Dynamic contrast enhanced MRI demonstrate altered placental perfusion in the STOX1A preeclampsia mouse model.

INTRODUCTION: Preeclampsia, a hypertensive disorder of pregnancy triggered by placental dysfunction, is reproduced in the murine STOX1A model, with hypertension, proteinuria, and abnormalities in umbilical and uterine Dopplers. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an innovative technique that provides insights into tissue perfusion. The present study aims at analyzing placental perfusion using DCE-MRI to further characterize placental defects in the STOX1A model. METHODS: Two study groups were formed: the "TgSTOX13 pregnancy group" from mating TgSTOX13 genotype males with wild-type females, and the "wild-type pregnancy group" from mating wild-type males with wild-type females. Blood pressure, urinary albumin to creatinine ratio, and fetal weights were measured and compared between the groups, while perfusion parameters were analyzed using both conventional compartmental (1C) and free-time point-Hermite (FTPH) models in the DCE analysis. RESULTS: Seventeen pregnant mice in the "TgSTOX13 pregnancy group" and thirteen in the "wild-type pregnant group" were included in the analysis. During late gestation, the TgSTOX13 pregnancy group exhibited higher blood pressure, elevated albumin/creatinine ratio, and decreased fetal weights compared to the wild-type pregnancy group. In the DCE analysis utilizing the 1C model, blood flow (Fb) was significantly reduced by approximately 31.8 % in the TgSTOX13 pregnancy group compared to the wild-type pregnancy group (p < 0.01), a finding corroborated by the FTPH model with a reduction estimated at 31.5 % (p < 0.01). DISCUSSION: Our investigation successfully utilized DCE MRI to assess placental perfusion in a mouse model of preeclampsia, revealing a significant reduction of approximately 30 % in the preeclamptic mice, mirroring human pathophysiology.

Improved survival and decreased cancer deaths in young adults with cancer after passage of the Affordable Care Act Dependent Coverage Expansion.

BACKGROUND: The Patient Protection and Affordable Care Act (ACA) allowed Americans aged 19-25 years to remain on their parents' health insurance plans until age 26 years (the Dependent Care Expansion [DCE]). Have those with cancer diagnoses benefited? METHODS: The ACE DCE 7-year age range of 19-25 years was compared for changes in cancer survival and mortality before and after enactment of the ACA with groups that were younger and older (in 7-year age spans: ages 12-18 and 26-32 years, respectively). Cancer death data for the entire United States were obtained from the Centers for Disease Control and Prevention, and relative survival data of patients who were diagnosed with cancer were obtained from the National Cancer Institute Surveillance, Epidemiology, and End Results regions representing 42%-44% of the country. RESULTS: Joinpoint analysis identified the DCE-eligible cohort as the only age group of the three groups evaluated that have had improvements in both cancer survival and death rate trends after ACA implementation and that 2010, the year the ACA was passed, was the inflection year for both survival and deaths. By 6 years, the relative survival after cancer diagnosis was 2.6 and 3.9 times greater in the DCE-eligible age group than in the younger and older control groups, respectively (both p < .001), and the cancer death rate in the DCE-eligible age group improved 2.1 and 1.5 times greater than in the younger and older control age groups, respectively (both p < .01). CONCLUSIONS: During the first decade of the ACA, eligible young adults with cancer have had significantly improved survival and mortality. Additional policies expanding insurance coverage and enabling earlier cancer diagnosis among young adults are needed. PLAIN LANGUAGE SUMMARY: The Patient Protection and Affordable Care Act (ACA) Dependent Care Expansion (DCE) that began in the United States in 2011 allowed young adults aged 19-25 years to remain on their parents' health insurance plans until age 26 years. The survival rate at 6 years in young adult patients diagnosed with cancer was 2.6 to 3.9 times greater in the DCE-eligible age group compared with the younger and older age groups, and the rate of deaths from cancer improved 1.5 to 2.1 times more. During the first decade of the ACA, young adults with cancer who were in the eligible group had significantly longer survival and reduced deaths from cancer. Additional policies that expand insurance coverage and allow the diagnosis of cancer sooner are needed in young adults.

An adaptive parameter decoupling algorithm-based image reconstruction model (ADAIR) for rapid golden-angle radial DCE-MRI.

Objective. The acceleration of magnetic resonance imaging (MRI) acquisition is crucial for both clinical and research applications, particularly in dynamic MRI. Existing compressed sensing (CS) methods, despite being effective for fast imaging, face limitations such as the need for incoherent sampling and residual noise, which restrict their practical use for rapid MRI.Approach. To overcome these challenges, we propose a novel image reconstruction framework that integrates the MRI physical model with a flexible, self-adjusting, decoupling data-driven model. We validated this method through experiments using both simulated andin vivodynamic contrast-enhanced MRI datasets.Main results. The experimental results demonstrate that the proposed framework achieves high spatial and temporal resolution reconstructions. Additionally, when compared to state-of-the-art image reconstruction approaches, our method significantly enhances acceleration capabilities, enabling sparse and rapid imaging with high resolution.Significance. Our proposed framework offers a promising solution for real-time imaging and image-guided radiation therapy applications by providing superior performance in achieving high spatial and temporal resolution reconstructions, thus addressing the limitations of existing CS schemes.

Barriers of the CNS transfer rate dynamics in patients with vascular cognitive impairment and dementia.

Background: Advances in in vivo MRI techniques enable cerebral barrier transfer rates (K trans ) measurement in patients with vascular cognitive impairment and dementia (VCID). However, a consensus has not been reached on the dynamic contribution and importance of cerebral barrier abnormalities to the differential diagnosis of dementia subtypes. Our goal was to investigate the dynamics of blood-brain barrier (BBB) and blood-CSF barrier (BCSFB) K trans in patients with VCID longitudinally and determine the effect of aging. Methods: We studied subjects at two time points over two years; they were 65.5 years of age (SD = 15.94, M/F = 24/14) at the first visit. We studied 38 patients, 18 of whom had two visits. We calculated the BBB and BCSFB K trans with dynamic contrast-enhanced T1 MR, and we used 1H-MR spectroscopy to measure N-acetylaspartate (NAA) levels in the white matter as a marker of injury. In addition, we measured CSF levels of active-matrix metalloproteinase-3 (MMP3) as an inflammatory biomarker to aid in patient clustering. Results: Longitudinal BBB measurements revealed variable dynamic behavior: after two years, the BBB K trans increased in 55% of patients and decreased in the remaining 45% unpredictably. We did not find a significant linear model of BBB K trans versus age for VCID. For healthy controls, the model was K trans = 0.0014 + 0.0002 × age, which was significant (p = 0.046). VCID patients showed a reduction in BCSFB K trans compared to healthy controls (p = 0.01). Combining NAA, CSF MMP3, and K trans in a clustering analysis separated patients into groups. Conclusion: These results suggest that BBB K trans in VCID is dynamic and BCSFB K trans reduced by age. By combining inflammatory biomarkers with BBB K trans data, it is possible to separate VCID patients into distinct groups with different underlying pathologies.

Neuroinflammation and blood-brain barrier breakdown in acute, clinical intracerebral hemorrhage.

Neuroinflammation is a promising therapeutic target in intracerebral hemorrhage (ICH), characterized in the brain by microglial activation and blood-brain barrier (BBB) breakdown. In this study, 36 acute, spontaneous, supratentorial ICH patients underwent dynamic contrast-enhanced MRI to measure BBB permeability (Ktrans) 1-3 days post-onset and 16 returned for [11C](R)-PK11195 PET to quantify microglial activation (BPND), 2-7 days post-onset. We first tested if these markers were increased and co-localized in the perihematomal brain and found that perihematomal Ktrans and BPND were increased vs. the contralateral brain, but regions of high Ktrans and BPND only overlapped by a mean of 4.9%. We then tested for associations of perihematomal Ktrans and BPND with clinical characteristics (age, ICH volume & location, blood pressure), other markers of inflammation (edema, IL-6, and CRP), and long-term functional outcome (90-day mRS). Lower perihematomal BPND was associated with increasing age. Lobar hemorrhage was associated with greater Ktrans than deep, but Ktrans and BPND were not associated with ICH volume, or other inflammatory markers. While perihematomal Ktrans and BPNDwere not associated with outcome, contralateral Ktrans was significantly associated with greater 90-day mRS. Exploratory analyses demonstrated that blood pressure variability over 72 h was also associated with contralateral Ktrans.

Role of Intravoxel Incoherent Motion and Dynamic-Contrast-Enhanced Perfusion in Response Prediction of Patients with Colorectal Cancer after Neoadjuvant Chemotherapy.

BACKGROUND: Colorectal cancer (CRC) is a complex malignancy requiring multimodal treatment strategies, including neoadjuvant chemoradiation therapy (Neo-CRT), to improve patient outcomes. However, the response to Neo-CRT varies among individuals, which necessitates the development of reliable predictors of treatment response. The present study aimed to investigate the role of intravoxel incoherent motion (IVIM) and dynamic contrast-enhanced (DCE) perfusion in predicting treatment response in CRC patients after Neo-CRT. METHODS: This study was conducted on patients diagnosed with locally advanced CRC who received Neo-CRT. IVIM and DCE perfusion imaging were performed before and after CRT. Quantitative parameters, including perfusion fraction (f), diffusion coefficient (D), and transfer constant (Ktrans), were calculated from the imaging data. Treatment response was assessed based on the pathological response after surgery. Statistical data were analyzed in SPSS v. 26 using the t-test and the chi-square method. RESULTS: A total of 51 patients (female: male = 22:29, mean age = 58.14±3.49) participated in the study. Among all the patients, 15 (29.4%) cases had good responses, while 36 (70.58%) cases did not respond to treatment. All DCE parameters showed higher sensitivity and specificity than IVIM D*. Ve, Kep, and DCE Ktrans indicated significant predictive power for treatment response. Ktrans was the most accurate parameter for predicting response to treatment. Overall sensitivity and specificity of DCE were 88.8% [95% CI: 80-95.6], and 80 % [95% CI: 65-90], and those of IVIM were 65.5% and 81%, respectively. Sensitivity and specificity for DCE + IVIM were 79.5% and 93.5%, and those of DCE + IVIM + standard magnetic resonance imaging were 80.2% and 86%, respectively. CONCLUSION: IVIM and DCE perfusion imaging could serve as promising tools for predicting treatment response in CRC patients after Neo-CRT.

Exploring the Neural Mechanisms of Mirrored-Self Misidentification in Alzheimer's Disease.

OBJECTIVE: Alzheimer's disease (AD) is a complex neurodegenerative condition that causes a range of cognitive disturbances, including mirror-self misidentification syndrome (MSM), in which patients cannot recognize themselves in a mirror. However, the mechanism of action of MSM is not precisely known. This study aimed to explore the possible neural mechanisms of action of MSM in AD using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). METHODS: This study included 48 AD patients, 13 in the MSM group and 35 in the non-MSM group. The permeability of the blood-brain barrier (BBB) was quantitatively monitored by measuring the transfer rate (Ktrans) of the contrast agent from the vasculature to the surrounding tissue using DCE-MRI. The concentration of contrast agents in different brain regions was measured, and the Patlak model was used to calculate Ktrans. Ktrans values were compared between the left and right cerebral hemispheres in different brain areas between the MSM and non-MSM groups. Additionally, the difference in Ktrans values between mild and severe MSM was assessed. Logistic regression analysis was used to examine the risk factors for MSM. RESULTS: The Mann‒Whitney U test was used to compare two groups and revealed elevated Ktrans values in the left thalamus, left putamen, left globus pallidus, left corona radiata, and right caudate in the MSM group (p < 0.05). Logistic regression analysis revealed that increased Ktrans values in the left putamen (OR = 1.53, 95% CI = 1.04, 2.26) and left globus pallidus (OR = 1.54, 95% CI = 1.02, 2.31) may be risk factors for MSM. After dividing MSM patients into mild and moderate-severe groups, the Ktrans values of the thalamus in the moderate-severe group were greater than those in the mild group (p < 0.05). CONCLUSION: Our study revealed the relationship between BBB permeability and MSM in AD. MSM is associated with BBB breakdown in the left putamen and globus pallidus. The left putamen and globus pallidus may function in mirror self-recognition. Higher BBB permeability in the thalamus may reflect the severity of AD in MSM.

Dynamic Contrast Enhanced MRI Mapping of Vascular Permeability for Evaluation of Breast Cancer Neoadjuvant Chemotherapy Response Using Image-to-Image Conditional Generative Adversarial Networks.

Dynamic contrast enhanced (DCE) MRI is a non-invasive imaging technique that has become a quantitative standard for assessing tumor microvascular permeability. Through the application of a pharmacokinetic (PK) model to a series of T1-weighed MR images acquired after an injection of a contrast agent, several vascular permeability parameters can be quantitatively estimated. These parameters, including Ktrans, a measure of capillary permeability, have been widely implemented for assessing tumor vascular function as well as tumor therapeutic response. However, conventional PK modeling for translation of DCE MRI to PK vascular permeability parameter maps is complex and time-consuming for dynamic scans with thousands of pixels per image. In recent years, image-to-image conditional generative adversarial network (cGAN) is emerging as a robust approach in computer vision for complex cross-domain translation tasks. Through a sophisticated adversarial training process between two neural networks, image-to-image cGANs learn to effectively translate images from one domain to another, producing images that are indistinguishable from those in the target domain. In the present study, we have developed a novel image-to-image cGAN approach for mapping DCE MRI data to PK vascular permeability parameter maps. The DCE-to-PK cGAN not only generates high-quality parameter maps that closely resemble the ground truth, but also significantly reduces computation time over 1000-fold. The utility of the cGAN approach to map vascular permeability is validated using open-source breast cancer patient DCE MRI data provided by The Cancer Imaging Archive (TCIA). This data collection includes images and pathological analyses of breast cancer patients acquired before and after the first cycle of neoadjuvant chemotherapy (NACT). Importantly, in good agreement with previous studies leveraging this dataset, the percentage change of vascular permeability Ktrans derived from the DCE-to-PK cGAN enables early prediction of responders to NACT.

Predictive potential of dynamic contrast-enhanced MRI and plasma-derived angiogenic factors for response to concurrent chemoradiotherapy in human papillomavirus-negative oropharyngeal cancer.

BACKGROUND: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can assess tumour vascularity, which depends on the process of angiogenesis and affects tumour response to treatment. Our study explored the associations between DCE-MRI parameters and the expression of plasma angiogenic factors in human papilloma virus (HPV)-negative oropharyngeal cancer, as well as their predictive value for response to concurrent chemoradiotherapy (cCRT). PATIENTS AND METHODS: Twenty-five patients with locally advanced HPV-negative oropharyngeal carcinoma were prospectively enrolled in the study. DCE-MRI and blood plasma sampling were conducted before cCRT, after receiving a radiation dose of 20 Gy, and after the completion of cCRT. Perfusion parameters ktrans, kep, Ve, initial area under the curve (iAUC) and plasma expression levels of angiogenic factors (vascular endothelial growth factor [VEGF], connective tissue growth factor [CTGF], platelet-derived growth factor [PDGF]-AB, angiogenin [ANG], endostatin [END] and thrombospondin-1 [THBS1]) were measured at each time-point. Patients were stratified into responders and non-responders based on clinical evaluation. Differences and correlations between measures were used to generate prognostic models for response prediction. RESULTS: Higher perfusion parameter ktrans and higher plasma VEGF levels successfully discriminated responders from non-responders across all measured time-points, whereas higher iAUC and higher plasma PDGF-AB levels were also discriminative at selected time points. Using early intra-treatment measurements of ktrans and VEGF, a predictive model was created with cut-off values of 0.259 min-1 for ktrans and 62.5 pg/mL for plasma VEGF. CONCLUSIONS: Early intra-treatment DCE-MRI parameter ktrans and plasma VEGF levels may be valuable early predictors of response to cCRT in HPV-negative oropharyngeal cancer.

A Deep Learning-Based Framework for Highly Accelerated Prostate MR Dispersion Imaging.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) measures microvascular perfusion by capturing the temporal changes of an MRI contrast agent in a target tissue, and it provides valuable information for the diagnosis and prognosis of a wide range of tumors. Quantitative DCE-MRI analysis commonly relies on the nonlinear least square (NLLS) fitting of a pharmacokinetic (PK) model to concentration curves. However, the voxel-wise application of such nonlinear curve fitting is highly time-consuming. The arterial input function (AIF) needs to be utilized in quantitative DCE-MRI analysis. and in practice, a population-based arterial AIF is often used in PK modeling. The contribution of intravascular dispersion to the measured signal enhancement is assumed to be negligible. The MR dispersion imaging (MRDI) model was recently proposed to account for intravascular dispersion, enabling more accurate PK modeling. However, the complexity of the MRDI hinders its practical usability and makes quantitative PK modeling even more time-consuming. In this paper, we propose fast MR dispersion imaging (fMRDI) to effectively represent the intravascular dispersion and highly accelerated PK parameter estimation. We also propose a deep learning-based, two-stage framework to accelerate PK parameter estimation. We used a deep neural network (NN) to estimate PK parameters directly from enhancement curves. The estimation from NN was further refined using several steps of NLLS, which is significantly faster than performing NLLS from random initializations. A data synthesis module is proposed to generate synthetic training data for the NN. Two data-processing modules were introduced to improve the model's stability against noise and variations. Experiments on our in-house clinical prostate MRI dataset demonstrated that our method significantly reduces the processing time, produces a better distinction between normal and clinically significant prostate cancer (csPCa) lesions, and is more robust against noise than conventional DCE-MRI analysis methods.

DCE-MRI of the liver with sub-second temporal resolution using GRASP-Pro with navi-stack-of-stars sampling.

Respiratory motion-induced image blurring and artifacts can compromise image quality in dynamic contrast-enhanced MRI (DCE-MRI) of the liver. Despite remarkable advances in respiratory motion detection and compensation in past years, these techniques have not yet seen widespread clinical adoption. The accuracy of image-based motion detection can be especially compromised in the presence of contrast enhancement and/or in situations involving deep and/or irregular breathing patterns. This work proposes a framework that combines GRASP-Pro (Golden-angle RAdial Sparse Parallel MRI with imProved performance) MRI with a new radial sampling scheme called navi-stack-of-stars for free-breathing DCE-MRI of the liver without the need for explicit respiratory motion compensation. A prototype 3D golden-angle radial sequence with a navi-stack-of-stars sampling scheme that intermittently acquires a 2D navigator was implemented. Free-breathing DCE-MRI of the liver was conducted in 24 subjects at 3T including 17 volunteers and 7 patients. GRASP-Pro reconstruction was performed with a temporal resolution of 0.34-0.45 s per volume, whereas standard GRASP reconstruction was performed with a temporal resolution of 15 s per volume. Motion compensation was not performed in all image reconstruction tasks. Liver images in different contrast phases from both GRASP and GRASP-Pro reconstructions were visually scored by two experienced abdominal radiologists for comparison. The nonparametric paired two-tailed Wilcoxon signed-rank test was used to compare image quality scores, and the Cohen's kappa coefficient was calculated to evaluate the inter-reader agreement. GRASP-Pro MRI with sub-second temporal resolution consistently received significantly higher image quality scores (P < 0.05) than standard GRASP MRI throughout all contrast enhancement phases and across all assessment categories. There was a substantial inter-reader agreement for all assessment categories (ranging from 0.67 to 0.89). The proposed technique using GRASP-Pro reconstruction with navi-stack-of-stars sampling holds great promise for free-breathing DCE-MRI of the liver without respiratory motion compensation.

Evaluating Preferences of Hospitalized Diabetes Patients for Hospital-Wide Glycemic Control Programme: A Discrete Choice Experiment.

Background: Effective glycemic control is crucial for hospitalized patients, leading to benefits such as shorter hospital stays and reduced postoperative infection rates. While previous studies have emphasized the effectiveness of multidisciplinary collaborative stewardship for hospital-wide hyperglycemia management, patient perspectives and preferences have not been adequately considered. Objective: To identify factors influencing treatment preferences of Chinese hospitalized diabetes patients using discrete choice experiments (DCEs) and provide practical insights for the construction of a hospital-wide glycemic control programme. Methods: A face-to-face survey was conducted among diabetes patients admitted to nonendocrine departments in a tertiary hospital in Nanjing, China. The attributes and levels were determined based on DCE principles, and a conditional logit model was used to quantify patients' preferences. Results: A total of 157 respondents were analyzed. Antihyperglycemic effectiveness, healthcare providers, treatment regimen, monitoring frequency, and adverse reactions were the five attributes that significantly influenced patient preference (p < 0.05). Notably, an 80% glycemic control rate (ß = 2.009) and a multidisciplinary management team involving clinical pharmacists (ß = 1.346) had the greatest impact. Negative effects were observed for hypoglycemia (ß = -1.008), insulin pump use (ß = -0.746), and frequent glucose monitoring (ß = -0.523). Female patients exhibited higher concern for healthcare providers (ß = 1.172) compared to males. Younger and shorter-course patients prioritized antihyperglycemic effectiveness (ß = 3.330, ß = 1.510), while older patients preferred multidisciplinary management (ß = 1.186) and opposed increased monitoring frequency (ß = -0.703). Patients with higher educational backgrounds showed greater acceptance of continuous glucose monitoring (ß = 1.983), and those with higher annual income placed more emphasis on glycemic control rate. Conclusion: Treatment preferences of hospitalized diabetes patients are mainly influenced by antihyperglycemic effectiveness, adverse reactions, healthcare providers, and individual characteristics. Comprehensive consideration and an individualized therapy strategy should be given when constructing a hospital-wide glycemic control programme.

Tumor enhancement and shrinkage pattern in dynamic contrast-enhanced magnetic resonance imaging for predicting pathologic complete response after human epidermal growth factor receptor 2 (HER2)-targeted therapy in breast cancer.

Background: Targeted therapy with neoadjuvant chemotherapy for patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer has increased the rates of pathological complete response (pCR) and breast preservation surgery and improved the overall disease-free survival rate. This study aimed to determine whether tumor enhancement and shrinkage patterns in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict the efficacy of targeted therapy in patients with HER2-positive breast cancer and differentiate pCR from non-pCR. Methods: The data of 64 patients with HER2-positive breast cancer who received targeted therapy prior to surgery were retrospectively collected. All patients had complete postoperative pathological data. The pretreatment evaluation of the tumor enhancement pattern and the shrinkage pattern after two treatment cycles were assessed. The difference in the enhancement and shrinkage patterns between the pCR and non-pCR groups was evaluated via the χ2 test. Logistic regression analysis was used to assess the value of enhancement and shrinkage patterns for predicting pCR in patients with HER2-positive breast cancer. Results: There were statistically significant differences in tumor size, estrogen receptor (ER) status, lymph node metastasis, enhancement pattern, and shrinkage pattern between the pCR and non-pCR cases. Patients with a tumor size ≤20 mm were likely to achieve pCR. ER status, lymph node metastasis, and enhancement and shrinkage patterns each had good precision for predicting pCR, and the combination of enhancement and shrinkage patterns had the highest prediction accuracy. Multivariate logistic regression analysis indicated that only enhancement pattern had a significant predictive value. Conclusions: Among patients with HER2-positive breast cancer, those with tumor size ≤20 mm, ER-negative status, no lymph node metastases, and mass enhancement and concentric shrinkage patterns are more likely to achieve pCR. Mass enhancement combined with concentric shrinkage had the highest accuracy in predicting pCR, indicating that preoperative imaging may be useful for guiding clinical decisions regarding targeted treatments.

Perspectives and Preferences of People with Type 2 Diabetes for the Attributes of Weekly Insulin.

INTRODUCTION: Daily insulin administration can be burdensome for people with type 2 diabetes (PwT2D) and can impact treatment adherence. This study investigated preferences for once-weekly, long-acting basal insulin for treatment of PwT2D. METHODS: An online discrete-choice experiment was administered to PwT2D in the USA. Qualitative interviews informed the selection of six attributes: reduction in A1c level after 6 months, amount of time spent in optimal blood sugar range each day, number of serious low blood sugar events, number of nighttime low blood sugar events, change in weight because of the insulin over 6 months, and frequency of administration. Each participant completed eight questions offering a choice between two long-acting insulins; questions varied according to an experimental design. A fixed treatment choice question asked about preferences for daily versus weekly insulin, holding other treatment features constant. Data were analyzed using random-parameters logit models, and heterogeneity was explored through subgroup analyses. RESULTS: Four hundred sixty-six PwT2D completed the survey (mean age, 57; mean A1c, 7.5%; 59.0% female); 33.3% of these were currently on a basal/bolus regimen, 34.3% used basal only, and 32.4% were insulin naive. Respondents placed the most importance on avoiding a 10-pound weight change and equal importance on the largest change in the number of serious and nighttime low blood sugar events per year and achieving the longest time in range included in the choice questions. There was significant heterogeneity in preferences by experience: insulin-naive respondents had stronger preferences for scheduled and flexible weekly insulin over daily insulin; 67.6% preferred flexible weekly over daily insulin, all else being equal. CONCLUSION: PwT2D valued insulin efficacy and reducing treatment-related adverse events, with heterogeneity in the relative importance of administration frequency. All else being equal, respondents preferred weekly over daily basal insulin. These findings provide insights into the preferences of PwT2D considering weekly long-acting insulin.

Perfusion-weighted imaging with dynamic contrast enhancement (PWI/DCE) morphologic, qualitative, semiquantitative, and radiomics features predicting undifferentiated pleomorphic sarcoma (UPS) treatment response.

Undifferentiated pleomorphic sarcoma (UPS) is the largest subgroup of soft tissue sarcomas. This study determined the value of perfusion-weighted imaging with dynamic-contrast-enhancement (PWI/DCE) morphologic, qualitative, and semiquantitative features for predicting UPS pathology-assessed treatment effect (PATE). This retrospective study included 33 surgically excised extremity UPS patients with pre-surgical MRI. Volumetric tumor segmentation from PWI/DCE was obtained at Baseline (BL), Post-Chemotherapy (PC), and Post-Radiation Therapy (PRT). The surgical specimens' PATE separated cases into Responders (R) (≥ 90%, 16 patients), Partial-Responders (PR) (89 - 31%, 10 patients), and Non-Responders (NR) (≤ 30%, seven patients). Seven semiquantitative kinetic parameters and maps were extracted from time-intensity curves (TICs), and 107 radiomic features were derived. Statistical analyses compared R vs. PR/NR. At PRT, 79% of R displayed a "Capsular" morphology (P = 1.49 × 10-7), and 100% demonstrated a TIC-type II (P = 8.32 × 10-7). 80% of PR showed "Unipolar" morphology (P = 1.03 × 10-5), and 60% expressed a TIC-type V (P = 0.06). Semiquantitative wash-in rate (WiR) was able to separate R vs. PR/NR (P = 0.0078). The WiR radiomics displayed significant differences in the first_order_10 percentile (P = 0.0178) comparing R vs. PR/NR at PRT. The PWI/DCE TIC-type II curve, low WiR, and "Capsular" enhancement represent PRT patterns typically observed in successfully treated UPS and demonstrate potential for UPS treatment response assessment.

Blood-Brain Barrier Disruption and Imaging Assessment in Stroke.

Disruption of the blood-brain barrier (BBB) is an important pathological hallmark of ischemic stroke. Blood-brain barrier disruption (BBBD) is a consequence of ischemia and may also exacerbate damage to brain parenchyma. Therefore, maintaining BBB integrity is critical for the central nervous system (CNS) homeostasis. This review offers a concise overview of BBB structure and function, along with the mechanisms underlying its impairment following a stroke. In addition, we review the recent imaging techniques employed to study blood-brain barrier permeability (BBBP) in the context of ischemic brain injury with the goal of providing imaging guidance for stroke diagnosis and treatment from the perspective of the BBBD. This knowledge is vital for developing strategies to safeguard the BBB during cerebral ischemia.

Development of a novel patient reported outcome measure for health-related quality of life in amyotrophic lateral sclerosis (PROQuALS): study protocol.

BACKGROUND: Patient reported outcome measures (PROMs) can be used to assess the impact of health conditions upon an individual's health-related quality of life (HRQoL). Whilst PROMs have been used to quantify the HRQoL impact of amyotrophic lateral sclerosis (ALS), existing instruments may not fully capture what matters to people living with ALS (plwALS) or be appropriate to be used directly to inform the cost-effectiveness of new treatments. This highlights a need for a new condition-specific PROM that can both capture what's important to plwALS and be used in economic evaluation. This study has two key aims: 1) to produce a novel PROM for measuring HRQoL in plwALS (PROQuALS). 2) to value a set of items from the novel PROM to generate an associated preference-weighted measure (PWM) that will enable utility values to be generated. METHODS: A mixed-methods study design will be conducted across three stages. Stage 1 involves concept elicitation and the generation of draft PROM content from a robust and comprehensive systematic review of HRQoL in ALS, with input from plwALS. Stage 2 consists of cognitive debriefing of the draft PROM content to ascertain its content validity (Stage 2a), followed by a psychometric survey (Stage 2b) to assess statistical performance. Evidence from Stage 2 will be used to make decisions on the final content and format of the novel PROM. Stage 3 will involve valuation and econometric modeling using health economics methods to generate preference weights, so a PWM derived from the novel PROM can be used in the cost-effectiveness analyses of treatments. Patient and clinical advisory groups will have critical, collaborative input throughout the project. DISCUSSION: The novel PROM will be designed to comprehensively assess important aspects of HRQoL to plwALS and to quantify HRQoL in terms of subjective impact. The PROQuALS measure will be available for use in research and healthcare settings. The associated PWM component will extend and enable the use of PROQuALS in cost-effective analyses of new treatments for ALS. TRIAL REGISTRATION: Not applicable.

Preliminary study on DCE-MRI radiomics analysis for differentiation of HER2-low and HER2-zero breast cancer.

Purpose: This study aims to evaluate the utility of radiomic features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in distinguishing HER2-low from HER2-zero breast cancer. Patients and methods: We retrospectively analyzed 118 MRI cases, including 78 HER2-low and 40 HER2-zero patients confirmed by immunohistochemistry or fluorescence in situ hybridization. From each DCE-MRI case, 960 radiomic features were extracted. These features were screened and reduced using intraclass correlation coefficient, Mann-Whitney U test, and least absolute shrinkage to establish rad-scores. Logistic regression (LR) assessed the model's effectiveness in distinguishing HER2-low from HER2-zero. A clinicopathological MRI characteristic model was constructed using univariate and multivariate analysis, and a nomogram was developed combining rad-scores with significant MRI characteristics. Model performance was evaluated using the receiver operating characteristic (ROC) curve, and clinical benefit was assessed with decision curve analysis. Results: The radiomics model, clinical model, and nomogram successfully distinguished between HER2-low and HER2-zero. The radiomics model showed excellent performance, with area under the curve (AUC) values of 0.875 in the training set and 0.845 in the test set, outperforming the clinical model (AUC = 0.691 and 0.672, respectively). HER2 status correlated with increased rad-score and Time Intensity Curve (TIC). The nomogram outperformed both models, with AUC, sensitivity, and specificity values of 0.892, 79.6%, and 82.8% in the training set, and 0.886, 83.3%, and 90.9% in the test set. Conclusions: The DCE-MRI-based nomogram shows promising potential in differentiating HER2-low from HER2-zero status in breast cancer patients.

DCE-Qnet: deep network quantification of dynamic contrast enhanced (DCE) MRI.

INTRODUCTION: Quantification of dynamic contrast-enhanced (DCE)-MRI has the potential to provide valuable clinical information, but robust pharmacokinetic modeling remains a challenge for clinical adoption. METHODS: A 7-layer neural network called DCE-Qnet was trained on simulated DCE-MRI signals derived from the Extended Tofts model with the Parker arterial input function. Network training incorporated B1 inhomogeneities to estimate perfusion (Ktrans, vp, ve), tissue T1 relaxation, proton density and bolus arrival time (BAT). The accuracy was tested in a digital phantom in comparison to a conventional nonlinear least-squares fitting (NLSQ). In vivo testing was conducted in ten healthy subjects. Regions of interest in the cervix and uterine myometrium were used to calculate the inter-subject variability. The clinical utility was demonstrated on a cervical cancer patient. Test-retest experiments were used to assess reproducibility of the parameter maps in the tumor. RESULTS: The DCE-Qnet reconstruction outperformed NLSQ in the phantom. The coefficient of variation (CV) in the healthy cervix varied between 5 and 51% depending on the parameter. Parameter values in the tumor agreed with previous studies despite differences in methodology. The CV in the tumor varied between 1 and 47%. CONCLUSION: The proposed approach provides comprehensive DCE-MRI quantification from a single acquisition. DCE-Qnet eliminates the need for separate T1 scan or BAT processing, leading to a reduction of 10 min per scan and more accurate quantification.

A Comprehensive Model Outperformed the Single Radiomics Model in Noninvasively Predicting the HER2 Status in Patients with Breast Cancer.

RATIONALE AND OBJECTIVES: This study aimed to develop predictive models based on conventional magnetic resonance imaging (cMRI) and radiomics features for predicting human epidermal growth factor receptor 2 (HER2) status of breast cancer (BC) and compare their performance. MATERIALS AND METHODS: A total of 287 patients with invasive BC in our hospital were retrospectively analyzed. All patients underwent preoperative breast MRI consisting of fat-suppressed T2-weighted imaging, axial dynamic contrast-enhanced MRI, and diffusion-weighted imaging sequences. From these sequences, radiomics features were derived. Three distinct models were established utilizing cMRI features, radiomics features, and a comprehensive model that amalgamated both. The predictive capabilities of these models were assessed using the receiver operating characteristic curve analysis. The comparative performance was then determined through the DeLong test and net reclassification improvement (NRI). RESULTS: In a randomized split, the 287 patients with BC were allotted to either training (234; 46 HER2-zero, 107 HER2-low, 81 HER2-positive) or test (53; 8 HER2-zero, 27 HER2-low, 18 HER2-positive) at an 8:2 ratio. The mean area under the curve (AUCs) for cMRI, radiomics, and comprehensive models predicting HER2 status were 0.705, 0.819, and 0.859 in training set and 0.639, 0.797, and 0.842 in test set, respectively. DeLong's test indicated that the combined model's AUC surpassed the radiomics model significantly (p < 0.05). NRI analysis verified superiority of the combined model over the radiomics for BC HER2 prediction (NRI 25.0) in the test set. CONCLUSION: The comprehensive model based on the combination of cMRI and radiomics features outperformed the single radiomics model in noninvasively predicting the three-tiered HER2 status in patients with BC.