Texture analysis of deep medullary veins on susceptibility-weighted imaging in infants: evaluating developmental and ischemic changes

Evaluation of Neonatal Cerebral Circulation Under Hypoxic Ischemic Risk Factors Based on Quantitative Analysis of Cerebral Veins with Magnetic Resonance Susceptibility Weighted Imaging

PURPOSE

To observe the regulation of cerebral circulation in vivo based on image segmentation algorithms for deep learning in medical imaging to automatically detect and quantify the neonatal deep medullary veins (DMVs) on susceptibility weighted imaging (SWI) images. To evaluate early cerebral circulation self-rescue for neonates undergoing risk of cerebral hypoxia-ischaemia in vivo.

METHODS

SWI images and clinical data of 317 neonates with or without risk of cerebral hypoxia-ischaemia were analyzed. Quantitative parameters showing the number, width, and curvature of DMVs were obtained using an image segmentation algorithm.

RESULTS

The number of DMVs was greater in males than in females (p < 0.01), and in term than in preterm infants (p = 0.001). The width of DMVs was greater in term than in preterm infants (p < 0.01), in low-risk than in high-risk group (p < 0.01), and in neonates without intracranial extracerebral haemorrhage (ICECH) than with ICECH (p < 0.05). The curvature of DMVs was greater in term than in preterm infants (P < 0.05). The width of both bilateral thalamic veins and anterior caudate nucleus veins were positively correlated with the number of DMVs; the width of bilateral thalamic veins was positively correlated with the width of DMVs.

CONCLUSION

The DMVs quantification based on image segmentation algorithm may provide more detailed and stable quantitative information in neonate. SWI vein quantification may be an observable indicator for in vivo assessment of cerebral circulation self-regulation in neonatal hypoxic-ischemic brain injury.

Advancements in investigating the role of cerebral small vein loss in Alzheimer's disease-related pathological changes

Alzheimer's disease (AD) is the prevailing type of dementia in the elderly, yet a comprehensive comprehension of its precise underlying mechanisms remains elusive. The investigation of the involvement of cerebral small veins in the advancement of AD has yet to be sufficiently explored in previous studies, primarily due to constraints associated with pathological staining techniques. However, recent research has provided valuable insights into multiple pathophysiological occurrences concerning cerebral small veins in AD, which may manifest sequentially, concurrently, or in a self-perpetuating manner. These events are presumed to be among the initial processes in the disease's progression. The impact of cerebral small vein loss on amyloid beta (Aβ) clearance through the glial lymphatic system is noteworthy. There exists a potential interdependence between collagen deposition and Aβ deposition in cerebral small veins. The compromised functionality of cerebral small veins can result in decreased cerebral perfusion pressure, potentially leading to cerebral tissue ischemia and edema. Additionally, the reduction of cerebral small veins may facilitate the infiltration of inflammatory factors into the brain parenchyma, thereby eliciting neuroinflammatory responses. Susceptibility-weighted imaging (SWI) is a valuable modality for the efficient assessment of cerebral small veins, precisely the deep medullary vein (DMV), and holds promise for the identification of precise and reliable imaging biomarkers for AD. This review presents a comprehensive overview of the current advancements and obstacles to the impairment of cerebral small veins in AD. Additionally, we emphasize future research avenues and the importance of conducting further investigations in this domain.

Deep Medullary Vein and MRI Markers Were Related to Cerebral Hemorrhage Subtypes

BACKGROUND

To explore the performance of deep medullary vein (DMV) and magnetic resonance imaging (MRI) markers in different intracerebral hemorrhage (ICH) subtypes in patients with cerebral small vessel disease (CSVD).

METHODS

In total, 232 cases of CSVD with ICH were included in this study. The clinical and image data were retrospectively analyzed. Patients were divided into hypertensive arteriopathy (HTNA)-related ICH, cerebral amyloid angiopathy (CAA)-related ICH, and mixed ICH groups. The DMV score was determined in the cerebral hemisphere contralateral to the ICH.

RESULTS

The DMV score was different between the HTNA-related and mixed ICH groups (p < 0.01). The MRI markers and CSVD burden score were significant among the ICH groups (p < 0.05). Compared to mixed ICH, HTNA-related ICH diagnosis was associated with higher deep white matter hyperintensity (DWMH) (OR: 0.452, 95% CI: 0.253-0.809, p < 0.05) and high-degree perivascular space (PVS) (OR: 0.633, 95% CI: 0.416-0.963, p < 0.05), and CAA-related ICH diagnosis was associated with increased age (OR: 1.074; 95% CI: 1.028-1.122, p = 0.001). The DMV score correlated with cerebral microbleed (CMB), PVS, DWMH, periventricular white matter hyperintensity (PWMH), and CSVD burden score (p < 0.05) but not with lacuna (p > 0.05). Age was an independent risk factor for the severity of DMV score (OR: 1.052; 95% CI: 0.026-0.076, p < 0.001).

CONCLUSION

DMV scores, CSVD markers, and CSVD burden scores were associated with different subtypes of ICH. In addition, DMV scores were associated with the severity of CSVD and CSVD markers.

Can radiomics be used to detect hypoxic-ischemic encephalopathy in neonates without magnetic resonance imaging abnormalities?

BACKGROUND

No study has assessed normal magnetic resonance imaging (MRI) findings to predict potential brain injury in neonates with hypoxic-ischemic encephalopathy (HIE).

OBJECTIVE

We aimed to evaluate the efficacy of MRI-based radiomics models of the basal ganglia, thalami and deep medullary veins to differentiate between HIE and the absence of MRI abnormalities in neonates.

MATERIALS AND METHODS

In this study, we included 38  full-term neonates with HIE and normal MRI findings and 89 normal neonates. Radiomics features were extracted from T1-weighted images, T2-weighted images, diffusion-weighted imaging and susceptibility-weighted imaging (SWI). The different models were evaluated using receiver operating characteristic curve analysis. Clinical utility was evaluated using decision curve analysis.

RESULTS

The SWI model exhibited the best performance among the seven single-sequence models. For the training and validation cohorts, the area under the curves (AUCs) of the SWI model were 1.00 and 0.98, respectively. The combined nomogram model incorporating SWI Rad-scores and independent predictors of clinical characteristics was not able to distinguish HIE in patients without MRI abnormalities from the control group (AUC, 1.00). A high degree of fitting and favorable clinical utility was detected using the calibration curve with the Hosmer-Lemeshow test. Decision curve analysis was used for the SWI, clinical and combined nomogram models. The decision curve showed that the SWI and combined nomogram models had better predictive performance than the clinical model.

CONCLUSIONS

HIE can be detected in patients without MRI abnormalities using an MRI-based radiomics model. The SWI model performed better than the other models.

Deep Medullary Vein Thrombosis in Newborns: A Systematic Literature Review

BACKGROUND

Deep medullary vein (DMV) thrombosis is a rare cause of brain damage in both preterm and full-term neonates. In this study, we aimed to collect data on clinical and radiological presentation, treatment, and outcome of neonatal DMV thrombosis.

METHODS

Systematic literature review on neonatal DMV thrombosis was carried out in PubMed, ClinicalTrial.gov, Scopus, and Web of Science up to December 2022.

RESULTS

Seventy-five published cases of DMV thrombosis were identified and analysed (preterm newborns were 46%). Neonatal distress, respiratory resuscitation, or need for inotropes were present in 34/75 (45%) of patients. Signs and symptoms at presentation included seizures (38/75, 48%), apnoea (27/75, 36%), lethargy or irritability (26/75, 35%). At magnetic resonance imaging (MRI), fan-shaped linear T2 hypointense lesions were documented in all cases. All had ischaemic injuries, most often involving the frontal (62/74, 84%) and parietal lobes (56/74, 76%). Signs of haemorrhagic infarction were present in 53/54 (98%). Antithrombotic treatment was not mentioned in any of the studies included. Although mortality was low (2/75, 2.6%), a large proportion of patients developed neurological sequelae (intellectual disability in 19/51 [37%] and epilepsy in 9/51 [18%] cases).

CONCLUSIONS

DMV thrombosis is rarely identified in the literature, even if it is possibly under-recognized or under-reported. Presentation in neonatal age is with seizures and non-specific systemic signs/symptoms that often cause diagnostic delay, despite the pathognomonic MRI picture. The high rate of morbidity, which determines significant social and health costs, requires further in-depth studies aimed at earlier diagnosis and evidence-based prevention and therapeutic strategies.

A radiomics-based study of deep medullary veins in infants: Evaluation of neonatal brain injury with hypoxic-ischemic encephalopathy via susceptibility-weighted imaging

Objective

The deep medullary veins (DMVs) can be evaluated using susceptibility-weighted imaging (SWI). This study aimed to apply radiomic analysis of the DMVs to evaluate brain injury in neonatal patients with hypoxic-ischemic encephalopathy (HIE) using SWI.

Methods

This study included brain magnetic resonance imaging of 190 infants with HIE and 89 controls. All neonates were born at full-term (37+ weeks gestation). To include the DMVs in the regions of interest, manual drawings were performed. A Rad-score was constructed using least absolute shrinkage and selection operator (LASSO) regression to identify the optimal radiomic features. Nomograms were constructed by combining the Rad-score with a clinically independent factor. Receiver operating characteristic curve analysis was applied to evaluate the performance of the different models. Clinical utility was evaluated using a decision curve analysis.

Results

The combined nomogram model incorporating the Rad-score and clinical independent predictors, was better in predicting HIE (in the training cohort, the area under the curve was 0.97, and in the validation cohort, it was 0.95) and the neurologic outcomes after hypoxic-ischemic (in the training cohort, the area under the curve was 0.91, and in the validation cohort, it was 0.88).

Conclusion

Based on radiomic signatures and clinical indicators, we developed a combined nomogram model for evaluating neonatal brain injury associated with perinatal asphyxia.

Radiomics based of deep medullary veins on susceptibility-weighted imaging in infants: predicting the severity of brain injury of neonates with perinatal asphyxia

OBJECTIVE

This study aimed to apply radiomics analysis of the change of deep medullary veins (DMV) on susceptibility-weighted imaging (SWI), and to distinguish mild hypoxic-ischemic encephalopathy (HIE) from moderate-to-severe HIE in neonates.

METHODS

A total of 190 neonates with HIE (24 mild HIE and 166 moderate-to-severe HIE) were included in this study. All of them were born at 37 gestational weeks or later. The DMVs were manually included in the regions of interest (ROI). For the purpose of identifying optimal radiomics features and to construct Rad-scores, 1316 features were extracted. LASSO regression was used to identify the optimal radiomics features. Using the Red-score and the clinical independent factor, a nomogram was constructed. In order to evaluate the performance of the different models, receiver operating characteristic (ROC) curve analysis was applied. Decision curve analysis (DCA) was implemented to evaluate the clinical utility.

RESULTS

A total of 15 potential predictors were selected and contributed to Red-score construction. Compared with the radiomics model, the nomogram combined model incorporating Red-score and urea nitrogen did not better distinguish between the mild HIE and moderate-to-severe HIE group. For the training cohort, the AUC of the radiomics model and the combined nomogram model was 0.84 and 0.84. For the validation cohort, the AUC of the radiomics model and the combined nomogram model was 0.80 and 0.79, respectively. The addition of clinical characteristics to the nomogram failed to distinguish mild HIE from moderate-to-severe HIE group.

CONCLUSION

We developed a radiomics model and combined nomogram model as an indicator to distinguish mild HIE from moderate-to-severe HIE group.

The role of MRI-based texture analysis to predict the severity of brain injury in neonates with perinatal asphyxia

OBJECTIVE

To evaluate the efficacy of the MRI-based texture analysis (TA) of the basal ganglia and thalami to distinguish moderate-to-severe hypoxic-ischemic encephalopathy (HIE) from mild HIE in neonates.

METHODS

This study included 68 neonates (15 with mild, 20 with moderate-to-severe HIE, and 33 control) were born at 37 gestational weeks or later and underwent MRI in first 10 days after birth. The basal ganglia and thalami were delineated for TA on the apparent diffusion coefficient (ADC) maps, T₁-, and T₂ weighted images. The basal ganglia, thalami, and the posterior limb of the internal capsule (PLIC) were also evaluated visually on diffusion-weighted imaging and T₁ weighted sequence. Receiver operating characteristic curve and logistic regression analyses were used.

RESULTS

Totally, 56 texture features for the basal ganglia and 46 features for the thalami were significantly different between the HIE groups on the ADC maps, T₂-, and T₂ weighted sequences. Using a Histogram_entropy log-10 value as >1.8 from the basal ganglia on the ADC maps (p < 0.001; OR, 266) and the absence of hyperintensity of the PLIC on T₁ weighted images (p = 0.012; OR, 17.11) were found as independent predictors for moderate-to-severe HIE. Using only a Histogram_entropy log-10 value had an equal diagnostic yield when compared to its combination with other texture features and imaging findings.

CONCLUSION

The Histogram_entropy log-10 value can be used as an indicator to differentiate from moderate-to-severe to mild HIE.

ADVANCES IN KNOWLEDGE

MRI-based TA may provide quantitative findings to indicate different stages in neonates with perinatal asphyxia.

Highly accelerated 3D MPRAGE using deep neural network-based reconstruction for brain imaging in children and young adults

OBJECTIVES

This study aimed to accelerate the 3D magnetization-prepared rapid gradient-echo (MPRAGE) sequence for brain imaging through the deep neural network (DNN).

METHODS

This retrospective study used the k-space data of 240 scans (160 for the training set, mean ± standard deviation age, 93 ± 80 months, 94 males; 80 for the test set, 106 ± 83 months, 44 males) of conventional MPRAGE (C-MPRAGE) and 102 scans (77 ± 74 months, 52 males) of both C-MPRAGE and accelerated MPRAGE. All scans were acquired with 3T scanners. DNN was developed with simulated-acceleration data generated by under-sampling. Quantitative error metrics were compared between images reconstructed with DNN, GRAPPA, and E-SPIRIT using the paired t-test. Qualitative image quality was compared between C-MPRAGE and accelerated MPRAGE reconstructed with DNN (DNN-MPRAGE) by two readers. Lesions were segmented and the agreement between C-MPRAGE and DNN-MPRAGE was assessed using linear regression.

RESULTS

Accelerated MPRAGE reduced scan times by 38% compared to C-MPRAGE (142 s vs. 320 s). For quantitative error metrics, DNN showed better performance than GRAPPA and E-SPIRIT (p < 0.001). For qualitative evaluation, overall image quality of DNN-MPRAGE was comparable (p > 0.999) or better (p = 0.025) than C-MPRAGE, depending on the reader. Pixelation was reduced in DNN-MPRAGE (p < 0.001). Other qualitative parameters were comparable (p > 0.05). Lesions in C-MPRAGE and DNN-MPRAGE showed good agreement for the dice similarity coefficient (= 0.68) and linear regression (R² = 0.97; p < 0.001).

CONCLUSIONS

DNN-MPRAGE reduced acquisition time by 38% and revealed comparable image quality to C-MPRAGE.

KEY POINTS

• DNN-MPRAGE reduced acquisition times by 38%. • DNN-MPRAGE outperformed conventional reconstruction on accelerated scans (SSIM of DNN-MPRAGE = 0.96, GRAPPA = 0.43, E-SPIRIT = 0.88; p < 0.001). • Compared to C-MPRAGE scans, DNN-MPRAGE showed improved mean scores for overall image quality (2.46 vs. 2.52; p < 0.001) or comparable perceived SNR (2.56 vs. 2.58; p = 0.08).

Brain MRI Radiomics Analysis of School-Aged Children with Tetralogy of Fallot

Introduction

Radiomics could be potential imaging biomarkers by capturing and analyzing the features. Children and adolescents with CHD have worse neurodevelopmental and functional outcomes compared with their peers. Early diagnosis and intervention are the necessity to improve neurological outcomes in CHD patients.

Methods

School-aged TOF patients and their healthy peers were recruited for MRI and neurodevelopmental assessment. LASSO regression was used for dimension reduction. ROC curve graph showed the performance of the model.

Results

Six related features were finally selected for modeling. The final model AUC was 0.750. The radiomics features can be potential significant predictors for neurodevelopmental diagnoses.

Conclusion

The radiomics on the conventional MRI can help predict the neurodevelopment of school-aged children and provide parents with rehabilitation advice as early as possible.

The role of MRI texture analysis based on susceptibility-weighted imaging in predicting Fuhrman grade of clear cell renal cell carcinoma

BACKGROUND

The Fuhrman nuclear grade system is one of the most important independent indicators in patients with clear cell renal cell carcinoma (ccRCC) for aggressiveness and prognosis. Preoperative assessment of tumor aggressiveness is important for surgical decision-making.

PURPOSE

To explore the role of magnetic resonance imaging (MRI) texture analysis based on susceptibility-weighted imaging (SWI) in predicting Fuhrman grade of ccRCC.

MATERIAL AND METHODS

A total of 45 patients with SWI and surgically proven ccRCC were divided into two groups: the low-grade group (Fuhrman I/II, n = 29) and the high-grade group (Fuhrman III/IV, n = 16). Texture features were extracted from SWI images. Feature selection was performed, and multivariable logistic regression analysis was performed to develop the SWI-based texture model for grading ccRCCs. Receiver operating characteristic (ROC) curve analysis and leave-group-out cross-validation (LGOCV) were performed to test the reliability of the model.

RESULTS

A total of 396 SWI-based texture features were extracted from each SWI image. The SWI-based texture model developed by multivariable logistic regression analysis was: SWIscore = -0.59 + 1.60 * ZonePercentage. The area under the ROC curve of the SWI-based texture model for differentiating high-grade ccRCC from low-grade ccRCC was 0.81 (95% confidence interval 0.67-0.94), with 80% accuracy, 56.25% sensitivity, and 93.10% specificity. After 100 LGOCVs, the mean accuracy, sensitivity, and specificity were 90.91%, 91.83%, and 89.89% for the training sets, and 77.29%, 80.52%, and 71.44% for the test sets, respectively.

CONCLUSION

SWI-based texture analysis might be a reliable quantitative approach for differentiating high-grade ccRCC from low-grade ccRCC.

Brain MRI radiomics analysis may predict poor psychomotor outcome in preterm neonates

OBJECTIVES

This study aimed to apply a radiomics approach to predict poor psychomotor development in preterm neonates using brain MRI.

METHODS

Prospectively enrolled preterm neonates underwent brain MRI near or at term-equivalent age and neurodevelopment was assessed at a corrected age of 12 months. Two radiologists visually assessed the degree of white matter injury. The radiomics analysis on white matter was performed using T1-weighted images (T1WI) and T2-weighted images (T2WI). A total of 1906 features were extracted from the images and the minimum redundancy maximum relevance algorithm was used to select features. A prediction model for the binary classification of the psychomotor developmental index was developed and eightfold cross-validation was performed. The diagnostic performance of the model was evaluated using the AUC with and without including significant clinical and DTI parameters.

RESULTS

A total of 46 preterm neonates (median gestational age, 29 weeks; 26 males) underwent brain MRI (median corrected gestational age, 37 weeks). Thirteen of 46 (28.3%) neonates showed poor psychomotor outcomes. There was one neonate among 46 with moderate to severe white matter injury on visual assessment. For the radiomics analysis, twenty features were selected for each analysis. The AUCs of prediction models based on T1WI, T2WI, and both T1WI and T2WI were 0.925, 0.834, and 0.902. Including gestational age or DTI parameters did not improve the prediction performance of T1WI.

CONCLUSIONS

A radiomics analysis of white matter using early T1WI or T2WI could predict poor psychomotor outcomes in preterm neonates.

KEY POINTS

• Radiomics analysis on T1-weighted images of preterm neonates showed the highest diagnostic performance (AUC, 0.925) for predicting poor psychomotor outcomes. • In spite of 45 of 46 neonates having no significant white matter injury on visual assessment, the radiomics analysis of early brain MRI showed good diagnostic performance (sensitivity, 84.6%; specificity, 78.8%) for predicting poor psychomotor outcomes. • Radiomics analysis on early brain MRI can help to predict poor neurodevelopmental outcomes in preterm neonates.

Radiomics Analysis of Susceptibility Weighted Imaging for Hepatocellular Carcinoma: Exploring the Correlation between Histopathology and Radiomics Features

Purpose:

No previous researches have extracted radiomics features from susceptibility weighted imaging (SWI) for biomedical applications. This research aimed to explore the correlation between histopathology of hepatocellular carcinoma (HCC) and radiomics features extracted from SWI.

Methods:

A total of 53 patients were ultimately enrolled into this retrospective study with MR examinations undertaken at a 3T scanner. About 107 radiomics features were extracted from SWI images of each patient. Then, the Spearman correlation test was performed to evaluate the correlation between the SWI-derived radiomics features and histopathologic indexes including histopathologic grade, microvascular invasion (MVI) as well as the expression status of cytokeratin 7 (CK-7), cytokeratin 19 (CK-19) and Glypican-3 (GPC-3). With SWI-derived radiomics features utilized as independent variables, four logistic regression-based diagnostic models were established for diagnosing patients with positive CK-7, CK-19, GPC-3 and high histopathologic grade, respectively. Then, receiver operating characteristic analysis was performed to evaluate the diagnostic performance.

Results:

A total of 11, 32, 18 and one SWI-derived radiomics features were significantly correlated with histopathologic grade, the expression of CK-7, the expression of CK-19 and the expression of GPC-3 (P < 0.05), respectively. None of the SWI-derived radiomics features was correlated with MVI status. The areas under the curve were 0.905, 0.837, 0.800 and 0.760 for diagnosing patients with positive CK-19, positive CK-7, high histopathologic grade and positive GPC-3.

Conclusion:

Extracting the radiomics features from SWI images was feasible to evaluate multiple histopathologic indexes of HCC.