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Objective : To investigate the feasibility of a machine learning (ML) model based on radiomic features to identify active giant cell arteritis (GCA) in the aorta and differentiate it from atherosclerosis in follow-up [ 18 F]FDG-PET/CT images for therapy monitoring. Methods : To train the ML model, 64 [ 18 F]FDG-PET scans of 34 patients with proven GCA and 34 control subjects with type 2 diabetes mellitus were retrospectively included. The aorta was delineated into the ascending, arch, descending, and abdominal aorta. From each segment, 95 features were extracted. All segments were randomly split into a training/validation ( n = 192; 80%) and test set ( n = 46; 20%). In total, 441 ML models were trained, using combinations of seven feature selection methods, seven classifiers, and nine different numbers of features. The performance was assessed by area under the curve (AUC). The best performing ML model was compared to the clinical report of nuclear medicine physicians in 19 follow-up scans (7 active GCA, 12 inactive GCA). For explainability, an occlusion map was created to illustrate the important regions of the aorta for the decision of the ML model. Results : The ten-feature model with ANOVA as the feature selector and random forest classifier demonstrated the highest performance (AUC = 0.92 ± 0.01). Compared with the clinical report, this model showed a higher PPV (0.83 vs. 0.80), NPV (0.85 vs. 0.79), and accuracy (0.84 vs. 0.79) in the detection of active GCA in follow-up scans. Conclusions : The current radiomics ML model was able to identify active GCA and differentiate GCA from atherosclerosis in follow-up [ 18 F]FDG-PET/CT scans. This demonstrates the potential of the ML model as a monitoring tool in challenging [ 18 F]FDG-PET scans of GCA patients.

The complement system, a coordinator and facilitator of the innate immune response, plays an essential role in maintaining host homeostasis. It promotes clearance of pathogen- and danger-associated molecular patterns, regulates adaptive immunity, and can modify various metabolic processes such as energy expenditure, lipid metabolism, and glucose homeostasis. In this review, we will focus on the intricate interplay between complement components and lipid metabolism. More precisely, we will display how alterations in the activation and regulation of the complement system affect pathological outcome in lipid-associated diseases, such as atherosclerosis, obesity, metabolic syndrome, age-related macular degeneration, and metabolic dysfunction-associated steatotic liver disease. In addition to that, we will present and evaluate underlying complement-mediated physiological mechanisms, observed both in vitro and in vivo . Our manuscript will demonstrate the clinical significance of the complement system as a bridging figure between innate immunity and lipid homeostasis., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could influence the work reported in this paper., (Copyright © 2024 Alic, Dendinovic and Papac-Milicevic.)

Introduction: Neovascular glaucoma (NVG) is a severe type characterized by forming new blood vessels on the iris and the anterior chamber angle, often resulting from ischemic retinal diseases. Pars plana vitrectomy (PPV) is a standard surgical procedure for treating various retinal and vitreous conditions. Understanding the risk factors associated with NVG development following PPV is crucial for improving patient outcomes., Objective: To identify and evaluate demographic, clinical, and surgical risk factors associated with developing NVG following PPV., Patients and Methods: A prospective cohort study was conducted over two years, involving 60 type 2 diabetes mellitus (T2DM) patients (31 males and 29 females; mean age 60.48±9.63 years) who underwent PPV at the Eye Clinic and Department of Clinical Immunology, University Clinical Center Sarajevo, Sarajevo, Bosnia and Herzegovina. Patients were thoroughly informed about the study, and written informed consent was obtained. Comprehensive data collection included demographic information, medical history, preoperative and postoperative eye examinations, and intraoperative details. Statistical analyses were performed using IBM SPSS Statistics for Windows, Version 21 (Released 2012; IBM Corp., Armonk, New York, United States)., Results:  Within 12 months postoperatively, 17 patients (28.3%) developed NVG. Significant preoperative risk factors for NVG included prolonged duration of T2DM (p=0.037), elevated preoperative intraocular pressure (IOP) (p=0.024), and higher levels of vascular endothelial growth factor (VEGF) (p=0.011). Intraoperative factors, such as sharp dissection (p=0.000) and operative complications (p=0.004), were also significantly associated with NVG development. Multivariate logistic regression analysis identified prolonged T2DM duration (OR 1.132, p=0.023), increased preoperative IOP (OR 1.192, p=0.029), elevated VEGF levels (OR 1.002, p=0.016), and intraoperative sharp dissection (OR 0.114, p=0.006) as independent risk factors., Conclusions: Multiple preoperative and intraoperative factors influence the development of NVG post-PPV. Prolonged T2DM duration, elevated preoperative IOP, high VEGF levels, and specific intraoperative techniques significantly increase the risk of NVG. These findings underscore the importance of careful preoperative assessment and tailored intraoperative strategies to mitigate NVG risk in PPV patients., Competing Interests: Human subjects: Consent was obtained or waived by all participants in this study. Ethics Committe of the University Clinical Center Sarajevo issued approval 02-07-8095. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Nisic et al.)

Endothelin-1/endothelin A receptor (ET-1/ETAR) pathway plays an important role in the progression of liver fibrosis by activating hepatic stellate cells (HSCs) - a key cell type involved in the pathogenesis of liver fibrosis. Inactivating HSCs by blocking the ET-1/ETAR pathway using a selective ETAR antagonist (ERA) represents a promising therapeutic approach for liver fibrosis. Unfortunately, small-molecule ERAs possess limited clinical potential due to poor bioavailability, short half-life, and rapid renal clearance. To improve the clinical applicability, we conjugated ERA to superparamagnetic iron-oxide nanoparticles (SPIONs) and investigated the therapeutic efficacy of ERA and ERA-SPIONs in vitro and in vivo and analyzed liver uptake by in vivo and ex vivo magnetic resonance imaging (MRI), HSCs-specific localization, and ET-1/ETAR-pathway antagonism in vivo. In murine and human liver fibrosis/cirrhosis, we observed overexpression of ET-1 and ETAR that correlated with HSC activation, and HSC-specific localization of ETAR. ERA and successfully synthesized ERA-SPIONs demonstrated significant attenuation in TGFβ-induced HSC activation, ECM production, migration, and contractility. In an acute CCl 4 -induced liver fibrosis mouse model, ERA-SPIONs exhibited higher liver uptake, HSC-specific localization, and ET-1/ETAR pathway antagonism. This resulted in significantly reduced liver-to-body weight ratio, plasma ALT levels, and α-SMA and collagen-I expression, indicating attenuation of liver fibrosis. In conclusion, our study demonstrates that the delivery of ERA using SPIONs enhances the therapeutic efficacy of ERA in vivo. This approach holds promise as a theranostic strategy for the MRI-based diagnosis and treatment of liver fibrosis., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Background: In vivo diffuse reflectance spectroscopy provides additional contrast in discriminating nerves embedded in adipose tissue during surgery. However, large datasets are required to achieve clinically acceptable classification levels. This study assesses the spectral similarity between ex vivo porcine and in vivo human spectral data of nerve and adipose tissue, as porcine tissue could contribute to generate large datasets., Methods: Porcine diffuse reflectance spectra were measured at 124 nerve and 151 adipose locations. A previously recorded dataset of 32 in vivo human nerve and 23 adipose tissue locations was used for comparison. In total, 36 features were extracted from the raw porcine to generate binary logistic regression models for all combinations of two, three, four and five features. Feature selection was performed by assessing similar means between normalized features of nerve and of adipose tissue (Kruskal-Wallis test, p < 0.05) and for models performing best on the porcine cross validation set. The human test set was used to assess classification performance., Results: The binary logistic regression models with selected features showed an accuracy of 60% on the test set., Conclusions: Spectral similarity between ex vivo porcine and in vivo human adipose and nerve tissue was present, but further research is required.

Cellular death, aging, and tissue damage trigger inflammation that leads to enzymatic and non-enzymatic lipid peroxidation of polyunsaturated fatty acids present on cellular membranes and lipoproteins. This results in the generation of highly reactive degradation products, such as malondialdehyde (MDA) and 4-hydroxynonenal (4-HNE), that covalently modify free amino groups of proteins and lipids in their vicinity. These newly generated neoepitopes represent a unique set of damage-associated molecular patterns (DAMPs) associated with oxidative stress termed oxidation-specific epitopes (OSEs). OSEs are enriched on oxidized lipoproteins, microvesicles, and dying cells, and can trigger sterile inflammation. Therefore, prompt recognition and removal of OSEs is required to maintain the homeostatic balance. This is partially achieved by various humoral components of the innate immune system, such as natural IgM antibodies, pentraxins and complement components that not only bind OSEs but in some cases modulate their pro-inflammatory potential. Natural IgM antibodies are potent complement activators, and 30% of them recognize OSEs such as oxidized phosphocholine (OxPC-), 4-HNE-, and MDA-epitopes. Furthermore, OxPC-epitopes can bind the complement-activating pentraxin C-reactive protein, while MDA-epitopes are bound by C1q, C3a, complement factor H (CFH), and complement factor H-related proteins 1, 3, 5 (FHR-1, FHR-3, FHR-5). In addition, CFH and FHR-3 are recruited to 2-(ω-carboxyethyl)pyrrole (CEP), and full-length CFH also possesses the ability to attenuate 4-HNE-induced oxidative stress. Consequently, alterations in the innate humoral defense against OSEs predispose to the development of diseases associated with oxidative stress, as shown for the prototypical OSE, MDA-epitopes. In this mini-review, we focus on the mechanisms of the accumulation of OSEs, the pathophysiological consequences, and the interactions between different OSEs and complement components. Additionally, we will discuss the clinical potential of genetic variants in OSE-recognizing complement proteins - the OSE complotype - in the risk estimation of diseases associated with oxidative stress., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Alic, Binder and Papac-Milicevic.)

A magnetic sentinel lymph node biopsy ((SLN)B) procedure has recently been shown feasible in oral cancer patients. However, a grading system is absent for proper identification and classification, and thus for clinical reporting. Based on data from eight complete magnetic SLNB procedures, we propose a provisional grading system. This grading system includes: (1) a qualitative five-point grading scale for MRI evaluation to describe iron uptake by LNs; (2) an ex vivo count of resected SLN with a magnetic probe to quantify iron amount; and (3) a qualitative five-point grading scale for histopathologic examination of excised magnetic SLNs. Most SLNs with iron uptake were identified and detected in level II. In this level, most variance in grading was seen for MRI and histopathology; MRI and medullar sinus were especially highly graded, and cortical sinus was mainly low graded. On average 82 ± 58 µg iron accumulated in harvested SLNs, and there were no significant differences in injected tracer dose (22.4 mg or 11.2 mg iron). In conclusion, a first step was taken in defining a comprehensive grading system to gain more insight into the lymphatic draining system during a magnetic SLNB procedure.

Background: A procedure for sentinel lymph node biopsy (SLNB) using superparamagnetic iron-oxide (SPIO) nanoparticles and intraoperative sentinel lymph node (SLN) detection was developed to overcome drawbacks associated with the current standard-of-care SLNB. However, residual SPIO nanoparticles can result in void artefacts at follow-up magnetic resonance imaging (MRI) scans. We present a grading protocol to quantitatively assess the severity of these artefacts and offer an option to minimise the impact of SPIO nanoparticles on diagnostic imaging., Methods: Follow-up mammography and MRI of two patient groups after a magnetic SLNB were included in the study. They received a 2-mL subareolar dose of SPIO (high-dose, HD) or a 0.1-mL intratumoural dose of SPIO (low-dose, LD). Follow-up mammography and MRI after magnetic SLNB were acquired within 4 years after breast conserving surgery (BCS). Two radiologists with over 10-year experience in breast imaging assessed the images and analysed the void artefacts and their impact on diagnostic follow-up., Results: A total of 19 patients were included (HD, n = 13; LD, n = 6). In the HD group, 9/13 patients displayed an artefact on T1-weighted images up to 3.6 years after the procedure, while no impact of the SPIO remnants was observed in the LD group., Conclusions: SLNB using a 2-mL subareolar dose of magnetic tracer in patients undergoing BCS resulted in residual artefacts in the breast in the majority of patients, which may hamper follow-up MRI. This can be avoided by using a 0.1-mL intratumoural dose., (© 2021. The Author(s) under exclusive licence to European Society of Radiology.)

Objective: Sentinel lymph node harvesting is an essential step in the surgical treatment of a growing number of malignancies. Various techniques are available to facilitate this purpose. The present study reports a new laparoscopic technique for lymph node harvesting using magnetic nanoparticles containing a superparamagnetic iron-oxide core and dextran coating. This study assesses the clinical relevance of the prototype and provides input for further technological development on the way to clinical implementation., Methods: A laparoscopic differential magnetometer prototype was built, utilizing a nonlinear detection principle (differential magnetometry) for magnetic identification of lymph nodes. The iron content sensitivity, depth & spatial sensitivity, and angular sensitivity were analyzed to investigate clinical options., Results: The minimum detectable amount of iron was 9.8 μg at a distance of 1 mm. The detection depth was 5, 8, and 10 mm for samples containing 126, 252, and 504 μg iron, respectively. The maximum lateral detection distance was 5, 7, and 8 mm for samples containing 126, 252, and 504 μg iron, respectively. A sample containing 504 μg iron was detectable at all angulations assessed (0°, 30°, 60° and 90°)., Conclusion: The laparoscopic differential magnetometer demonstrates promising results for further investigation and development towards laparoscopic lymph node harvesting using magnetic nanoparticles., Significance: The laparoscopic differential magnetometer facilitates a novel method for sentinel lymph node harvesting, which helps to determine prognosis and treatment of cancer patients.

The efficient development and utilisation of magnetic nanoparticles (MNPs) for applications in enhanced biosensing relies on the use of magnetisation dynamics, which are primarily governed by the time-dependent motion of the magnetisation due to externally applied magnetic fields. An accurate description of the physics involved is complex and not yet fully understood, especially in the frequency range where Néel and Brownian relaxation processes compete. However, even though it is well known that non-zero, non-static local fields significantly influence these magnetisation dynamics, the modelling of magnetic dynamics for MNPs often uses zero-field dynamics or a static Langevin approach. In this paper, we developed an approximation to model and evaluate its performance for MNPs exposed to a magnetic field with varying amplitude and frequency. This model was initially developed to predict superparamagnetic nanoparticle behaviour in differential magnetometry applications but it can also be applied to similar techniques such as magnetic particle imaging and frequency mixing. Our model was based upon the Fokker-Planck equations for the two relaxation mechanisms. The equations were solved through numerical approximation and they were then combined, while taking into account the particle size distribution and the respective anisotropy distribution. Our model was evaluated for Synomag ® -D70, Synomag ® -D50 and SHP-15, which resulted in an overall good agreement between measurement and simulation.

Sentinel lymph node biopsy (SLNB) is a diagnostic staging procedure that aims to identify the first draining lymph node(s) from the primary tumor, the sentinel lymph nodes (SLN), as their histopathological status reflects the histopathological status of the rest of the nodal basin. The routine SLNB procedure consists of peritumoral injections with a technetium-99m [ 99m Tc]-labelled radiotracer followed by lymphoscintigraphy and SPECT-CT imaging. Based on these imaging results, the identified SLNs are marked for surgical extirpation and are subjected to histopathological assessment. The routine SLNB procedure has proven to reliably stage the clinically negative neck in early-stage oral squamous cell carcinoma (OSCC). However, an infamous limitation arises in situations where SLNs are located in close vicinity of the tracer injection site. In these cases, the hotspot of the injection site can hide adjacent SLNs and hamper the discrimination between tracer injection site and SLNs (shine-through phenomenon). Therefore, technical developments are needed to bring the diagnostic accuracy of SLNB for early-stage OSCC to a higher level. This review evaluates novel SLNB imaging techniques for early-stage OSCC: MR lymphography, CT lymphography, PET lymphoscintigraphy and contrast-enhanced lymphosonography. Furthermore, their reported diagnostic accuracy is described and their relative merits, disadvantages and potential applications are outlined.

Recent emerging hybrid technology of positron emission tomography/magnetic resonance (PET/MR) imaging has generated a great need for an accurate MR image-based PET attenuation correction. MR image segmentation, as a robust and simple method for PET attenuation correction, has been clinically adopted in commercial PET/MR scanners. The general approach in this method is to segment the MR image into different tissue types, each assigned an attenuation constant as in an X-ray CT image. Machine learning techniques such as clustering, classification and deep networks are extensively used for brain MR image segmentation. However, only limited work has been reported on using deep learning in brain PET attenuation correction. In addition, there is a lack of clinical evaluation of machine learning methods in this application. The aim of this review is to study the use of machine learning methods for MR image segmentation and its application in attenuation correction for PET brain imaging. Furthermore, challenges and future opportunities in MR image-based PET attenuation correction are discussed.

Genetic variants within complement factor H (CFH), a major alternative complement pathway regulator, are associated with the development of age-related macular degeneration (AMD) and other complementopathies. This is explained with the reduced binding of CFH or its splice variant factor H-like protein 1 (FHL-1) to self-ligands or altered self-ligands (e.g., malondialdehyde [MDA]-modified molecules) involved in homeostasis, thereby causing impaired complement regulation. Considering the critical role of CFH in inhibiting alternative pathway activation on MDA-modified surfaces, we performed an unbiased genome-wide search for genetic variants that modify the ability of plasma CFH to bind MDA in 1,830 individuals and characterized the mechanistic basis and the functional consequences of this. In a cohort of healthy individuals, we identified rs1061170 in CFH and the deletion of CFHR3 and CFHR1 as dominant genetic variants that modify CFH/FHL-1 binding to MDA. We further demonstrated that FHR1 and FHR3 compete with CFH for binding to MDA-epitopes and that FHR1 displays the highest affinity toward MDA-epitopes compared to CFH and FHR3. Moreover, FHR1 bound to MDA-rich areas on necrotic cells and prevented CFH from mediating its cofactor activity on MDA-modified surfaces, resulting in enhanced complement activation. These findings provide a mechanistic explanation as to why the deletion of CFHR3 and CFHR1 is protective in AMD and highlight the importance of genetic variants within the CFH/CFHR3/CFHR1 locus in the recognition of altered-self in tissue homeostasis., Competing Interests: The authors declare no competing interest., (Copyright © 2020 the Author(s). Published by PNAS.)

Diabetes is a large healthcare burden worldwide. There is substantial evidence that lifestyle modifications and drug intervention can prevent diabetes, therefore, an early identification of high risk individuals is important to design targeted prevention strategies. In this paper, we present an automatic tool that uses machine learning techniques to predict the development of type 2 diabetes mellitus (T2DM). Data generated from an oral glucose tolerance test (OGTT) was used to develop a predictive model based on the support vector machine (SVM). We trained and validated the models using the OGTT and demographic data of 1,492 healthy individuals collected during the San Antonio Heart Study. This study collected plasma glucose and insulin concentrations before glucose intake and at three time-points thereafter (30, 60 and 120 min). Furthermore, personal information such as age, ethnicity and body-mass index was also a part of the data-set. Using 11 OGTT measurements, we have deduced 61 features, which are then assigned a rank and the top ten features are shortlisted using minimum redundancy maximum relevance feature selection algorithm. All possible combinations of the 10 best ranked features were used to generate SVM based prediction models. This research shows that an individual's plasma glucose levels, and the information derived therefrom have the strongest predictive performance for the future development of T2DM. Significantly, insulin and demographic features do not provide additional performance improvement for diabetes prediction. The results of this work identify the parsimonious clinical data needed to be collected for an efficient prediction of T2DM. Our approach shows an average accuracy of 96.80% and a sensitivity of 80.09% obtained on a holdout set., Competing Interests: The authors have declared that no competing interests exist.

Objective: Histology is often used as a gold standard to evaluate noninvasive imaging modalities such as a magnetic resonance imaging (MRI). Spatial correspondence between histology and MRI is a critical step in quantitative evaluation of skeletal muscle in golden retriever muscular dystrophy (GRMD). Registration becomes technically challenging due to nonorthogonal histology section orientation, section distortion, and the different image contrast and resolution., Methods: This study describes a three-step procedure to register histology images with multiparametric MRI, i.e., interactive slice localization controlled by a three-dimensional mouse, followed by an affine transformation refinement, and a B-spline deformable registration using a new similarity metric. This metric combines mutual information and gradient information., Results: The methodology was verified using ex vivo high-resolution multiparametric MRI with a resolution of 117.19 μm (i.e., T1-weighted and T2-weighted MRI images) and trichrome stained histology images acquired from the pectineus muscles of ten dogs (nine GRMD and one healthy control). The proposed registration method yielded a root mean squares (RMS) error of 148.83 ± 34.96 μm averaged for ten muscle samples based on landmark points validated by five observers. The best RMS error averaged for ten muscles, was 128.48 ± 25.39 μm., Conclusion: The established correspondence between histology and in vivo MRI enables accurate extraction of MRI characteristics for histologically confirmed regions (e.g., muscle, fibrosis, and fat)., Significance: The proposed methodology allows creation of a database of spatially registered multiparametric MRI and histology. This database will facilitate accurate monitoring of disease progression and assess treatment effects noninvasively.

Introduction: Renalase is a protein secreted in kidneys and considered as a blood pressure modulator. High rates of hypertension and its regulation in patients on hemodialysis demands search for potential cause and treatment. The aim of this study was to determine the genotype and allele frequencies of renalase gene rs2576178 polymorphism in population from Bosnia and Herzegovina. Also, the objective of present study was to find the possible association between renalase gene rs2576178 polymorphism and hypertension in patients on hemodialysis., Material and Methods: The genotype of renalase gene rs2576178 polymorphism was determined in 137 participants (100 patients on hemodialysis and 37 controls), using polymerase chain reaction (PCR) and subsequent cleavage with MspI restriction endonuclease. Genotype and allele frequencies were assessed for Hardy-Weinberg equilibrium using a Chi-squared test. The value of P<0.05 was considered as statistically significant., Results: Comparison of genotype distribution and allele frequency in participants on hemodialysis with and without hypertension, and healthy control showed no statistical difference., Conclusion: The results of the study suggest that renalase gene rs2576178 polymorphism is not a factor that influences blood pressure in patients on hemodialysis.

Background: Many techniques are proposed for the quantification of tumor heterogeneity as an imaging biomarker for differentiation between tumor types, tumor grading, response monitoring and outcome prediction. However, in clinical practice these methods are barely used. This study evaluates the reported performance of the described methods and identifies barriers to their implementation in clinical practice., Methodology: The Ovid, Embase, and Cochrane Central databases were searched up to 20 September 2013. Heterogeneity analysis methods were classified into four categories, i.e., non-spatial methods (NSM), spatial grey level methods (SGLM), fractal analysis (FA) methods, and filters and transforms (F&T). The performance of the different methods was compared., Principal Findings: Of the 7351 potentially relevant publications, 209 were included. Of these studies, 58% reported the use of NSM, 49% SGLM, 10% FA, and 28% F&T. Differentiation between tumor types, tumor grading and/or outcome prediction was the goal in 87% of the studies. Overall, the reported area under the curve (AUC) ranged from 0.5 to 1 (median 0.87). No relation was found between the performance and the quantification methods used, or between the performance and the imaging modality. A negative correlation was found between the tumor-feature ratio and the AUC, which is presumably caused by overfitting in small datasets. Cross-validation was reported in 63% of the classification studies. Retrospective analyses were conducted in 57% of the studies without a clear description., Conclusions: In a research setting, heterogeneity quantification methods can differentiate between tumor types, grade tumors, and predict outcome and monitor treatment effects. To translate these methods to clinical practice, more prospective studies are required that use external datasets for validation: these datasets should be made available to the community to facilitate the development of new and improved methods.

Background: Magnetic resonance imaging (MRI), together with histology, is widely used to diagnose and to monitor treatment in oncology. Spatial correspondence between these modalities provides information about the ability of MRI to characterize cancerous tissue. However, registration is complicated by deformations during pathological processing, and differences in scale and information content., Methodology/principal Findings: This study proposes a methodology for establishing an accurate 3D relation between histological sections and high resolution in vivo MRI tumor data. The key features of the methodology are: 1) standardized acquisition and processing, 2) use of an intermediate ex vivo MRI, 3) use of a reference cutting plane, 4) dense histological sampling, 5) elastic registration, and 6) use of complete 3D data sets. Five rat pancreatic tumors imaged by T2*-w MRI were used to evaluate the proposed methodology. The registration accuracy was assessed by root mean squared (RMS) distances between manually annotated landmark points in both modalities. After elastic registration the average RMS distance decreased from 1.4 to 0.7 mm. The intermediate ex vivo MRI and the reference cutting plane shared by all three 3D images (in vivo MRI, ex vivo MRI, and 3D histology data) were found to be crucial for the accurate co-registration between the 3D histological data set and in vivo MRI. The MR intensity in necrotic regions, as manually annotated in 3D histology, was significantly different from other histologically confirmed regions (i.e., viable and hemorrhagic). However, the viable and the hemorrhagic regions showed a large overlap in T2(*)-w MRI signal intensity., Conclusions: The established 3D correspondence between tumor histology and in vivo MRI enables extraction of MRI characteristics for histologically confirmed regions. The proposed methodology allows the creation of a tumor database of spatially registered multi-spectral MR images and multi-stained 3D histology.