Computer-based Image Analysis Research Articles

Advances in sickle cell retinopathy screening techniques, tests, and practices: A systematic review.

Sickle cell retinopathy (SCR) is a progressive, sight-threatening ophthalmic complication of sickle cell disease (SCD). Current SCR screening focuses on the detection of pathologic sea fan neovascularization, the first sign of proliferative sickle cell retinopathy (PSR). If untreated, PSR can lead to severe visual impairment and blindness through progression to vitreous hemorrhage and/or retinal detachment. SCR screening with dilated fundus examination (DFE) is recommended every 1-2 years starting at age 10, but data underlying this recommendation are of poor quality and based upon expert consensus. We performed a systematic review to characterize imaging techniques, laboratory-based tests, and clinical practices for SCR screening. This PROSPERO-registered systematic review included relevant texts identified through predetermined searches in online databases. Collected test accuracy data facilitated the calculation of likelihood ratios. Forty-four studies evaluating 4928 patients were included. DFE demonstrated moderate test accuracy (LR+ of 8.0, LR- of 0.3). Ultra-widefield-fundus photography demonstrated superior accuracy (LR+ 32.5, LR- 0.03). Optical coherence tomography angiography applications were highly accurate for PSR identification (machine learning LR+ 32.5, LR- 0.03; human grader LR+ 2.8-213.1, LR- 0.1-0.2). Most techniques and tests were more accurate at detecting PSR than staging SCR or detecting lower-grade SCR. Our findings support the integration of advanced image-based approaches, such as computer-based image analysis and ultra-wide-field fundus imaging, for SCR screening in SCD patients given the superior accuracy in PSR detection compared with the current standard of care. Rigorous SCR screening implementation studies are needed to support evidence-based SCR screening recommendations.

Weakly Supervised Deep Learning in Radiology.

Deep learning (DL) is currently the standard artificial intelligence tool for computer-based image analysis in radiology. Traditionally, DL models have been trained with strongly supervised learning methods. These methods depend on reference standard labels, typically applied manually by experts. In contrast, weakly supervised learning is more scalable. Weak supervision comprises situations in which only a portion of the data are labeled (incomplete supervision), labels refer to a whole region or case as opposed to a precisely delineated image region (inexact supervision), or labels contain errors (inaccurate supervision). In many applications, weak labels are sufficient to train useful models. Thus, weakly supervised learning can unlock a large amount of otherwise unusable data for training DL models. One example of this is using large language models to automatically extract weak labels from free-text radiology reports. Here, we outline the key concepts in weakly supervised learning and provide an overview of applications in radiologic image analysis. With more fundamental and clinical translational work, weakly supervised learning could facilitate the uptake of DL in radiology and research workflows by enabling large-scale image analysis and advancing the development of new DL-based biomarkers.

Automatic analysis of nuclear features reveals a non-tumoral predictor of tumor grade in bladder cancer

Background & objectivesTumor grade determines prognosis in urothelial carcinoma. The classification of low and high grade is based on nuclear morphological features that include nuclear size, hyperchromasia and pleomorphism. These features are subjectively assessed by the pathologists and are not numerically measured, which leads to high rates of interobserver variability. The purpose of this study is to assess the value of a computer-based image analysis tool for identifying predictors of tumor grade in bladder cancer.MethodsFour hundred images of urothelial tumors were graded by five pathologists and two expert genitourinary pathologists using a scale of 1 (lowest grade) to 5 (highest grade). A computer algorithm was used to automatically segment the nuclei and to provide morphometric parameters for each nucleus, which were used to establish the grading algorithm. Grading algorithm was compared to pathologists’ agreement.ResultsComparison of the grading scores of the five pathologists with the expert genitourinary pathologists score showed agreement rates between 88.5% and 97.5%.The agreement rate between the two expert genitourinary pathologists was 99.5%. The quantified algorithm based conventional parameters that determine the grade (nuclear size, pleomorphism and hyperchromasia) showed > 85% agreement with the expert genitourinary pathologists. Surprisingly, the parameter that was most associated with tumor grade was the 10th percentile of the nuclear area, and high grade was associated with lower 10th percentile nuclei, caused by the presence of more inflammatory cells in the high-grade tumors.ConclusionQuantitative nuclear features could be applied to determine urothelial carcinoma grade and explore new biologically explainable parameters with better correlation to grade than those currently used.

Histological Evaluation of Alveolar Ridge Preservation Using Different Bone Grafts: Clinical Study Analysis Part II.

To compare histologically the percentage of bone formation 12-20 weeks after ridge augmentation using 2 different techniques. Tooth loss is associated with 3-dimensional bone remodeling and ridge atrophy. Ridge preservation procedures can prevent alveolar bone volume loss. Different techniques and materials are used to preserve the alveolar ridge. Computer-generated randomization software was used to assign 2 ridge preservation techniques for 11 extraction sites. In group I, type I bovine Achilles tendon collagen plugs with bioactive resorbable calcium apatite crystals (CPCAC) were placed, and in group II, cortico-cancellous bone chips (CCBC) mix and an expanded polytetrafluoroethylene (ePTFE) barrier membrane were placed. The histomorphometric studies were performed using a computer-based image analysis system (ImageJ 1.4, National Institute of Health, Bethesda, Md) to calculate the pixel area of bone tissue and the remaining bone graft material. The histomorphometric data were analyzed using a Student t test to compare the measurements between the 2 experimental groups. This parametric statistical test was employed to determine if there were any statistically significant differences in the quantitative histological parameters between the groups. The sockets that received CPCAC showed a lower (31.89%) percentage of native bone surface area compared with the CCBC group (43.87%). However, the difference was not statistically significant (P < .05). In addition, the CPCAC group showed evidence of foreign-body reaction. The CCBC graft covered with an ePTFE barrier may induce more bone formation with minimal inflammation in an extraction socket compared with a collagen plug with calcium apatite crystals. In addition, histological analysis of the CPCAC graft showed evidence of foreign-body reaction, which indicates a negative clinical impact.

From Pixels to Prognosis: Unlocking the Potential of Deep Learning in Fibrotic Lung Disease Imaging Analysis.

The licensing of antifibrotic therapy for fibrotic lung diseases, including idiopathic pulmonary fibrosis IPF has created an urgent need for reliable biomarkers to predict disease progression and treatment response. Some patients experience stable disease trajectories, while others deteriorate rapidly, making treatment decisions challenging. High-resolution chest CT has become crucial for diagnosis, but visual assessments by radiologists suffer from low reproducibility and high interobserver variability. To address these issues, computer-based image analysis, called quantitative CT, has emerged. However, many quantitative CT methods rely on human input for training, therefore potentially incorporating human error into computer training. Rapid advances in artificial intelligence, specifically deep learning, aim to overcome this limitation by enabling autonomous quantitative analysis. While promising, deep learning also presents challenges including the need to minimize algorithm biases, ensuring explainability, and addressing accessibility and ethical concerns. This review explores the development and application of deep learning in improving the imaging process for fibrotic lung disease.

Automated diagnosis of plus disease in retinopathy of prematurity using quantification of vessels characteristics

The condition known as Plus disease is distinguished by atypical alterations in the retinal vasculature of neonates born prematurely. It has been demonstrated that the diagnosis of Plus disease is subjective and qualitative in nature. The utilization of quantitative methods and computer-based image analysis to enhance the objectivity of Plus disease diagnosis has been extensively established in the literature. This study presents the development of a computer-based image analysis method aimed at automatically distinguishing Plus images from non-Plus images. The proposed methodology conducts a quantitative analysis of the vascular characteristics linked to Plus disease, thereby aiding physicians in making informed judgments. A collection of 76 posterior retinal images from a diverse group of infants who underwent screening for Retinopathy of Prematurity (ROP) was obtained. A reference standard diagnosis was established as the majority of the labeling performed by three experts in ROP during two separate sessions. The process of segmenting retinal vessels was carried out using a semi-automatic methodology. Computer algorithms were developed to compute the tortuosity, dilation, and density of vessels in various retinal regions as potential discriminative characteristics. A classifier was provided with a set of selected features in order to distinguish between Plus images and non-Plus images. This study included 76 infants (49 [64.5%] boys) with mean birth weight of 1305 ± 427 g and mean gestational age of 29.3 ± 3 weeks. The average level of agreement among experts for the diagnosis of plus disease was found to be 79% with a standard deviation of 5.3%. In terms of intra-expert agreement, the average was 85% with a standard deviation of 3%. Furthermore, the average tortuosity of the five most tortuous vessels was significantly higher in Plus images compared to non-Plus images (p ≤ 0.0001). The curvature values based on points were found to be significantly higher in Plus images compared to non-Plus images (p ≤ 0.0001). The maximum diameter of vessels within a region extending 5-disc diameters away from the border of the optic disc (referred to as 5DD) exhibited a statistically significant increase in Plus images compared to non-Plus images (p ≤ 0.0001). The density of vessels in Plus images was found to be significantly higher compared to non-Plus images (p ≤ 0.0001). The classifier's accuracy in distinguishing between Plus and non-Plus images, as determined through tenfold cross-validation, was found to be 0.86 ± 0.01. This accuracy was observed to be higher than the diagnostic accuracy of one out of three experts when compared to the reference standard. The implemented algorithm in the current study demonstrated a commendable level of accuracy in detecting Plus disease in cases of retinopathy of prematurity, exhibiting comparable performance to that of expert diagnoses. By engaging in an objective analysis of the characteristics of vessels, there exists the possibility of conducting a quantitative assessment of the disease progression's features. The utilization of this automated system has the potential to enhance physicians' ability to diagnose Plus disease, thereby offering valuable contributions to the management of ROP through the integration of traditional ophthalmoscopy and image-based telemedicine methodologies.

Comparative immunohistochemical analysis of inflammatory cytokines in distinct subtypes of Sweet syndrome.

A dysregulated immune response has been implicated in Sweet syndrome (SS) pathogenesis; however, cytokine profiles across different conditions associated with SS - including adult-onset immunodeficiency (AOID) due to anti-interferon (IFN)-γ autoantibodies - remain unknown. To investigate alterations in inflammatory cytokines in skin lesions of distinct subtypes of SS. Skin biopsies were collected from 42 AOID- and 52 non-AOID-associated SS patients and 18 healthy controls. The comparative immunohistochemical study was conducted using monoclonal antibodies against interleukin (IL)-1β, IL-6, IL-17, IFN-γ, and tumor necrosis factor-α on paraffin-embedded sections. The quantitative percentage positivity and intensity were calculated using computer-based image analysis. The results showed stronger and more diffuse dermal immunoreactivity for IFN-γ and IL-17 in the AOID-associated (p < 0.001 and p < 0.001, respectively) and non-AOID-associated SS (p < 0.001 and p < 0.001, respectively) groups. However, no significant differences in the levels of these two cytokines were observed between the AOID- and non-AOID-associated SS groups. Increased expression of IFN-γ together with IL-17 was also noted in almost all subtypes among non-AOID-associated SS. These results demonstrate that IFN-γ and IL-17 are implicated in immunopathology of all SS subtypes, including AOID-associated SS, despite the presence of anti-IFN-γ autoantibodies.

Connectivity Matrix Seriation via Relaxation.

Volume electron microscopy together with computer-based image analysis are yielding neural circuit diagrams of ever larger regions of the brain. These datasets are usually represented in a cell-to-cell connectivity matrix and contain important information about prevalent circuit motifs allowing to directly test various theories on the computation in that brain structure. Of particular interest are the detection of cell assemblies and the quantification of feedback, which can profoundly change circuit properties. While the ordering of cells along the rows and columns doesn't change the connectivity, it can make special connectivity patterns recognizable. For example, ordering the cells along the flow of information, feedback and feedforward connections are segregated above and below the main matrix diagonal, respectively. Different algorithms are used to renumber matrices such as to minimize a given cost function, but either their performance becomes unsatisfying at a given size of the circuit or the CPU time needed to compute them scales in an unfavorable way with increasing number of neurons. Based on previous ideas, I describe an algorithm which is effective in matrix reordering with respect to both its performance as well as to its scaling in computing time. Rather than trying to reorder the matrix in discrete steps, the algorithm transiently relaxes the integer program by assigning a real-valued parameter to each cell describing its location on a continuous axis ('smooth-index') and finds the parameter set that minimizes the cost. I find that the smooth-index algorithm outperforms all algorithms I compared it to, including those based on topological sorting.

Identification of the complete particle size distribution of landslide debris by the combined method of scaled image analysis, line-grid analysis and laboratory sieve analysis

BackgroundGround failures in a slope due to gravity, are commonly known as landslides. Depend on the compositional, geological, and structural characteristics of the unstable initiation zone and the erosional composition of the propagation zone decide the complete particle size distribution of the moving mass and its gradation. This information is most important for the study of downslope movement. Only laboratory sieve analysis cannot fulfil this target because the natural debris contains a wide range of particle sizes, especially boulders. The combined method of scaled image analysis and laboratory sieve analysis or the combined method of line-grid analysis and laboratory sieve analysis was proposed to fulfil the requirement. To study the proposed combined methods, five different locations within the downslope propagation zone from the Aranayake landslide in Sri Lanka were surveyed and analyzed. In image analysis, the high-resolution scaled image of deposited debris was analyzed by computer-based image analysis for particle sizes. Small particles were addressed by the laboratory sieve analysis using the representative debris sample taken from the same location. If the boulder sizes within the debris are too big to address this method, then the Line-grid method was performed. The particles in every 0.5 m along a measured line of debris deposition were measured in this method. If the selected location contains small particles that cannot measured manually, the representative sample was used for the laboratory sieve analysis to fulfil this range.ResultsThe results of three locations indicated a 40% distribution of < 10 mm and a 60% distribution of > 10 mm representing the general distribution of the debris. Two distributions deviated from the general distribution that was surveyed and analyzed from special locations of the “near boundary of flow path” and “slope change zone” of the landslide.ConclusionsThe combined methodology yielded successful results of complete particle size distribution for the wide range of particle sizes in debris. The variation of the particle size distribution curves of debris along the downslope depositions is planned to be used for the study of downslope propagation, damage zone assessment studies, and predicting the representative composition of future failures.

A Preliminary Assessment of Cerebral Surface Strain

Background: Craniotomies are commonly performed neurosurgical procedures. Quantitating cerebral surface strain may facilitate the identification of intracerebral pathology and improve intraoperative management of underperfused brain tissue. Objective: The aim of this study was to test the use of digital image correlation software to quantify cerebral surface strain of intraoperative cerebral tissue during craniotomies. Methods: Ncorr, an open-source software program, was used to perform digital image correlation analysis from publicly available craniotomy videos. Mann-Whitney U tests were then utilized to assess statistical differences between craniotomy datasets. Results: Four different craniotomies were retrospectively examined, and the acquired cerebral surface strain data were subsequently extracted and analyzed. Statistically significant cerebral surface strain values were identified when comparing the four craniotomies. Additional prospective research is needed to establish baseline ranges of cerebral surface strain and to further understand the potential utility and limitations of this non-invasive intraoperative monitoring technique. Conclusion: This preliminary study successfully demonstrated the use of computer-based image analysis for the non-invasive quantification of cerebral surface strain during neurosurgery.

Long-Term Supplementation of Canthaxanthin Does Not Inhibit Gastric Epithelial Cell Proliferation in Helicobacter mustelae-Infected Ferrets

The effect of canthaxanthin (CX), a non-provitamin A carotenoid, on gastric epithelial proliferation was studied in ferrets colonized with Helicobacter mustelae, which causes a chronic gastritis and an increased gastric epithelial cell proliferation. Seven spayed female ferrets were dosed by gavage with CX beadlets (50 mg/kg body wt, 5 d/wk) over 2 y, whereas seven control animals were given placebo beadlets. At the end of the 2-y-period, ferrets were killed, and gastric tissues were obtained from the antrum and body regions. A cell-proliferating biomarker, proliferating cell nuclear antigen (PCNA), was assayed by immunohistochemistry. The PCNA positivity was measured by a computer-based image analysis system. Serum concentrations of carotenoids, retinoids and tocopherols were analyzed by HPLC. Serum antioxidant status was measured by the oxygen radical absorbance capacity (ORAC) assay. The PCNA positivity in both antrum and body regions was not significantly different between CX-fed ferrets and controls. Serum CX concentrations were significantly higher in CX-fed ferrets vs. controls (P<0.001), whereas levels of other carotenoids, retinoids and tocopherols were not significantly different. The ORAC values were not significantly different between groups. This study does not suggest inhibitory effects of CX on gastric epithelial cell proliferation in H. mustelae infected ferrets.

Georeferencing Accuracy Assessment of Historical Aerial Photos Using a Custom-Built Online Georeferencing Tool

As one of the earliest forms of remote sensing, aerial photography has been regarded as an important part of the mapmaking process. Aerial photos, especially historical aerial photos, provide significant amount of valuable information for many applications and fields. However, due to limited funding support, most historical aerial photos have not been digitized and georeferenced yet, which substantially limits their utility for today’s computer-based image processing and analysis. Traditionally, historical aerial photos are georeferenced with desktop GIS software applications. However, this method is expensive, time-consuming, and labor-intensive. To address these limitations, this research developed a custom-built online georeferencing tool to enable georeferencing digitized historical aerial photos in a web environment, which is able to georeference historical aerial photos in a rapid and cost-effective manner. To evaluate the georeferencing performance, a set of 50 historical aerial photos were georeferenced with not only the developed online georeferencing tool but also two commercial desktop software programs. Research results revealed the custom-built online georeferencing tool provided the highest degree of accuracy while maximizing its accessibility.

Homogeneous Chronic Subdural Hematoma with Diverse Recurrent Possibilities.

Background: Evidence suggests that hyperdense (HD) chronic subdural hematomas (CSDHs) have a higher recurrence than hypodense (LD) chronic subdural hematomas. The value of mean hematoma density (MHD) has been proven to be associated with postoperative recurrence. The MHD levels in homogeneous CSDHs likely underestimate the risk of recurrence in HD homogeneous subtypes. Methods: This study investigated 42 consecutive CSDH cases between July 2010 and July 2014. The area of the hematoma was quantified to determine the MHD level using computer-based image analysis of preoperative brain CT scans. Results: In terms of the MHD distribution of the four types of CSDHs (homogeneous, laminar, separated, and trabecular), wide 95% CI (11.80-16.88) and high standard deviation (4.59) can be found in homogeneous types, reflecting a high variability in the MHD levels between cases (from low to high density). The categorization of homogeneous types into LD and HD (type five) displayed a minor standard deviation in the MHD levels for LD and HD subtypes (1.15, and 0.88, respectively). MHD values demonstrated concentrated distributions among the respective five types, compared to the four-type setting. Conclusions: In the current research, we provide a consideration that if LD and HD hematomas are separated from homogeneous CSDHs, the variability of the MHD quantification can potentially be reduced, thereby avoiding the possibility of undetected high-risk groups.

Computer-Based Renal Sonographic Image Analysis on Renal Progression in Patients With Chronic Glomerulopathies

Computer-Based Renal Sonographic Image Analysis on Renal Progression in Patients With Chronic Glomerulopathies

Immunofluorescent and Immunohistochemical (Colorimetric) Characterization of B‐DNA, Z‐DNA and G4‐Quadruplex DNA in Human Tissues Demonstrating the Spatial Genomic Organization of Different DNA Structures: Genomesorganizomics

Spatial genomic organization, relative to DNA and RNA structures, has not been examined. DNA can adopt a variety of different shapes (helical conformations), i.e., canonical right‐handed double‐stranded (ds‐) B‐DNA, alternative left‐handed ds‐Z‐DNA and multi‐stranded G4‐quadruplex DNA. Each of these DNAs has a unique structure and plays a different role in cells. The structure of the nucleus is complex, and has a major role in gene expression. For the first time, our group has demonstrated the spatial genomic distribution of three totally different DNAs concurrently in HeLa cells, paraffin‐embedded tissue sections, and frozen sections. This was achieved by means of anti‐B‐DNA, anti‐Z‐DNA and anti‐G4 quadruplex DNA antibodies, via immunofluorescence (IF), and immunohistochemistry (IHC) (colorimetric). No one is investigating the relationship between spatial genomic organization and structures of different nucleic acids. All three types of DNA exist in the human genome and perform diverse cellular genomic functions. Tissues were processed in 10% NBF, and staining protocols were done both manually and by autostainer. Antigen retrieval enhanced all DNA‐based immunostaining procedures. IF was performed using Alexa Fluor conjugated secondary antibodies [i.e., Alexa Fluor 405 (blue), Alexa Fluor 488 (green) and Alexa Fluor 555 (red)], and wheat germ agglutinin conjugated with Alexa Fluor 647 (white) (counterstain). IHC protocols (i.e., enzyme: HRP or AP) were accomplished by using enzymes conjugated to secondary antibodies, and color developed by chromogens [i.e., B‐DNA (Blue) AP Substrate, Z‐DNA (Red/Magenta) AP Substrate, and G4‐quadruplex DNA (Brown), DAB Peroxidase (HRP) Substrate]. Negative and positive controls, isotype controls, and blocking background were performed in triplicate. The order of primary antibodies (and substrates: IHC) is critical for obtaining accurate data. Images were obtained by confocal and light microscopes (i.e., 200x, 600x). Advanced computer‐based image analysis (e.g., HALO, Image Pro Plus) allowed for qualitative and quantitative characterization of immunostained DNAs. The largest percentage of DNA was in the form of B‐DNA, followed by the other two remaining structures of DNA. The novel method signifies a new subfield of structural and functional genomics, which we have named Spatial Genomic Organization of DNA Structures, i.e., Genomesorganizomics. It will aid spatial transcriptomics in the analysis of gene expression and provide information on transcriptional activity based upon the conformation of DNA in genes and telomeres. An in‐depth comparative analysis between IF and IHC will yield information about specific advantages and disadvantages of these immuno‐based tissue staining systems (e.g., sensitivity, dynamic range, easier multiplexing, photobleaching, better target colocalization). This genomics approach is both sensitive and reliable, and will allow for the characterization of nuclear genomic architecture. Future projects are examining both DNA and RNA structures of four or more nucleic acids.

Accurate Quantitative Histomorphometric-Mathematical Image Analysis Methodology of Rodent Testicular Tissue and Its Possible Future Research Perspectives in Andrology and Reproductive Medicine.

Infertility is increasing worldwide; male factors can be identified in nearly half of all infertile couples. Histopathologic evaluation of testicular tissue can provide valuable information about infertility; however, several different evaluation methods and semi-quantitative score systems exist. Our goal was to describe a new, accurate and easy-to-use quantitative computer-based histomorphometric-mathematical image analysis methodology for the analysis of testicular tissue. On digitized, original hematoxylin-eosin (HE)-stained slides (scanned by slide-scanner), quantitatively describable characteristics such as area, perimeter and diameter of testis cross-sections and of individual tubules were measured with the help of continuous magnification. Immunohistochemically (IHC)-stained slides were digitized with a microscope-coupled camera, and IHC-staining intensity measurements on digitized images were also taken. Suggested methods are presented with mathematical equations, step-by-step detailed characterization and representative images are given. Our novel quantitative histomorphometric-mathematical image analysis method can improve the reproducibility, objectivity, quality and comparability of andrological-reproductive medicine research by recognizing even the mild impairments of the testicular structure expressed numerically, which might not be detected with the present semi-quantitative score systems. The technique is apt to be subjected to further automation with machine learning and artificial intelligence and can be named ‘Computer-Assisted or -Aided Testis Histology’ (CATHI).

4D imaging analysis of the aging mouse neural stem cell niche reveals a dramatic loss of progenitor cell dynamism regulated by the RHO-ROCK pathway.

SummaryIn the adult ventricular-subventricular zone (V-SVZ), neural stem cells (NSCs) give rise to transit-amplifying progenitor (TAP) cells. These progenitors reside in different subniche locations, implying that cell movement must accompany lineage progression, but the dynamic behaviors of adult NSCs and TAPs remain largely unexplored. Here, we performed live time-lapse imaging with computer-based image analysis of young and aged 3D V-SVZ wholemounts from transgenic mice with fluorescently distinguished NSCs and TAP cells. Young V-SVZ progenitors are highly dynamic, with regular process outgrowth and retraction and cell migration. However, these activities dramatically declined with age. An examination of single-cell RNA sequencing (RNA-seq) data revealed age-associated changes in the Rho-Rock pathway that are important for cell motility. Applying a small molecule to inhibit ROCK transformed young into old V-SVZ progenitor cell dynamic behaviors. Hence RHO-ROCK signaling is critical for normal adult NSC and TAP movement and interactions, which are compromised with age, concomitant with the loss of regenerative ability.

Prediction of Clinically Significant Cancer Using Radiomics Features of Pre-Biopsy of Multiparametric MRI in Men Suspected of Prostate Cancer.

Simple SummaryRadiomics is the field of computer-based medical image analysis that incorporates various radiological imaging features, such as texture and shape parameters, from scans to derive algorithms. These mathematical algorithms have the potential to predict the biological characteristics of disease. In this study, we obtained quantitative imaging texture features of pre-biopsy multiparametric MRI of men suspected of prostate cancer and extracted from the T2WI and ADC images focusing on gray-level co-occurrence matrices (GLCM). These were correlated with the Gleason score of the histopathology of radical prostatectomy specimen, including the prediction of clinically significant prostate cancer. The knowledge gained through this prospective protocol-based study should facilitate establishing that GLCM texture features alone can be used as a biomarker for predicting the presence of clinically significant PCa.Background: Texture features based on the spatial relationship of pixels, known as the gray-level co-occurrence matrix (GLCM), may play an important role in providing the accurate classification of suspected prostate cancer. The purpose of this study was to use quantitative imaging parameters of pre-biopsy multiparametric magnetic resonance imaging (mpMRI) for the prediction of clinically significant prostate cancer. Methods: This was a prospective study, recruiting 200 men suspected of having prostate cancer. Participants were imaged using a protocol-based 3T MRI in the pre-biopsy setting. Radiomics parameters were extracted from the T2WI and ADC texture features of the gray-level co-occurrence matrix were delineated from the region of interest. Radical prostatectomy histopathology was used as a reference standard. A Kruskal–Wallis test was applied first to identify the significant radiomic features between the three groups of Gleason scores (i.e., G1, G2 and G3). Subsequently, the Holm–Bonferroni method was applied to correct and control the probability of false rejections. We compared the probability of correctly predicting significant prostate cancer between the explanatory GLCM radiomic features, PIRADS and PSAD, using the area under the receiver operation characteristic curves. Results: We identified the significant difference in radiomic features between the three groups of Gleason scores. In total, 12 features out of 22 radiomics features correlated with the Gleason groups. Our model demonstrated excellent discriminative ability (C-statistic = 0.901, 95%CI 0.859–0.943). When comparing the probability of correctly predicting significant prostate cancer between explanatory GLCM radiomic features (Sum Variance T2WI, Sum Entropy T2WI, Difference Variance T2WI, Entropy ADC and Difference Variance ADC), PSAD and PIRADS via area under the ROC curve, radiomic features were 35.0% and 34.4% more successful than PIRADS and PSAD, respectively, in correctly predicting significant prostate cancer in our patients (p < 0.001). The Sum Entropy T2WI score had the greatest impact followed by the Sum Variance T2WI. Conclusion: Quantitative GLCM texture analyses of pre-biopsy MRI has the potential to be used as a non-invasive imaging technique to predict clinically significant cancer in men suspected of having prostate cancer.

Image-based high-throughput mapping of TGF-\u03b2-induced phosphocomplexes at a single-cell level

Protein interactions and posttranslational modifications orchestrate cellular responses to e.g. cytokines and drugs, but it has been difficult to monitor these dynamic events in high-throughput. Here, we describe a semi-automated system for large-scale in situ proximity ligation assays (isPLA), combining isPLA in microtiter wells with automated microscopy and computer-based image analysis. Phosphorylations and interactions are digitally recorded along with subcellular morphological features. We investigated TGF-β-responsive Smad2 linker phosphorylations and complex formations over time and across millions of individual cells, and we relate these events to cell cycle progression and local cell crowding via measurements of DNA content and nuclear size of individual cells, and of their relative positions. We illustrate the suitability of this protocol to screen for drug effects using phosphatase inhibitors. Our approach expands the scope for image-based single cell analyses by combining observations of protein interactions and modifications with morphological details of individual cells at high throughput.

The Use of Color to Determine Fish Freshness: European Seabass (Dicentrarchus labrax)

ABSTRACT In this study, the relationship between color and the quality of fish was investigated. Additionally, how the color properties alter as a result of whether the fish is kept in cold water during the storage was also observed. For this purpose, the color changes on both halves of the sea bass (the bottom side dipped in cold water (WS: wet side) and the top side exposed to air (AR: air side)) were analyzed during 4°C storage by computer-based image analysis (CBI). The eyes, gills, and skin of the samples were monitored separately. Along with the color measurements, some freshness parameters, including total volatile basic nitrogen (TVB-N), thiobarbituric acid reactive substances (TBARs), TVC (total viable count), quality index method (QIM), and Torry score sensory analysis were assessed. The results indicated that the shelf life of sea bass was six days. High correlations were obtained between CBI results and the freshness parameters analyzed (min R2 = 0.844). Among the CBI analyses of skin, gill, and eye segmentation, the best results were achieved via monitoring the skin color. Regardless of the side of the fish (AS or WS) used, our study has revealed that applicable color parameters for the determination of shelf life of sea bass are a* and b*.