Spin-lattice Relaxation Research Articles

Correlating clinical course with baseline subcortical shape in provisional tic disorder.

This study examined children at the onset of tic disorder (tics for less than 9months: NT group), a population on which little research exists. Here, we investigate relationships between the baseline shape and volume of subcortical nuclei, diagnosis, and tic symptom outcomes. 187 children were assessed at baseline and a 12-month follow-up: 88 with NT, 60 tic-free healthy controls (HC), and 39 with chronic tic disorder/Tourette syndrome (TS), using T1-weighted MRI and total tic scores (TTS) from the Yale Global Tic Severity Scale to evaluate symptom change. Subcortical surface maps were generated using FreeSurfer-initialized large deformation diffeomorphic metric mapping. Linear regression models correlated baseline structural shapes with follow-up TTS while accounting for covariates, with relationships mapped onto structure surfaces. We found that the NT group had a larger right hippocampus compared to HC. Surface maps illustrate distinct patterns of inward deformation in the putamen and outward deformation in the thalamus for NT compared to controls. We also found patterns of outward deformation in almost all studied structures when comparing the TS group to controls. The NT group also showed consistent outward deformation compared to TS in the caudate, accumbens, putamen, and thalamus. Subsequent analyses including clinical symptoms revealed that a larger pallidum and thalamus at baseline correlated with less improvement of tic symptoms at follow-up. These observations constitute some of the first prognostic biomarkers for tic disorders and suggest that these subregional shape and volume differences may be associated with the outcome of tic disorders.

Enhancing MRI radiomics feature reproducibility and classification performance in Parkinson's disease: a harmonization approach to gray-level discretization variability.

This study aimed to assess the reproducibility of MRI-derived radiomic features across multiple gray-level discretization levels for classifying Parkinson's disease (PD) subtypes, and to evaluate the impact of ComBat harmonization on feature stability and machine learning performance. T1-weighted MRI scans from 140 PD patients (70 tremor-dominant, 70 postural instability gait difficulty) and 70 healthy controls were obtained from the Parkinson's progression markers initiative (PPMI) database. Radiomic features were extracted from 16 brain regions using 6 discretization levels (8, 16, 32, 64, 128, and 256 bins). ComBat harmonization was applied using a combined batch variable incorporating both scanner models and discretization levels. Intraclass correlation coefficients (ICC) and Kruskal-Wallis tests assessed feature reproducibility before and after harmonization. Support vector machine classifiers were used for PD subtype classification. ComBat harmonization significantly improved feature reproducibility across all feature groups. The percentage of features showing excellent robustness (ICC ≥ 0.90) increased substantially after harmonization. The proportion of features significantly affected by discretization levels was reduced following harmonization. Classification accuracy improved dramatically, from a range of 0.42-0.49 before harmonization to 0.86-0.96 after harmonization across most discretization levels. AUC values similarly increased from 0.60-0.67 to 0.93-0.99 after harmonization. ComBat harmonization significantly enhanced the reproducibility of radiomic features across discretization levels and improved PD subtype classification performance. This study highlights the importance of harmonization in radiomics research for PD and suggests potential clinical applications in personalized treatment planning.

EARLY DETECTION OF BRAIN TUMOR USING MRI AND TRANSFER LEARNING

Brain tumors pose significant risks to cognitive functions, making early detection crucial for improving patient survival rates. Accurate tumor detection aids neuro-oncologists in diagnosing tumor types and recommending appropriate treatments. However, manual detection is challenging, time-consuming, and prone to human error. Recently, Deep Learning (DL) models have demonstrated substantial potential in efficiently classifying large image datasets. This paper presents three novel and efficient multi-class DL architectures leveraging transfer learning to classify different brain tumors. Our primary contribution is to enhance the predictive accuracy while minimizing the usage of computational resource compared to other state-of-art models in the literature by forgoing preprocessing, segmentation, hybrid models, and augmentation techniques. Additionally, we reduce the number of FC layers to streamline computation. We conduct extensive experiments to evaluate the performance of our models using the brain tumor Figshare T1-weighted contrast-enhanced MRI dataset, comprising 3064 images from three distinct tumor types. The InceptionV3 model records a 98.36% accuracy level with five-fold cross-validation. By incorporating batch normalization and optimizing the learning rate for the Adam optimizer, the Xception model reached 99.19% accuracy. Finally, we utilize Particle Swarm Optimization (PSO) to fine-tune the learning rate of the Stochastic Gradient Descent (SGD) optimizer. The Xception model attained 98.85% accuracy. These results highlight the novelty of our approach, offering a practical solution for neuro-oncologists, particularly through the fine-tuned Xception model, which delivers early and accurate tumor detection with minimal computational resources.

Pancreatic volume, fat content and T1 relaxation time using magnetic resonance imaging in patients with prior gestational diabetes mellitus.

To compare the volume, fat content, and T1 relaxation time of the pancreas in participants with and without prior gestational diabetes mellitus (GDM). In this prospective case-control study, we enrolled 29 women with prior GDM, and divided them into three groups (normoglycemic, prediabetic, and diabetic) based on their glycaemic status in the postpartum period; and a group of 13 participants as controls who had normoglycemia during pregnancy. Participants underwent MR examination including an axial multi-echo DIXON-based sequence and an axial-oblique T1 mapping sequence. The average proton density fat fraction (PDFF) and T1 value of the pancreas were measured by drawing multiple circular regions of interest (ROIs). Average hepatic PDFF was similarly calculated. Pancreatic volume was computed by summating cleaned freehand ROIs outlining pancreatic parenchyma. Women were evaluated at a mean age of 32.9 ± 4.0 years and with a mean BMI of 25.2 ± 2.9kg/m2. The median (IQR) duration of follow-up after childbirth was 24 (34-44) months. In controls, the pancreatic PDFF, T1 value, and volume were 5.4 ± 2.3%, 850.6 ± 101.6 ms, and 69.3 ± 16.2cm³, respectively, while in subjects with a history of GDM, these values were 4.7 ± 2.0%, 844.6 ± 91.8 ms, and 69.8 ± 16.6cm³, respectively. No statistical differences were observed (all p > 0.05). Mean hepatic PDFF was significantly higher in the diabetic and prediabetic groups (12.2 ± 6.4% and 11.0 ± 7.6% respectively) than in the control and normoglycemic groups (7.9 ± 7.3 and 4.7 ± 1.6% respectively; p -0.01). We did not find any significant changes in pancreatic volume, fat content, or fibrosis in women with prior GDM and with different glycaemic categories when evaluated in the early postpartum period, compared to the control group. These findings may be a consequence of the transient state of insulin resistance in GDM and the finite duration of the disease course in new-onset prediabetes and diabetes patients.

Disentangling Neurodegeneration From Aging in Multiple Sclerosis Using Deep Learning: The Brain-Predicted Disease Duration Gap.

Disentangling brain aging from disease-related neurodegeneration in patients with multiple sclerosis (PwMS) is increasingly topical. The brain-age paradigm offers a window into this problem but may miss disease-specific effects. In this study, we investigated whether a disease-specific model might complement the brain-age gap (BAG) by capturing aspects unique to MS. In this retrospective study, we collected 3D T1-weighted brain MRI scans of PwMS to build (1) a cross-sectional multicentric cohort for age and disease duration (DD) modeling and (2) a longitudinal single-center cohort of patients with early MS as a clinical use case. We trained and evaluated a 3D DenseNet architecture to predict DD from minimally preprocessed images while age predictions were obtained with the DeepBrainNet model. The brain-predicted DD gap (the difference between predicted and actual duration) was proposed as a DD-adjusted global measure of MS-specific brain damage. Model predictions were scrutinized to assess the influence of lesions and brain volumes while the DD gap was biologically and clinically validated within a linear model framework assessing its relationship with BAG and physical disability measured with the Expanded Disability Status Scale (EDSS). We gathered MRI scans of 4,392 PwMS (69.7% female, age: 42.8 ± 10.6 years, DD: 11.4 ± 9.3 years) from 15 centers while the early MS cohort included 749 sessions from 252 patients (64.7% female, age: 34.5 ± 8.3 years, DD: 0.7 ± 1.2 years). Our model predicted DD better than chance (mean absolute error = 5.63 years, R2 = 0.34) and was nearly orthogonal to the brain-age model (correlation between DD and BAGs: r = 0.06 [0.00-0.13], p = 0.07). Predictions were influenced by distributed variations in brain volume and, unlike brain-predicted age, were sensitive to MS lesions (difference between unfilled and filled scans: 0.55 years [0.51-0.59], p < 0.001). DD gap significantly explained EDSS changes (B = 0.060 [0.038-0.082], p < 0.001), adding to BAG (ΔR2 = 0.012, p < 0.001). Longitudinally, increasing DD gap was associated with greater annualized EDSS change (r = 0.50 [0.39-0.60], p < 0.001), with an incremental contribution in explaining disability worsening compared with changes in BAG alone (ΔR2 = 0.064, p < 0.001). The brain-predicted DD gap is sensitive to MS-related lesions and brain atrophy, adds to the brain-age paradigm in explaining physical disability both cross-sectionally and longitudinally, and may be used as an MS-specific biomarker of disease severity and progression.

The Developing Human Connectome Project: A fast deep learning-based pipeline for neonatal cortical surface reconstruction

The Developing Human Connectome Project (dHCP) aims to explore developmental patterns of the human brain during the perinatal period. An automated processing pipeline has been developed to extract high-quality cortical surfaces from structural brain magnetic resonance (MR) images for the dHCP neonatal dataset. However, the current implementation of the pipeline requires more than 6.5 h to process a single MRI scan, making it expensive for large-scale neuroimaging studies. In this paper, we propose a fast deep learning (DL) based pipeline for dHCP neonatal cortical surface reconstruction, incorporating DL-based brain extraction, cortical surface reconstruction and spherical projection, as well as GPU-accelerated cortical surface inflation and cortical feature estimation. We introduce a multiscale deformation network to learn diffeomorphic cortical surface reconstruction end-to-end from T2-weighted brain MRI. A fast unsupervised spherical mapping approach is integrated to minimize metric distortions between cortical surfaces and projected spheres. The entire workflow of our DL-based dHCP pipeline completes within only 24 s on a modern GPU, which is nearly 1000 times faster than the original dHCP pipeline. The qualitative assessment demonstrates that for 82.5% of the test samples, the cortical surfaces reconstructed by our DL-based pipeline achieve superior (54.2%) or equal (28.3%) surface quality compared to the original dHCP pipeline.

Vertebral Bone Quality Score as a Predictor of Subsequent Fractures After Cement Augmentation for Osteoporotic Vertebral Compression Fracture.

Dual-energy X-ray absorptiometry (DXA) T-scores have been shown to predict fragility fractures in population-based studies. Recently, a novel MRI-based vertebral bone quality (VBQ) score has been proposed, showing better predictability for fragility fractures compared with DXA T-scores. The aim of this study was to explore the correlation between VBQ scores and DXA T-scores and to determine the impact of VBQ scores on the risk of subsequent fragility fractures after cement augmentation for osteoporotic vertebral compression fracture (OVCF). Between January and December 2018, 251 consecutive patients who received cement augmentation for OVCF were included in the study. VBQ scores were calculated using noncontrast T1-weighted MRI. Correlations between VBQ and T-scores were assessed. Patients were divided into 2 groups based on the presence or absence of subsequent fragility fractures after cement augmentation: (1) no Subsequent fracture group and (2) subsequent fracture group. Comparisons between the groups were conducted, and risk factors of subsequent fractures were evaluated using multivariable logistic regression analysis. Of the patients, 42 (16.7%) experienced subsequent fractures after cement augmentation. The VBQ score showed moderate correlations with the T-score of the hip (r = -0.523, P < .001) and the T-score of the lumbar spine (r = -0.383, P < .001). The subsequent fracture group had a significantly higher VBQ score (4.02 ± 0.56 vs 3.52 ± 0.62, P < .001) and a worse T-score of hip (-3.06 ± 1.28 vs -2.42 ± 0.98, P = .004). In the multivariable analysis, the VBQ score was the only independent predictor of subsequent fractures with 2.799 odds ratio (1.342 to 5.841, P = .006). In patients who received cement augmentation for OVCF, the VBQ score is significantly correlated with the DXA T-score and may be a more reliable predictor of subsequent fragility fractures.

Diabetic striatopathy (hyperglycemic hemichorea-hemiballismus syndrome) in a young patient with type 1 diabetes mellitus in Dar es Salaam, Tanzania: a case report

Introduction: Diabetic striatopathy, or nonketotic hyperglycemic hemichorea-hemiballismus syndrome, is a rare movement disorder linked to poorly controlled diabetes mellitus. It predominantly affects older women with type 2 diabetes mellitus and presents with characteristic basal ganglia abnormalities on CT and MRI. Even rarer is the presentation in a young patient, which may pose diagnostic and management challenges. Case Presentation: We report a 17-year-old male with poorly controlled type 1 diabetes mellitus presenting with left-sided hemichorea-hemiballismus of acute onset associated with hyperglycemia without ketoacidosis. Brain imaging revealed increased attenuation in the right caudate and putamen on CT and hyperintensity on T1-weighted MRI, consistent with diabetic striatopathy. The abnormal movements abated after one month through dietary counseling, increased insulin dosage, and anti-chorea therapy. Conclusion: Diabetic striatopathy may occur in young patients with type 1 diabetes mellitus. In resource-limited settings, its management can be challenging. There is a need for increased awareness among physicians of this potentially reversible condition, especially when seeing atypical patient populations. Strict glycemic control is an essential part of treatment.

Misdiagnosis of Clival Lesion as Macroadenoma and Subsequent Identification as Clival Chordoma: Emphasizing the Role of MRI

ABSTRACT Clival chordomas are rare, malignant tumors arising from remnants of the notochord, comprising less than 1% of all bone tumors. Due to their insidious growth and location at the skull base, they are frequently misdiagnosed as pituitary macroadenomas, which present with similar clinical symptoms, including headaches and visual disturbances. This case study highlights the diagnostic challenges encountered in a 65-year-old male, initially diagnosed with pituitary macroadenoma based on clinical presentation and early imaging findings. Despite the proximity of the lesion to the sellar region, initial CT imaging failed to differentiate the clival chordoma from a pituitary macroadenoma due to overlapping features such as bone destruction and soft-tissue mass appearance. However, subsequent magnetic resonance imaging (MRI) provided crucial details that led to the correct diagnosis. T2-weighted MRI revealed heterogeneous hyperintensity, necrotic and cystic components, and low-signal-intensity septations—features not typically seen in macroadenomas. Post-contrast enhancement patterns and evidence of local invasion into surrounding structures further supported the diagnosis of clival chordoma. This case underscores the importance of MRI in the differential diagnosis of skull base tumors, especially when clinical and initial imaging findings are inconclusive. Early and precise use of MRI can prevent misdiagnosis and guide appropriate treatment planning, particularly in rare pathologies like clival chordomas.

Approximation of bone mineral density and subcutaneous adiposity using T1-weighted images of the human head

Abstract Bones and brain are intricately connected and scientific interest in their interaction is growing. This has become particularly evident in the framework of clinical applications for various medical conditions, such as obesity and osteoporosis. The adverse effects of obesity on brain health have long been recognised, but few brain imaging studies provide sophisticated body composition measures. Here we propose to extract the following bone- and adiposity-related measures from T1-weighted MR images of the head: an approximation of skull bone mineral density (BMD), skull bone thickness, and two approximations of subcutaneous fat (i.e., the intensity and thickness of soft non-brain head tissue). The reliability and validity of these four distinct measures were tested in two large-scale databases, the UK Biobank and OASIS-3. The measures pertaining to skull BMD, skull bone thickness, and intensity-based adiposity proxy proved to be reliable (ICC=.95/.83/.66, p&amp;lt;.001) and valid, with high correlations to DXA-derived head BMD values (rho=.70, p&amp;lt;.001) and MRI-derived abdominal subcutaneous adipose volume (rho=.62, p&amp;lt;.001). Thickness-based adiposity proxy had only a low retest reliability (ICC=.53, p&amp;lt;.001). The outcomes of this study constitute an important step towards extracting relevant non-brain features from available brain scans.

Associations of demyelination in the right middle temporal gyrus and right praecuneus with visuospatial cognitive dysfunction in Alzheimer's disease.

Alzheimer's disease (AD) is associated with impairments in not only memory but also visuospatial cognitive function. Despite its adverse effects on the quality of life, patients with early-stage AD are often neglected. Emerging evidence suggests that patients with AD exhibit increased vulnerability of myelin, a crucial component for neuronal conduction and survival. To test our hypothesis that myelin damage was associated with cognitive deficits in AD, we examined correlations of myelin integrity, quantified by T1-weighted/T2-weighted (T1w/T2w) ratios, with visuospatial cognitive abilities and compared them between patients with AD and cognitively normal (CN) individuals. Fifty-seven patients with AD and 22 CN subjects were enrolled in this study. To assess subjects' visuo-constructive abilities, we employed the Rey-Osterrieth Complex Figure Copy Test (ROCFT-c) paired with analysis of T1- and T2-weighted magnetic resonance imaging brain images. Voxel-based associations between T1w/T2w ratios and ROCFT-c scores in the AD group were assessed, controlling for age and handedness (voxel threshold uncorrected P < 0.001, cluster threshold uncorrected P < 0.05). Additionally, we compared the T1w/T2w ratios of these identified brain regions between the AD and CN groups. The voxel-based analysis demonstrated positive correlations between T1w/T2w ratios and ROCFT-c scores in the right middle temporal gyrus and right praecuneus in patients with AD who exhibited significantly lower T1w/T2w ratios in the right middle temporal gyrus (P = 0.038) and a trend toward lower T1w/T2w ratios in the right praecuneus (P = 0.055). Our results demonstrated a strong association between reduced myelin integrity in the right middle temporal gyrus and right praecuneus and visuospatial cognitive dysfunction in patients with AD. These findings are believed to shed light on the neural basis of visuospatial processing in patients with AD, underlining the necessity for developing objective biomarkers for assessing patients' visuospatial cognitive function.

Reduced Vestibular Function is Associated with Cortical Surface Shape Changes in the Frontal Cortex.

Aging-associated decline in peripheral vestibular function is linked to deficits in executive ability, self-motion perception, and motor planning and execution. While these behaviors are known to rely on the sensorimotor and frontal cortices, the precise pathways involving the frontal and sensorimotor cortices in these vestibular-associated behaviors are unknown. To fill this knowledge gap, this cross-sectional study investigates the relationship between age-related variation in vestibular function and surface shape alterations of the frontal and sensorimotor cortices, considering age, intracranial volume, and sex. Data from 117 participants aged 60+ from the Baltimore Longitudinal Study of Aging, who underwent endorgan-specific vestibular tests (cVEMP for the saccule, oVEMP for the utricle, and vHIT for the horizontal canal) and T1-weighted MRI scans on the same visit, were analyzed. We examined ten brain structures in the putative "vestibular cortex": the middle-superior part of the prefrontal cortex (SFG_PFC), frontal pole (SFG_pole), and posterior pars of the superior frontal gyrus (SFG), the dorsal prefrontal cortex and posterior pars of middle frontal gyrus (MFG_DPFC, MFG), the pars opercularis, pars triangularis, and pars orbitalis of the inferior frontal gyrus, as well as the precentral gyrus and postcentral gyrus (PoCG) of the sensorimotor cortex. For each region of interest (ROI), shape descriptors were estimated as local compressions and expansions of the population average ROI surface using surface LDDMM. Shape descriptors were linearly regressed onto standardized vestibular variables, age, intracranial volume, and sex. Lower utricular function was linked with surface compression in the left MFG and expansion in the bilateral SFG_pole and left SFG. Reduced canal function was associated with surface compression in the right SFG_PFC and SFG_pole and left SFG. Both reduced saccular and utricular function correlated with surface compression in the posterior medial part of the left MFG. Our findings illuminate the complexity of the relationship between vestibular function and the morphology of the frontal and sensorimotor cortices in aging. Improved understanding of these relationships could help in developing interventions to enhance quality of life in aging and populations with cognitive impairment.

Combined T1-weighted MRI and diffusion MRI tractography of paraventricular, locus coeruleus, and dorsal vagal complex connectivity in brainstem-hypothalamic nuclei

Background: Current multimodal neuroimaging plays a critical role in studying clinical conditions such as cardiovascular disease, major depression, and other disorders related to chronic stress. These conditions involve the brainstem-hypothalamic network, specifically the locus coeruleus (LC), dorsal vagal complex (DVC), and paraventricular nucleus (PVN) of the hypothalamus, collectively referred to as the &amp;ldquo;DVC-LC-PVN circuitry.&amp;rdquo; This circuitry is strongly associated with the norepinephrine (NE) and epinephrine (E) neurotransmitter systems, which are implicated in the regulation of key autonomic functions, such as cardiovascular and respiratory control, stress response, and cognitive and emotional behaviors. Objectives: To develop a methodology for delineating the DVC-LC-PVN circuitry in the human brain using multimodal neuroimaging. Methods: We combined structural T1-weighted morphometric magnetic resonance imaging (MRI) and diffusion MRI-based tractography to map the DVC-LC-PVN circuitry in the human brain. This methodology was applied to a pilot sample of brain datasets from five healthy adult subjects obtained from the publicly available Human Connectome Project repository and to one post-mortem human dataset. Results: The DVC-LC-PVN circuitry was delineated in vivo in five human subjects and one ultra-high resolution post-mortem dataset, allowing for refined anatomical observations. Conclusion: NE and E neurotransmitter systems engender substantial interest in both basic and clinical neuroscience due to their roles in the regulation of key autonomic functions, such as cardiovascular and respiratory control, stress responses, and cognitive and emotional behaviors. As demonstrated in this study, multimodal neuroimaging techniques provide a valuable approach for mapping small brainstem and hypothalamic structures and complex circuitries such as the DVC-LC-PVN circuitry.

Value of MRI-visible perivascular spaces in predicting levodopa responsiveness of patients with Parkinson’s disease

PurposeInter-individual difference in levodopa responsiveness is a challenge for physicians to administer personalized treatment for patients with Parkinson’s disease (PD). Previous studies demonstrated that magnetic resonance imaging (MRI)-visible perivascular spaces (PVS) might lead to an incomplete response to levodopa. This study aimed to investigate the association between MRI-visible PVS and levodopa responsiveness in patients with PD. MethodsThis cross-sectional study enrolled a total of 327 patients with PD (median age 64.0[57.0–68.0] years, 180 male) who had undergone high-resolution T2-weighted structural MRI at our hospital between 2019 and 2023. An acute levodopa challenge test was performed to evaluate levodopa responsiveness. The patients were divided into two groups: levodopa responsive (MDS-UPDRS-III reduction ≥ 33 %, n = 274) and irresponsive groups (MDS-UPDRS-III reduction < 33 %, n = 53). We employed quantitative and semi-quantitative methods to evaluate MRI-visible PVS in patients with PD, including PVS number, volume fraction, and visual score. Additionally, the imaging features of the levodopa-responsive and irresponsive groups were compared. ResultsThere were no significant differences in PVS number, volume fraction, and visual score between the levodopa-responsive and −irresponsive groups. The indicators from quantitative and semi-quantitative analyses of PVS were not found to be independent predictors of levodopa responsiveness. None of the indicators from the quantitative or semi-quantitative analyses of PVS were significantly associated with poor responsiveness to levodopa treatment. ConclusionsMRI-visible PVS are not independently associated with levodopa responsiveness, and their value in predicting levodopa responsiveness in patients with PD is limited.

Radiomics based on brain-to-tumor interface enables prediction of metastatic tumor type of brain metastasis: a proof-of-concept study.

Early and accurate identification of the metastatic tumor types of brain metastasis (BM) is essential for appropriate treatment and management. A total of 450 patients were enrolled from two centers as a primary cohort who carry 764 BMs originated from non-small cell lung cancer (NSCLC, patient = 173, lesion = 187), small cell lung cancer (SCLC, patient = 84, lesion = 196), breast cancer (BC, patient = 119, lesion = 200), and gastrointestinal cancer (GIC, patient = 74, lesion = 181). A third center enrolled 28 patients who carry 67 BMs (NSCLC = 24, SCLC = 22, BC = 10, and GIC = 11) to form an external test cohort. All patients received contrast-enhanced T1-weighted (T1CE) and T2-weighted (T2W) MRI scans at 3.0T before treatment. Radiomics features were calculated from BM and brain-to-tumor interface (BTI) region in the MRI image and screened using least absolute shrinkage and selection operator (LASSO) to construct the radiomics signature (RS). Volume of peritumor edema (VPE) was calculated and combined with RS to create a joint model. Performance of the models was assessed by receiver operating characteristic (ROC). The BTI-based RS showed better performance compared to BM-based RS. The combined models integrating BTI features and VPE can improve identification performance in AUCs in the training (LC/NLC vs. SCLC/NSCLC vs. BC/GIC, 0.803 vs. 0.949 vs. 0.918), internal validation (LC/NLC vs. SCLC/NSCLC vs. BC/GIC, 0.717 vs. 0.854 vs. 0.840), and external test (LC/NLC vs. SCLC/NSCLC vs. BC/GIC, 0.744 vs. 0.839 vs. 0.800) cohorts. This study indicated that BTI-based radiomics features and VPE are associated with the metastatic tumor types of BM.

Intratumoral and peritumoral MRI-based radiomics for predicting extrapelvic peritoneal metastasis in epithelial ovarian cancer

ObjectivesTo investigate the potential of intratumoral and peritumoral radiomics derived from T2-weighted MRI to preoperatively predict extrapelvic peritoneal metastasis (EPM) in patients with epithelial ovarian cancer (EOC).MethodsIn this retrospective study, 488 patients from four centers were enrolled and divided into training (n = 245), internal test (n = 105), and external test (n = 138) sets. Intratumoral and peritumoral models were constructed based on radiomics features extracted from the corresponding regions. A combined intratumoral and peritumoral model was developed via a feature-level fusion. An ensemble model was created by integrating this combined model with specific independent clinical predictors. The robustness and generalizability of these models were assessed using tenfold cross-validation and both internal and external testing. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC). The Shapley Additive Explanation method was employed for model interpretation.ResultsThe ensemble model showed superior performance across the tenfold cross-validation, with the highest mean AUC of 0.844 ± 0.063. On the internal test set, the peritumoral and ensemble models significantly outperformed the intratumoral model (AUC = 0.786 and 0.832 vs. 0.652, p = 0.007 and p < 0.001, respectively). On the external test set, the AUC of the ensemble model significantly exceeded those of the intratumoral and peritumoral models (0.843 vs. 0.750 and 0.789, p = 0.008 and 0.047, respectively).ConclusionPeritumoral radiomics provide more informative insights about EPM than intratumoral radiomics. The ensemble model based on MRI has the potential to preoperatively predict EPM in EOC patients.Critical relevance statementIntegrating both intratumoral and peritumoral radiomics information based on MRI with clinical characteristics is a promising noninvasive method to predict EPM to guide preoperative clinical decision-making for EOC patients.Key PointsPeritumoral radiomics can provide valuable information about extrapelvic peritoneal metastasis in epithelial ovarian cancer.The ensemble model demonstrated satisfactory performance in predicting extrapelvic peritoneal metastasis.Combining intratumoral and peritumoral MRI radiomics contributes to clinical decision-making in epithelial ovarian cancer.Graphical

Prediction of methylphenidate treatment response for ADHD using conventional and radiomics T1 and DTI features: Secondary analysis of a randomized clinical trial

BackgroundAttention-Deficit/Hyperactivity Disorder (ADHD) is commonly treated with methylphenidate (MPH). Although highly effective, MPH treatment still has a relatively high non-response rate of around 30%, highlighting the need for a better understanding of treatment response. Radiomics of T1-weighted images and Diffusion Tensor Imaging (DTI) combined with machine learning approaches could offer a novel method for assessing MPH treatment response. PurposeTo evaluate the accuracy of both conventional and radiomics approaches in predicting treatment response based on baseline T1 and DTI data in stimulant-naive ADHD participants. MethodsWe performed a secondary analysis of a randomized clinical trial (ePOD-MPH), involving 47 stimulant-naive ADHD participants (23 boys aged 11.4 ± 0.4 years, 24 men aged 28.1 ± 4.3 years) who underwent 16 weeks of treatment with MPH. Baseline T1-weighted and DTI MRI scans were acquired. Treatment response was assessed at 8 weeks (during treatment) and one week after cessation of 16-week treatment (post-treatment) using the Clinical Global Impressions − Improvement scale as our primary outcome. We compared prediction accuracy using a conventional model and a radiomics model. The conventional approach included the volume of bilateral caudate, putamen, pallidum, accumbens, and hippocampus, and for DTI the mean fractional anisotropy (FA) of the entire brain white matter, bilateral Anterior Thalamic Radiation (ATR), and the splenium of the corpus callosum, totaling 14 regional features. For the radiomics approach, 380 features (shape-based and first-order statistics) were extracted from these 14 regions. XGBoost models with nested cross-validation were used and constructed for the total cohort (n = 47), as well as children (n = 23) and adults (n = 24) separately. Exact binomial tests were employed to compare model performance. ResultsFor the conventional model, balanced accuracy (bAcc) in predicting treatment response during treatment was 63 % for the total cohort, 32 % for children, and 36 % for adults (Area Under the Receiver Operating Characteristic Curve (AUC-ROC): 0.69, 0.33, 0.41 respectively). Radiomics models demonstrated bAcc’s of 68 %, 64 %, and 64 % during treatment (AUC-ROCs of 0.73, 0.62, 0.69 respectively). These predictions were better than chance for both conventional and radiomics models in the total cohort (p = 0.04, p = 0.003 respectively). The radiomics models outperformed the conventional models during treatment in children only (p = 0.02). At post-treatment, performance was markedly reduced. ConclusionWhile conventional and radiomics models performed equally well in predicting clinical improvement across children and adults during treatment, radiomics features offered enhanced structural information beyond conventional region-based volume and FA averages in children. Prediction of symptom improvement one week after treatment cessation was poor, potentially due to the transient effects of stimulant treatment on symptom improvement.

Dendrimer/Copper(II) Complex-Mediated siRNA Delivery Disrupts Lactate Metabolism to Reprogram the Local Immune Microenvironment against Tumor Growth and Metastasis.

Solid tumors reprogram metabolic pathways to meet their biosynthesis demands, resulting in elevated levels of metabolites in the tumor microenvironment (TME), including lactate. Excessive accumulation and active transportation of lactate within the TME drives tumor progression, metastasis, and immunosuppression. Interruption of TME lactate metabolism is expected to restore antitumor responses and sensitize tumor immunotherapy. Herein, we developed phenylboronic acid- and pyridine-modified poly(amidoamine) dendrimer/copper(II) (Cu(II)) complexes, namely, D-Cu complexes, to deliver monocarboxylate transporter 4 siRNA (siMCT4) and disrupt the tumor lactate shuttle. The D-Cu complexes are shown to have a Cu(II)-mediated chemodynamic effect and T1-weighted magnetic resonance imaging potential (r1 relaxivity = 1.19 mM-1 s-1), enabling effective siMCT4 delivery to inhibit lactate efflux within cancer cells. In combination with a CD11b immune agonist, the treatment of D-Cu/siMCT4 polyplexes in a mouse breast tumor model alleviates local TME immunosuppression, leading to excellent inhibition of both primary tumor growth and lung metastasis.

Diagnostic Confidence of Contrast-Enhanced T1-Weighted MRI for the Detection of Brain Metastases: 3D FSE-vs. 3D GRE-Based Sequences.

This retrospective study evaluated the utility of contrast-enhanced (CE) T1-weighted 3D fast spinecho-based SPACE sequences for brain metastasis detection on 3T MRI compared to gradient recalled-echo-based 3D fast low-angle shot (FLASH) sequence. We identified all patients at a single institution who underwent SPACE and 3D FLASH sequences as part of a practice quality improvement project. Their medical records were retrospectively reviewed. Five certified neuroradiologists reviewed the images, with at least 2 weeks separation between scoring sequences for the same patient. The following parameters were evaluated: number of metastatic lesions, number of indeterminate lesions, lesion margin, contrast-to-noise ratio (CNR), extent of image artifacts, and overall image quality. CNR was also quantified for solidly enhancing lesions > 1 cm. We identified 220 patients who underwent SPACE and 3D FLASH sequences (the order of the sequences was equally distributed). Of these, 79 had brain metastases on imaging, and 7 were excluded; thus, 72 patients were included in the study. Twenty patients were scored by 2 radiologists. Out of the 92 evaluations, SPACE detected more lesions than did 3D FLASH in 35, while 3D FLASH detected more lesions in 10. More indeterminate lesions were seen on 3D FLASH (27) than on SPACE (9). For lesion margin, CNR, and overall image quality on a Likert scale, SPACE performed significantly better than did 3D FLASH, with less image artifacts (P < 0.00001). Higher quantitative CNRs were found on SPACE than on 3D FLASH images, although this result was not statistically significant (median = 22.9 vs. 15.5, respectively, P = 0.134). There was a high inter-reader lesion detection concordance with Krippendorf's alpha ordinals at 0.962 for SPACE, 0.870 for 3D FLASH, and 0.918 for the two sequences combined. Compared with 3D FLASH, the SPACE sequence detected more metastatic lesions and was rated higher for image quality, lesion margin, and CNR, with fewer artifacts. Importantly, the SPACE sequence resulted in increased reader confidence, with fewer indeterminate lesions detected. FLASH = fast low-angle shot; FSE = fast spin-echo; GRE = gradient-recalled echo; MP-RAGE = magnetization-prepared rapid gradient echo; SPACE = Sampling Perfection with Application-optimized Contrasts using different flip angle Evolution; VIBE = volumetric interpolated breath-hold examination.

Designing T1 and T2 Relaxation Time Phantoms: From Material Selection to Measurement

Designing T1 and T2 Relaxation Time Phantoms: From Material Selection to Measurement