T2-weighted Images Research Articles

Metastasis Detection Using True and Artificial T1-Weighted Postcontrast Images in Brain MRI

Objectives Small lesions are the limiting factor for reducing gadolinium-based contrast agents in brain magnetic resonance imaging (MRI). The purpose of this study was to compare the sensitivity and precision in metastasis detection on true contrast-enhanced T1-weighted (T1w) images and artificial images synthesized by a deep learning method using low-dose images. Materials and Methods In this prospective, multicenter study (5 centers, 12 scanners), 917 participants underwent brain MRI between October 2021 and March 2023 including T1w low-dose (0.033 mmol/kg) and full-dose (0.1 mmol/kg) images. Forty participants with metastases or unremarkable brain findings were evaluated in a reading (mean age ± SD, 54.3 ± 15.1 years; 24 men). True and artificial T1w images were assessed for metastases in random order with 4 weeks between readings by 2 neuroradiologists. A reference reader reviewed all data to confirm metastases. Performances were compared using mid-P McNemar tests for sensitivity and Wilcoxon signed rank tests for false-positive findings. Results The reference reader identified 97 metastases. The sensitivity of reader 1 did not differ significantly between sequences (sensitivity [precision]: true, 66.0% [98.5%]; artificial, 61.9% [98.4%]; P = 0.38). With a lower precision than reader 1, reader 2 found significantly more metastases using true images (sensitivity [precision]: true, 78.4% [87.4%]; artificial, 60.8% [80.8%]; P < 0.001). There was no significant difference in sensitivity for metastases ≥5 mm. The number of false-positive findings did not differ significantly between sequences. Conclusions One reader showed a significantly higher overall sensitivity using true images. The similar detection performance for metastases ≥5 mm is promising for applying low-dose imaging in less challenging diagnostic tasks than metastasis detection.

Solitary fibrous tumor of the gallbladder: a case report

Abstract Background Primary solitary fibrous tumors (SFTs) of the gallbladder are rare. Here, we report the case of a patient who underwent surgical treatment for a primary SFT originating in the gallbladder. Case presentation A 48-mm gallbladder tumor was detected in a 70-year-old man using abdominal ultrasonography at a primary hospital, and he was subsequently referred to our department. A 50-mm enhanced tumor in the gallbladder was identified using computed tomography. Magnetic resonance imaging revealed a smooth-marginated tumor with hyperintensity on T2-weighted imaging. 18F-Fluorodeoxyglucose positron emission tomography confirmed high-level fluorodeoxyglucose uptake in the gallbladder tumor in the early phase without increasing uptake in the later phase. Surgical resection was planned to evaluate the tumor diagnosis. Initially, we performed open cholecystectomy with wedge resection of the gallbladder bed. Intraoperative pathological examination suggested gallbladder cancer; therefore, we performed radical surgery, including resection of the common bile duct, extended radical lymphadenectomy, and choledochojejunostomy. Ultimately, the final pathological examination revealed an SFT originating from the gallbladder with a negative surgical margin. Postoperatively, the patient developed bile leakage that was treated with tube drainage. The patient recovered satisfactorily and was discharged on postoperative day 20. At 24 months postoperatively, the patient was in good general condition without recurrence. Conclusions We report a rare case of a primary SFT originating in the gallbladder. Clinicians should be aware that SFT can be found in the gallbladder, and when it is difficult to make a preoperative diagnosis, surgical treatment should be considered.

Comparative analysis of image quality and diagnostic performance among SS-EPI, MS-EPI, and rFOV DWI in bladder cancer.

To compare image quality and diagnostic performance among SS-EPI diffusion weighted imaging (DWI), multi-shot (MS) EPI DWI, and reduced field-of-view (rFOV) DWI for muscle-invasive bladder cancer (MIBC). This retrospective study included 73 patients with bladder cancer who underwent multiparametric MRI in our referral center between August 2020 and February 2023. Qualitative image assessment was performed in 73; and quantitative assessment was performed in 66 patients with maximum lesion diameter > 10mm. The diagnostic performance of the imaging finding of muscle invasion was evaluated in 47 patients with pathological confirmation of MIBC. T2-weighted imaging, SS-EPI DWI, MS-EPI DWI, rFOV DWI, and dynamic contrast-enhanced imaging were acquired with 3T-MRI. Qualitative image assessment was performed by three readers who rated anatomical distortion, clarity of bladder wall, and lesion conspicuity using a four-point scale. Quantitative assessment included calculation of SNR and CNR, and grading of the presence of muscle layer invasion according to the VI-RADS diagnostic criteria. Wilcoxon matched pairs signed rank test was used to compare qualitative and quantitative image quality. McNemar test and receiver-operating characteristic analysis were used to compare diagnostic performance. Anatomical distortion was less in MS-EPI DWI, rFOV DWI, and SS-EPI DWI, in that order with significant difference. Clarity of bladder wall was greater for MS-EPI DWI, SS-EPI DWI, and rFOV DWI, in that order. There were significant differences between any two combinations of the three DWI types, except between SS-EPI DWI and MS-EPI in Reader 1. Lesion conspicuity, diagnostic performance, SNR and CNR were not significantly different among the three DWI types. Among the three DWI sequences evaluated, MS-EPI DWI showed the least anatomical distortion and superior bladder wall delineation but no improvement in diagnostic performance for MIBC. MS-EPI DWI may be considered for additional imaging if SS-EPI DWI is of poor quality.

Magnetic Resonance Imaging Texture Analysis Based on Intraosseous and Extraosseous Lesions to Predict Prognosis in Patients with Osteosarcoma

Objectives: To construct an optimal magnetic resonance imaging (MRI) texture model to evaluate histological patterns and predict prognosis in patients with osteosarcoma (OS). Methods: Thirty-four patients underwent pretreatment MRI and were diagnosed as having OS by surgical resection or biopsy between September 2008 and June 2018. Histological patterns and 3-year survival were recorded. Manual segmentation was performed in intraosseous, extraosseous, and entire lesions on T1-weighted, T2-weighted, and contrast-enhanced T1-weighted images to extract texture features and perform principal component analysis. A support vector machine algorithm with 3-fold cross-validation was used to construct and validate the models. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate diagnostic performance in evaluating histological patterns and 3-year survival. Results: Eight patients were chondroblastic and the remaining twenty-six patients were non-chondroblastic patterns. Twenty-seven patients were 3-year survivors, and the remaining seven patients were non-survivors. In discriminating chondroblastic from non-chondroblastic patterns, the model from extraosseous lesions on the T2-weighted images showed the highest diagnostic performance (AUCs of 0.94 and 0.89 in the training and validation sets). The model from intraosseous lesions on the T1-weighted images showed the highest diagnostic performance in discriminating 3-year non-survivors from survivors (AUCs of 0.99 and 0.88 in the training and validation sets) with a sensitivity, specificity, positive predictive value, and negative predictive value of 85.7%, 92.6%, 75.0%, and 96.2%, respectively. Conclusions: The texture models of extraosseous lesions on T2-weighted images can discriminate the chondroblastic pattern from non-chondroblastic patterns, while the texture models of intraosseous lesions on T1-weighted images can discriminate 3-year non-survivors from survivors.

Predictive value of contrast-enhanced MRI for the regrowth of residual uterine fibroids after high-intensity focused ultrasound treatment.

To investigate whether the signal intensity (SI) ratio of residual fibroid (RF) to myometrium using Contrast-Enhanced Magnetic Resonance Imaging (CE-MRI) could predict fibroid regrowth after high-intensity focused ultrasound (HIFU) treatment. A retrospective analysis was conducted among 164 patients with uterine fibroids who underwent HIFU. To predict the RF regrowth, the SI perfusion parameters were quantified using the RF-myometrium SI ratio on CE-MRI on day 1 post-HIFU and then compared with the fibroid-myometrium SI ratio on the T2-weighted image (T2WI) and Funaki classification 1 year later. Thirty cases from another center were used as an external validation set to evaluate the performance of RF-myometrium SI ratio. The predictive performance of the RF-myometrium SI ratio on CE-MRI on day 1 post-HIFU (Area Under Curve, AUC: 0.869) was superior to that of the preoperative and postoperative fibroid-myometrium SI ratios on the T2WI (AUC: 0.724, 0.696) and Funaki classification (AUC: 0.663, 0.623). Multivariate analysis showed that the RF- myometrium SI ratio and RF thickness were independent factors. The RF-myometrium SI ratio reflects the long-term rate of re-intervention (r = 0.455, p < 0.001). The RF-myometrium SI ratio on CE-MRI exhibits greater accuracy in predicting RF regrowth compared to the SI classification and the SI ratio on T2WI. The ratio of residual uterine fibroid to myometrial signal intensity on contrast-enhanced (CE)-MRI can reflect residual blood supply, predict regrowth of fibroids, and thus reflect long-term re-intervention rate and recovery situation of clinical high-intensity focused ultrasound (HIFU) treatment. Contrast-enhanced (CE)-MRI can indicate the blood supply of residual uterine fibroids after high-intensity focused ultrasound (HIFU) treatment. The predictive capability of CE-MRI ratio surpasses T2WI ratio and the Funaki. Residual fibroids can serve as a measure of the long-term efficacy of HIFU.

Visualizing the association between the location and prognosis of isocitrate dehydrogenase wild-type glioblastoma: a voxel-wise Cox regression analysis with open-source datasets.

This study examined the correlation between tumor location and prognosis in patients with glioblastoma using magnetic resonance images of various isocitrate dehydrogenase (IDH) wild-type glioblastomas from The Cancer Imaging Archive (TCIA). The relationship between tumor location and prognosis was visualized using voxel-wise Cox regression analysis. Participants with IDH wild-type glioblastoma were selected, and their survival and demographic data and tumor characteristics were collected from TCIA datasets. Post-contrast-enhanced T1-weighted imaging, T2-fluid attenuated inversion recovery imaging, and tumor segmentation data were also compiled. Following affine registration of each image and tumor segmentation region of interest to the MNI standard space, a voxel-wise Cox regression analysis was conducted. This analysis determined the association of the presence or absence of the tumor with the prognosis in each voxel after adjusting for the covariates. The study included 769 participants of 464 men and 305 women (mean age, 63years ± 12 [standard deviation]). The hazard ratio map indicated that tumors in the medial frontobasal region and around the third and fourth ventricles were associated with poorer prognoses, underscoring the challenges of complete resection and treatment accessibility in these areas regardless of the tumor volume. Conversely, tumors located in the right temporal and occipital lobes had favorable prognoses. This study showed an association between tumor location and prognosis. These findings may assist clinicians in developing more precise and effective treatment plans for patients with glioblastoma to improve their management.

MRI characteristics of ovarian metastasis: differentiation from stomach and colorectal cancer.

To evaluate the efficacy of MRI findings for differentiating between ovarian metastasis from stomach cancer (OMSC) and colorectal cancer (OMCC). Twenty-six patients with histopathologically proven ovarian metastasis (n = 8 with 12 OMSCs and n = 18 with 25 OMCCs) were enrolled in the study. All patients had undergone pelvic MRI before surgery. We retrospectively reviewed MRI findings and compared them between the two pathologies. The black scrunchie sign was defined as a thick (> 5mm) and lobulated hypointense rim (> 180°) with central hyperintense areas on T2-weighted images. Predominantly solid lesions (100% vs. 20%, p < 0.01), black scrunchie sign (33% vs. 0%, p < 0.01), and flow void (67% vs. 20%, p < 0.01) were frequently observed in OMSCs than in OMCCs. The signal intensity ratio of solid components on T2-weighted images (3.30 ± 0.70 vs. 2.52 ± 0.77, p < 0.01) and gadolinium-enhanced T1-weighted images (2.21 ± 0.57 vs. 1.43 ± 0.32, p < 0.01) were significantly higher in OMSCs than in OMCCs. Furthermore, hyperintense areas within cystic components on T1-weighted images (71% vs. 18%, p < 0.01) and stained-glass appearance (44% vs. 0%, p < 0.01) were frequently observed in OMCCs than in OMSCs. The black scrunchie sign was only observed in OMSCs. OMSCs always exhibited predominantly solid lesions and had higher signal intensity of solid components on T2- and gadolinium-enhanced T1-weighted images. OMCCs usually presented as cystic lesions, usually accompanied by hyperintense areas within the cystic components on T1-weighted images.

Anatomical Characteristics of Cervicomedullary Compression on MRI Scans in Children with Achondroplasia

This retrospective study assessed anatomical characteristics of cervicomedullary compression in children with achondroplasia. Twelve anatomical parameters were analyzed (foramen magnum diameter and area; myelon area; clivus length; tentorium and occipital angles; brainstem volume outside the posterior fossa; and posterior fossa, cerebellum, supratentorial ventricular system, intracranial cerebrospinal fluid, and fourth ventricle volumes) from sagittal and transversal T1- and T2-weighted magnetic resonance imaging (MRI) scans from 37 children with achondroplasia aged ≤ 4 years (median [range] 0.8 [0.1–3.6] years) and compared with scans from 37 children without achondroplasia (median age 1.5 [0–3.9] years). Mann–Whitney U testing was used for between-group comparisons. Foramen magnum diameter and area were significantly smaller in children with achondroplasia compared with the reference group (mean 10.0 vs. 16.1 mm [p &lt; 0.001] and 109.0 vs. 160.8 mm2 [p = 0.005], respectively). The tentorial angle was also steeper in children with achondroplasia (mean 47.6 vs. 38.1 degrees; p &lt; 0.001), while the clivus was significantly shorter (mean 23.5 vs. 30.3 mm; p &lt; 0.001). Significant differences were also observed in myelon area, occipital angle, fourth ventricle, intracranial cerebrospinal fluid and supratentorial ventricular volumes, and the volume of brainstem protruding beyond the posterior fossa (all p &lt; 0.05). MRI analysis of brain structures may provide a standardized value to indicate decompression surgery in children with achondroplasia.

Deep learning of structural MRI predicts fluid, crystallized, and general intelligence.

Can brain structure predict human intelligence? T1-weighted structural brain magnetic resonance images (sMRI) have been correlated with intelligence. However, the population-level association does not fully account for individual variability in intelligence. To address this, studies have emerged recently to predict individual subject's intelligence or neurocognitive scores. However, they are mostly on predicting fluid intelligence (the ability to solve new problems). Studies are lacking to predict crystallized intelligence (the ability to accumulate knowledge) or general intelligence (fluid and crystallized intelligence combined). This study tests whether deep learning of sMRI can predict an individual subject's verbal, comprehensive, and full-scale intelligence quotients (VIQ, PIQ, and FSIQ), which reflect fluid and crystallized intelligence. We performed a comprehensive set of 432 experiments, using different input image channels, six deep learning models, and two outcome settings, in 850 healthy and autistic subjects 6-64 years of age. Our findings indicate a statistically significant potential of T1-weighted sMRI in predicting intelligence, with a Pearson correlation exceeding 0.21 (p < 0.001). Interestingly, we observed that an increase in the complexity of deep learning models does not necessarily translate to higher accuracy in intelligence prediction. The interpretations of our 2D and 3D CNNs, based on GradCAM, align well with the Parieto-Frontal Integration Theory (P-FIT), reinforcing the theory's suggestion that human intelligence is a result of interactions among various brain regions, including the occipital, temporal, parietal, and frontal lobes. These promising results invite further studies and open new questions in the field.

Assessing the Performance of Artificial Intelligence Assistance for Prostate MRI: A Two-Center Study Involving Radiologists With Different Experience Levels.

Artificial intelligence (AI) assistance may enhance radiologists' performance in detecting clinically significant prostate cancer (csPCa) on MRI. Further validation is needed for radiologists with different experiences. To assess the performance of experienced and less-experienced radiologists in detecting csPCa, with and without AI assistance. Retrospective. Nine hundred patients who underwent prostate MRI and biopsy (median age 67 years; 356 with csPCa and 544 with non-csPCa). 3-T and 1.5-T, diffusion-weighted imaging using a single-shot gradient echo-planar sequence, turbo spin echo T2-weighted image. CsPCa regions based on biopsy results served as the reference standard. Ten less-experienced (<500 prostate MRIs) and six experienced (>1000 prostate MRIs) radiologists reviewed each case twice using Prostate Imaging Reporting and Data System v2.1, with and without AI, separated by 4-week intervals. Cases were equally distributed among less-experienced radiologists, and 90 cases were randomly assigned to each experienced radiologist. Reading time and diagnostic confidence were assessed. Area under the curve (AUC), sensitivity, specificity, reading time, and diagnostic confidence were compared using the DeLong test, Chi-squared test, Fisher exact test, or Wilcoxon rank-sum test between the two sessions. A P-value <0.05 was considered significant. Adjusting threshold using Bonferroni correction was performed for multiple comparisons. For less-experienced radiologists, AI assistance significantly improved lesion-level sensitivity (0.78 vs. 0.88), sextant-level AUC (0.84 vs. 0.93), and patient-level AUC (0.84 vs. 0.89). For experienced radiologists, AI assistance only improved sextant-level AUC (0.82 vs. 0.91). AI assistance significantly reduced median reading time (250 s [interquartile range, IQR: 157, 402] vs. 130 s [IQR: 88, 209]) and increased diagnostic confidence (5 [IQR: 4, 5] vs. 5 [IQR: 4, 5]) irrespective of experience and enhanced consistency among experienced radiologists (Fleiss κ: 0.53 vs. 0.61). AI-assisted reading improves the performance of detecting csPCa on MRI, particularly for less-experienced radiologists. 3 TECHNICAL EFFICACY: Stage 2.

An SBM and TBSS Analysis in Early-stage Patients With Alzheimer's Disease, Lewy Body Dementias, and Corticobasal Syndrome.

To compare gray matter (GM) and white matter (WM) changes in patients with Alzheimer's disease (AD), Lewy body dementias (LBD), corticobasal syndrome (CBS), and healthy controls (HC). Surface-based morphometry (SBM) was assessed on 3D T1-weighted images using FreeSurfer image analysis and WM microstructure was studied using Tract-Based Spatial Statistics (TBSS) in 12 AD, 15 LBD, 10 CBS patients, and 10 HC. Patients with AD, compared with HC, exhibited reduced cortical surface area and volume in the superior frontal, middle frontal, and medial orbitofrontal cortex. In TBSS, AD patients, compared with HC and LBD, displayed decreased fractional anisotropy, axial diffusivity, and increased radial diffusivity in all major WM tracts. Other comparisons between the groups yielded no differences, either in the SBM or the TBSS analysis. The results indicate significant early structural changes in the GM of the frontal lobe, along with WM alterations early in AD patients.

Bright middle cerebellar peduncle sign in multiple system atrophy with predominant cerebellar ataxia is more apparent in double-inversion recovery imaging than in conventional imaging.

Multiple system atrophy (MSA) is a neurodegenerative disorder that presents as parkinsonism, cerebellar ataxia, and autonomic dysfunction. Magnetic resonance imaging (MRI) findings of MSA are reported to be the atrophy of the putamen/pons/cerebellum, hot cross bun sign, and bright middle cerebellar peduncle (MCP) sign. Here, we assessed the sensitivity of detecting the bright MCP sign in patients with MSA cerebellar variant (MSA-C) using a double inversion recovery (DIR) procedure, comparing it to the sensitivity of detection by T2-weighted image (T2WI) and fluid-attenuated inversion recovery (FLAIR) sequences on conventional MRI. We evaluated 6 MSA-C patients and 6 control patients (multiple sclerosis, neuromyelitis optica, and spinocerebellar atrophy). Characteristics of all the patients were collected, and MRI was analyzed. Two neurologists independently evaluated the visualization of the bright MCP sign using a 4-point visual grade from Grade 0 to Grade 3. Differences in grade between DIR and T2WI or FLAIR were statistically analyzed. Also, as a quantitative analysis, the signal intensity of the MCP lesion was compared with that of the ipsilateral thalamus, and the MCP/thalamus ratio was evaluated. As a result, DIR more sensitively showed the bright MCP signs of MSA-C patients than T2WI or FLAIR. Also, the bright MCP sign deteriorated and expanded over time in the cases we followed with MRI. We also evaluated hot cross bun sign in the pons, but the hot cross bun sign in MSA-C patients was not significantly different between the DIR and conventional MRI sequences. The DIR procedure can be a more sensitive method for detecting the involvement of MCP lesions in MSA-C.

Application of deep learning techniques for breath-hold, high-precision T2-weighted magnetic resonance imaging of the abdomen.

To evaluate the feasibility of a high-precision single-shot fast spin-echo (SS-FSE) sequence using the deep learning-based Precise IQ Engine (PIQE) algorithm in comparison with standard SS-FSE for T2-weighted MR imaging of the abdomen, and to compare the image quality with a multi-shot (MS)-FSE sequence using the PIQE algorithm. This retrospective study included 105 patients who underwent abdominal MR including T2-weighted sequences using the PIQE reconstruction algorithm. The image quality, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) in high-precision SS-FSE sequences using PIQE were compared to those in standard SS-FSE without PIQE and MS-FSE sequences using PIQE. The scores for all qualitative parameters were significantly higher in high-precision SS-FSE sequence using PIQE than in standard SS-FSE sequence without PIQE (all p < 0.001). In the comparison between two high-precision sequences using PIQE, the SS-FSE sequence showed significantly better scores for the blurring, ghosts or motion/flow artifacts, conspicuity of intrahepatic structures, focal nonsolid hepatic and pancreatic cystic lesions, and overall image quality, in comparison to the MS-FSE sequence (all p < 0.001). Additionally, the SS-FSE sequence using PIQE showed significantly higher SNR of the liver and CNR of nonsolid hepatic lesions than the MS-FSE sequence using PIQE (p < 0.001). A high-precision SS-FSE sequence using the PIQE algorithm is a feasible alternative to the standard FSE sequence in T2-weighted MR imaging of the abdomen. It can improve image quality, the SNR of the liver, and the ability to visualize nonsolid focal liver lesions and pancreatic cystic lesions in comparison to a high-precision MS-FSE sequence using PIQE although this study was limited by single-center design and lack of pathological confirmation.

Radio-pathomic estimates of cellular growth kinetics predict survival in recurrent glioblastoma.

Aim: A radio-pathomic machine learning (ML) model has been developed to estimate tumor cell density, cytoplasm density (Cyt) and extracellular fluid density (ECF) from multimodal MR images and autopsy pathology. In this multicenter study, we implemented this model to test its ability to predict survival in patients with recurrent glioblastoma (rGBM) treated with chemotherapy.Methods: Pre- and post-contrast T1-weighted, FLAIR and ADC images were used to generate radio-pathomic maps for 51 patients with longitudinal pre- and post-treatment scans. Univariate and multivariate Cox regression analyses were used to test the influence of contrast-enhancing tumor volume, total cellularity, mean Cyt and mean ECF at baseline, immediately post-treatment and the pre- and post-treatment rate of change in volume and cellularity on overall survival (OS).Results: Smaller Cyt and larger ECF after treatment were significant predictors of OS, independent of tumor volume and other clinical prognostic factors (HR=3.23×10-6, p<0.001 and HR=2.39×105, p<0.001, respectively). Both post-treatment volumetric growth rate and the rate of change in cellularity were significantly correlated with OS (HR=1.17, p=0.003 and HR=1.14, p=0.01, respectively).Conclusion: Changes in histological characteristics estimated from a radio-pathomic ML model are a promising tool for evaluating treatment response and predicting outcome in rGBM.

Sex differences in brain MRI using deep learning toward fairer healthcare outcomes

This study leverages deep learning to analyze sex differences in brain MRI data, aiming to further advance fairness in medical imaging. We employed 3D T1-weighted Magnetic Resonance images from four diverse datasets: Calgary-Campinas-359, OASIS-3, Alzheimer's Disease Neuroimaging Initiative, and Cambridge Center for Aging and Neuroscience, ensuring a balanced representation of sexes and a broad demographic scope. Our methodology focused on minimal preprocessing to preserve the integrity of brain structures, utilizing a Convolutional Neural Network model for sex classification. The model achieved an accuracy of 87% on the test set without employing total intracranial volume (TIV) adjustment techniques. We observed that while the model exhibited biases at extreme brain sizes, it performed with less bias when the TIV distributions overlapped more. Saliency maps were used to identify brain regions significant in sex differentiation, revealing that certain supratentorial and infratentorial regions were important for predictions. Furthermore, our interdisciplinary team, comprising machine learning specialists and a radiologist, ensured diverse perspectives in validating the results. The detailed investigation of sex differences in brain MRI in this study, highlighted by the sex differences map, offers valuable insights into sex-specific aspects of medical imaging and could aid in developing sex-based bias mitigation strategies, contributing to the future development of fair AI algorithms. Awareness of the brain's differences between sexes enables more equitable AI predictions, promoting fairness in healthcare outcomes. Our code and saliency maps are available at https://github.com/mahsadibaji/sex-differences-brain-dl.

Deep Learning-Based Pediatric Brain Region Segmentation and Volumetric Analysis for General Growth Pattern in Healthy Children.

To establish a quantitative reference for brain structural changes in children with neurological disorders, we employed deep learning technique to brain region segmentation and volumetric analysis within a cohort of healthy children. In this study, we recruited 312 participants aged 1.5 to 14.5years (210 boys and 102 girls), dividing them into five age groups. High-resolution structural T1-weighted images were obtained, and an established toolkit utilizing deep learning algorithms was employed for brain region segmentation. For each age group, the volumes of gray matter and white matter, along with the thickness and surface area of the cortex, were calculated and compared between boys and girls. The results indicated that the volumes of gray matter and white matter in both bilateral cerebral hemispheres, as well as the total brain volume, increased with age. Furthermore, the volumes of the left and right hippocampus, amygdala, and thalamus also demonstrated an increase as age progressed. Conversely, cortical thickness and surface area decreased with age. Our findings provide a quantitative reference for understanding brain structural changes in children with neurological disorders.

Prognostic significance of collagen content in solitary fibrous tumors of the central nervous system

PurposeWe aimed to explore the prognostic significance of collagen content in solitary fibrous tumors (SFTs) of the central nervous system (CNS) and preliminarily investigate its relationship with magnetic resonance imaging (MRI) features of SFTs.MethodsCollagen content was identified using Masson’s trichrome staining, and quantitatively assessed. Radiomic methods were applied to extract quantitative MRI features of SFTs, which were then analyzed in relation to collagen content.ResultsThe collagen content in CNS SFTs was categorized into high- and low-content groups, with a cutoff value of 6%. Survival analysis indicated a positive correlation between collagen content and overall survival (OS). In multivariate Cox regression analysis, incorporating factors such as mitosis, necrosis, Ki67, and collagen content and other indicators, collagen content emerged as an independent prognostic factor. Collagen content demonstrated a negative correlation with tumor histological phenotype, Ki67, WHO grade, mitosis, necrosis, and brain invasion. Additionally, the signal intensity of SFTs on T2-weighted imaging (T2WI) decreased with increasing collagen content. Radiomics analysis identified 1,702 features from each patient’s region of interest, with 12 features showing significant differences between the high and low collagen content groups. Among the quantitative parameters and radiomic models, the combined T1- and T2WI models exhibited the highest diagnostic performance.ConclusionThese findings suggest that collagen content is an independent prognostic risk factor for OS. Furthermore, combined radiomic models based on T1-and T2WI sequences may offer a more comprehensive, objective, and accurate assessment of collagen content in CNS SFTs.

Functional localization of the human auditory and visual thalamus using a thalamic localizer functional magnetic resonance imaging task

Abstract Functional magnetic resonance imaging (fMRI) of the auditory and visual sensory systems of the human brain is an active area of investigation in the study of human health and disease. The medial geniculate nucleus (MGN) and lateral geniculate nucleus (LGN) are key thalamic nuclei involved in the processing and relay of auditory and visual information, respectively, and are the subject of blood-oxygen-level-dependent (BOLD) fMRI studies of neural activation and functional connectivity in human participants. However, localization of BOLD fMRI signal originating from neural activity in MGN and LGN remains a technical challenge, due, in part, to the poor definition of boundaries of these thalamic nuclei in standard T1-weighted and T2-weighted magnetic resonance imaging sequences. Here, we report the development and evaluation of an auditory and visual sensory thalamic localizer (TL) fMRI task that produces participant-specific functionally-defined regions of interest (fROIs) of both MGN and LGN, using 3 Tesla multiband fMRI and a clustered-sparse temporal acquisition sequence, in less than 16 minutes of scan time. We demonstrate the use of MGN and LGN fROIs obtained from the TL fMRI task in standard resting-state functional connectivity (RSFC) fMRI analyses in the same participants. In RSFC analyses, we validated the specificity of MGN and LGN fROIs for signals obtained from primary auditory and visual cortex, respectively, and benchmarked their performance against alternative atlas- and segmentation-based localization methods. The TL fMRI task and analysis code (written in Presentation and MATLAB, respectively) have been made freely available to the wider research community.

Structural covariance alterations reveal motor damage in periventricular leukomalacia

Abstract Periventricular leukomalacia is a common neuroimaging finding in patients with spastic cerebral palsy. Myelin damage disrupts neuronal connectivity. However, specific alterations in the gray matter structure and their impact on the whole brain remain unclear, particularly when differentiating between preterm and full-term periventricular leukomalacia. This study investigated the gray matter network alterations following early white matter injury in infants and young children. High-resolution T1-weighted 3T brain magnetic resonance imaging, clinical data, and motor function scores were collected from 42 children with periventricular leukomalacia and 38 age- and sex-matched healthy controls. Based on gestational age, the periventricular leukomalacia group was stratified into preterm (n = 27) and full-term (n = 15) groups. Voxel-based morphometry was used to analyze whole-brain structural metrics, and motor-related regions were selected as nodes for network construction. Structural covariance analysis was used to quantify the strength of the structural connections between gray matter regions, and graph theory metrics were used to assess network properties. Motor assessments included gross and fine motor skills, and their associations with brain regions were analyzed. Both preterm and full-term periventricular leukomalacia groups exhibited abnormal motor networks. Preterm periventricular leukomalacia showed more extensive central gray matter nuclei atrophy, whereas full-term periventricular leukomalacia was predominantly localized to the motor cortex. Children with periventricular leukomalacia displayed decreased connectivity between the central gray matter nuclei and other regions, coupled with increased connectivity between the motor cortex and cerebellar hemispheres. Thalamic volume correlated with gross motor scores in preterm infants. These findings suggest that ischemic-hypoxic injury disrupts motor gray matter networks, with preterm infants being more severely affected. This study highlights the potential of structural covariance patterns for monitoring brain development and advancing our understanding of aberrant brain development in children with periventricular leukomalacia.

EPID-20. PRIMARY INTRAMEDULLARY PILOMYXOID ASTROCYTOMA OF SPINAL CORD (PIPASC): A SYSTEMATIC REVIEW OF CASE REPORTS AND CASE SERIES OF TWO DECADES

Abstract BACKGROUND Pilomyxoid Astrocytoma (PMA), new variant of pilocytic astrocytoma, is more aggressive and relatively rare, mostly seen in children and commonly found in brain. Primary intramedullary pilomyxoid astrocytoma of spinal cord (PIPASC) is extremely rare. The distinct aggressive and pathological behavior, poor prognosis and the diversity in response to various treatment modalities makes the PIPASC an oncologic enigma. We aim to evaluate the clinical manifestations, histopathologic spectrum and onco-surgical management of this neoplasm. MATERIALS AND METHODS After an extensive literature search using PubMed Central and Google Scholar, eight case reports and one case series of patients with spinal PA were retrieved and included using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines without applying any filter of time, study type or place. Case presentations of 12 patients were analyzed in this review. RESULTS Pediatric preponderance of the neoplasm (75%) with the mean age of 7.90 ± 4.51 years was noted. Sensory deficits and scoliotic deformity were the most common presenting symptom and sign in 83.33% and 33.33% of patients, respectively. Thoracic spine (50%) was most commonly affected region. MRI showed the most consistent findings of cord compression in 100% cases and the lesion appeared hypointense on T1- weighted images (T1WI) and hyperintense on T2-weighted images (33.33%). The average post treatment survival duration was found to be more than 2 years. CONCLUSIONS Occurrence of PIPASC among pediatric population is rare. They have possibility of extraneural metastasis, can differentiate into more malignant glioma and get recurrent due to which they require a long-term follow-up of the patients. Further studies are required to investigate the utility of chemotherapy and radiotherapy in its management.