Fetal brain MRI atlases and datasets: A review

Fetal brain development is a complex process involving different stages of growth and organization which are crucial for the development of brain circuits and neural connections. Fetal atlases and labeled datasets are promising tools to investigate prenatal brain development. They support the identification of atypical brain patterns, providing insights into potential early signs of clinical conditions. In a nutshell, prenatal brain imaging and post-processing via modern tools are a cutting-edge field that will significantly contribute to the advancement of our understanding of fetal development. In this work, we first provide terminological clarification for specific terms (i.e., "brain template" and "brain atlas"), highlighting potentially misleading interpretations related to inconsistent use of terms in the literature. We discuss the major structures and neurodevelopmental milestones characterizing fetal brain ontogenesis. Our main contribution is the systematic review of 18 prenatal brain atlases and 3 datasets. We also tangentially focus on clinical, research, and ethical implications of prenatal neuroimaging.

A comparison of traditional and net structured intersomatic cages in the lombosacral region: A biomechanical analysis for enhancing discopathy treatment

The vertebral column represents an essential element for support, mobility, and the protection of the central nervous system. Various pathologies can compromise these vital functions, leading to pain and a decrease in the quality of life. Within the scope of this study, a novel redesign of the Intersomatic Cage, traditionally used in the presence of discopathy, was proposed. The adoption of additive manufacturing technology allowed for the creation of highly complex geometries, focusing on the lumbosacral tract, particularly on the L4-L5 and L5-S1 intervertebral discs. In addition to the tensile analysis carried out using Finite Element Analysis (FEA) in static simulations, a parallel study on the range of motion (ROM) of the aforementioned vertebral pairs was conducted. The ROM represents the relative movement range between various vertebral pairs. The introduction of the intersomatic cage between the vertebrae, replacing the pulpy nucleus of the intervertebral disc, could influence the ROM, thus having significant clinical implications. For the analysis, the ligaments were modelled using a 1D approach. Their constraint reaction and deformability upon load application were analysed to better understand the potential biomechanical implications arising from the adoption of the cages. During the FEA simulations, two types of cages were analysed: LLIF for L4-L5 and ALIF for L5-S1, subjecting them to four different loading conditions. The results indicate that the stresses exhibited by cages with a NET structure are generally lower compared to those of traditional cages. This stress reduction in cages with NET structure suggests a more optimal load distribution, but it is essential to assess potential repercussions on the surrounding bone structure.

Application of texture signatures based on multiparameter-magnetic resonance imaging for predicting microvascular invasion in hepatocellular carcinoma: Retrospective study

BACKGROUND

Despite continuous changes in treatment methods, the survival rate for advanced hepatocellular carcinoma (HCC) patients remains low, highlighting the importance of diagnostic methods for HCC.

AIM

To explore the efficacy of texture analysis based on multi-parametric magnetic resonance (MR) imaging (MRI) in predicting microvascular invasion (MVI) in preoperative HCC.

METHODS

This study included 105 patients with pathologically confirmed HCC, categorized into MVI-positive and MVI-negative groups. We employed Original Data Analysis, Principal Component Analysis, Linear Discriminant Analysis (LDA), and Non-LDA (NDA) for texture analysis using multi-parametric MR images to predict preoperative MVI. The effectiveness of texture analysis was determined using the B11 program of the MaZda4.6 software, with results expressed as the misjudgment rate (MCR).

RESULTS

Texture analysis using multi-parametric MRI, particularly the MI + PA + F dimensionality reduction method combined with NDA discrimination, demonstrated the most effective prediction of MVI in HCC. Prediction accuracy in the pulse and equilibrium phases was 83.81%. MCRs for the combination of T2-weighted imaging (T2WI), arterial phase, portal venous phase, and equilibrium phase were 22.86%, 16.19%, 20.95%, and 20.95%, respectively. The area under the curve for predicting MVI positivity was 0.844, with a sensitivity of 77.19% and specificity of 91.67%.

CONCLUSION

Texture analysis of arterial phase images demonstrated superior predictive efficacy for MVI in HCC compared to T2WI, portal venous, and equilibrium phases. This study provides an objective, non-invasive method for preoperative prediction of MVI, offering a theoretical foundation for the selection of clinical therapy.

The new morphologic classification of the hand motor cortex with magnetic resonance imaging in glioma patients

Purpose

The hand motor cortex (HMC) is a reliable anatomical landmark for identifying the precentral gyrus. The current study aimed to investigate the morphology of HMC on axial MRI of glioma patients, propose a new morphological classification of HMC and analyze the effect of tumors on the morphology of HMC.

Methods

A retrospective study of 276 adult right-handed glioma patients was conducted. The morphology of HMC was assessed using T2 axial images. Subsequently, the distribution of morphological subtypes was compared between the bilateral hemispheres and the tumor-affected and healthy hemispheres. Finally, the influence of tumor pathology on the morphology of HMC was investigated.

Results

A new morphological classification of HMC with four subtypes (Ω, ε, Ω-ε and ε-Ω) was proposed. No significant difference was identified in the distribution of morphological subtypes between the bilateral hemispheres (p = 0.0901, Chi-square test), or between the tumor-affected and healthy hemispheres (p = 0.3507, Chi-square test), and the morphology of HMC between the bilateral hemispheres were consistent (p < 0.0001, Kappa test). In addition, a significant difference was identified in the distribution of morphological subtypes between astrocytic and oligodendroglial tumors (p = 0.0135, Chi-square test).

Conclusion

In the current study, we proposed a new morphological classification of HMC, and found that tumor could affect the morphology of HMC in glioma patients. The results can help our clinical practice, enabling us to further understand the spatial structure of the cerebral hemispheres.

Post-treatment magnetic resonance imaging predicts outcomes of maxillary sinus cancer treatment using super-selective intra-arterial infusion of high-dose cisplatin with concomitant radiotherapy (RADPLAT)

OBJECTIVES

This study aimed to evaluate the prognostic value of magnetic resonance imaging (MRI) findings in predicting local recurrence in patients with maxillary sinus cancer treated with super-selective intra-arterial infusion of high-dose cisplatin with concomitant radiotherapy (RADPLAT).

METHODS

This single-center retrospective study included consecutive patients with maxillary sinus squamous cell carcinoma, who underwent RADPLAT between October 2016 and September 2021. MRI was performed before (within 2 weeks) and 1 month after (post-treatment MRI) the start of treatment. Tumor reduction rates and pre-treatment cross-sectional areas were calculated from the maximum cross-sectional areas on pre- and post-treatment MRI T2-weighted axial images. Statistical analyses, including receiver operating characteristic analysis, were performed to assess the predictive value of the tumor reduction rates.

RESULTS

Twenty-four patients were included in this study. Recurrence occurred in seven patients with a median time of 213 days. The tumor reduction rates were significantly higher in the benign post-treatment changes group compared to the recurrence group (median, 0.814 vs. 0.174; p < 0.001). The cut-off value for the reduction rate between the groups was 0.3578. No significant difference was observed in the maximum pre-treatment cross-sectional area between the groups (p = 0.664). The inter-observer agreement for the tumor areas was excellent.

CONCLUSIONS

The tumor reduction rate calculated from MRI T2-weighted images may be a predictor of local recurrence in patients with maxillary sinus cancer treated with RADPLAT. Patients with lower reduction rates may benefit from early salvage surgeries.

Artificial intelligence powered advancements in upper extremity joint MRI: A review

Magnetic resonance imaging (MRI) is an indispensable medical imaging examination technique in musculoskeletal medicine. Modern MRI techniques achieve superior high-quality multiplanar imaging of soft tissue and skeletal pathologies without the harmful effects of ionizing radiation. Some current limitations of MRI include long acquisition times, artifacts, and noise. In addition, it is often challenging to distinguish abutting or closely applied soft tissue structures with similar signal characteristics. In the past decade, Artificial Intelligence (AI) has been widely employed in musculoskeletal MRI to help reduce the image acquisition time and improve image quality. Apart from being able to reduce medical costs, AI can assist clinicians in diagnosing diseases more accurately. This will effectively help formulate appropriate treatment plans and ultimately improve patient care. This review article intends to summarize AI's current research and application in musculoskeletal MRI, particularly the advancement of DL in identifying the structure and lesions of upper extremity joints in MRI images.

A two-center study of a combined nomogram based on mammography and MRI to predict ALN metastasis in breast cancer

OBJECTIVES

To develop and validate a predictive method for axillary lymph node (ALN) metastasis of breast cancer by using radiomics based on mammography and MRI.

MATERIALS AND METHODS

A retrospective analysis of 492 women from center 1 (The affiliated Hospital of Qingdao University) and center 2 (Yantai Yuhuangding Hospital) with primary breast cancer from August 2013 to May 2021 was carried out. The radscore was calculated using the features screened based on preoperative mammography and MRI from the training cohort of Center 1 (n = 231), then tested in the validation cohort (n = 99), an internal test cohort (n = 90) from Center 1, and an external test cohort (n = 72) from Center 2. Univariate and multivariate analyses were used to screen for the clinical and radiological characteristics most associated with ALN metastasis. A combined nomogram was established in combination with radscore that predicted the clinicopathological and radiological characteristics. Calibration curves were used to test the effectiveness of the combined nomogram. The receiver operating characteristic (ROC) curve was used to evaluate the performance of the combined nomogram and then compare with the clinical and radiomic models. The decision curve analysis (DCA) value was used to evaluate the combined nomogram for clinical applications.

RESULTS

The constructed combined nomogram incorporating the radscore and MRI-reported ALN metastasis status exhibited good calibration and outperformed the radiomics signatures in predicting ALN metastasis (area under the curve [AUC]: 0.886 vs. 0.846 in the training cohort; 0.826 vs. 0.762 in the validation cohort; 0.925 vs. 0.899 in the internal test cohort; and 0.902 vs. 0.793 in the external test cohort). The combination nomogram achieved a higher AUC in the training cohort (0.886 vs. 0.786) and the internal test cohort (0.925 vs. 0.780) and similar AUCs in the validation (0.826 vs. 0.811) and external test (0.902 vs. 0.837) cohorts than the clinical model.

CONCLUSION

A combined nomogram based on mammography and MRI can be used for preoperative prediction of ALN metastasis in primary breast cancer.

Altered static and dynamic functional brain network in knee osteoarthritis: A resting-state functional magnetic resonance imaging study: Static and dynamic FNC in KOA

This study aimed to investigate altered static and dynamic functional network connectivity (FNC) and its correlation with clinical symptoms in patients with knee osteoarthritis (KOA). One hundred and fifty-nine patients with KOA and 73 age- and gender-matched healthy subjects (HS) underwent resting-state functional magnetic resonance imaging (rs-fMRI) and clinical evaluations. Group independent component analysis (GICA) was applied, and seven resting-state networks were identified. Patients with KOA had decreased static FNC within the default mode network (DM), visual network (VS), and cerebellar network (CB) and increased static FNC between the subcortical network (SC) and VS (p < 0.05, FDR corrected). Four reoccurring FNC states were identified using k-means clustering analysis. Although abnormalities in dynamic FNCs of KOA patients have been found using the common window size (22 TR, 44 s), but the results of the clustering analysis were inconsistent when using different window sizes, suggesting dynamic FNCs might be an unstable method to compare brain function between KOA patients and HS. These recent findings illustrate that patients with KOA have a wide range of abnormalities in the static and dynamic FNCs, which provided a reference for the identification of potential central nervous therapeutic targets for KOA treatment and might shed light on the other musculoskeletal pain neuroimaging studies.

Distinct effects of first-episode and recurrent major depressive disorder on brain changes related to suicidal ideation: Evidence from the REST-meta-MDD Project

BACKGROUND

Significant differences in clinical manifestations between first-episode and recurrent major depression disorder (FE-MDD/R-MDD) have been demonstrated in previous studies, including the degree of suicide attempt. However, the potential brain mechanism underlying the effect of depressive episode frequency on suicidal ideation (SI) remains unclear.

METHODS

In this study, 102 patients with FE-MDD (SI/non-SI: N = 70/32) and 71 matched normal controls (NCs), as well as 75 patients with R-MDD (SI/non-SI: N = 37/38) and 49 matched NCs were screened from the Chinese REST-meta-MDD consortium. T1-weighted and resting-state fMRI images were used to calculate gray matter volume (GMV) and fractional amplitude of low-frequency fluctuations (fALFF), respectively.

RESULTS

Group comparisons revealed that FE-MDD showed changes only in GMV, while R-MDD showed changes in both GMV and fALFF compared to NCs. SI-specific GMV decreases were observed in the right cerebellum, superior marginal gyrus, and left middle temporal gyrus in FE-MDD patients, while SI-specific fALFF decreases in bilateral superior frontal gyrus and increases in bilateral cerebellum and left parahippocampal gyrus were obserevd in R-MDD patients. Moreover, a negative correlation was found between GMV value in right cerebellum and HAMD score.

CONCLUSIONS

These findings suggest that first-episode and recurrent MDD show different effects on brain structure and function in patients with SI, providing a potential explanation for the distinct clinical manifestations of MDD patients from a brain mechanisms perspective.

Reduction of ADC bias in diffusion MRI with deep learning-based acceleration: A phantom validation study at 3.0 T

PURPOSE

Further acceleration of DWI in diagnostic radiology is desired but challenging mainly due to low SNR in high b-value images and associated bias in quantitative ADC values. Deep learning-based reconstruction and denoising may provide a solution to address this challenge.

METHODS

The effects of SNR reduction on ADC bias and variability were investigated using a commercial diffusion phantom and numerical simulations. In the phantom, performance of different reconstruction methods, including conventional parallel (SENSE) imaging, compressed sensing (C-SENSE), and compressed SENSE acceleration with an artificial intelligence deep learning-based technique (C-SENSE AI), was compared at different acceleration factors and flip angles using ROI-based analysis. ADC bias was assessed by Lin's Concordance correlation coefficient (CCC) followed by bootstrapping to calculate confidence intervals (CI). ADC random measurement error (RME) was assessed by the mean coefficient of variation (CV¯) and non-parametric statistical tests.

RESULTS

The simulations predicted increasingly negative bias and loss of precision towards lower SNR. These effects were confirmed in phantom measurements of increasing acceleration, for which CCC decreased from 0.947 to 0.279 and CV¯ increased from 0.043 to 0.439, and of decreasing flip angle, for which CCC decreased from 0.990 to 0.063 and CV¯ increased from 0.037 to 0.508. At high acceleration and low flip angle, C-SENSE AI reconstruction yielded best denoised ADC maps. For the lowest investigated flip angle, CCC = {0.630, 0.771 and 0.987} and CV¯={0.508, 0.426 and 0.254} were obtained for {SENSE, C-SENSE, C-SENSE AI}, the improvement by C-SENSE AI being significant as compared to the other methods (CV: p = 0.033 for C-SENSE AI vs. C-SENSE and p < 0.001 for C-SENSE AI vs. SENSE; CCC: non-overlapping CI between reconstruction methods). For the highest investigated acceleration factor, CCC = {0.479,0.926,0.960} and CV¯={0.519,0.119,0.118} were found, confirming the reduction of bias and RME by C-SENSE AI as compared to C-SENSE (by trend) and to SENSE (CV: p < 0.001; CCC: non-overlapping CI).

CONCLUSION

ADC bias and random measurement error in DWI at low SNR, typically associated with scan acceleration, can be effectively reduced by deep-learning based C-SENSE AI reconstruction.

Reconstruction of 3D knee MRI using deep learning and compressed sensing: a validation study on healthy volunteers

BACKGROUND

To investigate the potential of combining compressed sensing (CS) and artificial intelligence (AI), in particular deep learning (DL), for accelerating three-dimensional (3D) magnetic resonance imaging (MRI) sequences of the knee.

METHODS

Twenty healthy volunteers were examined using a 3-T scanner with a fat-saturated 3D proton density sequence with four different acceleration levels (10, 13, 15, and 17). All sequences were accelerated with CS and reconstructed using the conventional and a new DL-based algorithm (CS-AI). Subjective image quality was evaluated by two blinded readers using seven criteria on a 5-point-Likert-scale (overall impression, artifacts, delineation of the anterior cruciate ligament, posterior cruciate ligament, menisci, cartilage, and bone). Using mixed models, all CS-AI sequences were compared to the clinical standard (sense sequence with an acceleration factor of 2) and CS sequences with the same acceleration factor.

RESULTS

3D sequences reconstructed with CS-AI achieved significantly better values for subjective image quality compared to sequences reconstructed with CS with the same acceleration factor (p ≤ 0.001). The images reconstructed with CS-AI showed that tenfold acceleration may be feasible without significant loss of quality when compared to the reference sequence (p ≥ 0.999).

CONCLUSIONS

For 3-T 3D-MRI of the knee, a DL-based algorithm allowed for additional acceleration of acquisition times compared to the conventional approach. This study, however, is limited by its small sample size and inclusion of only healthy volunteers, indicating the need for further research with a more diverse and larger sample.

TRIAL REGISTRATION

DRKS00024156.

RELEVANCE STATEMENT

Using a DL-based algorithm, 54% faster image acquisition (178 s versus 384 s) for 3D-sequences may be possible for 3-T MRI of the knee.

KEY POINTS

• Combination of compressed sensing and DL improved image quality and allows for significant acceleration of 3D knee MRI. • DL-based algorithm achieved better subjective image quality than conventional compressed sensing. • For 3D knee MRI at 3 T, 54% faster image acquisition may be possible.

Superficial small cerebellar infarcts in cerebral amyloid angiopathy on 3 T MRI: A preliminary study

BACKGROUND

Strictly superficial cerebellar microbleeds and cerebellar superficial siderosis have been considered markers of advanced cerebral amyloid angiopathy (CAA), but there are few studies on cerebellar ischemic lesions in CAA. We investigated the presence of superficial small cerebellar infarct (SCI) ≤15 mm and its relation to magnetic resonance imaging (MRI) markers in patients with probable CAA.

METHODS

Eighty patients with probable CAA were retrospectively evaluated. The presence of superficial SCIs was examined, along with cerebellar microbleeds and cerebellar superficial siderosis, using 3-T MRI. Lobar cerebral microbleeds, cortical superficial siderosis (cSS), enlargement of the perivascular space in the centrum semiovale, and white matter hyperintensity were assessed and the total CAA-small vessel disease (SVD) score was calculated.

RESULTS

Nine of the 80 patients (11.3%) had a total of 16 superficial SCIs. By tentatively defining SCI <4 mm as cerebellar microinfarcts, 8 out of 16 (50%) superficial SCIs corresponded to cerebellar microinfarcts. The total CAA-SVD score was significantly higher in patients with superficial SCIs (p = 0.01). The prevalence of cSS (p = 0.018), cortical cerebral microinfarct (p = 0.034), and superficial cerebellar microbleeds (p = 0.006) was significantly higher in patients with superficial SCIs. The number of superficial cerebellar microbleeds was also significantly higher in patients with superficial SCIs (p = 0.001).

CONCLUSIONS

Our results suggest that in patients with CAA, superficial SCIs (including microinfarcts) on MRI may indicate more severe, advanced-stage CAA. These preliminary findings should be verified by larger prospective studies in the future.

Simulation of time-intensity curve based on k-space filling in breast dynamic contrast-enhanced three-dimensional magnetic resonance imaging

This study elucidated the effects of a three-dimensional k-space trajectory incorporating the partial Fourier (PF) technique on a time-intensity curve (TIC) in a dynamic contrast-enhanced magnetic resonance imaging of a typical malignant breast tumor using a digital phantom. Images were obtained from the Cancer Imaging Archive Open Data for Breast Cancer, and 1-min scans with high temporal resolution were analyzed. The order of the k-space trajectory was set as Linear (sequential), Low-High (centric), PF (62.5%; Z-, Y-, and both directions), and Low-High Radial. k0 (center of the k-space) timing and TIC shape were affected by the chosen k-space trajectory and implementation of the PF technique. A small TIC gradient was obtained using a Low-High Radial order. If the k-space filling method (particularly the radial method) produces a gentle TIC gradient, misinterpretation could arise during the assessment of tumor malignancy status.

Diagnostic magnetic resonance imaging characteristics of congenital mesoblastic nephroma: a retrospective multi-center International Society of Pediatric Oncology-Renal Tumor Study Group (SIOP-RTSG) radiology panel study

BACKGROUND

Congenital mesoblastic nephroma is the most common solid renal tumor in neonates. Therefore, patients <3 months of age are advised to undergo upfront nephrectomy, whereas invasive procedures at diagnosis in patients ≥3 months of age are discouraged by the International Society of Pediatric Oncology-Renal Tumor Study Group (SIOP-RTSG). Nevertheless, discriminating congenital mesoblastic nephroma, especially from the more common Wilms tumor, solely based on imaging remains difficult. Recently, magnetic resonance imaging (MRI) has become the preferred modality. Studies focusing on MRI characteristics of congenital mesoblastic nephroma are limited.

OBJECTIVE

This study aims to identify diagnostic MRI characteristics of congenital mesoblastic nephroma in the largest series of patients to date.

MATERIALS AND METHODS

In this retrospective multicenter study, five SIOP-RTSG national review radiologists identified 52 diagnostic MRIs of histologically proven congenital mesoblastic nephromas. MRI was performed following SIOP-RTSG protocols, while radiologists assessed their national cases using a validated case report form.

RESULTS

Patients (24/52 classic, 11/52 cellular, and 15/52 mixed type congenital mesoblastic nephroma, 2/52 unknown) had a median age of 1 month (range 1 day-3 months). Classic type congenital mesoblastic nephroma appeared homogeneous with a lack of hemorrhage, necrosis and/or cysts, showing a concentric ring sign in 14 (58.3%) patients. Cellular and mixed type congenital mesoblastic nephroma appeared more heterogeneous and were larger (311.6 and 174.2 cm3, respectively, versus 41.0 cm3 for the classic type (P<0.001)). All cases were predominantly T2-weighted isointense and T1-weighted hypointense, and mean overall apparent diffusion coefficient values ranged from 1.05-1.10×10-3 mm2/s.

CONCLUSION

This retrospective international collaborative study showed classic type congenital mesoblastic nephroma predominantly presented as a homogeneous T2-weighted isointense mass with a typical concentric ring sign, whereas the cellular type appeared more heterogeneous. Future studies may use identified MRI characteristic of congenital mesoblastic nephroma for validation and for exploring the discriminative non-invasive value of MRI, especially from Wilms tumor.

Current role of magnetic resonance imaging on assessing and monitoring the efficacy of phototherapy

Phototherapy, also known as photobiological therapy, is a non-invasive and highly effective physical treatment method. Its broad use in clinics has led to significant therapeutic results. Phototherapy parameters, such as intensity, wavelength, and duration, can be adjusted to create specific therapeutic effects for various medical conditions. Meanwhile, Magnetic Resonance Imaging (MRI), with its diverse imaging sequences and excellent soft-tissue contrast, provides a valuable tool to understand the therapeutic effects and mechanisms of phototherapy. This review explores the clinical applications of commonly used phototherapy techniques, gives a brief overview of how phototherapy impacts different diseases, and examines MRI's role in various phototherapeutic scenarios. We argue that MRI is crucial for precise targeting, treatment monitoring, and prognosis assessment in phototherapy. Future research and applications will focus on personalized diagnosis and monitoring of phototherapy, expanding its applications in treatment and exploring multimodal imaging technology to enhance diagnostic and therapeutic precision and effectiveness.

Analysis of gait pattern related to high cerebral small vessel disease burden using quantitative gait data from wearable sensors

BACKGROUND AND OBJECTIVES

Sensor-based wearable devices help to obtain a wide range of quantitative gait parameters, which provides sufficient data to investigate disease-specific gait patterns. Although cerebral small vessel disease (CSVD) plays a significant role in gait impairment, the specific gait pattern associated with a high burden of CSVD remains to be explored.

METHODS

We analyzed the gait pattern related to high CSVD burden from 720 participants (aged 55-65 years, 42.5 % male) free of neurological disease in the Taizhou Imaging Study. All participants underwent detailed quantitative gait assessments (obtained from an insole-like wearable gait tracking device) and brain magnetic resonance imaging examinations. Thirty-three gait parameters were summarized into five gait domains. Sparse sliced inverse regression was developed to extract the gait pattern related to high CSVD burden.

RESULTS

The specific gait pattern derived from several gait domains (i.e., angles, phases, variability, and spatio-temporal) was significantly associated with the CSVD burden (OR=1.250, 95 % CI: 1.011-1.546). The gait pattern indicates that people with a high CSVD burden were prone to have smaller gait angles, more stance time, more double support time, larger gait variability, and slower gait velocity. Furthermore, people with this gait pattern had a 25 % higher risk of a high CSVD burden.

CONCLUSIONS

We established a more stable and disease-specific quantitative gait pattern related to high CSVD burden, which is prone to facilitate the identification of individuals with high CSVD burden among the community residents or the general population.

Diagnostic Performance of Radiomics and Deep Learning to Identify Benign and Malignant Soft Tissue Tumors: A Systematic Review and Meta-analysis

RATIONALE AND OBJECTIVES

To systematically evaluate the application value of radiomics and deep learning (DL) in the differential diagnosis of benign and malignant soft tissue tumors (STTs).

MATERIALS AND METHODS

A systematic review was conducted on studies published up to December 11, 2023, that utilized radiomics and DL methods for the diagnosis of STTs. The methodological quality and risk of bias were evaluated using the Radiomics Quality Score (RQS) 2.0 system and Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool, respectively. A bivariate random-effects model was used to calculate the summarized sensitivity and specificity. To identify factors contributing to heterogeneity, meta-regression and subgroup analyses were performed to assess the following covariates: diagnostic modality, region/volume of interest, imaging examination, study design, and pathology type. The asymmetry of Deeks' funnel plot was used to assess publication bias.

RESULTS

A total of 21 studies involving 3866 patients were included, with 13 studies using independent test/validation sets included in the quantitative statistical analysis. The average RQS was 21.31, with substantial or near-perfect inter-rater agreement. The combined sensitivity and specificity were 0.84 (95% CI: 0.76-0.89) and 0.88 (95% CI: 0.69-0.96), respectively. Meta-regression and subgroup analyses showed that study design and the region/volume of interest were significant factors affecting study heterogeneity (P < 0.05). No publication bias was observed.

CONCLUSION

Radiomics and DL can accurately distinguish between benign and malignant STTs. Future research should concentrate on enhancing the rigor of study designs, conducting multicenter prospective validations, amplifying the interpretability of DL models, and integrating multimodal data to elevate the diagnostic accuracy and clinical utility of soft tissue tumor assessments.

Longitudinal MRI analysis using a hybrid DenseNet-BiLSTM method for Alzheimer's disease prediction

Alzheimer's disease is a progressive neurological disorder characterized by brain atrophy and cell death, leading to cognitive decline and impaired functioning. Previous research has primarily focused on using cross-sectional data for Alzheimer's disease identification, but analyzing longitudinal sequential MR images is crucial for improved diagnostic accuracy and understanding disease progression. However, existing deep learning models face challenges in learning spatial and temporal features from such data. To address these challenges, this study presents a novel hybrid DenseNet-BiLSTM method for Alzheimer's disease prediction using longitudinal MRI analysis. The proposed framework combines Convolutional DenseNet for spatial information extraction and joined BiLSTM layers for capturing temporal characteristics and relationships between longitudinal images at different time points. This approach overcomes issues like overfitting, vanishing gradients, and incomplete patient data. We evaluated the model on 684 longitudinal MRI images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including normal controls, individuals with mild cognitive impairment, and Alzheimer's disease patients. The results demonstrate high classification accuracy, with 95.28% for AD/CN, 88.19% for NC/MCI, 83.51% for sMCI/pMCI, and 92.14% for MCI/AD. These findings highlight the substantial improvement in Alzheimer's disease diagnosis achieved through the utilization of longitudinal MRI images. The contributions of this study lie in both the deep learning and medical domains. In the deep learning domain, our hybrid framework effectively learns spatial and temporal features from longitudinal data, addressing the challenges associated with multi-dimensional and sequential time series data. In the medical domain, our study emphasizes the importance of analyzing baseline and longitudinal MR images for accurate diagnosis and understanding disease progression.

Reduced myelin content in bipolar disorder: A study of inhomogeneous magnetization transfer

BACKGROUND

Previous neuroimaging and pathological studies have found myelin-related abnormalities in bipolar disorder (BD), which prompted the use of magnetic resonance (MR) imaging technology sensitive to neuropathological changes to explore its neuropathological basis. We holistically investigated alterations in myelin within BD patients by inhomogeneous magnetization transfer (ihMT), which is sensitive and specific to myelin content.

METHODS

Thirty-one BD and 42 healthy controls (HC) were involved. Four MR metrics, i.e., ihMT ratio (ihMTR), pseudo-quantitative ihMT (qihMT), magnetization transfer ratio and pseudo-quantitative magnetization transfer (qMT), were compared between groups using analysis methods based on whole-brain voxel-level and white matter regions of interest (ROI), respectively.

RESULTS

The voxel-wise analysis showed significantly inter-group differences of ihMTR and qihMT in the corpus callosum. The ROI-wise analysis showed that ihMTR, qihMT, and qMT values in BD group were significantly lower than that in HC group in the genu and body of corpus callosum, left anterior limb of the internal capsule, left anterior corona radiate, and bilateral cingulum (p < 0.001). And the qihMT in genu of corpus callosum and right cingulum were negatively correlated with depressive symptoms in BD group.

LIMITATIONS

This study is based on cross-sectional data and the sample size is limited.

CONCLUSION

These findings suggest the reduced myelin content of anterior midline structure in the bipolar patients, which might be a critical pathophysiological feature of BD.

Five multivariate Duchenne muscular dystrophy progression models bridging six-minute walk distance and MRI relaxometry of leg muscles

The study aimed to provide quantitative information on the utilization of MRI transverse relaxation time constant (MRI-T2) of leg muscles in DMD clinical trials by developing multivariate disease progression models of Duchenne muscular dystrophy (DMD) using 6-min walk distance (6MWD) and MRI-T2. Clinical data were collected from the prospective and longitudinal ImagingNMD study. Disease progression models were developed by a nonlinear mixed-effect modeling approach. Univariate models of 6MWD and MRI-T2 of five muscles were developed separately. Age at assessment was the time metric. Multivariate models were developed by estimating the correlation of 6MWD and MRI-T2 model variables. Full model estimation approach for covariate analysis and five-fold cross validation were conducted. Simulations were performed to compare the models and predict the covariate effects on the trajectories of 6MWD and MRI-T2. Sigmoid Imax and Emax models best captured the profiles of 6MWD and MRI-T2 over age. Steroid use, baseline 6MWD, and baseline MRI-T2 were significant covariates. The median age at which 6MWD is half of its maximum decrease in the five models was similar, while the median age at which MRI-T2 is half of its maximum increase varied depending on the type of muscle. The models connecting 6MWD and MRI-T2 successfully quantified how individual characteristics alter disease trajectories. The models demonstrate a plausible correlation between 6MWD and MRI-T2, supporting the use of MRI-T2. The developed models will guide drug developers in using the MRI-T2 to most efficient use in DMD clinical trials.

Cost-Effectiveness of Cardiovascular Magnetic Resonance for Rejection Surveillance After Cardiac Transplantation in the Australian Health Care System

BACKGROUND

Heart transplantation is an effective treatment for end-stage congestive heart failure, however, achieving the right balance of immunosuppression to maintain graft function while minimising adverse effects is challenging. Serial endomyocardial biopsies (EMBs) are currently the standard for rejection surveillance, despite being invasive. Replacing EMB-based surveillance with cardiac magnetic resonance (CMR)-based surveillance for acute cardiac allograft rejection has shown feasibility. This study aimed to assess the cost-effectiveness of CMR-based surveillance in the first year after heart transplantation.

METHOD

A prospective clinical trial was conducted with 40 orthotopic heart transplant (OHT) recipients. Participants were randomly allocated into two surveillance groups: EMB-based, and CMR-based. The trial included economic evaluations, comparing the frequency and cost of surveillance modalities in relation to quality-adjusted life years (QALYs) within the first year post-transplantation. Sensitivity analysis encompassed modelled data from observed EMB and CMR arms, integrating two hypothetical models of expedited CMR-based surveillance.

RESULTS

In the CMR cohort, 238 CMR scans and 15 EMBs were conducted, versus (vs) 235 EMBs in the EMB group. CMR surveillance yielded comparable rejection rates (CMR 74 vs EMB 94 events, p=0.10) and did not increase hospitalisation risk (CMR 32 vs EMB 46 events, p=0.031). It significantly reduced the necessity for invasive EMBs by 94%, lowered costs by an average of AUD$32,878.61, and enhanced cumulative QALY by 0.588 compared with EMB. Sensitivity analysis showed that increased surveillance with expedited CMR Models 1 and 2 were more cost-effective than EMB (all p<0.01), with CMR Model 1 achieving the greatest cost savings (AUD$34,091.12±AUD$23,271.86 less) and utility increase (+0.62±1.49 QALYs, p=0.011), signifying an optimal cost-utility ratio. Model 2 showed comparable utility to the base CMR model (p=0.900) while offering the benefit of heightened surveillance frequency during periods of elevated rejection risk.

CONCLUSIONS

CMR-based rejection surveillance in orthotopic heart transplant recipients provides a cost-effective alternative to EMB-based surveillance. Furthermore, it reduces the need for invasive procedures, without increased risk of rejection or hospitalisation for patients, and can be incorporated economically for expedited surveillance. These findings have important implications for improving patient care and optimising resource allocation in post-transplant management.

Correlation between congenital pelvic floor muscle development assessed by magnetic resonance imaging and postoperative defecation

OBJECTIVE

Children with congenital anorectal malformation (CAM) experience challenges with defecation. This study aims to assess defecation in preschool-age children with CAM and to evaluate the correlation between pelvic floor muscle developed assessed by magnetic resonance imaging (MRI) and postoperative defecation.

METHODS

We collected clinical data and MRI results from 89 male children with CAM. The bowel function scores for children with Perineal (cutaneous) fistula, Rectourethral fistula(Prostatic or Bulbar), and Rectovesical fistula were computed. MRI scans were subjected to image analysis of the striated muscle complex (SMC). The association between pelvic floor muscle score and bowel function score was examined using the Cochran-Armitage Trend Test.

RESULTS

We observed that 77.4% of the SMC scores by MRI for Perineal fistula were good. The Rectourethral fistula SMC score was 40.6% for moderate and 59.4% for poor. The SMC score for Rectovesical fistula was 100% for moderate. Furthermore, 77.4% of patients with Perineal fistula had bowel function scores (BFS) ≥ 17 points. Among those with Rectourethral fistula and Rectovesical fistula, 12.5% and 0 had BFS ≥ 17 points, respectively. An analysis of muscle development and bowel function in patients with Rectovesical fistula, Rectourethral fistula, and Perineal fistula revealed a correlation between SMC development and BFS. Subgroup analysis showed that the Perineal fistula had statistical significance; however, the Rectourethral fistula and Rectovesical fistula were not statistically significant.

CONCLUSION

A correlation exists between pelvic floor muscle development and postoperative defecation in children with Perineal fistula.

Exosomal miR-1202 mediates Brodmann Area 44 functional connectivity changes in medication-free patients with major depressive disorder: An fMRI study

Previous large-sample postmortem study revealed that the expression of miR-1202 in brain tissues from Brodmann area 44 (BA44) was dysregulated in patients with major depressive disorder (MDDs). However, the specific in vivo neuropathological mechanism of miR-1202 as well as its interplay with BA44 circuits in the depressed brain are still unclear. Here, we performed a case-control study with imaging-genetic approach based on resting-state functional magnetic resonance imaging (MRI) data and miR-1202 quantification from 110 medication-free MDDs and 102 healthy controls. Serum-derived circulating exosomes that readily cross the blood-brain barrier were isolated to quantify miR-1202. For validation, repeated MR scans were performed after a six-week follow-up of antidepressant treatment on a cohort of MDDs. Voxelwise factorial analysis revealed two brain areas (including the striatal-thalamic region) in which the effect of depression on the functional connectivity with BA44 was significantly dependent on the expression level of exosomal miR-1202. Moreover, longitudinal change of the BA44 connectivity with the striatal-thalamic region in MDDs after antidepressant treatment was found to be significantly related to the level of miR-1202 expression. These findings revealed that the in vivo neuropathological effect of miR-1202 dysregulation in depression is possibly exerted by mediating neural functional abnormalities in BA44-striatal-thalamic circuits.

Neurogenic heterotopic ossification of the hip: Magnetic resonance imaging versus computed tomography for pre-surgical assessment

PURPOSE

Neurogenic heterotopic ossification (NHO) of the hip is a frequent complication of spinal cord injuries, often requiring surgical management. Pre-surgical imaging assessment is essential, usually with computed tomography (CT)-scan. We aimed to compare magnetic resonance imaging (MRI) and CT for pre-surgical imaging assessment of the NHO, particularly for their relationships with vessels and nerves.

METHOD

This prospective study included consecutive patients who underwent surgery for NHO from July 2019 to April 2022. All patients had CT angiography and MRI including Zero Echo Time and TRICKS sequences. Radiologists used standardized reports for CT and MRI to evaluate NHO and their features, bone mineralization, and relation to the arteries, veins and nerves. Agreement between pre-surgical CT and MRI was evaluated.

RESULTS

Twenty-four patients (mean age: 53.5 ± 12.2 years) were included, among which 7 had bilateral NHO (31 hips). NHO were anterior in 15/31 hips (48 %), multifragmented in 25/31 hips (81 %). Mild and significant demineralization was most frequent. Gutter and tunnel were reported in 11.1 % of the arteries. Nerves were more often identified in MRI than in CT-scan. Agreement coefficients between CT and MRI were excellent for NHO location (0.95) and implantation (0.92), good for fragmentation (0.70), contact with joint capsule (0.66), bone mineralization (0.74), and relation to arteries (0.85), veins (0.76), sciatic nerve (0.7) and moderate for femoral nerve (0.47).

CONCLUSION

MRI exhibited a good agreement with CT for pre-surgical assessment of NHO of the hip, especially to evaluate their relationships with the arteries, veins and sciatic nerve. Femoral nerves were more often identified in MRI than in CT-scan.

Predicting cytogenetic risk in multiple myeloma using conventional whole-body MRI, spinal dynamic contrast-enhanced MRI, and spinal diffusion-weighted imaging

OBJECTIVES

Cytogenetic abnormalities are predictors of poor prognosis in multiple myeloma (MM). This paper aims to build and validate a multiparametric conventional and functional whole-body MRI-based prediction model for cytogenetic risk classification in newly diagnosed MM.

METHODS

Patients with newly diagnosed MM who underwent multiparametric conventional whole-body MRI, spinal dynamic contrast-enhanced (DCE-)MRI, spinal diffusion-weighted MRI (DWI) and had genetic analysis were retrospectively included (2011-2020/Ghent University Hospital/Belgium). Patients were stratified into standard versus intermediate/high cytogenetic risk groups. After segmentation, 303 MRI features were extracted. Univariate and model-based methods were evaluated for feature and model selection. Testing was performed using receiver operating characteristic (ROC) and precision-recall curves. Models comparing the performance for genetic risk classification of the entire MRI protocol and of all MRI sequences separately were evaluated, including all features. Four final models, including only the top three most predictive features, were evaluated.

RESULTS

Thirty-one patients were enrolled (mean age 66 ± 7 years, 15 men, 13 intermediate-/high-risk genetics). None of the univariate models and none of the models with all features included achieved good performance. The best performing model with only the three most predictive features and including all MRI sequences reached a ROC-area-under-the-curve of 0.80 and precision-recall-area-under-the-curve of 0.79. The highest statistical performance was reached when all three MRI sequences were combined (conventional whole-body MRI + DCE-MRI + DWI). Conventional MRI always outperformed the other sequences. DCE-MRI always outperformed DWI, except for specificity.

CONCLUSIONS

A multiparametric MRI-based model has a better performance in the noninvasive prediction of high-risk cytogenetics in newly diagnosed MM than conventional MRI alone.

CRITICAL RELEVANCE STATEMENT

An elaborate multiparametric MRI-based model performs better than conventional MRI alone for the noninvasive prediction of high-risk cytogenetics in newly diagnosed multiple myeloma; this opens opportunities to assess genetic heterogeneity thus overcoming sampling bias.

KEY POINTS

• Standard genetic techniques in multiple myeloma patients suffer from sampling bias due to tumoral heterogeneity. • Multiparametric MRI noninvasively predicts genetic risk in multiple myeloma. • Combined conventional anatomical MRI, DCE-MRI, and DWI had the highest statistical performance to predict genetic risk. • Conventional MRI alone always outperformed DCE-MRI and DWI separately to predict genetic risk. DCE-MRI alone always outperformed DWI separately, except for the parameter specificity to predict genetic risk. • This multiparametric MRI-based genetic risk prediction model opens opportunities to noninvasively assess genetic heterogeneity thereby overcoming sampling bias in predicting genetic risk in multiple myeloma.

Amide proton transfer weighted and diffusion weighted imaging based radiomics classification algorithm for predicting 1p/19q co-deletion status in low grade gliomas

BACKGROUND

1p/19q co-deletion in low-grade gliomas (LGG, World Health Organization grade II and III) is of great significance in clinical decision making. We aim to use radiomics analysis to predict 1p/19q co-deletion in LGG based on amide proton transfer weighted (APTw), diffusion weighted imaging (DWI), and conventional MRI.

METHODS

This retrospective study included 90 patients histopathologically diagnosed with LGG. We performed a radiomics analysis by extracting 8454 MRI-based features form APTw, DWI and conventional MR images and applied a least absolute shrinkage and selection operator (LASSO) algorithm to select radiomics signature. A radiomics score (Rad-score) was generated using a linear combination of the values of the selected features weighted for each of the patients. Three neuroradiologists, including one experienced neuroradiologist and two resident physicians, independently evaluated the MR features of LGG and provided predictions on whether the tumor had 1p/19q co-deletion or 1p/19q intact status. A clinical model was then constructed based on the significant variables identified in this analysis. A combined model incorporating both the Rad-score and clinical factors was also constructed. The predictive performance was validated by receiver operating characteristic curve analysis, DeLong analysis and decision curve analysis. P < 0.05 was statistically significant.

RESULTS

The radiomics model and the combined model both exhibited excellent performance on both the training and test sets, achieving areas under the curve (AUCs) of 0.948 and 0.966, as well as 0.909 and 0.896, respectively. These results surpassed the performance of the clinical model, which achieved AUCs of 0.760 and 0.766 on the training and test sets, respectively. After performing Delong analysis, the clinical model did not significantly differ in predictive performance from three neuroradiologists. In the training set, both the radiomic and combined models performed better than all neuroradiologists. In the test set, the models exhibited higher AUCs than the neuroradiologists, with the radiomics model significantly outperforming resident physicians B and C, but not differing significantly from experienced neuroradiologist.

CONCLUSIONS

Our results suggest that our algorithm can noninvasively predict the 1p/19q co-deletion status of LGG. The predictive performance of radiomics model was comparable to that of experienced neuroradiologist, significantly outperforming the diagnostic accuracy of resident physicians, thereby offering the potential to facilitate non-invasive 1p/19q co-deletion prediction of LGG.

Ultrashort echo time MRI detects significantly lower collagen but higher pore water in the tibial cortex of female patients with osteopenia and osteoporosis

Ultrashort echo time (UTE) MRI can quantify the major proton pool densities in cortical bone, including total (TWPD), bound (BWPD), and pore water (PWPD) proton densities, as well as the macromolecular proton density (MMPD), associated with the collagen content, which is calculated using macromolecular fraction (MMF) from UTE magnetization transfer (UTE-MT) modeling. This study aimed to investigate the differences in water and collagen contents in tibial cortical bone, between female osteopenia (OPe) patients, osteoporosis (OPo) patients, and young participants (Young). Being postmenopausal and above 55 years old were the inclusion criteria for OPe and OPo groups. The tibial shaft of fourteen OPe (72.5 ± 6.8 years old), thirty-one OPo (72.0 ± 6.4 years old), and thirty-one young subjects (28.0 ± 6.1 years old) were scanned using a knee coil on a clinical 3 T scanner. Basic UTE, inversion recovery UTE, and UTE-MT sequences were performed. Investigated biomarkers were compared between groups using Kruskal-Wallis test. Spearman's correlation coefficients were calculated between the total hip dual-energy x-ray absorptiometry (DXA) T-score and UTE-MRI results. MMF, BWPD, and MMPD were significantly lower in OPo patients than in the young group. Whereas T1, TWPD, and PWPD were significantly higher in OPo patients. The largest OPo/Young average percentage differences were found in MMF (41.9%), PWPD (103.5%), and MMPD (64.0%). PWPD was significantly higher (50.7%), while BWPD was significantly lower (16.4%) in OPe than the Young group on average. MMF was found to be significantly lower (27%) in OPo patients compared with OPe group. T1, MMF, TWPD, PWPD, and MMPD values significantly correlated with the total hip DXA T-scores (provided by the patients and only available for OPe and OPo patients). DXA T-score showed the highest correlations with PWPD (R = 0.55) and MMF (R = 0.56) values. TWPD, PWPD, and MMF estimated using the UTE-MRI sequences were recommended to evaluate individuals with OPe and OPo.

How often should we perform magnetic resonance imaging (MRI) for the follow-up of pituitary adenoma?

Magnetic resonance imaging (MRI) is the examination of choice for diagnosing and monitoring pituitary adenoma (also known as pituitary neuroendocrine tumor or PitNET), whether treated or not. However, repeating the examination too often (and sometimes unnecessarily) is costly, and worrying data on tissue accumulation (brain, bone, etc.) of gadolinium atoms dissociated from their carrier molecule (chelator) have led European authorities to ban contrast agents based on linear chelators of gadolinium, which are particularly susceptible to rapid dissociation, in favor of chemically more stable macrocyclic chelators. It is therefore important to determine the optimal frequency for pituitary MRI monitoring in order to safely assess the natural history or therapeutic response of pituitary adenomas. The aim of this article is to summarize the most recent data on optimal follow-up intervals depending on the type, size and location of the pituitary tumor and the clinical situation in general, in order to generate monitoring algorithms to guide clinicians.

A simulation study to assess the potential benefits of MRI-based fetal weight estimation as a second-line test for suspected macrosomia

OBJECTIVE

To simulate the outcomes of Boulvain's trial by using magnetic resonance imaging (MRI) for estimated fetal weight (EFW) as a second-line confirmatory imaging.

STUDY DESIGN

Data derived from the Boulvain's trial and the study PREMACRO (PREdict MACROsomia) were used to simulate a 1000-patient trial. Boulvain's trial compared induction of labor (IOL) to expectant management in suspected macrosomia, whereas PREMACRO study compared the performance of ultrasound-EFW (US-EFW) and MRI-EFW in the prediction of birthweight. The primary outcome was the incidence of significant shoulder dystocia (SD). Cesarean delivery (CD), hyperbilirubinemia (HB), and IOL at < 39 weeks of gestation (WG) were selected as secondary outcomes. A subgroup analysis of the Boulvain's trial was performed to estimate the incidence of the primary and secondary outcomes in the true positive and false positive groups for the two study arms. Sensitivity, specificity, positive and negative predictive values (PPV, NPV) for the prediction of macrosomia by MRI-EFW at 36 WG were calculated, and a decision tree was constructed for each outcome.

RESULTS

The PPV of US-EFW for the prediction of macrosomia in the PREMACRO trial was 56.3 %. MRI-EFW was superior to US-EFW as a predictive tool resulting in lower rates of induction for false-positive cases. Repeating Boulvain's trial using MRI-EFW as a second-line test would result in similar rates of SD (relative risk [RR]:0.36), CD (RR:0.84), and neonatal HB (RR:2.6), as in the original trial. Increasing the sensitivity and specificity of MRI-EFW resulted in a similar relative risk for SD as in Boulvain's trial, but with reduced rates of IOL < 39 WG, and improved the RR of CD in favor of IOL. We found an inverse relationship between IOL rate and incidence of SD for both US-EFW and MRI-EFW, although overall rates of IOL, CD, and neonatal HB would be lower with MRI-derived estimates of fetal weight.

CONCLUSION

The superior accuracy of MRI-EFW over US-EFW for the diagnosis of macrosomia could result in lower rates of IOL without compromising the relative advantages of the intervention but fails to demonstrate a significant benefit to justify a replication of the original trial using MRI-EFW as a second-line test.

Pretreatment multiparametric MRI radiomics-integrated clinical hematological biomarkers can predict early rapid metastasis in patients with nasopharyngeal carcinoma

BACKGROUND

To establish and validate a predictive model combining pretreatment multiparametric MRI-based radiomic signatures and clinical characteristics for the risk evaluation of early rapid metastasis in nasopharyngeal carcinoma (NPC) patients.

METHODS

The cutoff time was used to randomly assign 219 consecutive patients who underwent chemoradiation treatment to the training group (n = 154) or the validation group (n = 65). Pretreatment multiparametric magnetic resonance (MR) images of individuals with NPC were employed to extract 428 radiomic features. LASSO regression analysis was used to select radiomic features related to early rapid metastasis and develop the Rad-score. Blood indicators were collected within 1 week of pretreatment. To identify independent risk variables for early rapid metastasis, univariate and multivariate logistic regression analyses were employed. Finally, multivariate logistic regression analysis was applied to construct a radiomics and clinical prediction nomogram that integrated radiomic features and clinical and blood inflammatory predictors.

RESULTS

The NLR, T classification and N classification were found to be independent risk indicators for early rapid metastasis by multivariate logistic regression analysis. Twelve features associated with early rapid metastasis were selected by LASSO regression analysis, and the Rad-score was calculated. The AUC of the Rad-score was 0.773. Finally, we constructed and validated a prediction model in combination with the NLR, T classification, N classification and Rad-score. The area under the curve (AUC) was 0.936 (95% confidence interval (95% CI): 0.901-0.971), and in the validation cohort, the AUC was 0.796 (95% CI: 0.686-0.905).

CONCLUSIONS

A predictive model that integrates the NLR, T classification, N classification and MR-based radiomics for distinguishing early rapid metastasis may serve as a clinical risk stratification tool for effectively guiding individual management.

3D amide proton transfer-weighted imaging may be useful for diagnosing early-stage breast cancer: a prospective monocentric study

BACKGROUND

We investigated the value of three-dimensional amide proton transfer-weighted imaging (3D-APTWI) in the diagnosis of early-stage breast cancer (BC) and its correlation with the immunohistochemical characteristics of malignant lesions.

METHODS

Seventy-eight women underwent APTWI and dynamic contrast-enhanced (DCE)-MRI. Pathological results were categorized as either benign (n = 43) or malignant (n = 37) lesions. The parameters of APTWI and DCE-MRI were compared between the benign and malignant groups. The diagnostic value of 3D-APTWI was evaluated using the area under the receiver operating characteristic curve (ROC-AUC) to establish a diagnostic threshold. Pearson's correlation was used to analyze the correlation between the magnetization transfer asymmetry (MTRasym) and immunohistochemical characteristics.

RESULTS

The MTRasym and time-to-peak of malignancies were significantly lower than those of benign lesions (all p < 0.010). The volume transfer constant, rate constant, and wash-in and wash-out rates of malignancies were all significantly greater than those of benign lesions (all p < 0.010). ROC-AUCs of 3D-APTWI, DCE-MRI, and 3D-APTWI+DCE to differential diagnosis between early-stage BC and benign lesions were 0.816, 0.745, and 0.858, respectively. Only the difference between AUCAPT+DCE and AUCDCE was significant (p < 0.010). When a threshold of MTRasym for malignancy for 2.42%, the sensitivity and specificity of 3D-APTWI for BC diagnosis were 86.5% and 67.6%, respectively; MTRasym was modestly positively correlated with pathological grade (r = 0.476, p = 0.003) and Ki-67 (r = 0.419, p = 0.020).

CONCLUSIONS

3D-APTWI may be used as a supplementary method for patients with contraindications of DCE-MRI. MTRasym can imply the proliferation activities of early-stage BC.

RELEVANCE STATEMENT

3D-APTWI can be an alternative diagnostic method for patients with early-stage BC who are not suitable for contrast injection.

KEY POINTS

• 3D-APTWI reflects the changes in the microenvironment of early-stage breast cancer. • Combined 3D-APTWI is superior to DCE-MRI alone for early-stage breast cancer diagnosis. • 3D-APTWI improves the diagnostic accuracy of early-stage breast cancer.

Differences in the prevalence of NAFLD, MAFLD, and MASLD according to changes in the nomenclature in a health check-up using MRI-derived proton density fat fraction

PURPOSE

International expert panels proposed new nomenclatures, metabolic dysfunction-associated fatty liver disease (MAFLD) in 2020 and metabolic dysfunction-associated steatotic liver disease (MASLD) in 2023, along with revised diagnostic criteria to replace non-alcoholic fatty liver disease (NAFLD). We aimed to investigate the differences in NAFLD, MAFLD, and MASLD prevalence with changing nomenclature in a health check-up using magnetic resonance imaging-derived proton density fat fraction (MRI-PDFF) to assess hepatic steatosis. We also examined the prevalence of the sub-classifications of steatotic liver disease (SLD) and the differences in characteristics among the sub-categories.

METHODS

We included 844 participants who underwent liver MRI-PDFF at our health check-up clinic between January 2020 and November 2022. Hepatic steatosis was defined as MRI-PDFF ≥ 5%. Participants were categorized according to NAFLD, MAFLD, MASLD, and sub-classifications of SLD.

RESULTS

The prevalence rates of NAFLD, MAFLD, and MASLD were 25.9%, 29.5%, and 25.2%, respectively. 30.5% of the participants was categorized as SLD. The prevalence rates of the SLD sub-categories were 25.2% for MASLD, 3.7% for MASLD and alcohol-associated liver disease (MetALD), 0.1% for alcohol-associated liver disease, 1.3% for specific etiology SLD, and 0.1% for cryptogenic SLD. Compared with patients in the MASLD group, those in the MetALD group were younger, predominantly male, and exhibited higher levels of serum aspartate aminotransferase, gamma-glutamyl transpeptidase, and triglycerides.

CONCLUSION

The prevalences of NAFLD and MASLD assessed using MRI-PDFF were similar, with MASLD accounting for 97.3% of the patients with NAFLD. The separate MetALD sub-category may have clinical characteristics that differ from those of MASLD.

Pulmonary Artery Angioplasty for Improving Ipsilateral Lung Perfusion in Adolescent and Adult Patients: An Analysis Based on Cardiac Magnetic Resonance Imaging and Lung Perfusion Scanning

Background

The left pulmonary artery (LPA) may be kinked and stenotic, especially in tetralogy of Fallot, because of ductal tissue and anterior deviation of the conal septum. If LPA stenosis is not effectively treated during total correction, surgical angioplasty is occasionally performed. However, whether pulmonary artery (PA) angioplasty in adolescents or adults improves perfusion in the ipsilateral lung remains unclear.

Methods

This retrospective review enrolled patients who underwent PA angioplasty for LPA stenosis between 2004 and 2019. Among patients who underwent a lung perfusion scan (LPS) or cardiac magnetic resonance imaging (cMRI) pre- and post-pulmonary angioplasty, those aged >13 years with <40% left lung perfusion (p-left) in the pre-angioplasty study were included. Preoperative and postoperative computed tomography, LPS, and cMRI data were collected. The perfusion ratio was analyzed according to the LPA's anatomical characteristics.

Results

Seventeen adolescents and 16 adults (≥18 years old) were finally included (median age, 17 years). The most common primary diagnosis was tetralogy of Fallot (87.9%). In all patients, LPA angioplasty was performed concomitantly with right ventricular outflow tract reconstruction. No patients died. Preoperative p-left was not significantly different between adolescents and adults; however, adolescents had significantly higher postoperative p-left than adults. P-left significantly increased in adolescents, but not in adults. Seven patients had significant stenosis (z-score <-2.0) confined only to the proximal LPA and demonstrated significantly increased p-left.

Conclusion

PA angioplasty significantly increased ipsilateral lung perfusion in adolescents. If focal stenosis is confined to the proximal LPA, PA angioplasty may improve ipsilateral lung perfusion, regardless of age.

Assessment of dental pulp response to carries via MR T2mapping and histological analysis

OBJECTIVES

The aim of our study was to assess the correlation between T2 relaxation times and their variability with the histopathological results of the same teeth in relation to caries progression.

MATERIALS AND METHODS

52 extracted permanent premolars were included in the study. Prior to extractions, patients underwent magnetic resonance imaging (MRI) scanning and teeth were evaluated using ICDAS classification. Pulps of extracted teeth were histologically analysed.

RESULTS

MRI T2 relaxation times (ms) were 111,9 ± 11.2 for ICDAS 0, 132.3 ± 18.5* for ICDAS 1, 124.6 ± 14.8 for ICDAS 2 and 112. 6 ± 18.2 for ICDAS 3 group (p = 0,013). A positive correlation was observed between MRI T2 relaxation times and macrophage and T lymphocyte density in healthy teeth. There was a positive correlation between vascular density and T2 relaxation times of dental pulp in teeth with ICDAS score 1. A negative correlation was found between T2 relaxation times and macrophage density. There was a positive correlation between T2 relaxation time variability and macrophage and T lymphocyte density in teeth with ICDAS score 2. In teeth with ICDAS score 3, a positive correlation between T2 relaxation times and T2 relaxation time variability and lymphocyte B density was found.

CONCLUSION

The results of our study confirm the applicability of MRI in evaluation of the true condition of the pulp tissue.

CLINICAL RELEVANCE

With the high correlation to histological validation, MRI method serves as a promising imaging implement in the field of general dentistry and endodontics.

A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound

Self-supervised pretraining has been observed to be effective at improving feature representations for transfer learning, leveraging large amounts of unlabelled data. This review summarizes recent research into its usage in X-ray, computed tomography, magnetic resonance, and ultrasound imaging, concentrating on studies that compare self-supervised pretraining to fully supervised learning for diagnostic tasks such as classification and segmentation. The most pertinent finding is that self-supervised pretraining generally improves downstream task performance compared to full supervision, most prominently when unlabelled examples greatly outnumber labelled examples. Based on the aggregate evidence, recommendations are provided for practitioners considering using self-supervised learning. Motivated by limitations identified in current research, directions and practices for future study are suggested, such as integrating clinical knowledge with theoretically justified self-supervised learning methods, evaluating on public datasets, growing the modest body of evidence for ultrasound, and characterizing the impact of self-supervised pretraining on generalization.

An open-source MRI compatible frame for multimodal presurgical mapping in macaque and capuchin monkeys

BACKGROUND

High-precision neurosurgical targeting in nonhuman primates (NHPs) often requires presurgical anatomical mapping with noninvasive neuroimaging techniques (MRI, CT, PET), allowing for translation of individual anatomical coordinates to surgical stereotaxic apparatus. Given the varied tissue contrasts that these imaging techniques produce, precise alignment of imaging-based coordinates to surgical apparatus can be cumbersome. MRI-compatible stereotaxis with radiopaque fiducial markers offer a straight-forward and reliable solution, but existing commercial options do not fit in conformal head coils that maximize imaging quality.

NEW METHOD

We developed a compact MRI-compatible stereotaxis suitable for a variety of NHP species (Macaca mulatta, Macaca fascicularis, and Cebus apella) that allows multimodal alignment through technique-specific fiducial markers.

COMPARISON WITH EXISTING METHODS

With the express purpose of compatibility with clinically available MRI, CT, and PET systems, the frame is no larger than a human head, while allowing for imaging NHPs in the supinated position. This design requires no marker implantation, special software, or additional knowledge other than the operation of a common large animal stereotaxis.

RESULTS

We demonstrated the applicability of this 3D-printable apparatus across a diverse set of experiments requiring presurgical planning: 1) We demonstrate the accuracy of the fiducial system through a within-MRI cannula insertion and subcortical injection of a viral vector. 2) We also demonstrated accuracy of multimodal (MRI and CT) alignment and coordinate transfer to guide a surgical robot electrode implantation for deep-brain electrophysiology.

CONCLUSIONS

The computer-aided design files and engineering drawings are publicly available, with the modular design allowing for low cost and manageable manufacturing.

Conversion map from quantitative parameter mapping to myelin water fraction: comparison with R1·R2* and myelin water fraction in white matter

OBJECTIVE

To clarify the relationship between myelin water fraction (MWF) and R1⋅R2* and to develop a method to calculate MWF directly from parameters derived from QPM, i.e., MWF converted from QPM (MWFQPM).

MATERIALS AND METHODS

Subjects were 12 healthy volunteers. On a 3 T MR scanner, dataset was acquired using spoiled gradient-echo sequence for QPM. MWF and R1⋅R2* maps were derived from the multi-gradient-echo (mGRE) dataset. Volume-of-interest (VOI) analysis using the JHU-white matter (WM) atlas was performed. All the data in the 48 WM regions measured VOI were plotted, and quadratic polynomial approximations of each region were derived from the relationship between R1·R2* and the two-pool model-MWF. The R1·R2* map was converted to MWFQPM map. MWF atlas template was generated using converted to MWF from R1·R2* per WM region.

RESULTS

The mean MWF and R1·R2* values for the 48 WM regions were 11.96 ± 6.63%, and 19.94 ± 4.59 s-2, respectively. A non-linear relationship in 48 regions of the WM between MWF and R1·R2* values was observed by quadratic polynomial approximation (R2 ≥ 0.963, P < 0.0001).

DISCUSSION

MWFQPM map improved image quality compared to the mGRE-MWF map. Myelin water atlas template derived from MWFQPM may be generated with combined multiple WM regions.

Paediatric hepatic angiosarcoma with consumptive hypothyroidism-an important diagnostic pitfall to avoid during evaluation of hepatic vascular tumours

Hepatic angiosarcoma is an extremely rare primary malignant vascular tumour in children with very poor prognosis. Radiological diagnosis of hepatic angiosarcoma is challenging due to overlapping imaging features with other benign vascular hepatic tumours, particularly infantile hepatic haemangioma. Consumptive hypothyroidism is a condition that is almost exclusively associated with infantile hepatic haemangioma and has never been reported in angiosarcoma. We present a case of hepatic angiosarcoma in a 20-month-old girl, associated with consumptive hypothyroidism and, as a result, initially misdiagnosed as infantile hepatic haemangioma. Radiologists should be aware that consumptive hypothyroidism is not a reliable feature to use in excluding paediatric hepatic angiosarcoma. Biopsy should be performed in patients older than 1 year of age or with atypical imaging features.

Ectopic pancreas tissue in the posterior mediastinum

The occurrence of ectopic pancreas in the mediastinum is rare. Herein, we report a 22-year-old female who presented with right shoulder pain, dysphagia, fever and headaches. Chest computer tomography revealed a mass in the posterior mediastinum with accompanying signs of acute mediastinitis. Needle biopsy and fine-needle aspiration revealed ectopic gastral tissue and ectopic pancreas tissue, respectively. Surgical resection was attempted due to recurring acute pancreatitis episodes. However, due to chronic-inflammatory adhesions of the mass to the tracheal wall, en-bloc resection was not possible without major tracheal resection. Since then, recurring pancreatitis episodes have been treated conservatively with antibiotics. We report this case due to its differing clinical and radiological findings in comparison to previous case reports, none of which pertained a case of ectopic pancreas tissue in the posterior mediastinum with recurring acute pancreatitis and mediastinitis.

Utility of visual rating scales in primary progressive aphasia

INTRODUCTION

Differential diagnosis among subjects with Primary Progressive Aphasia (PPA) can be challenging. Structural MRI can support the clinical profile. Visual rating scales are a simple and reliable tool to assess brain atrophy in the clinical setting. The aims of the study were to establish to what extent the visual rating scales could be useful in the differential diagnosis of PPA, to compare the clinical diagnostic impressions derived from routine MRI interpretations with those obtained using the visual rating scale and to correlate results of the scales in a voxel-based morphometry (VBM) analysis.

METHOD

Patients diagnosed with primary progressive aphasia (PPA) according to current criteria from two centers-Ospedale Maggiore Policlinico of Milan and Hospital Clínic de Barcelona-were included in the study. Two blinded clinicians evaluated the subjects MRIs for cortical atrophy and white matter hyperintensities using two protocols: routine readings and the visual rating scale. The diagnostic accuracy between patients and controls and within PPA subgroups were compared between the two protocols.

RESULTS

One hundred fifty Subjects were studied. All the scales showed a good to excellent intra and inter-rater agreement. The left anterior temporal scale could differentiate between semantic PPA and all other variants. The rater impression after the protocol can increase the accuracy just for the logopenic PPA. In the VBM analysis, the scores of visual rating scales correlate with the corresponding area of brain atrophy.

CONCLUSION

The Left anterior temporal rating scale can distinguish semantic PPA from other variants. The rater impression after structured view improved the diagnostic accuracy of logopenic PPA compared to normal readings. The unstructured view of the MRI was reliable for identifying semantic PPA and controls. Neither the structured nor the unstructured view could identify the nonfluent and undetermined variants.

Prediction of the Ki-67 expression level in head and neck squamous cell carcinoma with machine learning-based multiparametric MRI radiomics: a multicenter study

BACKGROUND

This study aimed to develop and validate a machine learning (ML)-based fusion model to preoperatively predict Ki-67 expression levels in patients with head and neck squamous cell carcinoma (HNSCC) using multiparametric magnetic resonance imaging (MRI).

METHODS

A total of 351 patients with pathologically proven HNSCC from two medical centers were retrospectively enrolled in the study and divided into training (n = 196), internal validation (n = 84), and external validation (n = 71) cohorts. Radiomics features were extracted from T2-weighted images and contrast-enhanced T1-weighted images and screened. Seven ML classifiers, including k-nearest neighbors (KNN), support vector machine (SVM), logistic regression (LR), random forest (RF), linear discriminant analysis (LDA), naive Bayes (NB), and eXtreme Gradient Boosting (XGBoost) were trained. The best classifier was used to calculate radiomics (Rad)-scores and combine clinical factors to construct a fusion model. Performance was evaluated based on calibration, discrimination, reclassification, and clinical utility.

RESULTS

Thirteen features combining multiparametric MRI were finally selected. The SVM classifier showed the best performance, with the highest average area under the curve (AUC) of 0.851 in the validation cohorts. The fusion model incorporating SVM-based Rad-scores with clinical T stage and MR-reported lymph node status achieved encouraging predictive performance in the training (AUC = 0.916), internal validation (AUC = 0.903), and external validation (AUC = 0.885) cohorts. Furthermore, the fusion model showed better clinical benefit and higher classification accuracy than the clinical model.

CONCLUSIONS

The ML-based fusion model based on multiparametric MRI exhibited promise for predicting Ki-67 expression levels in HNSCC patients, which might be helpful for prognosis evaluation and clinical decision-making.

Early features of pancreatic cancer on magnetic resonance imaging (MRI): a case-control study

PURPOSE

Magnetic resonance imaging has been recommended as a primary imaging modality among high-risk individuals undergoing screening for pancreatic cancer. We aimed to delineate potential precursor lesions for pancreatic cancer on MR imaging.

METHODS

We conducted a case-control study at Kaiser Permanente Southern California (2008-2018) among patients that developed pancreatic cancer who had pre-diagnostic MRI examinations obtained 2-36 months prior to cancer diagnosis (cases) matched 1:2 by age, gender, race/ethnicity, contrast status and year of scan (controls). Patients with history of acute/chronic pancreatitis or prior pancreatic surgery were excluded. Images underwent blind review with assessment of a priori defined series of parenchymal and ductal features. We performed logistic regression to assess the associations between individual factors and pancreatic cancer. We further assessed the interaction among features as well as performed a sensitivity analysis stratifying based on specific time-windows (2-3 months, 4-12 months, 13-36 months prior to cancer diagnosis).

RESULTS

We identified 141 cases (37.9% stage I-II, 2.1% III, 31.4% IV, 28.6% unknown) and 292 matched controls. A solid mass was noted in 24 (17%) of the pre-diagnostic MRI scans. Compared to controls, pre-diagnostic images from cancer cases more frequently exhibited the following ductal findings: main duct dilatation (51.4% vs 14.3%, OR [95% CI]: 7.75 [4.19-15.44], focal pancreatic duct stricture with distal (upstream) dilatation (43.6% vs 5.6%, OR 12.71 [6.02-30.89], irregularity (42.1% vs 6.0%, OR 9.73 [4.91-21.43]), focal pancreatic side branch dilation (13.6% vs1.6%, OR 11.57 [3.38-61.32]) as well as parenchymal features: atrophy (57.9% vs 27.4%, OR 46.4 [2.71-8.28], focal area of signal abnormality (39.3% vs 4.8%, OR 15.69 [6.72-44,78]), all p < 0.001).

CONCLUSION

In addition to potential missed lesions, we have identified a series of ductal and parenchymal features on MRI that are associated with increased odds of developing pancreatic cancer.

Clinical and ultrasound features of 46 children with suppurative osteoarthritis: experience from two centers

OBJECTIVE

Diagnosing musculoskeletal infections in children is challenging. In recent years, with the advancement of ultrasound technology, high-resolution ultrasound has unique advantages for musculoskeletal children. The aim of this work is to summarize the ultrasonographic and clinical characteristics of children with pyogenic arthritis and osteomyelitis. This study provides a simpler and more effective diagnostic basis for clinical treatment.

METHODS

Fifty children with osteomyelitis or arthritis were diagnosed via ultrasound, and the results of the ultrasound diagnosis were compared with those of magnetic resonance imaging and surgery. Clinical and ultrasound characteristics were also analyzed.

RESULTS

Out of 50 patients, 46 were confirmed to have suppurative infection by surgical and microbiological examination. Among these 46 patients, 26 were diagnosed with osteomyelitis and 20 had arthritis. The manifestations of osteomyelitis were subperiosteal abscess (15 patients), bone destruction (17 patients), bone marrow abscess (9 patients), and adjacent joint abscess (13 patients). Osteomyelitis mostly affects the long bones of the limbs, femur and humerus (10 and 9 patients, respectively), followed by the ulna, radius, tibia and fibula (one patient each). The manifestations of arthritis were joint pus (20 patients) and joint capsule thickening (20 patients), and hip dislocation (8 patients). All the patients had arthritis involving the hip joint.

CONCLUSION

Subperiosteal abscess, bone destruction, and joint abscess with dislocation are ultrasonographic features of pyogenic osteoarthritis. The findings of this work can improve the early diagnosis and differentiation of pyogenic osteoarthritis and provide a reliable basis for treatment.

Native T1-mapping as a predictor of progressive renal function decline in chronic kidney disease patients

BACKGROUND

To investigate the potential of Native T1-mapping in predicting the prognosis of patients with chronic kidney disease (CKD).

METHODS

We enrolled 119 CKD patients as the study subjects and included 20 healthy volunteers as the control group, with follow-up extending until October 2022. Out of these patients, 63 underwent kidney biopsy measurements, and these patients were categorized into high (25-50%), low (< 25%), and no renal interstitial fibrosis (IF) (0%) groups. The study's endpoint event was the initiation of renal replacement therapy, kidney transplantation, or an increase of over 30% in serum creatinine levels. Cox regression analysis determined factors influencing unfavorable kidney outcomes. We employed Kaplan-Meier analysis to contrast kidney survival rates between the high and low T1 groups. Additionally, receiver-operating characteristic (ROC) curve analysis assessed the predictive accuracy of Native T1-mapping for kidney endpoint events.

RESULTS

T1 values across varying fibrosis degree groups showed statistical significance (F = 4.772, P < 0.05). Multivariate Cox regression pinpointed 24-h urine protein, cystatin C(CysC), hemoglobin(Hb), and T1 as factors tied to the emergence of kidney endpoint events. Kaplan-Meier survival analysis revealed a markedly higher likelihood of kidney endpoint events in the high T1 group compared to the low T1 value group (P < 0.001). The ROC curves for variables (CysC, T1, Hb) tied to kidney endpoint events demonstrated area under the curves(AUCs) of 0.83 (95%CI: 0.75-0.91) for CysC, 0.77 (95%CI: 0.68-0.86) for T1, and 0.73 (95%CI: 0.63-0.83) for Hb. Combining these variables elevated the AUC to 0.88 (95%CI: 0.81-0.94).

CONCLUSION

Native T1-mapping holds promise in facilitating more precise and earlier detection of CKD patients most at risk for end-stage renal disease.

Exercise-induced calf muscle hyperemia quantified with dynamic blood oxygen level-dependent (BOLD) imaging

Muscle hyperemia in exercise is usually the combined result of increased cardiac output and local muscle vasodilation, with the latter reflecting muscle's capacity for increased blood perfusion to support exercise. In this study, we aim to quantify muscle's vasodilation capability with dynamic BOLD imaging. A deoxyhemoglobin-kinetics model is proposed to analyze dynamic BOLD signals acquired during exercise recovery, deriving a hyperemia index (HI) for a muscle group of interest. We demonstrated the method's validity with calf muscles of healthy subjects who performed plantar flexion for muscle stimulation. In a test with exercise load incrementally increasing from 0 to 16 lbs., gastrocnemius HI showed considerable variance among the 4 subjects, but with a consistent trend, i.e. low at light load (e.g. 0-6 lbs) and linearly increasing at heavy load. The high variability among different subjects was confirmed with the other 10 subjects who exercised with a same moderate load of 8 lbs., with coefficient of variance among subjects' medial gastrocnemius 87.8%, lateral gastrocnemius 111.8% and soleus 132.3%. These findings align with the fact that intensive exercise induces high muscle hyperemia, but a comparison among different subjects is hard to make, presumably due to the subjects' different rate of oxygen utilization. For the same 10 subjects who exercised with load of 8 lbs., we also performed dynamic contrast enhanced (DCE) MRI to measure muscle perfusion (F). With a moderate correlation of 0.654, HI and F displayed three distinctive responses of calf muscles: soleus of all the subjects were in the cluster of low F and low HI, and gastrocnemius of most subjects had high F and either low or high HI. This finding suggests that parameter F encapsulates blood flow through vessels of all sizes, but BOLD-derived HI focuses on capillary flow and therefore is a more specific indicator of muscle vasodilation. In conclusion, the proposed hyperemia index has the potential of quantitatively assessing muscle vasodilation induced with exercise.

Value of ultra-high b-value diffusion-weighted imaging for the evaluation of renal ischemia-reperfusion injury

To explore the feasibility of ultra-high b-value diffusion-weighted imaging (ubDWI) in assessment of renal IRI. Thirty-five rabbits were randomized into a control group (n = 7) and a renal IRI group (n = 28). The rabbits in the renal IRI group underwent left renal artery clamping for 60 min. Rabbits underwent axial ubDWI before and at 1, 12, 24, and 48 h after IRI. Apparent diffusion coefficient (ADCst) were calculated from ubDWI with two b-values (b = 0, 1000 s/mm2). Triexponential fits were applied to calculate the pure diffusion coefficients (D), perfusion-related diffusion coefficient (D⁎), and ultra-high ADC (ADCuh). The interobserver reproducibility were evaluated. The repeated measurement analysis of variance and Spearman correlation analysis was used for statistical analysis. The ADCst, D, and ADCuh values showed good reproducibility. The ADCst, D, and D⁎ values of renal Cortex (CO) and outer medulla (OM) significantly decreased after IRI (all P < 0.05). The ADCuh values significantly increased from pre-IRI to 1 h after IRI (P < 0.05) and significantly declined at 24 h and 48 h after IRI (all P < 0.05). ADCuh was strongly positively correlated with AQP-1 in the renal CO and OM (ρ = 0.643, P < 0.001; ρ = 0.662, P < 0.001, respectively). ubDWI can be used to non-invasively evaluate early renal IRI, ADCuh may be adopted to reflect AQP-1 expression.

Posterior cruciate ligament tibial attachment sacrifice percentage is higher in cruciate-retaining total knee arthroplasty in patients with discoid lateral meniscus

BACKGROUND

The posterior cruciate ligament (PCL) attachment may be damaged in cruciate-retaining total knee arthroplasty (CR-TKA) using the complete resection for tibial preparation, and resection amount varies greatly among individuals. Discoid lateral meniscus (DLM) is one of the most common anatomic knee variants. This study aimed to evaluate the difference in PCL attachment sacrifice in CR-TKA between patients with and without DLM.

METHODS

Fifty-one knees in the study group (DLM group) were matched 1:1 to 51 control knees (non-DLM group) by age, sex, and maximum width of the tibial plateau. The percentage of the sacrificed PCL attachment and the morphological parameters of the tibial plateau were evaluated using magnetic resonance imaging (MRI) in a blind manner.

RESULTS

With a tibial cut simulated at a 0°, 3°, and 7° osteotomy slope, the mean PCL attachment resection percentages in the non-DLM group were 40.5%, 53.6%, and 72.6%, respectively. The corresponding resection percentages in the DLM group were 61.0% (P < 0.001), 73.3% (P < 0.001), and 85.7% (P < 0.001), respectively. The percentage of the minimum meniscus width to the maximum tibia width showed a weak positive correlation with the percentage of PCL attachment sacrifice.

CONCLUSIONS

A significantly greater portion of PCL attachment was sacrificed in DLM patients undergoing CR-TKA using the complete proximal tibia resection. Attention should be paid to PCL attachment resection during CR-TKA in patients with DLM, and alternative techniques or prosthesis types should be considered.

Machine learning-based automated scan prescription of lumbar spine MRI acquisitions

PURPOSE

High quality scan prescription that optimally covers the area of interest with scan planes aligned to relevant anatomical structures is crucial for error-free radiologic interpretation. The goal of this project was to develop a machine learning pipeline for oblique scan prescription that could be trained on localizer images and metadata from previously acquired MR exams.

METHODS

A novel Multislice Rotational Region-based Convolutional Neural Network (MS-R2CNN) architecture was developed. Based on this architecture, models for automated prescription sagittal lumbar spine acquisitions from axial, sagittal, and coronal localizer slices were trained. The automated prescription pipeline was integrated with the scanner console software and evaluated in experiments with healthy volunteers (N = 3) and patients with lower-back pain (N = 20).

RESULTS

Experiments in healthy volunteers demonstrated high accuracy of automated prescription in all subjects. There was good agreement between alignment and coverage of manual and automated prescriptions, as well as consistent views of the lumbar spine at different positions of the subjects within the scanner bore. In patients with lower-back pain, the generated prescription was applied in 18 cases (90% of the total number). None of the cases required major adjustment, while in 11 cases (55%) there were minor manual adjustments to the generated prescription.

CONCLUSIONS

This study demonstrates the ability of oriented object detection-based models to be trained to prescribe oblique lumbar spine MRI acquisitions without the need of manual annotation or feature engineering and the feasibility of using machine learning-based pipelines on the scanner for automated prescription of MRI acquisitions.

Differentiation between adrenocortical carcinoma and lipid-poor adrenal adenoma using a multiparametric MRI-based diagnostic algorithm

PURPOSE

The purpose of this study was to evaluate the capabilities of multiparametric magnetic resonance imaging (MRI) in differentiating between lipid-poor adrenal adenoma (LPAA) and adrenocortical carcinoma (ACC).

MATERIALS AND METHODS

Patients of two centers who underwent surgical resection of LPAA or ACC after multiparametric MRI were retrospectively included. A training cohort was used to build a diagnostic algorithm obtained through recursive partitioning based on multiparametric MRI variables, including apparent diffusion coefficient and chemical shift signal ratio (i.e., tumor signal intensity index). The diagnostic performances of the multiparametric MRI-based algorithm were evaluated using a validation cohort, alone first and then in association with adrenal tumor size using a cut-off of 4 cm. Performances of the diagnostic algorithm for the diagnosis of ACC vs. LPAA were calculated using pathology as the reference standard.

RESULTS

Fifty-four patients (27 with LPAA and 27 with ACC; 37 women; mean age, 48.5 ± 13.3 [standard deviation (SD)] years) were used as the training cohort and 61 patients (24 with LPAA and 37 with ACC; 47 women; mean age, 49 ± 11.7 [SD] years) were used as the validation cohort. In the validation cohort, the diagnostic algorithm yielded best accuracy for the diagnosis of ACC vs. LPAA (75%; 46/61; 95% CI: 55-88) when used without lesion size. Best sensitivity was obtained with the association of the diagnostic algorithm with tumor size (96%; 23/24; 95% CI: 80-99). Best specificity was obtained with the diagnostic algorithm used alone (76%; 28/37; 95% CI: 60-87).

CONCLUSION

A multiparametric MRI-based diagnostic algorithm that includes apparent diffusion coefficient and tumor signal intensity index helps discriminate between ACC and LPAA with high degrees of specificity and accuracy. The association of the multiparametric MRI-based diagnostic algorithm with adrenal lesion size helps maximize the sensitivity of multiparametric MRI for the diagnosis of ACC.

Renal interstitial fibrotic assessment using non-Gaussian diffusion kurtosis imaging in a rat model of hyperuricemia

BACKGROUND

To investigate the feasibility of Diffusion Kurtosis Imaging (DKI) in assessing renal interstitial fibrosis induced by hyperuricemia.

METHODS

A hyperuricemia rat model was established, and the rats were randomly split into the hyperuricemia (HUA), allopurinol (AP), and AP + empagliflozin (AP + EM) groups (n = 19 per group). Also, the normal rats were selected as controls (CON, n = 19). DKI was performed before treatment (baseline) and on days 1, 3, 5, 7, and 9 days after treatment. The DKI indicators, including mean kurtosis (MK), fractional anisotropy (FA), and mean diffusivity (MD) of the cortex (CO), outer stripe of the outer medulla (OS), and inner stripe of the outer medulla (IS) were acquired. Additionally, hematoxylin and eosin (H&E) staining, Masson trichrome staining, and nuclear factor kappa B (NF-κB) immunostaining were used to reveal renal histopathological changes at baseline, 1, 5, and 9 days after treatment.

RESULTS

The HUA, AP, and AP + EM group MKOS and MKIS values gradually increased during this study. The HUA group exhibited the highest MK value in outer medulla. Except for the CON group, all the groups showed a decreasing trend in the FA and MD values of outer medulla. The HUA group exhibited the lowest FA and MD values. The MKOS and MKIS values were positively correlated with Masson's trichrome staining results (r = 0.687, P < 0.001 and r = 0.604, P = 0.001, respectively). The MDOS and FAIS were negatively correlated with Masson's trichrome staining (r = -626, P < 0.0014 and r = -0.468, P = 0.01, respectively).

CONCLUSION

DKI may be a non-invasive method for monitoring renal interstitial fibrosis induced by hyperuricemia.