The assessment of liver steatosis is crucial in both hepatology and liver transplantation (LT) surgery. Steatosis can negatively impact the success of LT. Steatosis is a factor for excluding donated organs for LT, but the increasing demand for transplantable organs has led to the use of organs from marginal donors. The current standard for evaluating steatosis is a semi-quantitative grading based on the visual examination of a hematoxylin and eosin (H&E)-stained liver biopsy, but this method is time-consuming, subjective, and lacks reproducibility. Recent research has shown that infrared (IR) spectroscopy could be used as a real-time quantitative tool to assess steatosis during abdominal surgery. However, the development of IR-based methods has been hindered by the lack of appropriate quantitative reference values. In this study, we developed and validated digital image analysis methods for the quantitation of steatosis in H&E-stained liver sections using univariate and multivariate strategies including linear discriminant analysis (LDA), quadratic DA, logistic regression, partial least squares-DA (PLS-DA), and support vector machines. The analysis of 37 tissue samples with varying grades of steatosis demonstrates that digital image analysis provides accurate and reproducible reference values that improve the performance of IR spectroscopic models for steatosis quantification. A PLS model in the 1810-1052 cm-1 region using first derivative ATR-FTIR spectra provided RMSECV = 0.99%. The gained improvement in accuracy critically enhances the applicability of Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) to support an objective graft evaluation at the operation room, which might be especially relevant in cases of marginal liver donors to avoid unnecessary graft explantation.

Comparing magnetic resonance liver fat fraction measurements with histology in fibrosis: the difference between proton density fat fraction and tissue mass fat fraction

ObjectiveMagnetic resonance spectroscopy (MRS) provides a powerful method of measuring fat fraction. However, previous studies have shown that MRS results give lower values compared with visual estimates from biopsies in fibrotic livers. This study investigated these discrepancies and considered whether a tissue water content correction, as assessed by MRI relaxometry, could provide better agreement.Materials and methods110 patients were scanned in a 1.5 T Philips scanner and biopsies were obtained. Multiple echo MRS (30 × 30 × 30 mm volume) was used to determine Proton Density Fat Fraction (PDFF). Biopsies were assessed by visual assessment for fibrosis and steatosis grading. Digital image analysis (DIA) was also used to quantify fat fraction within tissue samples. T1 relaxation times were then used to estimate tissue water content to correct PDFF for confounding factors.ResultsPDFF values across the four visually assessed steatosis grades were significantly less in the higher fibrosis group (F3–F4) compared to the lower fibrosis group (F0–F2). The slope of the linear regression of PDFF vs DIA fat fraction was ~ 1 in the low fibrosis group and 0.77 in the high fibrosis group. Correcting for water content based on T1 increased the gradient but it did not reach unity.DiscussionIn fibrotic livers, PDFF underestimated fat fraction compared to DIA methods. Values were improved by applying a water content correction, but fat fractions were still underestimated.

Whole-slide image analysis identifies a high content of Hodgkin Reed-Sternberg cells and a low content of T lymphocytes in tumor microenvironment as predictors of adverse outcome in patients with classic Hodgkin lymphoma treated with ABVD.

Classic Hodgkin lymphoma (cHL) constitutes the most frequent lymphoma in young adults. Its histopathology is unique as a scattered tumor population, termed Hodgkin Reed-Sternberg (HRS) cells is diluted in a prominent tumor microenvironment (TME) composed of T lymphocytes, B lymphocytes, macrophages, neutrophils, eosinophils and histiocytes. Traditionally, the identification of prognostic biomarkers in the cHL TME has required visual inspection and manual counting by pathologists. The advent of whole-slide imaging (WSI) and digital image analysis methods could significantly contribute to improve this essential objective in cHL research, as a 10-20% of patients are still refractory or relapsed after conventional chemotherapy. In this work, we have digitized a total of 255 diagnostic cHL slides and quantified the proportion of HRS cells (CD30), B cells (CD20) and T cells (CD3) by digital image analysis. Data obtained where then correlated with the overall survival (OS) and progression free survival (PFS) of cHL patients. Quantification of HRS cells, B cells and T cells reflects the biological heterogeneity of the different cHL histological subtypes analyzed. A percentage of 2.00% of HRS cells statistically significantly discriminated between patients achieving a complete metabolic response (CMR) and refractory or relapsed (R/R) patients both for the OS (P=0.001) and PFS (P=0.005). Furthermore, patients with a percentage of T cells below the 26.70% in the TME showed a statistically significantly shorter OS (P=0.019) and PFS (P=0.041) in comparison with patients above this threshold. A subgroup of patients with a low content of T cells and high content of HRS cells exhibited a special aggressive clinical course. Currently, there is the need to implement quantitative and easy scalable methods to enhance clinical translation, as the cHL TME plays a central role in the clinical course of the disease. The results of this study could contribute to the identification of prognostic biomarkers specifically looking at the cHL TME and their inclusion in future clinical trials.

Characteristics of Virus Symptoms in Chili Plants (Capsicum frutescens) Based on RGB Image Analysis

Virus infection in chili plants may cause various symptoms. The complexity of the symptoms and human vision ability often become limiting factors during disease investigations. Digital image analysis is expected to become a method to assist in comprehensively describing the symptoms of plant viruses. A disease survey was conducted on cayenne pepper fields in Southeast Sulawesi Province to observe symptoms of virus infection virtually and to record the symptomatic plant using an RGB camera. The split-channel method is used to process images, followed by multidimensional scaling statistical analysis. Later on, viruses associated with plants were detected serologically. Single or mixed infection of Tobacco mosaic virus, Cucumber mosaic virus, Chili veinal mottle virus, and Pepper mottle virus was confirmed by plant leaves showing yellow-mosaic and mottle symptoms. The digital image analysis method could show variations in the characteristics of symptoms based on digital numbers in that cannot be recognized based on the observation of visual symptoms. A new approach to study the interactions between plant infecting viruses and their effects based on image analysis has also been developed during this research. This method needs to be further validated through testing under controlled conditions, such as inoculating plants with a predetermined type of virus.

Systematically higher Ki67 scores on core biopsy samples compared to corresponding resection specimen in breast cancer: a multi-operator and multi-institutional study

AbstractKi67 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.