The ISMRM Open Science Initiative for Perfusion Imaging (OSIPI): Results from the OSIPI-Dynamic Contrast-Enhanced challenge.

PURPOSE: K trans $$ {K}^{\mathrm{trans}} $$ has often been proposed as a quantitative imaging biomarker for diagnosis, prognosis, and treatment response assessment for various tumors. None of the many software tools for K trans $$ {K}^{\mathrm{trans}} $$ quantification are standardized. The ISMRM Open Science Initiative for Perfusion Imaging-Dynamic Contrast-Enhanced (OSIPI-DCE) challenge was designed to benchmark methods to better help the efforts to standardize K trans $$ {K}^{\mathrm{trans}} $$ measurement. METHODS: A framework was created to evaluate K trans $$ {K}^{\mathrm{trans}} $$ values produced by DCE-MRI analysis pipelines to enable benchmarking. The perfusion MRI community was invited to apply their pipelines for K trans $$ {K}^{\mathrm{trans}} $$ quantification in glioblastoma from clinical and synthetic patients. Submissions were required to include the entrants' K trans $$ {K}^{\mathrm{trans}} $$ values, the applied software, and a standard operating procedure. These were evaluated using the proposed OSIP I gold $$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score defined with accuracy, repeatability, and reproducibility components. RESULTS: Across the 10 received submissions, the OSIP I gold $$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score ranged from 28% to 78% with a 59% median. The accuracy, repeatability, and reproducibility scores ranged from 0.54 to 0.92, 0.64 to 0.86, and 0.65 to 1.00, respectively (0-1 = lowest-highest). Manual arterial input function selection markedly affected the reproducibility and showed greater variability in K trans $$ {K}^{\mathrm{trans}} $$ analysis than automated methods. Furthermore, provision of a detailed standard operating procedure was critical for higher reproducibility. CONCLUSIONS: This study reports results from the OSIPI-DCE challenge and highlights the high inter-software variability within K trans $$ {K}^{\mathrm{trans}} $$ estimation, providing a framework for ongoing benchmarking against the scores presented. Through this challenge, the participating teams were ranked based on the performance of their software tools in the particular setting of this challenge. In a real-world clinical setting, many of these tools may perform differently with different benchmarking methodology.

An untrained deep learning method for reconstructing dynamic MR images from accelerated model-based data.

PURPOSE: To implement physics-based regularization as a stopping condition in tuning an untrained deep neural network for reconstructing MR images from accelerated data. METHODS: The ConvDecoder (CD) neural network was trained with a physics-based regularization term incorporating the spoiled gradient echo equation that describes variable-flip angle data. Fully-sampled variable-flip angle k-space data were retrospectively accelerated by factors of R = {8, 12, 18, 36} and reconstructed with CD, CD with the proposed regularization (CD + r), locally low-rank (LR) reconstruction, and compressed sensing with L1-wavelet regularization (L1). Final images from CD + r training were evaluated at the "argmin" of the regularization loss; whereas the CD, LR, and L1 reconstructions were chosen optimally based on ground truth data. The performance measures used were the normalized RMS error, the concordance correlation coefficient, and the structural similarity index. RESULTS: The CD + r reconstructions, chosen using the stopping condition, yielded structural similarity indexs that were similar to the CD (p = 0.47) and LR structural similarity indexs (p = 0.95) across R and that were significantly higher than the L1 structural similarity indexs (p = 0.04). The concordance correlation coefficient values for the CD + r T1 maps across all R and subjects were greater than those corresponding to the L1 (p = 0.15) and LR (p = 0.13) T1 maps, respectively. For R ≥ 12 (≤4.2 min scan time), L1 and LR T1 maps exhibit a loss of spatially refined details compared to CD + r. CONCLUSION: The use of an untrained neural network together with a physics-based regularization loss shows promise as a measure for determining the optimal stopping point in training without relying on fully-sampled ground truth data.

Analysis of simplicial complexes to determine when to sample for quantitative DCE MRI of the breast.

PURPOSE: A method is presented to select the optimal time points at which to measure DCE-MRI signal intensities, leaving time in the MR exam for high-spatial resolution image acquisition. THEORY: Simplicial complexes are generated from the Kety-Tofts model pharmacokinetic parameters Ktrans and ve . A geometric search selects optimal time points for accurate estimation of perfusion parameters. METHODS: The DCE-MRI data acquired in women with invasive breast cancer (N = 27) were used to retrospectively compare parameter maps fit to full and subsampled time courses. Simplicial complexes were generated for a fixed range of Kety-Tofts model parameters and for the parameter ranges weighted by estimates from the fully sampled data. The largest-area manifolds determined the optimal three time points for each case. Simulations were performed along with retrospectively subsampled data fits. The agreement was computed between the model parameters fit to three points and those fit to all points. RESULTS: The optimal three-point sample times were from the data-informed simplicial complex analysis and determined to be 65, 204, and 393 s after arrival of the contrast agent to breast tissue. In the patient data, tumor-median parameter values fit using all points and the three selected time points agreed with concordance correlation coefficients of 0.97 for Ktrans and 0.67 for ve . CONCLUSION: It is possible to accurately estimate pharmacokinetic parameters from three properly selected time points inserted into a clinical DCE-MRI breast exam. This technique can provide guidance on when to capture images for quantitative data between high-spatial-resolution DCE-MRI images.

Bayesian calibration of a stochastic, multiscale agent-based model for predicting in vitro tumor growth.

Hybrid multiscale agent-based models (ABMs) are unique in their ability to simulate individual cell interactions and microenvironmental dynamics. Unfortunately, the high computational cost of modeling individual cells, the inherent stochasticity of cell dynamics, and numerous model parameters are fundamental limitations of applying such models to predict tumor dynamics. To overcome these challenges, we have developed a coarse-grained two-scale ABM (cgABM) with a reduced parameter space that allows for an accurate and efficient calibration using a set of time-resolved microscopy measurements of cancer cells grown with different initial conditions. The multiscale model consists of a reaction-diffusion type model capturing the spatio-temporal evolution of glucose and growth factors in the tumor microenvironment (at tissue scale), coupled with a lattice-free ABM to simulate individual cell dynamics (at cellular scale). The experimental data consists of BT474 human breast carcinoma cells initialized with different glucose concentrations and tumor cell confluences. The confluence of live and dead cells was measured every three hours over four days. Given this model, we perform a time-dependent global sensitivity analysis to identify the relative importance of the model parameters. The subsequent cgABM is calibrated within a Bayesian framework to the experimental data to estimate model parameters, which are then used to predict the temporal evolution of the living and dead cell populations. To this end, a moment-based Bayesian inference is proposed to account for the stochasticity of the cgABM while quantifying uncertainties due to limited temporal observational data. The cgABM reduces the computational time of ABM simulations by 93% to 97% while staying within a 3% difference in prediction compared to ABM. Additionally, the cgABM can reliably predict the temporal evolution of breast cancer cells observed by the microscopy data with an average error and standard deviation for live and dead cells being 7.61±2.01 and 5.78±1.13, respectively.

Deep learning-based pancreas volume assessment in individuals with type 1 diabetes.

Pancreas volume is reduced in individuals with diabetes and in autoantibody positive individuals at high risk for developing type 1 diabetes (T1D). Studies investigating pancreas volume are underway to assess pancreas volume in large clinical databases and studies, but manual pancreas annotation is time-consuming and subjective, preventing extension to large studies and databases. This study develops deep learning for automated pancreas volume measurement in individuals with diabetes. A convolutional neural network was trained using manual pancreas annotation on 160 abdominal magnetic resonance imaging (MRI) scans from individuals with T1D, controls, or a combination thereof. Models trained using each cohort were then tested on scans of 25 individuals with T1D. Deep learning and manual segmentations of the pancreas displayed high overlap (Dice coefficient = 0.81) and excellent correlation of pancreas volume measurements (R2 = 0.94). Correlation was highest when training data included individuals both with and without T1D. The pancreas of individuals with T1D can be automatically segmented to measure pancreas volume. This algorithm can be applied to large imaging datasets to quantify the spectrum of human pancreas volume.

Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting.

BACKGROUND: The purpose of this study was to determine whether advanced quantitative magnetic resonance imaging (MRI) can be deployed outside of large, research-oriented academic hospitals and into community care settings to predict eventual pathological complete response (pCR) to neoadjuvant therapy (NAT) in patients with locally advanced breast cancer. METHODS: Patients with stage II/III breast cancer (N = 28) were enrolled in a multicenter study performed in community radiology settings. Dynamic contrast-enhanced (DCE) and diffusion-weighted (DW)-MRI data were acquired at four time points during the course of NAT. Estimates of the vascular perfusion and permeability, as assessed by the volume transfer rate (Ktrans) using the Patlak model, were generated from the DCE-MRI data while estimates of cell density, as assessed by the apparent diffusion coefficient (ADC), were calculated from DW-MRI data. Tumor volume was calculated using semi-automatic segmentation and combined with Ktrans and ADC to yield bulk tumor blood flow and cellularity, respectively. The percent change in quantitative parameters at each MRI scan was calculated and compared to pathological response at the time of surgery. The predictive accuracy of each MRI parameter at different time points was quantified using receiver operating characteristic curves. RESULTS: Tumor size and quantitative MRI parameters were similar at baseline between groups that achieved pCR (n = 8) and those that did not (n = 20). Patients achieving a pCR had a larger decline in volume and cellularity than those who did not achieve pCR after one cycle of NAT (p < 0.05). At the third and fourth MRI, changes in tumor volume, Ktrans, ADC, cellularity, and bulk tumor flow from baseline (pre-treatment) were all significantly greater (p < 0.05) in the cohort who achieved pCR compared to those patients with non-pCR. CONCLUSIONS: Quantitative analysis of DCE-MRI and DW-MRI can be implemented in the community care setting to accurately predict the response of breast cancer to NAT. Dissemination of quantitative MRI into the community setting allows for the incorporation of these parameters into the standard of care and increases the number of clinical community sites able to participate in novel drug trials that require quantitative MRI.

Recent trends in the age at diagnosis of colorectal cancer in the US National Cancer Data Base, 2004-2015.

BACKGROUND: The incidence of colorectal cancer (CRC) in adults younger than 50 years has increased in the United States over the past decades according to Surveillance, Epidemiology, and End Results data. National guidelines conflict over beginning screening at the age of 45 or 50 years. METHODS: This was a retrospective study of National Cancer Data Base data from 2004 to 2015. The Cochran-Armitage test for trend was used to assess changes in the proportion of cases diagnosed at an age younger than 50 years. RESULTS: This study identified 130,165 patients diagnosed at an age younger than 50 years and 1,055,598 patients diagnosed at the age of 50 years or older. The proportion of the total number of patients diagnosed with CRC at an age younger than 50 years rose (12.2% in 2015 vs 10.0% in 2004; P < .0001). Younger adults presented with more advanced disease (stage III/IV; 51.6% vs 40.0% of those 50 years old or older). Among men, diagnosis at ages younger than 50 years rose only in non-Hispanic whites (P < .0001), whereas among women, Hispanic and non-Hispanic whites had increases in younger diagnoses over time (P < .05). All income quartiles had a proportional increase in younger adults over time (P < .001), with the highest income quartile having the highest proportion of younger cases. The proportion of younger onset CRC cases rose in urban areas (P < .001) but did not rise in rural areas. CONCLUSIONS: The proportion of persons diagnosed with CRC at an age younger than 50 years in the United States has continued to increase over the past decade, and younger adults present with more advanced disease. These data should be considered in the ongoing discussion of screening guidelines.

Translating preclinical MRI methods to clinical oncology.

The complexity of modern in vivo magnetic resonance imaging (MRI) methods in oncology has dramatically changed in the last 10 years. The field has long since moved passed its (unparalleled) ability to form images with exquisite soft-tissue contrast and morphology, allowing for the enhanced identification of primary tumors and metastatic disease. Currently, it is not uncommon to acquire images related to blood flow, cellularity, and macromolecular content in the clinical setting. The acquisition of images related to metabolism, hypoxia, pH, and tissue stiffness are also becoming common. All of these techniques have had some component of their invention, development, refinement, validation, and initial applications in the preclinical setting using in vivo animal models of cancer. In this review, we discuss the genesis of quantitative MRI methods that have been successfully translated from preclinical research and developed into clinical applications. These include methods that interrogate perfusion, diffusion, pH, hypoxia, macromolecular content, and tissue mechanical properties for improving detection, staging, and response monitoring of cancer. For each of these techniques, we summarize the 1) underlying biological mechanism(s); 2) preclinical applications; 3) available repeatability and reproducibility data; 4) clinical applications; and 5) limitations of the technique. We conclude with a discussion of lessons learned from translating MRI methods from the preclinical to clinical setting, and a presentation of four fundamental problems in cancer imaging that, if solved, would result in a profound improvement in the lives of oncology patients. Level of Evidence: 5 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1377-1392.

Repeatability, reproducibility, and accuracy of quantitative mri of the breast in the community radiology setting.

BACKGROUND: Quantitative diffusion-weighted MRI (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI) have the potential to impact patient care by providing noninvasive biological information in breast cancer. PURPOSE/HYPOTHESIS: To quantify the repeatability, reproducibility, and accuracy of apparent diffusion coefficient (ADC) and T1 -mapping of the breast in community radiology practices. STUDY TYPE: Prospective. SUBJECTS/PHANTOM: Ice-water DW-MRI and T1 gel phantoms were used to assess accuracy. Normal subjects (n = 3) and phantoms across three sites (one academic, two community) were used to assess reproducibility. Test-retest analysis at one site in normal subjects (n = 12) was used to assess repeatability. FIELD STRENGTH/SEQUENCE: 3T Siemens Skyra MRI quantitative DW-MRI and T1 -mapping. ASSESSMENT: Quantitative DW-MRI and T1 -mapping parametric maps of phantoms and fibroglandular and adipose tissue of the breast. STATISTICAL TESTS: Average values of breast tissue were quantified and Bland-Altman analysis was performed to assess the repeatability of the MRI techniques, while the Friedman test assessed reproducibility. RESULTS: ADC measurements were reproducible across sites, with an average difference of 1.6% in an ice-water phantom and 7.0% in breast fibroglandular tissue. T1 measurements in gel phantoms had an average difference of 2.8% across three sites, whereas breast fibroglandular and adipose tissue had 8.4% and 7.5% average differences, respectively. In the repeatability study, we found no bias between first and second scanning sessions (P = 0.1). The difference between repeated measurements was independent of the mean for each MRI metric (P = 0.156, P = 0.862, P = 0.197 for ADC, T1 of fibroglandular tissue, and T1 of adipose tissue, respectively). DATA CONCLUSION: Community radiology practices can perform repeatable, reproducible, and accurate quantitative T1 -mapping and DW-MRI. This has the potential to dramatically expand the number of sites that can participate in multisite clinical trials and increase clinical translation of quantitative MRI techniques for cancer response assessment. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Quantitative Correlation of in Vivo Properties with in Vitro Assay Results: The in Vitro Binding of a Biotin-DNA Analogue Modifier with Streptavidin Predicts the in Vivo Avidin-Induced Clearability of the Analogue-Modified Antibody.

Quantitative prediction of in vivo behavior using an in vitro assay would dramatically accelerate pharmaceutical development. However, studies quantitatively correlating in vivo properties with in vitro assay results are rare because of the difficulty in quantitatively understanding the in vivo behavior of an agent. We now demonstrate such a correlation as a case study based on our quantitative understanding of the in vivo chemistry. In an ongoing pretargeting project, we designed a trifunctional antibody (Ab) that concomitantly carried a biotin and a DNA analogue (hereafter termed MORF). The biotin and the MORF were fused into one structure prior to conjugation to the Ab for the concomitant attachment. Because it was known that avidin-bound Ab molecules leave the circulation rapidly, this design would theoretically allow complete clearance by avidin. The clearability of the trifunctional Ab was determined by calculating the blood MORF concentration ratio of avidin-treated Ab to non-avidin-treated Ab using mice injected with these compounds. In theory, any compromised clearability should be due to the presence of impurities. In vitro, we measured the biotinylated percentage of the Ab-reacting (MORF-biotin)⊃-NH2 modifier, by addition of streptavidin to the radiolabeled (MORF-biotin)⊃-NH2 samples and subsequent high-performance liquid chromatography (HPLC) analysis. On the basis of our previous quantitative understanding, we predicted that the clearability of the Ab would be equal to the biotinylation percentage measured via HPLC. We validated this prediction within a 3% difference. In addition to the high avidin-induced clearability of the trifunctional Ab (up to ∼95%) achieved by the design, we were able to predict the required quality of the (MORF-biotin)⊃-NH2 modifier for any given in vivo clearability. This approach may greatly reduce the steps and time currently required in pharmaceutical development in the process of synthesis, chemical analysis, in vitro cell study, and in vivo validation.

Assessment of MRI issues at 7 T for 28 implants and other objects.

OBJECTIVE: Metallic implants are currently a contraindication for volunteer subjects and patients referred for 7-T examinations because of concerns related to magnetic field interactions and MRI-related heating. Artifacts may also be problematic. Therefore, the purpose of this investigation was to evaluate these MRI issues for 28 implants and other objects in association with a 7-T MR system. MATERIALS AND METHODS: Tests were performed at 7 T using standardized procedures to evaluate magnetic field interactions (translational attraction and torque) for all 28 items. MRI-related heating and artifacts were assessed using spin-echo and gradient-echo pulse sequences, respectively, for two aneurysm clips located within a transmit-receive head radiofrequency coil. RESULTS: Eight of the 28 items showed magnetic field interactions at levels that could pose risks to human subjects. The two aneurysm clips exhibited heating, but the temperature rise did not exceed 1°C. Artifacts were dependent on the material and dimensions of each aneurysm clip. CONCLUSION: These findings show that certain implants and objects may be acceptable for human subjects undergoing MRI examinations at 7 T, whereas others may involve possible risks. This information has important implications for individuals referred for MRI examinations at 7 T.

Multimodal image coregistration and inducible selective cell ablation to evaluate imaging ligands.

We combined multimodal imaging (bioluminescence, X-ray computed tomography, and PET), tomographic reconstruction of bioluminescent sources, and two unique, complementary models to evaluate three previously synthesized PET radiotracers thought to target pancreatic beta cells. The three radiotracers {[(18)F]fluoropropyl-(+)-dihydrotetrabenazine ([(18)F]FP-DTBZ), [(18)F](+)-2-oxiranyl-3-isobutyl-9-(3-fluoropropoxy)-10-methoxy-2,3,4,6,7,11b-hexahydro-1H-pyrido[2,1-a]isoquinoline ((18)F-AV-266), and (2S,3R,11bR)-9-(3-fluoropropoxy)-2-(hydroxymethyl)-3-isobutyl-10-methoxy-2,3,4,6,7,11b-hexahydro-1H-pyrido[2,1-a]isoquinolin-2-ol ((18)F-AV-300)} bind vesicular monoamine transporter 2. Tomographic reconstruction of the bioluminescent signal in mice expressing luciferase only in pancreatic beta cells was used to delineate the pancreas and was coregistered with PET and X-ray computed tomography images. This strategy enabled unambiguous identification of the pancreas on PET images, permitting accurate quantification of the pancreatic PET signal. We show here that, after conditional, specific, and rapid mouse beta-cell ablation, beta-cell loss was detected by bioluminescence imaging but not by PET imaging, given that the pancreatic signal provided by three PET radiotracers was not altered. To determine whether these ligands bound human beta cells in vivo, we imaged mice transplanted with luciferase-expressing human islets. The human islets were imaged by bioluminescence but not with the PET ligands, indicating that these vesicular monoamine transporter 2-directed ligands did not specifically bind beta cells. These data demonstrate the utility of coregistered multimodal imaging as a platform for evaluation and validation of candidate ligands for imaging islets.

Cross-sectional imaging of the pancreas in diabetes.

Diabetes mellitus presents a global health challenge characterized by dysregulated glucose metabolism and insulin resistance. Pancreas dysfunction contributes to the development and progression of diabetes. Cross-sectional imaging modalities have provided new insight into the structural and functional alterations of the pancreas in individuals with diabetes. This review summarizes MRI and CT studies that characterize pancreas alterations in both type 1 and type 2 diabetes and discusses future applications of these techniques.

Disparities in the Demographic Composition of The Cancer Imaging Archive.

Purpose To characterize the demographic distribution of The Cancer Imaging Archive (TCIA) studies and compare them with those of the U.S. cancer population. Materials and Methods In this retrospective study, data from TCIA studies were examined for the inclusion of demographic information. Of 189 studies in TCIA up until April 2023, a total of 83 human cancer studies were found to contain supporting demographic data. The median patient age and the sex, race, and ethnicity proportions of each study were calculated and compared with those of the U.S. cancer population, provided by the Surveillance, Epidemiology, and End Results Program and the Centers for Disease Control and Prevention U.S. Cancer Statistics Data Visualizations Tool. Results The median age of TCIA patients was found to be 6.84 years lower than that of the U.S. cancer population (P = .047) and contained more female than male patients (53% vs 47%). American Indian and Alaska Native, Black or African American, and Hispanic patients were underrepresented in TCIA studies by 47.7%, 35.8%, and 14.7%, respectively, compared with the U.S. cancer population. Conclusion The results demonstrate that the patient demographics of TCIA data sets do not reflect those of the U.S. cancer population, which may decrease the generalizability of artificial intelligence radiology tools developed using these imaging data sets. Keywords: Ethics, Meta-Analysis, Health Disparities, Cancer Health Disparities, Machine Learning, Artificial Intelligence, Race, Ethnicity, Sex, Age, Bias Published under a CC BY 4.0 license.

Changes in Pancreas Volume in Living Donor Liver Transplant Recipients.

BACKGROUND: Metabolic factors have a significant role in the morbidity and mortality associated with chronic liver disease. The pancreas has a central role in metabolism and metabolic risk factors but has been largely ignored in liver transplantation. Small pancreas volume has been demonstrated in pathologic conditions such as type 1 and 2 diabetes. METHODS: This study assessed abdominal imaging before and after liver transplantation to determine if liver transplantation induces changes in pancreas volume in living donor liver transplant recipients. Our secondary outcome is to correlate pancreas volume with demographic, clinical, and outcome data. We conducted a retrospective study of pancreas volume in patients enrolled in the adult-to-adult living donor liver transplantation cohort study. Pancreas volume was manually calculated from 413 MRI or computed tomography images and correlated with imaging and clinical data. RESULTS: Pancreas volume declined by an average of 24% (87.8 ±â€…25.2 mL to 66.8 ±â€…20.4 mL, P < 0.0001), regardless of liver disease etiology. Pancreas volume correlated with portal blood flow, spleen volume, and liver enzyme levels. We found a correlation between smaller pancreas volume pretransplant and longer intensive care unit (ICU) stay across all patients (P < 0.05). Individuals with an ICU stay of <2 d had a larger average pancreas volume pretransplant than those with an ICU stay of 2 d or longer (91.2 mL versus 82.2 mL, P < 0.05). CONCLUSIONS: Pancreas volume is dynamic in liver transplant recipients and may reflect altered metabolism and risk of posttransplantation complications.

Longitudinal Assessment of Pancreas Volume by MRI Predicts Progression to Stage 3 Type 1 Diabetes.

OBJECTIVE: This multicenter prospective cohort study compared pancreas volume as assessed by MRI, metabolic scores derived from oral glucose tolerance testing (OGTT), and a combination of pancreas volume and metabolic scores for predicting progression to stage 3 type 1 diabetes (T1D) in individuals with multiple diabetes-related autoantibodies. RESEARCH DESIGN AND METHODS: Pancreas MRI was performed in 65 multiple autoantibody-positive participants enrolled in the Type 1 Diabetes TrialNet Pathway to Prevention study. Prediction of progression to stage 3 T1D was assessed using pancreas volume index (PVI), OGTT-derived Index60 score and Diabetes Prevention Trial-Type 1 Risk Score (DPTRS), and a combination of PVI and DPTRS. RESULTS: PVI, Index60, and DPTRS were all significantly different at study entry in 11 individuals who subsequently experienced progression to stage 3 T1D compared with 54 participants who did not experience progression (P < 0.005). PVI did not correlate with metabolic testing across individual study participants. PVI declined longitudinally in the 11 individuals diagnosed with stage 3 T1D, whereas Index60 and DPTRS increased. The area under the receiver operating characteristic curve for predicting progression to stage 3 from measurements at study entry was 0.76 for PVI, 0.79 for Index60, 0.79 for DPTRS, and 0.91 for PVI plus DPTRS. CONCLUSIONS: These findings suggest that measures of pancreas volume and metabolism reflect distinct components of risk for developing stage 3 type 1 diabetes and that a combination of these measures may provide superior prediction than either alone.

A novel reporter system for molecular imaging and high-throughput screening of anticancer drugs.

Apoptosis is irreversible programmed cell death, characterized by a cellular cascade activation of caspase 3, which subsequently degrades proteins and other components of cells with a motif sequence. Here we report a novel reporter system to detect apoptosis, growth arrest, and cell death based on controlled and self-amplified protein degradation. The key element of the reporter system is an apoptotic sensor chimerical protein which consists of three components: procaspase 3, ubiquitin (Ub), and a strong consensus sequence of N-degron. Between each of these units is a DEVD (Asp-Glu-Val-Asp) sequence, which acts as the cleavage target of caspase 3. This non-conventional signal loss approach is much more sensitive than other native methods that are based on signal gain. The superior sensitivity is demonstrated by its effective application in 386-well high-throughput screening (HTS) with low drug concentrations and a short incubation time. The HTS selection process using this reporter system is very simple and economic. The simplicity eliminates potential errors introduced by multiple steps; there is no need for any substrate. Furthermore, the cells in the assay need not be disrupted, and the morphology of the cells can provide additional information on mechanisms. After HTS, the intact cells can also be used for other analytic analysis. This system thus has a potentially important role in the discovery and development of new anticancer drugs. It also appears to be very versatile, can be used both in vitro and in vivo with different linked reporter genes, and can be used for a variety of imaging applications.

Testing a new platform to screen disease-modifying therapy in type 1 diabetes.

Studies of new therapies to preserve insulin secretion in early type 1 diabetes require several years to recruit eligible subjects and to see a treatment effect; thus, there is interest in alternative study designs to speed this process. Most people with longstanding type 1 diabetes no longer secrete insulin. However, studies from pancreata of those with longstanding T1D show that beta cells staining for insulin can persist for decades after diagnosis, and this is paralleled in work showing proinsulin secretion in individuals with longstanding disease; collectively this suggests that there is a reserve of alive but "sleeping" beta cells. Here, we designed a novel clinical trial platform to test whether a short course of therapy with an agent known to have effects in type 1 diabetes with residual endogenous insulin could transiently induce insulin secretion in those who no longer produce insulin. A therapy that transiently "wakes up" sleeping beta cells might be tested next in a fully powered trial in those with endogenous insulin secretion. In this three-arm non-randomized pilot study, we tested three therapies known to impact disease: two beta-cell supportive agents, liraglutide and verapamil, and an immunomodulatory agent, golimumab. The golimumab treated arm was not fully enrolled due to uncertainties about immunotherapy during the COVID-19 pandemic. Participants had mixed-meal tolerance test (MMTT)-stimulated C-peptide below the quantitation limit (<0.02 ng/mL) at enrollment and received 8 to 12 weeks of therapy. At the completion of therapy, none of the individuals achieved the primary outcome of MMTT-stimulated C-peptide ≥ 0.02 ng/mL. An exploratory outcome of the verapamil arm was MRI-assessed pancreas size, diffusion, and longitudinal relaxation time, which showed repeatability of these measures but no treatment effect. The liraglutide and golimumab arms were registered on clinicaltrials.gov under accession number NCT03632759 and the verapamil arm under accession number NCT05847413. Trail registration: Protocols are registered in ClinicalTrials.gov under accession numbers NCT03632759 and NCT05847413.

Longitudinal MRI Shows Progressive Decline in Pancreas Size and Altered Pancreas Shape in Type 1 Diabetes.

CONTEXT: Individuals with type 1 diabetes (T1D) have a smaller pancreas, but longitudinal changes in pancreas size and shape are unclear. OBJECTIVE: We monitored changes in pancreas size and shape after diagnosis with T1D. DESIGN: We conducted a prospective cohort study at an academic medical center between 2014 and 2022. PATIENTS AND HEALTHY CONTROLS: Individuals with T1D (n = 91) or controls (n = 90) underwent magnetic resonance imaging (MRI) of the pancreas, including longitudinal MRI in 53 individuals with new-onset T1D. INTERVENTION: Interventions included MRI and continuous glucose monitoring (CGM). MAIN OUTCOME MEASURES: Pancreas size and shape were measured from MRI. For participants who used CGM, measures of glycemic variability were calculated. RESULTS: On longitudinal imaging, pancreas volume and pancreas volume index normalized for body weight declined during the first year after diagnosis. Pancreas volume index continued to decline through the fifth year after diagnosis. A cross-sectional study of individuals with diabetes duration up to 60 years demonstrated that pancreas size in adults negatively correlated with age and disease duration, whereas pancreas volume and pancreas volume index remained stable in controls. Pancreas volume index correlated inversely with low blood glucose index, a measure of risk for hypoglycemia. Pancreas shape was altered in individuals with T1D and further diverged from controls over the first 5 years after diagnosis. Pancreas size and shape are altered in nondiabetic individuals at genetic risk for T1D. Combined pancreas size and shape analysis better distinguished the pancreas of individuals with T1D from controls than size alone. CONCLUSIONS: Pancreas size declines most rapidly near the clinical diagnosis of T1D and continues to decline throughout adulthood. Declines in pancreas size are accompanied by changes in pancreas shape.