Surajit Ray

• Ph. D.
• Professor (Full) at University of Glasgow

Background: Somatostatin receptor SPECT-CT imaging is a small volume procedure and gathering sufficient data for clinical studies requires multiple centres. When quantitative data is acquired on different cameras (or with different acquisition or reconstruction parameters), values may not be directly comparable due to bias or variance: an effect no...

Aim Tumour Sink Effect (TSE) describes where avid tumours monopolise radiopharmaceutical uptake, resulting in limited uptake in smaller disease sites or physiological organs. TSE has been identified in [68Ga]Ga-DOTA-TATE imaging [1], with modelling suggesting significant effects occur with tumour burdens > 550 ml [2]. However a study of [68Ga]Ga-DO...

Background Textural Analysis features in molecular imaging require to be robust under repeat measurement and to be independent of volume for optimum use in clinical studies. Recent EANM and SNMMI guidelines for radiomics provide advice on the potential use of phantoms to identify robust features (Hatt in EJNMMI, 2022). This study applies the sugges...

Purpose The aim of this study is to assess inter-observer variability of the Krenning Score for 99m Tc-EDDA/HYNIC-TOC single photon emission computed tomography (SPECT)-computed tomography (CT) images and compare against quantitative metrics obtained from tumour and physiological uptake measurements. Methods Thirty-two patients with 117 lesions vi...

Background Peripheral nodal B‐cell lymphomas (PNBCL) represent the most common presentation of lymphomas in dogs. Multiagent CHOP (C = cyclophosphamide, H = hydroxydaunorubicin [Doxorubicin], O = Oncovin, P = prednisolone)‐based chemotherapy protocols have been widely accepted as gold standard 1st‐line treatment. CHOP‐25 and CHOP‐19 are most common...

The development and application of artificial intelligence-based computer vision systems in medicine, environment, and industry are playing an increasingly prominent role. Hence, the need for optimal and efficient hyperparameter tuning strategies is more than crucial to deliver the highest performance of the deep learning networks in large and dema...

The use of functional imaging such as PET in radiotherapy (RT) is rapidly expanding with new cancer treatment techniques. A fundamental step in RT planning is the accurate segmentation of tumours based on clinical diagnosis. Furthermore, recent tumour control techniques such as intensity modulated radiation therapy (IMRT) dose painting requires the...

Background The virus neutralization assay is a principal method to assess the efficacy of antibodies in blocking viral entry. Due to biosafety handling requirements of viruses classified as hazard group 3 or 4, pseudotyped viruses can be used as a safer alternative. However, it is often queried how well the results derived from pseudotyped viruses...

Aim/Introduction: Guidelines on radiomics in Nuclear Medicine [1] have recommended that users test for volume dependence and repeatability of radiomic parameters, including proposing the use of a “revolver” phantom, originally applied to PET imaging by Forgacs et al [2]. This study applies a modifed version of the suggested methodology to iden...

Introduction Semi-quantitative thyroid uptake measurements on a gamma camera using 99mTc-Pertechnetate can provide useful diagnostic information for patients with hyperthyroidism. Guidelines suggest that centres should determine a local normal range due to geographical variations in normal thyroid measurements [1]. When a gamma camera is replaced,...

Understanding the spatiotemporal dynamics of river water chemistry from its source to sinks is critical for constraining the origin, transformation, and “hotspots” of contaminants in a river basin. To provide new spatiotemporal constraints on river chemistry, dissolved trace element concentrations were measured at 17 targeted locations across the R...

With the increasing integration of functional imaging techniques like Positron Emission Tomography (PET) into radiotherapy (RT) practices, a paradigm shift in cancer treatment methodologies is underway. A fundamental step in RT planning is the accurate segmentation of tumours based on clinical diagnosis. Furthermore, novel tumour control methods, s...

Purpose: Withdrawal of long-acting release somatostatin analogue (LAR-SSA) treatment before somatostatin receptor imaging is based on empirical reasoning that it may block uptake at receptor sites. This study aims to quantify differences in uptake of 99mTc-EDDA/HYNIC-TOC between patients receiving LAR-SSA and those who were not. Methods: Quantif...

Objectives: To determine how the intrinsic severity of successively dominant SARS-CoV-2 variants changed over the course of the pandemic. Methods: A retrospective cohort analysis in the NHS Greater Glasgow and Clyde (NHS GGC) Health Board. All sequenced non-nosocomial adult COVID-19 cases in NHS GGC with relevant SARS-CoV-2 lineages (B.1.177/Alp...

Impaired water quality continues to be a serious problem in surface waters worldwide. Despite extensive regulatory water quality monitoring implemented by the Government of India over the past two decades, the spatial and temporal resolution of water quality observations, the range of monitored contaminants and data related to characterisation of p...

Aim: The Krenning Score is used in Somatostatin Receptor Imaging to identify patients suitable for Peptide Receptor Radionuclide Therapy (PRRT). As a visual metric it may be subject to inter-rater variability. This study will compare visual Krenning Score determined by an Expert Consensus (ECS) to scoring by less-experienced reporters. Quantitative...

Siemens BroadQuant allows quantitative SPECT imaging for any radionuclide and collimator pairing calibrated by the user. Recent EANM guidelines [1] recommend calibration should occur at least annually, but permit the user to determine the frequency of validation checks. This study aimed to measure the longitudinal stability for a range of radionucl...

Objectives The SARS-CoV-2 Alpha variant was associated with increased transmission relative to other variants present at the time of its emergence and several studies have shown an association between Alpha variant infection and increased hospitalisation and 28-day mortality. However, none have addressed the impact on maximum severity of illness in...

An outbreak of acute hepatitis of unknown aetiology in children was reported in Scotland in April 20221 and has now been identified in 35 countries2. Several recent studies have suggested an association with human adenovirus (HAdV), a virus not commonly associated with hepatitis. Here we report a detailed case-control investigation and find an asso...

Background Radionuclide ventriculography (RNVG) can be used to quantify mechanical dyssynchrony and may be a valuable adjunct in the assessment of heart failure with reduced ejection fraction (HFrEF). The study aims to investigate the effect of beta-blockers on mechanical dyssynchrony using novel RNVG phase parameters. Methods A retrospective stud...

There have been numerous risk tools developed to enable triaging of SARS-CoV-2 positive patients with diverse levels of complexity. Here we presented a simplified risk-tool based on minimal parameters and chest X-ray (CXR) image data that predicts the survival of adult SARS-CoV-2 positive patients at hospital admission. We analysed the NCCID databa...

In March 2020 mathematics became a key part of the scientific advice to the UK government on the pandemic response to COVID-19. Mathematical and statistical modelling provided critical information on the spread of the virus and the potential impact of different interventions. The unprecedented scale of the challenge led the epidemiological modellin...

Vaccines based on the spike protein of SARS-CoV-2 are a cornerstone of the public health response to COVID-19. The emergence of hypermutated, increasingly transmissible variants of concern (VOCs) threaten this strategy. Omicron (B.1.1.529), the fifth VOC to be described, harbours multiple amino acid mutations in spike, half of which lie within the...

Acute kidney injury (AKI) is a prevalent complication in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive inpatients, which is linked to an increased mortality rate compared to patients without AKI. Here we analysed the difference in kidney blood biomarkers in SARS-CoV-2 positive patients with non-fatal or fatal outcome, in ord...

Objective To determine how the severity of successively dominant SARS-CoV-2 variants changed over the course of the COVID-19 pandemic. Design Retrospective cohort analysis. Setting Community- and hospital-sequenced COVID-19 cases in the NHS Greater Glasgow and Clyde (NHS GG&C) Health Board. Participants All sequenced non-nosocomial adult COVID-1...

Vaccination-based exposure to spike protein derived from early SARS-CoV-2 sequences is the key public health strategy against COVID-19. Successive waves of SARS-CoV-2 infections have been characterised by the evolution of highly mutated variants that are more transmissible and that partially evade the adaptive immune response. Omicron is the fifth...

Introduction: Despite significant therapeutic advancements, Atherosclerotic Cardiovascular Disease (ASCVD) patients require frequent hospitalization. Machine learning (ML) algorithms present an opportunity to develop improved and more generalizable prediction models for 30-day hospital readmission due to ASCVD. Objectives: The current study aims to...

The global pandemic of coronavirus disease 2019 (COVID-19) is continuing to have a significant effect on the well-being of the global population, thus increasing the demand for rapid testing, diagnosis, and treatment. As COVID-19 can cause severe pneumonia, early diagnosis is essential for correct treatment, as well as to reduce the stress on the h...

Background There is an urgent need to develop a simplified risk tool that enables rapid triaging of SARS CoV-2 positive patients during hospital admission, which complements current practice. Many predictive tools developed to date are complex, rely on multiple blood results and past medical history, do not include chest X ray results and rely on A...

The global pandemic of COVID-19 is continuing to have a significant effect on the well-being of global population, increasing the demand for rapid testing, diagnosis, and treatment. Along with COVID-19, other etiologies of pneumonia and tuberculosis constitute additional challenges to the medical system. In this regard, the objective of this work i...

Background Accurate diagnostic tools to identify patients at risk of cancer therapy-related cardiac dysfunction (CTRCD) are critical. For patients undergoing cardiotoxic cancer therapy, ejection fraction assessment using radionuclide ventriculography (RNVG) is commonly used for serial assessment of left ventricular (LV) function.Methods In this ret...

Since December 2019 the novel coronavirus SARS-CoV-2 has been identified as the cause of the pandemic COVID-19. Early symptoms overlap with other common conditions such as common cold and Influenza, making early screening and diagnosis are crucial goals for health practitioners. The aim of the study was to use machine learning (ML), an artificial n...

Since December 2019 the novel coronavirus SARS-CoV-2 has been identified as the cause of the pandemic Covid 19. Early symptoms overlap with other common conditions such as common cold and Influenza, making early screening and diagnosis are crucial goals for health practitioners. The aim of the study was to use machine learning, an artificial neural...

We present a new approach to model selection and Bayes factor determination, based on Laplace expansions (as in BIC), which we call Prior-based Bayes Information Criterion (PBIC). In this approach, the Laplace expansion is only done with the likelihood function, and then a suitable prior distribution is chosen to allow exact computation of the (app...

In this paper, we compare three functional regression models from a growth curve perspective to predict the relationship between two economic variables, specifically we compare a functional concurrent model, a functional historical model and a functional autoregressive model (FAR). The dependent and the independent variables are cumulated over the...

Peatlands are spatially heterogeneous ecosystems that develop due to a complex set of autogenic physical and biogeochemical processes and allogenic factors such as the climate and topography. They are significant stocks of global soil carbon, and therefore predicting the depth of peatlands is an important part of establishing an accurate assessment...

Distribution of slope and elevation for the locations of peat depth. ST = stratified dataset, and GR = gridded dataset. Refer to main text for a description of the differences between the two observation datasets. (JPEG)

Estimated semi-variograms computed from the residuals of three datasets used for peat depth predictions after fitting a linear model of the square root of peat depth. Slope (°) and elevation (m asl) were used as predictors. The dashed lines are upper and lower 95% confidence intervals for the semi-variograms generated under independence using a Mon...

Covariate importance for predicting blanket peat depth for the spatial model with slope and elevation as covariates. Point estimates are the effect of a change in one unit of the covariate (slope (°), elevation (m asl)) on peat depth (cm). Horizontal lines are the 95% confidence intervals. ST.C3, GR.C3, and CMB.C3 are the stratified, gridded, and c...

Prediction bias and correlation coefficients for linear and spatial models for covariate groups (Peat depth). (PDF)

This paper focuses on the analysis of spatially correlated functional data. The between-curve correlation is modeled by correlating functional principal component scores of the functional data. We propose a Spatial Principal Analysis by Conditional Expectation framework to explicitly estimate spatial correlations and reconstruct individual curves....

Modalclust is an R package which performs Hierarchical Mode Association Clustering (HMAC) along with its parallel implementation over several processors. Modal clustering techniques are especially designed to efficiently extract clusters in high dimensions with arbitrary density shapes. Further, clustering is performed over several resolutions and...

This article develops methods of statistical monitoring of clinical trials with multiple co-primary endpoints, where success is defined as meeting both endpoints simultaneously. In practice, a group sequential design (GSD) method is used to stop trials early for promising efficacy, and conditional power (CP) is used for futility stopping rules. In...

The number of modes (also known as modality) of a kernel density estimator (KDE) draws lots of interests and is important in practice. In this paper, we develop an inference framework on the modality of a KDE under multivariate setting using Gaussian kernel. We applied the modal clus- tering method proposed by [1] for mode hunting. A test statistic...

In this article, we study the power properties of quadratic-distance-based goodness-of-fit tests. First, we introduce the concept of a root kernel and discuss the considerations that enter the selection of this kernel. We derive an easy to use normal approximation to the power of quadratic distance goodness-of-fit tests and base the construction of...

Selecting between competing structural equation models is a common problem. Often selection is based on the chi-square test statistic or other fit indices. In other areas of statistical research Bayesian information criteria are commonly used, but they are less frequently used with structural equation models compared to other fit indices. This arti...

We extend the concept of the ridgeline from Ray and Lindsay (2005) to finite mixtures of general elliptical densities with possibly distinct density generators in each component. This can be used to obtain bounds for the number of modes of two-component mixtures of t distributions in any dimension. In case of proportional dispersion matrices, these...

Pattern discovery in sequences is an important unsolved problem in biology, with many applications, including detecting regulation of genes by transcription factors, and differentiating proteins of infecting organisms such as viruses from an animal's own genome. In this article we describe some of the recent statistical approaches developed to addr...

The main result of this article states that one can get as many as D+1 modes from just a two component normal mixture in D dimensions. Multivariate mixture models are widely used for modeling homogeneous populations and for cluster analysis. Either the components directly or modes arising from these components are often used to extract individual c...

We present a new approach to factor rotation for functional data. This is achieved by rotating the functional principal components toward a predefined space of periodic functions designed to decompose the total variation into components that are nearly-periodic and nearly-aperiodic with a predefined period. We show that the factor rotation can be o...

Background: In recent years, intense research efforts have focused on developing methods for automated flow cytometric data analysis. However, while designing such applications, little or no attention has been paid to the human perspective that is absolutely central to the manual gating process of identifying and characterizing cell populations. I...

Bayes factors (BFs) play an important role in comparing the fit of statistical models. However, computational limitations or lack of an appropriate prior sometimes prevent researchers from using exact BFs. Instead, it is approximated, often using the Bayesian Information Criterion (BIC) or a variant of BIC. The authors provide a comparison of sever...

Results of transformation with the flowTrans package. We plot the distribution of Treg events after applying logicle transformation based on a single parameter that was optimized according to the flowTrans package. We note that the resulting transformation did not remove the negative cluster (left of 0) in any of the four markers. Apparently there...

Results of application of logicle transformation with default arguments. We plot the distribution of Treg events after applying logicle transformation based on its default parameter values, i.e. without any transformation. We note that the resulting transformation did not remove the negative cluster (left of 0) in any of the four markers. Apparentl...

Table for Figure 4. A table containing the simulation results for Figure 4.

The widely used k top scoring pair (k-TSP) algorithm is a simple yet powerful parameter-free classifier. It owes its success in many cancer microarray datasets to an effective feature selection algorithm that is based on relative expression ordering of gene pairs. However, its general robustness does not extend to some difficult datasets, such as t...

Protein microarrays are a high-throughput technology capable of generating large quantities of proteomics data. They can be used for general research or for clinical diagnostics. Bioinformatics and statistical analysis techniques are required for interpretation and reaching biologically relevant conclusions from raw data. We describe essential algo...

Modal Clustering (HMAC) along with their parallel implementation (PHMAC) over several processors. These model-based non-parametric clustering techniques can extract clusters in very high dimensions with arbitrary density shapes. By default clustering is performed over several resolutions and the results are summarized as a hierarchical tree. Associ...

This work builds a unified framework for the study of quadratic form distance measures as they are used in assessing the goodness of fit of models. Many important procedures have this structure, but the theory for these methods is dispersed and incomplete. Central to the statistical analysis of these distances is the spectral decomposition of the k...

This work builds a unified framework for the study of quadratic form distance measures as they are used in assessing the goodness of fit of models. Many important procedures have this structure, but the theory for these methods is dispersed and incomplete. Central to the statistical analysis of these distances is the spectral decomposition of the k...

Data sets used in this study. List of peptide binding affinity and well-characterized T-cell epitopes used in this study.

We propose a general class of risk measures which can be used for data-based evaluation of parametric models. The loss function is defined as the generalized quadratic distance between the true density and the model proposed. These distances are characterized by a simple quadratic form structure that is adaptable through the choice of a non-negativ...

Protein antigens and their specific epitopes are formulation targets for epitope-based vaccines. A number of prediction servers are available for identification of peptides that bind major histocompatibility complex class I (MHC-I) molecules. The lack of standardized methodology and large number of human MHC-I molecules make the selection of approp...

A new clustering approach based on mode identification is developed by applying new optimiza- tion techniques to a nonparametric density estimator. A cluster is formed by those sample points that ascend to the same local maximum (mode) of the density function. The path from a point to its associated mode is efficiently solved by an EM-style algorit...

The advancing technology for automatic segmentation of medical images should be accompanied by techniques to inform the user of the local credibility of results. To the extent that this technology produces clinically acceptable segmentations for a significant fraction of cases, there is a risk that the clinician will assume every result is acceptab...

A key step in the development of an adaptive immune response to pathogens or vaccines is the binding of short peptides to molecules of the Major Histocompatibility Complex (MHC) for presentation to T lymphocytes, which are thereby activated and differentiate into effector and memory cells. The rational design of vaccines consists in part in the ide...

In this article we propose a general class of risk measures which can be used for data based evaluation of parametric models. The loss function is defined as generalized quadratic distance between the true density and the proposed model. These distances are characterized by a simple quadratic form structure that is adaptable through the choice of a...

Multivariate normal mixtures provide a flexible method of fitting high-dimensional data. It is shown that their topography, in the sense of their key features as a density, can be analyzed rigorously in lower dimensions by use of a ridgeline manifold that contains all critical points, as well as the ridges of the density. A plot of the elevations o...

this paper we try to explain why the above behavior of the power divergence test statistics are natural, and make a preliminary attempt to provide some new tests with reasonably high power at both kinds of alternatives

Pearson's χ2- and the log-likelihood ratio χ2-statistics are fundamental tools in goodness-of-fit testing. Cressie and Read constructed a general family of divergences which includes both statistics as special cases. This family is indexed by a single parameter, and divergences at either end of the scale are more powerful against alternatives of on...

The main result of this article states that one can get as many as D + 1 modes from a two component normal mixture in D dimensions. Multivariate mixture models are widely used for modeling homogeneous populations and for cluster analysis. Either the components directly or modes arsing from these components are often used to extract individual clust...

When a clinician uses an automatic method to segment a medical image, either she must accept the computer's segmentation or she must manually evaluate the quality of the segmentation and correct it as needed. This paper introduces another option: a methodology for identifying regions where the segmentation is not credible. Our method-ology identifi...