Search Results (1,009)

Background: Infant brain tissue segmentation from MRI data is a critical task in medical imaging, particularly challenging due to the evolving nature of tissue contrasts in the early months of life. The difficulty increases as gray matter (GM) and white matter (WM) intensities converge, making accurate segmentation challenging. This study aims to develop an improved U-net-based model to enhance the precision of automatic segmentation of cerebro-spinal fluid (CSF), GM, and WM in 10 infant brain MRIs using the iSeg-2017 dataset. Methods: The proposed method utilizes a U-net architecture with (2+1)Dconvolutional layers and skip connections. Preprocessing includes intensity normalization using histogram alignment to standardize MRI data across different records. The model was trained on the iSeg-2017 dataset, which comprises T1-weighted and T2-weighted MRI data from ten infant subjects. Cross-validation was performed to evaluate the model’s segmentation performance. Results: The model achieved an average accuracy of 92.2%, improving on previous methods by 0.7%. Sensitivity, precision, and Dice similarity scores were used to evaluate the performance, showing high levels of accuracy across different tissue types. The model demonstrated a slight bias toward misclassifying GM and WM, indicating areas for potential improvement. Conclusions: The results suggest that the U-net architecture is highly effective in segmenting infant brain tissues from MRI data. Future work will explore enhancements such as attention mechanisms and dual-network processing for further improving segmentation accuracy. Full article

Background and Aims: Elevated lipoprotein(a) [Lp(a)] and familial hypercholesterolemia (FH) are both inherited dyslipidemias that are independently associated with cardiovascular disease. Surrogate markers to assess signs of atherosclerosis, such as arterial stiffness, might be useful to evaluate the cardiovascular risk in young patients. The aim of this study is to evaluate the contribution of Lp(a) to arterial stiffness, as measured by carotid pulse wave velocity (cPWV) in young adults with FH. Methods: For this cross-sectional study, 214 children with FH who participated in a randomized controlled trial between 1997 and 1999 on the efficacy and safety of pravastatin were eligible. After 20 years, these patients were invited for a hospital visit, including cPWV assessment (by 4D flow MRI) and Lp(a) measurement. Linear mixed-effects models were used to evaluate the association between Lp(a) and cPWV. Results: We included 143 patients (mean [standard deviation] age: 31.8 [3.2] years) from 108 families. Median (interquartile range) cPWV was 1.62 (1.31–2.06) m/s. Both the unadjusted (ß = −0.0014 m/s per 1 mg/dL increase in Lp(a), 95% CI: −0.0052 to 0.0023, p = 0.455) and adjusted model (ß = −0.0005 m/s per 1 mg/dL increase in Lp(a), 95% CI: −0.0042 to 0.0032, p = 0.785) showed no significant association between Lp(a) and cPWV. Conclusions: Our findings indicate that Lp(a) levels are not associated with carotid arterial stiffness in young adults with FH. Possibly, High Lp(a) might cause atherosclerosis by mechanisms beyond arterial stiffness in young adults. Other surrogate markers of early signs of atherosclerosis may be more suitable to evaluate the Lp(a)-mediated contribution to atherosclerosis in young FH patients. Full article

Purpose: Erdheim–Chester disease (ECD) is an L Group Langerhans histiocytosis associated with pathogenic variants within the MAPK pathways, most commonly the BRAF gene. We analyzed prevalence, genetic, biochemical, and pituitary imaging features associated with arginine vasopressin deficiency (AVP-D), one of the most common endocrinopathies in ECD. Methods: A cross-sectional descriptive study of 61 subjects with ECD was conducted at a clinical research center from January 2011 to December 2018, with molecular genetics, baseline biochemical and pituitary endocrine function studies, and dedicated pituitary MRI (or CT) studies. AVP-D and anterior pituitary endocrinopathies (hypothyroidism, hypogonadism, adrenal insufficiency and panhypopituitarism) were assessed. Students’ t-tests, nonparametric tests, Fisher’s exact tests, and logistic regression were employed for analysis. Results: In total, 22 out of 61 subjects (36%; 19 males and 3 females) had AVP-D; 18 subjects with AVP-D were in active treatment with desmopressin. Those with versus without AVP-D were younger [mean (±SD): 50.00 (±10.45) vs. 56.72 (±10.45) years], had higher prevalence of BRAF V600E pathogenic variants [68% vs. 43%], lower IGF-1 [mean (±SD): 137.05 (±67.97) vs. 175.92 (±61.89) ng/mL], lower urine osmolality [416.00 (250.00–690.00) vs. 644.50 (538.75–757.75)) mOsm/kg], and a higher burden of central hypogonadism [81.82% vs. 36.00%], central hypothyroidism [23% vs. 2.5%], panhypopituitarism [41% vs. 0%], anterior pituitary endocrine deficits, absent posterior pituitary bright spots [63.64% vs. 20.51%], and abnormal pituitary imaging. In adjusted models, [OR (95%CI)] BRAF V600E mutation [7.38 (1.84–39.01)], central hypogonadism [6.193 (1.44–34.80)], primary hypothyroidism [13.89 (1.401–406.5)], absent posterior pituitary bright spot [12.84 (3.275–65.04)], and abnormal pituitary imaging [10.60 (2.844–48.29)] were associated with higher odds of having AVP-D. Conclusions: AVP-D is common in ECD and accompanied by a higher burden of pituitary endocrinopathies, BRAF V600E pathogenic variants, abnormal pituitary imaging, and absent posterior pituitary bright spots. Full article

This study investigates the clinical efficacy of MRI-based adaptive brachytherapy (IGABT) using combined intracavitary and interstitial techniques in the curative treatment of patients with advanced cervical cancer (LACC). A retrospective analysis was conducted on 149 LACC patients treated at a single center. The therapeutic protocol included intensity-modulated external beam radiotherapy (IMRT) and IGABT. Dosimetric parameters were evaluated for relevance for local control (LC), progression-free survival (PFS), and overall survival (OS) using Kaplan–Meier estimation, Cox regression, and log-rank test. Patients predominantly presented with stage III/IV tumors (81%, FIGO 2018). The median high-risk clinical target volume (hrCTV) was 34 cm³, with a median D90% dose of 88.9 GyEQD2. At 24 months, OS, PFS, and LC rates were 86%, 57%, and 81%, respectively. FIGO stage, tumor volume, and histology were significant predictors of PFS. Higher total hrCTV doses were strongly correlated with improved LC and PFS, emphasizing the importance of precise dosimetric optimization in IGABT and confirming the critical role of IGABT in achieving very good LC rates for LACC. The reported LC rates are comparable to landmark studies, such as INTERLACE and KEYNOTE-A18. This study validates the effectiveness of MRI-guided IGABT in enhancing local tumor control in advanced-stage cervical cancer while providing insights into the prognostic implications of dosimetric parameters such as hrCTV and point A. Future research should address the persistent challenge of distant metastases by exploring the integration of novel systemic treatment options. Full article

There has been an exponential increase in the utility of mixed-reality (MR) software as a tool for medical education and training due to its immersive and interactive capabilities. Whilst it has been progressively used in surgical training or in simulation training, there is a significant lack of using it to train the “trainers”. In this single-centre prospective study, MR technology was used to deliver a dedicated 2-h tutorial in surgical training to two cohorts of postgraduate students attending a course on clinical research and education. The Microsoft HoloLens 2 was used to run mixed-reality software capable of rendering CT scan images of a normal brain, an MRI of a large meningioma, an abdominal–pelvic CT scan, and a 3D-printed cranioplasty scan. The participants were then asked to complete a post-usage questionnaire in an anonymous manner. Fourteen participants attended the teaching session and completed the post-usage questionnaire. Scores obtained on the User Experience Questionnaire (UEQ) revealed that MR technology is rated “Excellent” on quality aspects for Attractiveness, Stimulation and Novelty. This prospective study provides insight into incorporating MR in training the trainers, allowing them to be equipped with the technology to imparting education to the next generation across various disciplines. Full article

Introduction: Glioma Grade 4 (GG4) tumors, which include both IDH-mutated and IDH wild-type astrocytomas, are the most prevalent and aggressive form of primary brain tumor. Radiomics is gaining ground in neuro-oncology. The integration of this data into machine learning models has the potential to improve the accuracy of prognostic models for GG4 patients. Karnofsky Performance Status (KPS), an established preoperative prognostic factor for survival, is commonly used in these patients. In this study, we developed a nomogram to identify patients with improved functional performance as indicated by an increase in KPS after surgery by analyzing radiomic features from preoperative 3D MRI scans. Methods: Quantitative imaging features were extracted from the -3D T1 GRE sequence of 157 patients from a single center and were used to develop the machine learning (ML) model. To improve applicability and create a nomogram, multivariable logistic regression analysis was performed to build a model incorporating clinical characteristics and radiomics features. Results: We labeled 55 cases in which KPS was improved after surgery (35%, KPS-flag = 1). The resulting model was evaluated according to test series results. The best model was obtained by XGBoost using the features extracted by pyradiomics, with a Matthew coefficient score (MCC) of 0.339 (95% CI: 0.330–0.3483) in cross-validation. The out-of-sample evaluation on the test set yielded an MCC of 0.302. A nomogram evaluating the improvement of KPS post-surgery was built based on statistically significant variables from multivariate logistic regression including clinical and radiomics data (c-index = 0.760, test set). Conclusions: MRI radiomic analysis represents a powerful tool to predict postoperative functional outcomes, as evaluated by KPS. Full article

Background/Objectives: Interest in diversity is growing worldwide. Today, an understanding and social acceptance of diverse people is becoming increasingly important. Therefore, in this study, we aimed to clarify the relationship between an individual’s gray matter volume (GMV), which is thought to reflect brain health, and their understanding of diversity (gender, sexuality (LGBTQ), and origin). Methods: GMV was determined as the value of the Gray Matter Brain Healthcare Quotient (GM-BHQ) based on MRI image analysis. Meanwhile, participants’ understanding and acceptance of diversity was calculated based on their answers to the psychological questions included in the World Values Survey Wave 7 (WVS7). Results: Our analysis indicated that, in the group of participants with the highest understanding of diversity (PHUD. n = 11), not only the GMV at the whole brain level (t = 2.587, p = 0.027, Cohen’s d = 0.780) but also the GMV of the central executive network (CEN: t = 2.700, p= 0.022, Cohen’s d = 0.814) and saliency network (SN: t = 3.100, p = 0.011, Cohen’s d = 0.935) were shown to be significantly higher than the theoretical value estimated from sex, age, and BMI at the 5% level. In addition, the GMV of the default mode network (DMN: t = 2.063, p = 0.066, Cohen’s d = 0.622) was also higher than the theoretical value at the 10% level. Meanwhile, in the group of others (n = 10), there was no significant difference from the theoretical value. These differences between PHUD and others were also observed when comparing the two with and without controlling for educational and occupational covariates at the 5% or 10% levels. Conclusions: These results suggest that understanding diversity requires a healthy brain, centered on three networks that govern rational judgment, emotion regulation, other-awareness, self-awareness, and the valuing of actions. This is the first study to show that brain structure is related to an understanding and acceptance of the diversity of people. Full article

Background: Radiomics has become a valuable tool in medical imaging, but its clinical use is limited by data variability and a lack of reproducibility between centers. This study aims to assess the differences between two scanners and provide guidance on image acquisition methods to reduce variations between images obtained from different centers. Methods: This study utilized medical images obtained in two different imaging centers, with two different 3T MRI scanners. For each scanner, 3D T2 FLAIR sequences were acquired in two forms: the raw and the clinical practice images typically used in diagnostic workflows. The differences between images were analyzed regarding resolution, SNR, CNR, and radiomic features. To facilitate comparison, bias field correction was applied, and the data were standardized to the same scale using Z-score normalization. Descriptive and inferential statistical methods were used to analyze the data. Results: The results show that there are significant differences between centers. Filtering and zero-padding significantly influence the resolution, SNR, CNR values, and radiomics features. Applying Z-score normalization has resolved variations in features sensitive to scale differences, but features reflecting dispersion and extreme values remain significantly different between scanners. Some feature differences may be resolved by analyzing the raw images in both centers. Conclusions: Variations arise due to different acquisition parameters and the differing quality and sensitivity of the equipment. In multi-center studies, acquiring raw images and then applying standardized post-processing methods across all images can enhance the robustness of results. This approach minimizes technical differences, and preserves the integrity of the information, reflecting a more accurate representation of reality and contributing to more reliable and reproducible findings. Full article

Mitochondrial 3-hydroxy-3-methylglutaryl-CoA synthase deficiency (HMGCS2D) is a rare metabolic disorder that impairs the body’s ability to produce ketone bodies and regulate energy metabolism. Diagnosing HMGCS2D is challenging because patients typically remain asymptomatic unless they experience fasting or illness. Due to the absence of reliable biochemical markers, genetic testing has become the definitive method for diagnosing HMGCS2D. This study included 19 patients from 14 unrelated families diagnosed with HMGCS2D in our department between October 2018 and October 2024. The clinical presentations, biochemical findings, molecular characteristics, and management strategies were systematically summarized and analyzed. Of the 19 cases studied, 16 were symptomatic, and 3 were asymptomatic. The onset of the first acute episode occurred between 10 days and 28 months of age. Triggers for the initial crisis in the symptomatic cases included poor feeding (93.8%), vomiting (56.3%), diarrhea (25.0%), and fever (18.8%). Clinical manifestations during the first episode were lethargy/coma (81.3%), rapid breathing (68.8%), hepatomegaly (56.3%), shock (37.5%), and seizures (18.8%). The biochemical abnormalities observed included elevated plasma transaminases (100%), metabolic acidosis (75%), hypoglycemia (56.3%), and elevated plasma ammonia levels (31.3%). Additionally, low free carnitine levels were found in seven cases, elevated C2 levels were found in one case, dicarboxylic aciduria was found in two cases, and ketonuria was found in two cases. Abnormal brain MRI findings were detected in three patients. Genetic analysis revealed seven HMGCS2 gene variants across the 19 cases. Notably, a novel variant, c.407A>T (p.D136V), was identified and has not been reported in any existing databases. Two common variants, c.559+1G>A and c.1090T>A (p.F364I), were present in 11 out of 19 cases (57.9%) and 10 out of 19 cases (55.5%), respectively. The implementation of a high glucose infusion and proactive management strategies—such as preventing prolonged fasting and providing enteral carbohydrate/glucose infusion during illness—effectively reduced the rate of acute relapses following accurate diagnosis. Currently, all 19 patients are alive, with ages ranging from 5 months to 14 years, and exhibit normal physical development. To the best of our knowledge, this study represents the first reported cases of HMGCS2D in Vietnamese patients. Our findings contribute to a broader understanding of the clinical phenotype and expand the known spectrum of HMGCS2 gene variants, enhancing current knowledge of this rare metabolic disorder. Full article

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is characterized by structural abnormalities, arrhythmias, and a spectrum of genetic and clinical manifestations. Clinically, ARVC is structurally distinguished by right ventricular dilation due to increased adiposity and fibrosis in the ventricular walls, and it manifests as cardiac arrhythmias ranging from non-sustained ventricular tachycardia to sudden cardiac death. Its prevalence has been estimated to range from 1 in every 1000 to 5000 people, with its large range being attributed to the variability in genetic penetrance from asymptomatic to significant burden. It is even suggested that the prevalence is underestimated, as the presence of genotypic mutations does not always lead to clinical manifestations that would facilitate diagnosis. Additionally, while set criteria have been in place since the 1990s, newer understanding of this condition and advancements in cardiac technology have prompted multiple revisions in the diagnostic criteria for ARVC. Novel discoveries of gene variants predisposing patients to ARVC have led to established screening techniques while providing insight into genetic counseling and management. This review aims to provide an overview of the genetics, pathophysiology, and clinical approach to ARVC. It will also focus on clinical presentation, ARVC diagnostic criteria, electrophysiological findings, including electrocardiogram characteristics, and imaging findings from cardiac MRI, 2D, and 3D echocardiogram. Current management options—including anti-arrhythmic medications, device indications, and ablation techniques—and the effectiveness of treatment will also be reviewed. Full article

Background/Objectives: Differentiating between benign and malignant parotid gland tumors (PGT) is essential for establishing the treatment strategy, which is greatly influenced by the tumor’s histology. The objective of this study was to evaluate the role of MRI-based radiomics in the differentiation between Warthin’s tumors (WT) and malignant tumors (MT), two entities that proved to present overlapping imaging features on conventional and functional MRI sequences. Methods: In this retrospective study, a total of 106 PGT (66 WT, 40 MT) with confirmed histology were eligible for radiomic analysis, which were randomly split into a training group (79 PGT; 49 WT; 30 MT) and a testing group (27 PGT; 17 WT, 10 MT). The radiomic features were extracted from 3D segmentations of PGT performed on the following sequences: PROPELLER T2-weighted images and the ADC map, using a dedicated software. First- and second-order features were derived for each lesion, using original and filtered images. Results: After employing several feature reduction techniques, including LASSO regression, three final radiomic parameters were identified to be the most significant in distinguishing between the two studied groups, with fair AUC values that ranged between 0.703 and 0.767. All three radiomic features were used to construct a Radiomic Score that presented the highest diagnostic performance in distinguishing between WT and MT, achieving an AUC of 0.785 in the training set, and 0.741 in the testing set. Conclusions: MRI-based radiomic features have the potential to serve as promising novel imaging biomarkers for discriminating between Warthin’s tumors and malignant tumors in the parotid gland. Nevertheless, it is still to prove how radiomic features can consistently achieve higher diagnostic performance, and if they can outperform alternative imaging methods, ideally in larger, multicentric studies. Full article

Background/Objectives: We aimed to compare non-pharmacological (non-PHM) and pharmacological (PHM) treatment for patients with non-acute lumbar disc herniation (LDH) and determine the feasibility of a large-scale study. Methods: This was a two-armed, parallel, multicenter, pragmatic controlled trial performed in South Korea. All patients underwent magnetic resonance imaging (MRI) scans both at the screening stage and the last follow-up. Patients with LDH findings on MRI were randomly assigned to non-PHM and PHM groups. Treatment was administered twice a week for a total of 8 weeks, and follow-up assessments were performed at weeks 9, 13, and 27 post-randomization. The primary outcome was the Oswestry Disability Index (ODI) score. A linear mixed model was used for primary analysis from intention-to-treat perspectives. The incremental cost-effectiveness ratio (ICER) was calculated for economic evaluation. Results: Thirty-six patients were enrolled, and thirty-five were included in the final analysis. At Week 9, the difference in ODI scores between the two groups was 5.17 (95% CI: −4.00 to 14.35, p = 0.262), and the numeric rating scale scores for lower back and leg pains were 1.89 (95% CI: 0.68 to 3.10, p = 0.003) and 1.52 (95% CI: 0.27 to 2.77, p = 0.018), respectively, confirming greater improvement in the non-PHM group than in the PHM group. The non-PHM group showed lower costs and higher quality-adjusted life years than the PHM group. The ICER calculated using the EuroQoL-5 Dimension (EQ-5D) was USD 20,926. Conclusions: We confirm the possibility that a non-PHM strategy could be a more effective and cost-effective treatment option than PHM for patients with non-acute lumbar disc herniation. Furthermore, this pilot study confirmed the feasibility of the main study in terms of design and patient compliance. Full article

Three-dimensional (3D)-printing technology is revolutionizing orthopedic oncology by providing precise, customized solutions for complex bone defects following tumor resection. Traditional modular endoprostheses are prone to complications such as fretting corrosion and implant failure, underscoring the need for innovative approaches. This case series reports on three patients treated with 3D-printed, patient-specific prostheses and cutting guides. Preoperative CT and MRI data were used to design implants tailored to each patient’s anatomy, manufactured using electron beam melting technology with a titanium–aluminum–vanadium alloy. Functional outcomes showed significant improvements: in Case I, AOFAS improved from 71 to 96, and VAS decreased from 6 to 1; in Case II, AOFAS increased from 65 to 79, and VAS decreased from 5 to 3. Radiographic evaluations demonstrated stable prosthesis placement and early evidence of bone integration in Cases I and II, while in Case III, localized disease control was achieved before systemic progression. This case series highlights the transformative potential of 3D-printed prostheses in addressing the challenges of reconstructing anatomically complex defects. By enabling precise tumor resection and improving functional outcomes, this approach can advance current practices in orthopedic oncology. Further research should explore larger cohorts and use cost-effectiveness analyses to validate these findings and facilitate broader clinical adoption. Full article

Transformer-based image registration methods have achieved notable success, but they still face challenges, such as difficulties in representing both global and local features, the inability of standard convolution operations to focus on key regions, and inefficiencies in restoring global context using the decoder. To address these issues, we extended the Swin-UNet architecture and incorporated dynamic snake convolution (DSConv) into the model, expanding it into three dimensions. This improvement enables the model to better capture spatial information at different scales, enhancing its adaptability to complex anatomical structures and their intricate components. Additionally, multi-scale dense skip connections were introduced to mitigate the spatial information loss caused by downsampling, enhancing the model’s ability to capture both global and local features. We also introduced a novel optimization-based weakly supervised strategy, which iteratively refines the deformation field generated during registration, enabling the model to produce more accurate registered images. Building on these innovations, we proposed OSS DSC-STUNet+ (Swin-UNet+ with 3D dynamic snake convolution). Experimental results on the IXI, OASIS, and LPBA40 brain MRI datasets demonstrated up to a 16.3% improvement in Dice coefficient compared to five classical methods. The model exhibits outstanding performance in terms of registration accuracy, efficiency, and feature preservation. Full article

Background: Radiomics is a non-invasive and cost-effective method for predicting the biological characteristics of tumors. In this study, we explored the association between radiomic features derived from magnetic resonance imaging (MRI) and genetic alterations in patients with breast cancer. Methods: We reviewed electronic medical records of patients with breast cancer patients with available targeted next-generation sequencing data available between August 2018 and May 2021. Substraction imaging of T1-weighted sequences was utilized. The tumor area on MRI was segmented semi-automatically, based on a seeded region growing algorithm. Radiomic features were extracted using the open-source software 3D slicer (version 5.6.1) with PyRadiomics extension. The association between genetic alterations and radiomic features was examined. Results: In total, 166 patients were included in this study. Among the 50 panel genes analyzed, only TP53 mutations were significantly associated with radiomic features. Compared with TP53 wild-type tumors, TP53 mutations were associated with larger tumor size, advanced stage, negative hormonal receptor status, and HER2 positivity. Tumors with TP53 mutations exhibited higher values for Gray Level Non-Uniformity, Dependence Non-Uniformity, and Run Length Non-Uniformity, and lower values for Sphericity, Low Gray Level Emphasis, and Small Dependence Low Gray Level emphasis compared to TP53 wild-type tumors. Six radiomic features were selected to develop a composite radiomics score. Receiver operating characteristic curve analysis showed an area under the curve of 0.786 (95% confidence interval, 0.719–0.854; p < 0.001). Conclusions: TP53 mutations in breast cancer can be predicted using MRI-derived radiomic analysis. Further research is needed to assess whether radiomics can help guide treatment decisions in clinical practice. Full article