Clinical features and prognostic factors of patients with cancer-associated stroke

BACKGROUND

Cerebrovascular diseases in cancer patients significantly aggravate their condition and prognosis; therefore, prompt and accurate diagnosis and treatment are important. The purpose of this study was to investigate patient demographics, laboratory data, brain magnetic resonance imaging (MRI) findings, and prognosis among patients with stroke and cancer, especially cancer-associated ischemic stroke (CAIS).

METHODS

We performed a retrospective, single-center study. We enrolled consecutive patients who had acute stroke and were admitted to our hospital between January 2011 and December 2021. We collected general demographic characteristics, cancer histopathological type, laboratory data, brain MRI findings, and prognosis data.

RESULTS

Among 2040 patients with acute stroke, a total of 160 patients (7.8%) had active cancer. The types of strokes were cerebral infarction, cerebral hemorrhage, subarachnoid hemorrhage, and transient ischemic attack in 124, 25, 5, and 6 patients, respectively. Among the patients with ischemic stroke, there were 69 cases of CAIS. Pancreas and adenocarcinoma were the most frequent types of primary tumor and histopathology. Patients with adenocarcinoma and those with cerebral infarctions in both bilateral anterior and posterior cerebral circulation areas showed higher D-dimer levels. Pancreatic cancer and high plasma D-dimer levels were associated with poor survival rate.

CONCLUSION

CAIS was seen more frequently in patients with pancreatic cancer and adenocarcinoma. Pancreatic cancer and high plasma D-dimer levels were potential factors of poor prognosis in patients with CAIS.

Crop filling: A pipeline for repairing memory clinic MRI corrupted by partial brain coverage

Data-driven solutions offer great promise for improving healthcare. However, standard clinical neuroimaging data is subject to real-world imaging artefacts that can render the data unusable for computational research and quantitative neuroradiology. T1 weighted structural MRI is used in dementia research to obtain volumetric measurements from cortical and subcortical brain regions. However, clinical radiologists often prioritise T2 weighted or FLAIR scans for visual assessment. As such, T1 weighted scans are often acquired but may not be a priority, resulting in artefacts such as partial brain coverage being systematically present in memory clinic data. Here we present "MRI Crop Filling", a pipeline to replace the missing T1 data with synthetic data generated from the T2 scan, making real-world clinical T1 data usable for computational research including the latest AI innovations. Our method consists of the following steps:•Register scans: T2 and (cropped) T1.•Synthesise a new T1 using an open source deep learning tool.•Replace missing (cropped) T1 data in original T1 scan and super-resolve to improve image quality.

Magnetic resonance imaging findings in patients with dropped head syndrome

BACKGROUND

Dropped head syndrome (DHS) is difficult to diagnose only by clinical examination. Although characteristic images on X-rays of DHS have been studied, changes in soft tissue of the disease have remained largely unknown. Magnetic resonance imaging (MRI) is useful for evaluating soft tissue, and we therefore performed this study with the purpose of investigating the characteristic signal changes of DHS on MRI by a comparison with those of cervical spondylosis.

METHODS

The study involved 35 patients diagnosed with DHS within 6 months after the onset and 32 patients with cervical spondylosis as control. The signal changes in cervical extensor muscles, interspinous tissue, anterior longitudinal ligament (ALL) and Modic change on MRI were analyzed.

RESULTS

Signal changes of cervical extensor muscles were 51.4% in DHS and 6.3% in the control group, those of interspinous tissue were 85.7% and 18.8%, and those of ALL were 80.0% and 21.9%, respectively, suggesting that the frequency of signal changes of cervical extensor muscles, interspinous tissue and ALL was significantly higher in the DHS group (p < 0.05). The presence of Modic change of acute phase (Modic type I) was also significantly higher in the DHS group than in the control group (p < 0.001).

CONCLUSION

MRI findings of DHS within 6 months after the onset presented the characteristic signal changes in cervical extensor muscles, interspinous tissue, ALL and Modic change. Evaluation of MRI signal changes is useful for an objective evaluation of DHS.

Cross-site prognosis prediction for nasopharyngeal carcinoma from incomplete multi-modal data

Accurate prognosis prediction for nasopharyngeal carcinoma based on magnetic resonance (MR) images assists in the guidance of treatment intensity, thus reducing the risk of recurrence and death. To reduce repeated labor and sufficiently explore domain knowledge, aggregating labeled/annotated data from external sites enables us to train an intelligent model for a clinical site with unlabeled data. However, this task suffers from the challenges of incomplete multi-modal examination data fusion and image data heterogeneity among sites. This paper proposes a cross-site survival analysis method for prognosis prediction of nasopharyngeal carcinoma from domain adaptation viewpoint. Utilizing a Cox model as the basic framework, our method equips it with a cross-attention based multi-modal fusion regularization. This regularization model effectively fuses the multi-modal information from multi-parametric MR images and clinical features onto a domain-adaptive space, despite the absence of some modalities. To enhance the feature discrimination, we also extend the contrastive learning technique to censored data cases. Compared with the conventional approaches which directly deploy a trained survival model in a new site, our method achieves superior prognosis prediction performance in cross-site validation experiments. These results highlight the key role of cross-site adaptability of our method and support its value in clinical practice.

Automatic segmentation of fat metaplasia on sacroiliac joint MRI using deep learning

OBJECTIVE

To develop a deep learning (DL) model for segmenting fat metaplasia (FM) on sacroiliac joint (SIJ) MRI and further develop a DL model for classifying axial spondyloarthritis (axSpA) and non-axSpA.

MATERIALS AND METHODS

This study retrospectively collected 706 patients with FM who underwent SIJ MRI from center 1 (462 axSpA and 186 non-axSpA) and center 2 (37 axSpA and 21 non-axSpA). Patients from center 1 were divided into the training, validation, and internal test sets (n = 455, 64, and 129). Patients from center 2 were used as the external test set. We developed a UNet-based model to segment FM. Based on segmentation results, a classification model was built to distinguish axSpA and non-axSpA. Dice Similarity Coefficients (DSC) and area under the curve (AUC) were used for model evaluation. Radiologists' performance without and with model assistance was compared to assess the clinical utility of the models.

RESULTS

Our segmentation model achieved satisfactory DSC of 81.86% ± 1.55% and 85.44% ± 6.09% on the internal cross-validation and external test sets. The classification model yielded AUCs of 0.876 (95% CI: 0.811-0.942) and 0.799 (95% CI: 0.696-0.902) on the internal and external test sets, respectively. With model assistance, segmentation performance was improved for the radiological resident (DSC, 75.70% vs. 82.87%, p < 0.05) and expert radiologist (DSC, 85.03% vs. 85.74%, p > 0.05).

CONCLUSIONS

DL is a novel method for automatic and accurate segmentation of FM on SIJ MRI and can effectively increase radiologist's performance, which might assist in improving diagnosis and progression of axSpA.

CRITICAL RELEVANCE STATEMENT

DL models allowed automatic and accurate segmentation of FM on sacroiliac joint MRI, which might facilitate quantitative analysis of FM and have the potential to improve diagnosis and prognosis of axSpA.

KEY POINTS

• Deep learning was used for automatic segmentation of fat metaplasia on MRI. • UNet-based models achieved automatic and accurate segmentation of fat metaplasia. • Automatic segmentation facilitates quantitative analysis of fat metaplasia to improve diagnosis and prognosis of axial spondyloarthritis.

A Deep-Learning Model for Diagnosing Fresh Vertebral Fractures on Magnetic Resonance Images

BACKGROUND

The accurate diagnosis of fresh vertebral fractures (VFs) was critical to optimizing treatment outcomes. Existing studies, however, demonstrated insufficient accuracy, sensitivity, and specificity in detecting fresh fractures using magnetic resonance imaging (MRI), and fall short in localizing the fracture sites.

METHODS

This prospective study comprised 716 patients with fresh VFs. We obtained 849 Short TI Inversion Recovery (STIR) image slices for training and validation of the AI model. The AI models employed were yolov7 and resnet50, to detect fresh VFs.

RESULTS

The AI model demonstrated a diagnostic accuracy of 97.6% for fresh VFs, with a sensitivity of 98% and a specificity of 97%. The performance of the model displayed a high degree of consistency when compared to the evaluations by spine surgeons. In the external testing dataset, the model exhibited a classification accuracy of 92.4%, a sensitivity of 93%, and a specificity of 92%.

CONCLUSIONS

Our findings highlighted the potential of AI in diagnosing fresh VFs, offering an accurate and efficient way to aid physicians with diagnosis and treatment decisions.

Quantitative analysis of molecular transport in the extracellular space using physics-informed neural network

The brain extracellular space (ECS), an irregular, extremely tortuous nanoscale space located between cells or between cells and blood vessels, is crucial for nerve cell survival. It plays a pivotal role in high-level brain functions such as memory, emotion, and sensation. However, the specific form of molecular transport within the ECS remain elusive. To address this challenge, this paper proposes a novel approach to quantitatively analyze the molecular transport within the ECS by solving an inverse problem derived from the advection-diffusion equation (ADE) using a physics-informed neural network (PINN). PINN provides a streamlined solution to the ADE without the need for intricate mathematical formulations or grid settings. Additionally, the optimization of PINN facilitates the automatic computation of the diffusion coefficient governing long-term molecule transport and the velocity of molecules driven by advection. Consequently, the proposed method allows for the quantitative analysis and identification of the specific pattern of molecular transport within the ECS through the calculation of the Péclet number. Experimental validation on two datasets of magnetic resonance images (MRIs) captured at different time points showcases the effectiveness of the proposed method. Notably, our simulations reveal identical molecular transport patterns between datasets representing rats with tracer injected into the same brain region. These findings highlight the potential of PINN as a promising tool for comprehensively exploring molecular transport within the ECS.

Comparative Performance of Susceptibility Map-Weighted MRI According to the Acquisition Planes in the Diagnosis of Neurodegenerative Parkinsonism

OBJECTIVE

To evaluate the diagnostic performance of susceptibility map-weighted imaging (SMwI) taken in different acquisition planes for discriminating patients with neurodegenerative parkinsonism from those without.

MATERIALS AND METHODS

This retrospective, observational, single-institution study enrolled consecutive patients who visited movement disorder clinics and underwent brain MRI and 18F-FP-CIT PET between September 2021 and December 2021. SMwI images were acquired in both the oblique (perpendicular to the midbrain) and the anterior commissure-posterior commissure (AC-PC) planes. Hyperintensity in the substantia nigra was determined by two neuroradiologists. 18F-FP-CIT PET was used as the reference standard. Inter-rater agreement was assessed using Cohen's kappa coefficient. The diagnostic performance of SMwI in the two planes was analyzed separately for the right and left substantia nigra. Multivariable logistic regression analysis with generalized estimating equations was applied to compare the diagnostic performance of the two planes.

RESULTS

In total, 194 patients were included, of whom 105 and 103 had positive results on 18F-FP-CIT PET in the left and right substantia nigra, respectively. Good inter-rater agreement in the oblique (κ = 0.772/0.658 for left/right) and AC-PC planes (0.730/0.741 for left/right) was confirmed. The pooled sensitivities for two readers were 86.4% (178/206, left) and 83.3% (175/210, right) in the oblique plane and 87.4% (180/206, left) and 87.6% (184/210, right) in the AC-PC plane. The pooled specificities for two readers were 83.5% (152/182, left) and 82.0% (146/178, right) in the oblique plane, and 83.5% (152/182, left) and 86.0% (153/178, right) in the AC-PC plane. There were no significant differences in the diagnostic performance between the two planes (P > 0.05).

CONCLUSION

There are no significant difference in the diagnostic performance of SMwI performed in the oblique and AC-PC plane in discriminating patients with parkinsonism from those without. This finding affirms that each institution may choose the imaging plane for SMwI according to their clinical settings.

Inferior temporal quadrantanopia associated with pituitary adenomas and a potential mechanism of excessive optic nerve bending

Background

Pituitary adenomas show typical visual field defects that begin superiorly and progress inferiorly. The cause of atypical visual field defects that start inferiorly remains unclear. This study aimed to understand this phenomenon using magnetic resonance imaging (MRI).

Methods

A total of 220 patients with pituitary adenomas underwent a visual field assessment of both eyes. Preoperative visual fields were assessed and classified into two types: superior quadrantanopia (typical) and inferior quadrantanopia (atypical). Several parameters related to tumor characteristics and optic nerve compression were evaluated using MRI.

Results

Of the 440 eyes examined, 174 (39.5%) had visual field defects. Of these, 28 (16.1%) had typical and 11 (6.3%) had atypical visual field defects. Patient age, tumor size, degree of cavernous sinus invasion, tumor pathology, and intratumor bleeding were similar between the two groups. The angle formed by the optic nerve in the optic canal and in the intracranial subarachnoid space at the exit of the optic canal (degree of optic nerve bending) was significantly larger in the atypical group than in the typical group (42.6° vs. 23.9°, P = 0.046).

Conclusion

In some pituitary adenomas, visual field defects begin inferiorly. This may be caused by optic nerve compression on the superior surface by the bony margin of the optic canal exit. Therefore, pituitary adenomas should be considered in patients with atypical visual field defects.

Examination of knee extensor and valgus moment arms of the patellar tendon in older individuals with and without knee osteoarthritis

BACKGROUND

Joint moment arm is a major element that determines joint torque. This study aimed to investigate factors associated with knee extensor and valgus moment arms of the patellar tendon in older individuals with and without knee osteoarthritis.

METHODS

Thirty-six participants with knee osteoarthritis (mean age, 78.1 ± 6.0 years) and 43 healthy controls (mean age, 73.0 ± 6.3 years) were analyzed. Magnetic resonance images (MRI) from the knee joint and thigh were acquired using a 3.0 T MRI scanner. The three-dimensional moment arm was defined as the distance between the contact point of the tibiofemoral joint and the patellar tendon line. The three-dimensional moment arm was decomposed into sagittal and coronal components, which were calculated as knee extensor and valgus moment arms, respectively. Quadriceps muscle volume, epicondylar width, bisect offset, Insall-Salvati ratio, and Kellgren-Lawrence grade were assessed. Multiple regression analyses were performed in the healthy control and knee osteoarthritis groups, with knee extensor and valgus moment arms as dependent variables.

FINDINGS

Knee extensor moment arm was significantly associated with epicondylar width and the Insall-Salvati ratio in the healthy control group and with Kellgren-Lawrence grade, epicondylar width, and quadriceps muscle volume in the knee osteoarthritis group. Valgus knee moment arm was significantly associated with bisect offset in both the groups.

INTERPRETATION

Knee size, osteoarthritis severity, and quadriceps muscle volume affect the knee extensor moment arm in knee osteoarthritis, whereas lateral patellar displacement affects the valgus knee moment arms in older individuals with and without knee osteoarthritis.

MRI Manifestations of Breast Cancer Stroma and their Role in Predicting Molecular Subtype: A Case-control Study

OBJECTIVE

This study explored whether breast MRI manifestations could be used to predict the stroma distribution of breast cancer (BC) and the role of tumor stroma-based MRI manifestations in molecular subtype prediction.

METHODS

57 patients with pathologically confirmed invasive BC (non-special type) who had lumpy BC on MRI within one week before surgery were retrospectively collected in the study. Stroma distributions were classified according to their characteristics in the pathological sections. The stromal distribution patterns among molecular subtypes were compared with the MRI manifestations of BC with different stroma distribution types (SDTs).

RESULTS

SDTs were significantly different and depended on the BC hormone receptor (HR) (P<0.001). There were also significant differences among five SDTs on T2WI, ADC map, internal delayed enhanced features (IDEF), marginal delayed enhanced features (MDEF), and time signal intensity (TSI) curves. Spiculated margin and the absence of type-I TSI were independent predictors for BC with star grid type stroma. The appearance frequency of hypo-intensity on T2WI in HR- BCs was significantly lower (P=0.043) than in HR+ BCs. Star grid stroma and spiculated margin were key factors in predicting HR+ BCs, and the AUC was 0.927 (95% CI: 0.867-0.987).

CONCLUSION

Breast MRI can be used to predict BC's stromal distribution and molecular subtypes.

[Neurological evolution in traumatic spinal cord injury according to the size of the intraparenchymal hemorrhage]

INTRODUCTION AND OBJECTIVES

The presence of spinal cord hemorrhage is considered as a poor prognostic factor in traumatic spinal cord injury (SCI). However, it has been suggested in published works that the prognosis of small hemorrhages is not so negative. The aim of this paper is to assess the neurological evolution in individuals with intraparenchymal hemorrhage according to its size.

MATERIAL AND METHODS

Retrospective observational study. Selected all the patients admitted for acute traumatic SCI between 2010 and 2018 with early magnetic resonance study and spinal cord hemorrhage. Two groups were established depending on the size of the bleeding: microhemorrhages (less than 4mm) and macrohemorrhages (greater than 4mm). The neurological examination at admission and discharge was compared according to the AIS grade and the motor score (MS).

RESULTS

Forty-six cases collected, 17 microhemorrhages and 29 macrohemorrhages. 70.6% of the microhemorrhages were AIS A while among macrohemorrhages the percentage was 89.6%. At the time of discharge, an improvement in the AIS grade was observed in 40.0% of the microhemorrhages compared to 4.0% of the macrohemorrhages (P=.008). Initial MS was similar, 45.2±22.2 in the microhemorrhages and 40.9±20.4 in the macrohemorrhages (P=.459), but at discharge it was higher in the first group: 60.4±20.5 for 42.7±22.8 (P=.033). Eight patients (17.4%) died during admission.

CONCLUSIONS

There is a relationship between the size of the intraparenchymal hemorrhage and the neurological prognosis of SCI, with hemorrhages smaller than 4mm presenting a better evolution.

Central Nervous System Dissemination of Solitary Sporadic Supratentorial Hemangioblastoma: A Case Report and Literature Review

We report a patient with whole neuroaxis dissemination of a sporadic supratentorial hemangioblastoma (HB) for more than 15 years. A 68-year-old female patient presented with severe radiating pain in the right leg. Gadolinium-enhanced lumbar spine MRI showed an intradural mass (2.5 cm in diameter) at the L4 level. The patient had been severely disabled for 22 years after a previous intraventricular brain tumor resection. At that time, the diagnosis was angioblastic meningioma, which was thought to be incorrect. At 14 years after the brain surgery, gamma knife radiosurgery was performed three times for newly developed or recurred supratentorial and infratentorial tumors in the cerebrospinal fluid pathway. The patient underwent lumbar spinal surgery, and a gross total removal of the mass was performed, which confirmed the histopathological diagnosis of HB. We reexamined the old histopathological specimen of the intraventricular tumor from 20 years ago and changed the diagnosis from angioblastic meningioma to supratentorial HB. Six months after spinal surgery, the patient underwent a second spinal surgery and brain surgery, and the histopathological diagnosis was HB following both surgeries, which was the same following the first spinal surgery. Here, we report a sporadic supratentorial HB patient who showed cranial and spinal disseminations for more than two decades along with a literature review.

A 3-hour time interval may not be sufficient for delayed enhancement magnetic resonance imaging with intravenous gadoteridol injection based on 3d-real IR sequence of the inner ear in Meniere's disease patient

BACKGROUND

Magnetic resonance imaging (MRI) can be applied to visualize endolymphatic hydrops (EH).

AIMS/OBJECTIVES

To explore whether a 3-h time interval was feasible for clinical practice.

MATERIALS AND METHODS

We prospectively enrolled 15 patients with unilateral Meniere's disease, each of whom underwent delayed enhancement MRI scan of the inner ear after intravenous gadoteridol injection at a 3-h interval. The ears of these patients were divided into two groups (group A: the affected ears; group B: the unaffected ears). Among the two groups, the signal intensity in perilymphatic area of the basal turn of cochlea, the results of visual evaluations in the vestibule, cochlea and semicircular canal and the detection results of EH were compared.

RESULTS

Regarding the signal intensity, a difference was found between group A and group B (p = .016). Besides, no difference was found between the visual evaluations in the vestibule, cochlea and semicircular canal of the two groups. Regarding the detection results of EH, group A (6 vestibules were undiagnosable; 8 cochleae were undiagnosable); group B (9 vestibules were undiagnosable; 10 cochleae were undiagnosable).

CONCLUSIONS AND SIGNIFICANCE

In the clinical application of gadoteridol for the inner ear, 3-h delayed MR imaging may not be sufficient.

Automatic detection, segmentation, and classification of primary bone tumors and bone infections using an ensemble multi-task deep learning framework on multi-parametric MRIs: a multi-center study

OBJECTIVES

To develop an ensemble multi-task deep learning (DL) framework for automatic and simultaneous detection, segmentation, and classification of primary bone tumors (PBTs) and bone infections based on multi-parametric MRI from multi-center.

METHODS

This retrospective study divided 749 patients with PBTs or bone infections from two hospitals into a training set (N = 557), an internal validation set (N = 139), and an external validation set (N = 53). The ensemble framework was constructed using T1-weighted image (T1WI), T2-weighted image (T2WI), and clinical characteristics for binary (PBTs/bone infections) and three-category (benign/intermediate/malignant PBTs) classification. The detection and segmentation performances were evaluated using Intersection over Union (IoU) and Dice score. The classification performance was evaluated using the receiver operating characteristic (ROC) curve and compared with radiologist interpretations.

RESULT

On the external validation set, the single T1WI-based and T2WI-based multi-task models obtained IoUs of 0.71 ± 0.25/0.65 ± 0.30 for detection and Dice scores of 0.75 ± 0.26/0.70 ± 0.33 for segmentation. The framework achieved AUCs of 0.959 (95%CI, 0.955-1.000)/0.900 (95%CI, 0.773-0.100) and accuracies of 90.6% (95%CI, 79.7-95.9%)/78.3% (95%CI, 58.1-90.3%) for the binary/three-category classification. Meanwhile, for the three-category classification, the performance of the framework was superior to that of three junior radiologists (accuracy: 65.2%, 69.6%, and 69.6%, respectively) and comparable to that of two senior radiologists (accuracy: 78.3% and 78.3%).

CONCLUSION

The MRI-based ensemble multi-task framework shows promising performance in automatically and simultaneously detecting, segmenting, and classifying PBTs and bone infections, which was preferable to junior radiologists.

CLINICAL RELEVANCE STATEMENT

Compared with junior radiologists, the ensemble multi-task deep learning framework effectively improves differential diagnosis for patients with primary bone tumors or bone infections. This finding may help physicians make treatment decisions and enable timely treatment of patients.

KEY POINTS

• The ensemble framework fusing multi-parametric MRI and clinical characteristics effectively improves the classification ability of single-modality models. • The ensemble multi-task deep learning framework performed well in detecting, segmenting, and classifying primary bone tumors and bone infections. • The ensemble framework achieves an optimal classification performance superior to junior radiologists' interpretations, assisting the clinical differential diagnosis of primary bone tumors and bone infections.

RTAU-Net: A novel 3D rectal tumor segmentation model based on dual path fusion and attentional guidance

BACKGROUND AND OBJECTIVE

According to the Global Cancer Statistics 2020, colorectal cancer has the third-highest diagnosis rate (10.0 %) and the second-highest mortality rate (9.4 %) among the 36 types. Rectal cancer accounts for a large proportion of colorectal cancer. The size and shape of the rectal tumor can directly affect the diagnosis and treatment by doctors. The existing rectal tumor segmentation methods are based on two-dimensional slices, which cannot analyze a patient's tumor as a whole and lose the correlation between slices of MRI image, so the practical application value is not high.

METHODS

In this paper, a three-dimensional rectal tumor segmentation model is proposed. Firstly, image preprocessing is performed to reduce the effect caused by the unbalanced proportion of background region and target region, and improve the quality of the image. Secondly, a dual-path fusion network is designed to extract both global features and local detail features of rectal tumors. The network includes two encoders, a residual encoder for enhancing the spatial detail information and feature representation of the tumor and a transformer encoder for extracting global contour information of the tumor. In the decoding stage, we merge the information extracted from the dual paths and decode them. In addition, for the problem of the complex morphology and different sizes of rectal tumors, a multi-scale fusion channel attention mechanism is designed, which can capture important contextual information of different scales. Finally, visualize the 3D rectal tumor segmentation results.

RESULTS

The RTAU-Net is evaluated on the data set provided by Shanxi Provincial Cancer Hospital and Xinhua Hospital. The experimental results showed that the Dice of tumor segmentation reached 0.7978 and 0.6792, respectively, which improved by 2.78 % and 7.02 % compared with suboptimal model.

CONCLUSIONS

Although the morphology of rectal tumors varies, RTAU-Net can precisely localize rectal tumors and learn the contour and details of tumors, which can relieve physicians' workload and improve diagnostic accuracy.

Comparative analysis of discoid lateral meniscus size: a distinction between symptomatic and asymptomatic cases

PURPOSE

This study evaluated the differences in meniscal sizes and occupancy between symptomatic and asymptomatic patients diagnosed with discoid lateral meniscus (DLM) using magnetic resonance imaging (MRI) to understand how these variations relate to the presence of symptoms and the patients' age.

METHODS

A retrospective review of 98 patients with DLM was conducted, excluding those with meniscal displacement. Both the width and extrusion of DLM and the percentage of the meniscus to the tibia were measured using mid-coronal and mid-sagittal MRI and compared between symptomatic and asymptomatic DLM groups. The relationships among each parameter, meniscal size, and patient age were evaluated. Symptomatic cases were divided into those with and without horizontal tears on MRI to compare the differences in meniscal morphology.

RESULTS

A total of 92 knees from 74 patients were included. Sixty-one knees required surgical intervention for symptomatic DLM, while 31 were asymptomatic and included the contralateral side of symptomatic knees. The symptomatic group exhibited larger morphological variations than the asymptomatic group. Moreover, the sagittal meniscal ratio reduced with age in the asymptomatic group (r = - 0.54, p = 0.002) but remained constant in the symptomatic group. The symptomatic cases with horizontal tears demonstrated larger meniscal dimensions and smaller posterior capsule distances than those without tears.

CONCLUSION

Symptomatic patients with DLM had larger knee morphological changes than asymptomatic ones. Age affected the meniscal occupancy in the sagittal plane only in asymptomatic patients.

LEVEL OF EVIDENCE

III.

Intraneural lipoma of the ulnar nerve: A rare case report and review

Intraneural lipoma of the ulnar nerve is a rare peripheral nerve tumor in an uncommon location. Although its benign course, it can cause disabling symptoms such as pain, diminished sensation or paraesthesia, tenderness, and occasionally even loss of strength. We present the case of a middle age woman with insidious paresthesias and swelling of the hypothenar eminence of the left hand for over 1 year. A hand and wrist radiograph first confirmed a focal soft tissue mass with fat density and excluded potential bone lesions. Then, an ultrasound was performed that showed a slightly hyperechoic mass with a fibrillated pattern in contiguity with the proximal aspect of the ulnar nerve. The morphological arrangement of this mass, its location along ulnar nerve distribution and the main signal characteristics in magnetic resonance imaging such as hyperintensity in T1- and T2-weighted images and hypointensity in fat saturation sequences inferred an intraneural lipoma. Due to the progressive symptoms, elective resection of the lesion was performed with full recovery of the symptoms.

Brain radial enhancement pattern in patients with negative glial fibrillary acidic protein-IgG: A cases series study

BACKGROUND AND OBJECTIVES

Brain radial enhancement pattern on magnetic resonance imaging (MRI) has been identified as typical lesions in autoimmune glial fibrillary acidic protein astrocytopathy (GFAP-A). However, the authors encountered several patients without GFAP-IgG showing that such specific imaging. In the present study, we reported the clinical pictures of 5 GFAP-IgG-negative patients with GFAP-A specific imaging pattern.

METHODS

Data was retrospectively obtained from June 2013 through April 2023, and five GFAP-IgG-negative patients with valid data were recruited. Clinical information was either obtained by the investigators or retrieved from the referring clinicians and included prodromal symptoms, neurologic manifestations, comorbidities, results of ancillary studies.

RESULTS

Altogether five GFAP-IgG-negative patients with "meningoencephalitis/encephalitis" manifestations and brain radial perivascular enhancement were confirmed. One patient had peripheral lymphoma. Four patients had other autoimmune antibody in serum and/or cerebrospinal fluid, of which one patient had positive aquaporin IgG. Clinical features of the five patients included headache, fever, epilepsy and abnormal behavioral symptoms. MRI of patients revealed radial perivascular gadolinium enhancement extending from the lateral ventricles to the white matter suggestive of autoimmune GFAP-A.

CONCLUSION

GFAP-A-like disorders with radial perivascular enhancement could be found in GFAP-IgG-negative patients with or without neoplasm, which could provide new insight into the differential diagnosis of GFAP-A.

Multimodal hybrid convolutional neural network based brain tumor grade classification

An abnormal growth or fatty mass of cells in the brain is called a tumor. They can be either healthy (normal) or become cancerous, depending on the structure of their cells. This can result in increased pressure within the cranium, potentially causing damage to the brain or even death. As a result, diagnostic procedures such as computed tomography, magnetic resonance imaging, and positron emission tomography, as well as blood and urine tests, are used to identify brain tumors. However, these methods can be labor-intensive and sometimes yield inaccurate results. Instead of these time-consuming methods, deep learning models are employed because they are less time-consuming, require less expensive equipment, produce more accurate results, and are easy to set up. In this study, we propose a method based on transfer learning, utilizing the pre-trained VGG-19 model. This approach has been enhanced by applying a customized convolutional neural network framework and combining it with pre-processing methods, including normalization and data augmentation. For training and testing, our proposed model used 80% and 20% of the images from the dataset, respectively. Our proposed method achieved remarkable success, with an accuracy rate of 99.43%, a sensitivity of 98.73%, and a specificity of 97.21%. The dataset, sourced from Kaggle for training purposes, consists of 407 images, including 257 depicting brain tumors and 150 without tumors. These models could be utilized to develop clinically useful solutions for identifying brain tumors in CT images based on these outcomes.

Relation of lumbar intervertebral disc height and severity of disc degeneration based on Pfirrmann scores

Background

Disc height (DH) change is considered one of the most critical factors in assessing intervertebral disc degeneration (IVD). Pfirrmann et al. developed a scoring system for disc degeneration evaluation based on changes in DH in magnetic resonance imaging (MRI). While the relationship between DH measurements and Pfirrmann scores for disc degeneration has been explored, the validity of different DH measuring techniques or their connection with disc degeneration is yet uncertain. The present study investigates intra-rater and inter-rater agreement and reliability of different DH measurement methods on MRI and evaluates the relationship between different DH measurement methods and Pfirrmann scores of IVD degeneration, as well as between different Pfirrmann scores and clinical outcomes.

Methods

Adult patients with MRI scans of the lumbar spine were recruited. Eight DH measuring techniques were tested for intra-rater and inter-rater agreement and reliability. Bland and Altman's Limits of Agreement (LOA) was used to evaluate intra-rater and inter-rater agreements. Intra-rater and inter-rater reliability were evaluated using intra-class correlations (ICC) with 95 % confidence intervals (95 % CI). The association between DH and Pfirrmann scores was examined using one-way ANOVA.

Results

Excellent intra-rater reliability was reported for 332 participants on DH (ranging from 0.912 (0.901, 0.923) to 0.973 (0.964, 0.981) and from 0.902 (0.892, 0.915) to 0.975 (0.962, 0.985) by two independent raters). All measuring methods had high intra-rater agreement, except for methods 4 and 5. All methods had good-to-excellent of inter-rater reliability on DH (ICCs ranging from 0.812 (0.795, 0.828) to 0.995 (0.994, 0.995)) except for the posterior disc material length of method 5 (ICC 0.740 (0.718, 0.761)). Methods 1 to 6 for evaluating DH in patients with spondylolisthesis had poor inter-rater reliability. The IVD levels with grades IV and V in Pfirrmann scores had significantly lower DH than the IVD levels with grades I to III in Pfirrmann scores. IVD levels with grades IV and V in Pfirrmann scores had significantly higher VAS and ODI than IVD levels with grades I in Pfirrmann scores.

Conclusion

A good-to-excellent intra-rater and inter-rater reliability was achieved on most DH measuring methods on MRI following a standardized and structured protocol. However, small anatomical structures and different tissue borders could influence measurements. Additionally, DH can differentiate between grade IV and V Pfirrmann scores, and severe IVD degeneration (IV and V Pfirrmann) is linked to clinical outcomes.

Differential diagnosis of atypical and anaplastic meningiomas based on conventional MRI features and ADC histogram parameters using a logistic regression model nomogram

The purpose of the study was to determine the value of a logistic regression model nomogram based on conventional magnetic resonance imaging (MRI) features and apparent diffusion coefficient (ADC) histogram parameters in differentiating atypical meningioma (AtM) from anaplastic meningioma (AnM). Clinical and imaging data of 34 AtM and 21 AnM diagnosed by histopathology were retrospectively analyzed. The whole tumor delineation along the tumor edge on ADC images and ADC histogram parameters were automatically generated and comparisons between the two groups using the independent samples t test or Mann-Whitney U test. Univariate and multivariate logistic regression analyses were used to construct the nomogram of the AtM and AnM prediction model, and the model's predictive efficacy was evaluated using calibration and decision curves. Significant differences in the mean, enhancement, perc.01%, and edema were noted between the AtM and AnM groups (P < 0.05). Age, sex, location, necrosis, shape, max-D, variance, skewness, kurtosis, perc.10%, perc.50%, perc.90%, and perc.99% exhibited no significant differences (P > 0.05). The mean and enhancement were independent risk factors for distinguishing AtM from AnM. The area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the nomogram were 0.871 (0.753-0.946), 80.0%, 81.0%, 79.4%, 70.8%, and 87.1%, respectively. The calibration curve demonstrated that the model's probability to predict AtM and AnM was in favorable agreement with the actual probability, and the decision curve revealed that the prediction model possessed satisfactory clinical availability. A logistic regression model nomogram based on conventional MRI features and ADC histogram parameters is potentially useful as an auxiliary tool for the preoperative differential diagnosis of AtM and AnM.

Dynamic contrast-enhanced MRI radiomics model predicts epidermal growth factor receptor amplification in glioblastoma, IDH-wildtype

PURPOSE

To develop and validate a dynamic contrast-enhanced (DCE) MRI-based radiomics model to predict epidermal growth factor receptor (EGFR) amplification in patients with glioblastoma, isocitrate dehydrogenase (IDH) wildtype.

METHODS

Patients with pathologically confirmed glioblastoma, IDH wildtype, from January 2015 to December 2020, with an EGFR amplification status, were included. Patients who did not undergo DCE or conventional brain MRI were excluded. Patients were categorized into training and test sets by a ratio of 7:3. DCE MRI data were used to generate volume transfer constant (Ktrans) and extracellular volume fraction (Ve) maps. Ktrans, Ve, and conventional MRI were then used to extract the radiomics features, from which the prediction models for EGFR amplification status were developed and validated.

RESULTS

A total of 190 patients (mean age, 59.9; male, 55.3%), divided into training (n = 133) and test (n = 57) sets, were enrolled. In the test set, the radiomics model using the Ktrans map exhibited the highest area under the receiver operating characteristic curve (AUROC), 0.80 (95% confidence interval [CI], 0.65-0.95). The AUROC for the Ve map-based and conventional MRI-based models were 0.74 (95% CI, 0.58-0.90) and 0.76 (95% CI, 0.61-0.91).

CONCLUSION

The DCE MRI-based radiomics model that predicts EGFR amplification in glioblastoma, IDH wildtype, was developed and validated. The MRI-based radiomics model using the Ktrans map has higher AUROC than conventional MRI.

Accuracy of skull stripping in a single-contrast convolutional neural network model using eight-contrast magnetic resonance images

In automated analyses of brain morphometry, skull stripping or brain extraction is a critical first step because it provides accurate spatial registration and signal-intensity normalization. Therefore, it is imperative to develop an ideal skull-stripping method in the field of brain image analysis. Previous reports have shown that convolutional neural network (CNN) method is better at skull stripping than non-CNN methods. We aimed to evaluate the accuracy of skull stripping in a single-contrast CNN model using eight-contrast magnetic resonance (MR) images. A total of 12 healthy participants and 12 patients with a clinical diagnosis of unilateral Sturge-Weber syndrome were included in our study. A 3-T MR imaging system and QRAPMASTER were used for data acquisition. We obtained eight-contrast images produced by post-processing T1, T2, and proton density (PD) maps. To evaluate the accuracy of skull stripping in our CNN method, gold-standard intracranial volume (ICVG) masks were used to train the CNN model. The ICVG masks were defined by experts using manual tracing. The accuracy of the intracranial volume obtained from the single-contrast CNN model (ICVE) was evaluated using the Dice similarity coefficient [= 2(ICVE ⋂ ICVG)/(ICVE + ICVG)]. Our study showed significantly higher accuracy in the PD-weighted image (WI), phase-sensitive inversion recovery (PSIR), and PD-short tau inversion recovery (STIR) compared to the other three contrast images (T1-WI, T2-fluid-attenuated inversion recovery [FLAIR], and T1-FLAIR). In conclusion, PD-WI, PSIR, and PD-STIR should be used instead of T1-WI for skull stripping in the CNN models.

Utility of ultrasonography in the diagnosis of lumbar spondylolysis in adolescent patients

BACKGROUND

This study aims to investigate the utility of the Doppler effect on ultrasonography for the diagnosis of very early- and early-stage lumbar spondylolysis in adolescent patients.

METHODS

In total, 76 adolescent patients with acute and subacute low back pain were prospectively enrolled, with 46 having lumbar spondylolysis and the remaining 30 having low back pain without spondylolysis. MRI and/or computed tomograms scans revealed very early- and early-stage lumbar spondylolysis. Furthermore, positive Doppler findings in ultrasonography around the area from the facet joint to the laminae were investigated.

RESULTS

There were no significant differences in age (p > 0.99) and body mass index (p = 0.11) between cases with and without spondylolysis. Very early- and early-stage spondylolysis were observed in 27.6% and 72.4% of patients, respectively. Positive power Doppler was 91.3% and 33.3% in cases with and without spondylolysis, respectively, which was significantly higher in spondylolysis (p < 0.001). The sensitivity and specificity of this positive power Doppler were 91.4% and 66.7%, respectively. Furthermore, the rate of positive power Doppler was significantly higher in early-stage spondylolysis (p = 0.02), with 75.0% and 97.6% sensitivity in very early- and early-stage spondylolysis, respectively.

CONCLUSIONS

A positive Doppler effect on ultrasonography is effective for screening very early- and early-stage spondylolysis in adolescent patients in an outpatient clinic.

A 3D Radiomics-Based Artificial Neural Network Model for Benign Versus Malignant Vertebral Compression Fracture Classification in MRI

To train an artificial neural network model using 3D radiomic features to differentiate benign from malignant vertebral compression fractures (VCFs) on MRI. This retrospective study analyzed sagittal T1-weighted lumbar spine MRIs from 91 patients (average age of 64.24 ± 11.75 years) diagnosed with benign or malignant VCFs from 2010 to 2019, of them 47 (51.6%) had benign VCFs and 44 (48.4%) had malignant VCFs. The lumbar fractures were three-dimensionally segmented and had their radiomic features extracted and selected with the wrapper method. The training set consisted of 100 fractured vertebral bodies from 61 patients (average age of 63.2 ± 12.5 years), and the test set was comprised of 30 fractured vertebral bodies from 30 patients (average age of 66.4 ± 9.9 years). Classification was performed with the multilayer perceptron neural network with a back-propagation algorithm. To validate the model, the tenfold cross-validation technique and an independent test set (holdout) were used. The performance of the model was evaluated using the average with a 95% confidence interval for the ROC AUC, accuracy, sensitivity, and specificity (considering the threshold = 0.5). In the internal validation test, the best model reached a ROC AUC of 0.98, an accuracy of 95% (95/100), a sensitivity of 93.5% (43/46), and specificity of 96.3% (52/54). In the validation with independent test set, the model achieved a ROC AUC of 0.97, an accuracy of 93.3% (28/30), a sensitivity of 93.3% (14/15), and a specificity of 93.3% (14/15). The model proposed in this study using radiomic features could differentiate benign from malignant vertebral compression fractures with excellent performance and is promising as an aid to radiologists in the characterization of VCFs.

Prediction of Tumor Progression During Neoadjuvant Chemotherapy and Survival Outcome in Patients With Triple-Negative Breast Cancer

OBJECTIVE

To investigate the association of clinical, pathologic, and magnetic resonance imaging (MRI) variables with progressive disease (PD) during neoadjuvant chemotherapy (NAC) and distant metastasis-free survival (DMFS) in patients with triple-negative breast cancer (TNBC).

MATERIALS AND METHODS

This single-center retrospective study included 252 women with TNBC who underwent NAC between 2010 and 2019. Clinical, pathologic, and treatment data were collected. Two radiologists analyzed the pre-NAC MRI. After random allocation to the development and validation sets in a 2:1 ratio, we developed models to predict PD and DMFS using logistic regression and Cox proportional hazard regression, respectively, and validated them.

RESULTS

Among the 252 patients (age, 48.3 ± 10.7 years; 168 in the development set; 84 in the validation set), PD was occurred in 17 patients and 9 patients in the development and validation sets, respectively. In the clinical-pathologic-MRI model, the metaplastic histology (odds ratio [OR], 8.0; P = 0.032), Ki-67 index (OR, 1.02; P = 0.044), and subcutaneous edema (OR, 30.6; P = 0.004) were independently associated with PD in the development set. The clinical-pathologic-MRI model showed a higher area under the receiver-operating characteristic curve (AUC) than the clinical-pathologic model (AUC: 0.69 vs. 0.54; P = 0.017) for predicting PD in the validation set. Distant metastases occurred in 49 patients and 18 patients in the development and validation sets, respectively. Residual disease in both the breast and lymph nodes (hazard ratio [HR], 6.0; P = 0.005) and the presence of lymphovascular invasion (HR, 3.3; P < 0.001) were independently associated with DMFS. The model consisting of these pathologic variables showed a Harrell's C-index of 0.86 in the validation set.

CONCLUSION

The clinical-pathologic-MRI model, which considered subcutaneous edema observed using MRI, performed better than the clinical-pathologic model for predicting PD. However, MRI did not independently contribute to the prediction of DMFS.

Diagnosis of cerebral microbleed via VGG and extreme learning machine trained by Gaussian map bat algorithm

Cerebral microbleed (CMB) is a serious public health concern. It is associated with dementia, which can be detected with brain magnetic resonance image (MRI). CMBs often appear as tiny round dots on MRIs, and they can be spotted anywhere over brain. Therefore, manual inspection is tedious and lengthy, and the results are often short in reproducible. In this paper, a novel automatic CMB diagnosis method was proposed based on deep learning and optimization algorithms, which used the brain MRI as the input and output the diagnosis results as CMB and non-CMB. Firstly, sliding window processing was employed to generate the dataset from brain MRIs. Then, a pre-trained VGG was employed to obtain the image features from the dataset. Finally, an ELM was trained by Gaussian-map bat algorithm (GBA) for identification. Results showed that the proposed method VGG-ELM-GBA provided better generalization performance than several state-of-the-art approaches.

[Translated article] Relationship between low lying peroneus brevis muscle belly and peroneal tendons dislocation

INTRODUCTION

Peroneal tendon pathologies are an important cause of pain in the lateral aspect of the ankle. It has been proposed in the literature that low lying peroneus brevis muscle belly occupies more space in the retromalleolar groove and could cause laxity of the superior retinaculum which would promote tendon dislocation, tenosynovitis or ruptures. The objective of the study is to characterise the population with low lying peroneus brevis muscle belly and determine the association between the low lying peroneus brevis muscle belly found on magnetic resonance imaging and clinical peroneal tendon dislocation.

METHODS

A case-control study was developed with a sample of 103 patients. The cases were patients with low lying peroneus brevis muscle belly and peroneal dislocation and the controls were patients with normal implantation of the peroneus brevis muscle and peroneal tendon dislocation.

RESULTS

The prevalence of clinical peroneal dislocation in patients with low implantation of the peroneal brevis muscle belly was 7.64%, and the prevalence of clinical peroneal dislocation in patients with normal implantation of the peroneus brevis muscle belly was 8.88%. The OR was 0.85 (CI 0.09-7.44, p=0.88).

DISCUSSION

Our findings indicate that there is no statistically significant relationship between low lying peroneus brevis muscle belly and clinical dislocation of the peroneal tendons.

[Multiresolution discrete optimization registration method of ultrasound and magnetic resonance images based on key points]

The registration of preoperative magnetic resonance (MR) images and intraoperative ultrasound (US) images is very important in the planning of brain tumor surgery and during surgery. Considering that the two-modality images have different intensity range and resolution, and the US images are degraded by lots of speckle noises, a self-similarity context (SSC) descriptor based on local neighborhood information was adopted to define the similarity measure. The ultrasound images were considered as the reference, the corners were extracted as the key points using three-dimensional differential operators, and the dense displacement sampling discrete optimization algorithm was adopted for registration. The whole registration process was divided into two stages including the affine registration and the elastic registration. In the affine registration stage, the image was decomposed using multi-resolution scheme, and in the elastic registration stage, the displacement vectors of key points were regularized using the minimum convolution and mean field reasoning strategies. The registration experiment was performed on the preoperative MR images and intraoperative US images of 22 patients. The overall error after affine registration was (1.57 ± 0.30) mm, and the average computation time of each pair of images was only 1.36 s; while the overall error after elastic registration was further reduced to (1.40 ± 0.28) mm, and the average registration time was 1.53 s. The experimental results show that the proposed method has prominent registration accuracy and high computational efficiency.

Accurate tumor segmentation and treatment outcome prediction with DeepTOP

BACKGROUND

Accurate outcome prediction prior to treatment can facilitate trial design and clinical decision making to achieve better treatment outcome.

METHOD

We developed the DeepTOP tool with deep learning approach for region-of-interest segmentation and clinical outcome prediction using magnetic resonance imaging (MRI). DeepTOP was constructed with an automatic pipeline from tumor segmentation to outcome prediction. In DeepTOP, the segmentation model used U-Net with a codec structure, and the prediction model was built with a three-layer convolutional neural network. In addition, the weight distribution algorithm was developed and applied in the prediction model to optimize the performance of DeepTOP.

RESULTS

A total of 1889 MRI slices from 99 patients in the phase III multicenter randomized clinical trial (NCT01211210) on neoadjuvant treatment for rectal cancer was used to train and validate DeepTOP. We systematically optimized and validated DeepTOP with multiple devised pipelines in the clinical trial, demonstrating a better performance than other competitive algorithms in accurate tumor segmentation (Dice coefficient: 0.79; IoU: 0.75; slice-specific sensitivity: 0.98) and predicting pathological complete response to chemo/radiotherapy (accuracy: 0.789; specificity: 0.725; and sensitivity: 0.812). DeepTOP is a deep learning tool that could avoid manual labeling and feature extraction and realize automatic tumor segmentation and treatment outcome prediction by using the original MRI images.

CONCLUSION

DeepTOP is open to provide a tractable framework for the development of other segmentation and predicting tools in clinical settings. DeepTOP-based tumor assessment can provide a reference for clinical decision making and facilitate imaging marker-driven trial design.

A feasibility study of reduced full-of-view synthetic high-b-value diffusion-weighted imaging in uterine tumors

OBJECTIVES

This study aimed to evaluate the feasibility of reduced full-of-view synthetic high-b value diffusion-weighted images (rFOV-syDWIs) in the clinical application of cervical cancer based on image quality and diagnostic efficacy.

METHODS

We retrospectively evaluated the data of 35 patients with cervical cancer and 35 healthy volunteers from May to November 2021. All patients and volunteers underwent rFOV-DWI scans, including a 13b-protocol: b = 0, 25, 50, 75, 100, 150, 200, 400, 600, 800, 1000, 1200, and 1500 s/mm² and a 5b-protocol: b = 0, 100, 400, 800,1500 s/mm². rFOV-syDWIs with b values of 1200 (rFOV-syDWI_b=1200) and 1500 (rFOV-syDWI_b=1500) were generated from two different multiple-b-value image datasets using a mono-exponential fitting algorithm. According to homoscedasticity and normality assessed by the Levene's test and Shapiro-Wilk test, the inter-modality differences of quantitative measurements were, respectively, examined by Wilcoxon signed-rank test or paired t test and the inter-group differences of ADC values were examined by independent t test or Mann-Whitney U test.

RESULTS

A higher inter-reader agreement between SNRs and CNRs was found in 13b-protocol and 5b-protocol rFOV-syDWI_b=1200/1500 compared to 13b-protocol rFOV-sDWI_b=1200/1500 (p < 0.05). AUC of 5b-protocol syADC_{mean,b=1200/1500} and syADC_{minimum,b=1200/1500} was equal or higher than that of 13b-protocol sADC_{mean,b=1200/1500} and sADC_{minimum,b=1200/1500}.

CONCLUSIONS

rFOV-syDWIs provide better lesion clarity and higher image quality than rFOV-sDWIs. 5b-protocol rFOV-syDWIs shorten scan time, and synthetic ADCs offer reliable diagnosis value as scanned 13b-protocol DWIs.

Multimodal medical image fusion algorithm based on pulse coupled neural networks and nonsubsampled contourlet transform

Combining two medical images from different modalities is more helpful for using the resulting image in the healthcare field. Medical image fusion means combining two or more images coming from multiple sensors. This technology obtains an output image that presents more effective and useful information from two images. This paper proposes a multi-modal medical image fusion algorithm based on the nonsubsampled contourlet transform (NSCT) and pulse coupled neural networks (PCNN) methods. The input images are decomposed using the NSCT method into low- and high-frequency subbands. The PCNN is a fusion rule for integrating both low- and high-frequency subbands. The inverse of the NSCT method is to reconstruct the fused image. The results of medical image fusion help doctors with disease diagnosis and patient treatment. The proposed algorithm is tested on six groups of multi-modal medical images using 100 pairs of input images. The proposed algorithm is compared with eight fusion methods. We evaluate the performance of the proposed algorithm using the fusion metrics: peak signal to noise ratio (PSNR), mutual information (MI), entropy (EN), weighted edge information (Q[Formula: see text]), nonlinear correlation information entropy (Q[Formula: see text]), standard deviation (SD), and average gradient (AG). Experimental results show that the proposed algorithm can perform better than other medical image fusion methods and achieve promising results.

Transformer-based multilevel region and edge aggregation network for magnetic resonance image segmentation

To improve the quality of magnetic resonance (MR) image edge segmentation, some researchers applied additional edge labels to train the network to extract edge information and aggregate it with region information. They have made significant progress. However, due to the intrinsic locality of convolution operations, the convolution neural network-based region and edge aggregation has limitations in modeling long-range information. To solve this problem, we proposed a novel transformer-based multilevel region and edge aggregation network for MR image segmentation. To the best of our knowledge, this is the first literature on transformer-based region and edge aggregation. We first extract multilevel region and edge features using a dual-branch module. Then, the region and edge features at different levels are inferred and aggregated through multiple transformer-based inference modules to form multilevel complementary features. Finally, the attention feature selection module aggregates these complementary features with the corresponding level region and edge features to decode the region and edge features. We evaluated our method on a public MR dataset: Medical image computation and computer-assisted intervention atrial segmentation challenge (ASC). Meanwhile, the private MR dataset considered infrapatellar fat pad (IPFP). Our method achieved a dice score of 93.2% for ASC and 91.9% for IPFP. Compared with other 2D segmentation methods, our method improved a dice score by 0.6% for ASC and 3.0% for IPFP.

Outcome Analysis of External Neurolysis in Posture-Induced Compressive Peroneal Neuropathy and the Utility of Magnetic Resonance Imaging in the Treatment Process

Objective

We aimed to analyze the effectiveness of external neurolysis on the common peroneal nerve (CPN) in patients with posture-induced compressive peroneal neuropathy (PICPNe). Further, we aimed to examine the utility of magnetic resonance imaging (MRI) in assessing the severity of denervation status and predicting the postoperative prognosis.

Methods

We included 13 patients (eight males and five females) with foot drop who underwent CPN decompression between 2018 and 2020. We designed a grading system for assessing the postoperative functional outcome. Additionally, we performed MRI to evaluate the denervation status of the affected musculature and its effect on postoperative recovery.

Results

The median time to surgery was 3 months. The median preoperative ankle dorsiflexion and eversion grades were both 3, while the average functional grade was 1. Posterior crural intermuscular septum was the most common cause of nerve compression, followed by deep tendinous fascia and anterior crural intermuscular septum. There was a significant postoperative improvement in the median postoperative ankle dorsiflexion and eversion grades and average postoperative functional (4, 5, and 2.38, respectively). Preoperative ankle eversion was significantly correlated with denervation status. Additionally, the devernation status on MRI was positively correlated with the outcome favorability. However, denervation atrophy led to a less favorable outcome.

Conclusion

Among patients with intractable PICPNe despite conservative management, surgical intervention could clinically improve motor function and functional ability. Additionally, MRI examination of the affected muscle could help diagnose CPNe and assess the postoperative prognosis.

Associations of time to return to performance following acute posterior thigh injuries with running biomechanics, hamstring function, and structure in collegiate sprinters: A prospective cohort design

BACKGROUND

The time to return to sport from acute hamstring strain injuries is associated with several functional and structural impairments. However, not all previous studies assessed the preinjury level before acute hamstring strain injuries directly. The purpose of this study was to examine the associations of the time to return to performance following acute hamstring strain injuries with deficits in running biomechanics, hamstring function and structure in collegiate sprinters by a prospective study.

METHODS

Using a prospective cohort design, 72 participants were recruited from a collegiate track and field team. At the preinjury assessment, a 60-m running-specific test, passive straight leg raise test and isometric knee flexion strength test were assessed at the beginning of the competitive season for three consecutive years (2017-2019). Afterwards, postinjury examinations were performed only in sprinters with acute hamstring strain injuries.

FINDINGS

Twelve sprinters strained their hamstring muscle (incidence rate of hamstring strain injuries: 16.7%); the majority (n = 10) were classified as grades 0-2. The running speed deficit of the running-specific test was associated with the time to return to performance as well as the passive straight leg raise test deficit. In the running-specific test, lower-limb kinetic deficits were more strongly associated with the time to return to performance compared to lower-limb kinematic deficits.

INTERPRETATION

A running-specific test may be considered one of the most convenient and valid tests for assessing rehabilitation progress after acute hamstring strain injuries.

Anterior talofibular ligament remnant quality is important for achieving a stable ankle after arthroscopic lateral ankle ligament repair

PURPOSE

The relationship between ligament remnant quality and postoperative outcomes after arthroscopic lateral ankle ligament repair for chronic lateral ankle instability is controversial. This study aimed to determine whether the signal intensity of the anterior talofibular ligament on preoperative magnetic resonance imaging and ligament remnant quality identified on arthroscopy are associated with recurrent ankle instability after arthroscopic lateral ankle ligament repair.

METHODS

A total of 68 ankles from 67 patients with chronic lateral ankle instability who underwent arthroscopic lateral ankle ligament repair were retrospectively studied. The signal intensity of the anterior talofibular ligament was evaluated using T2-weighted magnetic resonance imaging. Arthroscopy was used to evaluate the thickness and mechanical resistance of the anterior talofibular ligament by hook palpation and to classify ankles into two groups: the present anterior talofibular ligament group with adequate mechanical resistance and the absent anterior talofibular ligament group with no mechanical resistance. The outcomes included recurrent ankle instability (respraining of the operated ankle after surgery) and Self-Administered Foot Evaluation Questionnaire scores.

RESULTS

Thirteen ankles were diagnosed with recurrent ankle instability. Patients with a high anterior talofibular ligament T2 signal intensity experienced more recurrent ankle instability than those with a low intensity. As determined via arthroscopy, the absent anterior talofibular ligament group had a higher rate of recurrent ankle instability than the present anterior talofibular ligament group. There were no significant differences in Self-Administered Foot Evaluation Questionnaire scores between patients with high and low anterior talofibular ligament T2 signal intensity, as well as between absent and present anterior talofibular ligament groups based on arthroscopy.

CONCLUSION

Poor quality of the anterior talofibular ligament remnant could result in recurrent ankle instability after arthroscopic lateral ankle ligament repair. Therefore, when treating chronic lateral ankle instability, surgeons should consider ligament quality.

LEVEL OF EVIDENCE

IV.

DGRUnit: Dual graph reasoning unit for brain tumor segmentation

Many fully automatic segmentation models have been created to solve the difficulty of brain tumor segmentation, thanks to the rapid growth of deep learning. However, few approaches focus on the long-range relationships and contextual interdependence in multimodal Magnetic Resonance (MR) images. In this paper, we propose a novel approach for brain tumor segmentation called the dual graph reasoning unit (DGRUnit). Two parallel graph reasoning modules are included in our proposed method: a spatial reasoning module and a channel reasoning module. The spatial reasoning module models the long-range spatial dependencies between distinct regions in an image using a graph convolutional network (GCN). The channel reasoning module uses a graph attention network (GAT) to model the rich contextual interdependencies between different channels with similar semantic representations. Our experimental results clearly demonstrate the superior performance of the proposed DGRUnit. The ablation study shows the flexibility and generalizability of our model, which can be easily integrated into a wide range of neural networks and further improve them. When compared to several state-of-the-art methods, experimental results show that the proposed approach significantly improves both visual inspection and quantitative metrics for brain tumor segmentation tasks.

Diagnostic performance of magnetic resonance image for malignant intraductal papillary mucinous neoplasms: the importance of size of enhancing mural nodule within cyst

PURPOSE

To investigate the clinical significance of enhancing mural nodules ≥ 5 mm by comparing the diagnostic performance of high-risk stigmata for diagnosing the malignant IPMN between the international consensus guideline (ICG) 2012 and 2017 in pancreatic magnetic resonance image (MRI).

MATERIALS AND METHODS

In this retrospective study, we reviewed preoperative pancreatic MRI with surgically confirmed IPMNs between May 2009 and April 2021. High-risk stigmata, defined by ICG 2012 and ICG 2017, associated with malignant IPMN were evaluated using logistic regression analysis. We calculated and compared the sensitivity and specificity of ICG 2012 and ICG 2017 for diagnosing malignant IPMNs. Receiver-operating characteristic (ROC) curves were used to compare ICG 2012 to ICG 2017.

RESULTS

A total of 73 patients (43 men and 30 women; mean age, 69 years; standard deviation, 8 years) with 34 malignant IPMNs and 39 benign IPMNs were included. Among high-risk stigmata, enhancing mural nodule ≥ 5 mm, and MPD diameter ≥ 10 mm were the significant predictor of malignant IPMN, in multivariate logistic regression (P < 0.001 for all). For the diagnosis of malignant IPMN, the specificity of ICG 2017 for enhancing mural nodules ≥ 5 mm as the high-risk stigmata was significantly higher than that of ICG 2012 (87.2% vs. 64.1%, P = 0.008). However, there was no significant difference in sensitivity between the two guidelines (94.1% vs. 97.1%, P = 1.0). The comparison of the ROC curves showed that the diagnostic performance of ICG 2017 for malignant IPMNs (AUC, 0.91) significantly improved when compared to that of ICG 2012 (AUC, 0.81) (P = 0.01).

CONCLUSION

When applying enhancing mural nodule ≥ 5 mm as a high-risk stigmata, ICG 2017 provided a significantly higher specificity than ICG 2012 without a reduction in sensitivity.

Ingenuity using 3D-MRI fusion image in evaluation before and after microvascular decompression for hemifacial spasm

Background

Hemifacial spasm (HFS) is most often caused by blood vessels touching a facial nerve. In particular, responsible vessels compress the root exit zone (REZ) of the facial nerve. Although we recognize these causes of HFS, it is difficult to evaluate the findings of precise lesion in radiological imaging when vessels compress REZ. Hence, we tried to obtain precise images of pre- and postoperative neuroradiological findings of HFS by creating a fusion image of MR angiography and the REZ of facial nerve extracted by magnetic resonance imaging (MRI) diffusion tensor image (DTI).

Case Description

A 52-year-old woman had a 2-year history of HFS on the left side of her face. It was confirmed that the left vertebral artery and anterior inferior cerebellar artery were presented near the facial nerve on MRI. REZ of the facial nerve was visualized using DTI and fusion image was created with vascular components, making it possible to recognize the relationship between compression vessels and REZ of the facial nerve in detail. She underwent microvascular decompression and her HFS completely disappeared. We confirmed that the REZ of the facial nerve was decompressed by MRI imaging, in the same way as before surgery.

Conclusion

We describe that the REZ of facial nerve and compressive vessels was delineated in detail on MRI and this technique is useful for pre- and postoperative evaluation of HFS.

Cervical disc degeneration is associated with a reduction in mobility: A cross-sectional study of 1211 asymptomatic healthy subjects

The aim of this study was to establish the age-related changes and gender-specific differences of cervical disc degeneration using magnetic resonance image (MRI) and to evaluate the correlation between the severity of cervical disc degeneration and mobility in asymptomatic subjects. A total of 1,211 relatively healthy volunteers (606 males and 605 females, mean age 49.5 years) without neurological symptoms underwent MRI. At least 100 males and 100 females in each decade of life between the 20 s and the 70 s were included. This study was part of a larger project and used some previously published data. Cervical disc degeneration was defined according to the modified Pfirrmann classification system. A total disc degeneration score (DDS) was calculated by the summation of individual Pfirrmann scores from C2/C3 to C7/T1. Cervical range of motion (ROM) was measured by radiograph. The total DDS increased gradually with increasing age in both genders. DDSs were lower in females than in males in all decades. A DDS of 13 or more was found in more than half the cases in the 40 s or older age groups. The total DDS was 13 or more in over 95% of the cases in the 70 s age group. The total DDS was significantly and negatively correlated with cervical ROM overall (r =  - 0.46, p < 0.0001) and in both men (r =  - 0.52, p < 0.0001) and women (r =  - 0.40, p < 0.0001). This large-scale cross-sectional analysis of cervical spine MRI data in healthy subjects demonstrated that cervical disc degeneration progresses with age, and is correlated with a reduction in mobility.

Delayed-onset subdural hematoma after mild head injury with negative initial brain imaging

Mild head injuries are commonly encountered in the neurosurgical field and emergency room (ER). The usual step is to discharge if the mental status of the patient is good and the initial brain computed tomography (CT) findings are normal. Here, we report a rare case of an 82-year-old male patient who developed delayed-onset bilateral subdural hematoma five weeks after a mild head injury. He was not on anticoagulant or antiplatelet therapy. The initial CT scan on the day of injury and magnetic resonance (MR) imaging performed seven days after the injury did not reveal any intracranial pathology or skull fracture. However, he presented with severe headaches and an unsteady ataxic gait five weeks later. Brain CT revealed bilateral subdural hematoma compressing the lateral ventricles with a midline shift to the right side. The possible pathophysiological mechanisms underlying this uncommon entity are discussed with a review of the relevant literature.

Self-paced learning and privileged information based KRR classification algorithm for diagnosis of Parkinson's disease

Computer aided diagnosis (CAD) methods for Parkinson's disease (PD) can assist clinicians in diagnosis and treatment. Magnetic resonance imaging (MRI) based CAD methods can help reveal structural changes in brain. Classifier is a key component in CAD system, which directly affects the classification performance. Privileged information (PI) can assist to train the classifier by providing additional information, which makes test samples have less error and improves the classification accuracy. In this paper, we proposed a PI based kernel ridge regression plus (KRR+) in diagnosis of PD. Specifically, morphometric features and brain network features are extracted from MRI. Then, empirical kernel mapping feature expression method is used to make the data separable in high-dimensional space. Besides, we introduce self-paced learning that can adaptively select the sample in training of the model, which can further improve the classification performance. The experimental results show that the proposed method is effective for PD diagnosis, its performance is superior to existing classification model. This method is helpful to assist clinicians to find out possible neuroimaging biomarkers in the diagnosis of PD.

Obturator hernia - a rare etiology of lateral thigh pain: A case report

BACKGROUND

Lateral thigh pain is a common complaint in patients visiting a pain clinic. Herein, we describe the case of a patient with lateral thigh pain caused by an obturator hernia.

CASE SUMMARY

An 83-year-old woman visited the emergency room with suddenly aggravated right lateral thigh pain. Magnetic resonance imaging of the thigh revealed no abnormal findings in the lateral thigh area. However, an obturator hernia between the pectineus and obturator externus muscles was observed by chance. Retroperitoneal computed tomography revealed a herniated small bowel with an incarceration point at the right obturator canal and a dilated loop of the small bowel upstream. Ultrasonography of the right inguinal region revealed a distended bowel loop in the right pectineus muscle.

CONCLUSION

Our report provides clinicians with information that an obturator hernia can cause lateral thigh pain.

Adjustable shrinkage-thresholding projection algorithm for compressed sensing magnetic resonance imaging

Compressed sensing (CS) aims to reconstruct a high quality images with as little sample data as possible. Magnetic resonance imaging (MRI) plays an important role in medical imaging tools but has a slower data acquisition process. Applying CS to MRI offers significant scan time reductions. In this paper, we proposed a fast and efficient algorithm for compressed sensing magnetic resonance imaging (CS-MRI) reconstruction, denoted as adjustable shrinkage-thresholding projection algorithm (ASTP). It is designed to use adjustable shrinkage rules for l_p-norm based CS-MRI model. This algorithm is established by using an iterative projection and acceleration scheme. In each iteration, the proposed adjustable shrinkage-thresholding rules are employed to ensure global convergence to accurate solution. Furthermore, the parameter p can be selected flexibly according to different practical application situations, and the orthogonal projection operation is used to reduce the dimension of the solution space to accelerate the convergence speed and improve the reconstruction quality. Numerical experiments show that proposed ASTP algorithm provides a higher accuracy, convergence speed and ability to suppress noise compared with some certain state-of-the-art algorithms.

Clinical and MRI Evaluation of Silicone Gel Implants with RFID-M Traceability System: A Prospective Controlled Cohort Study Related to Safety and Image Quality in MRI Follow-Up

BACKGROUND

SmoothSilk implants (SSI) are the first generation of implants to incorporate a radio-frequency identification device (RFID-M), a non-invasive traceability system. Although the RFID-M is considered compatible with magnetic resonance imaging (MRI), the size of the artifact and its influence on breast tissue vary. This prospective study assessed safety and MRI issues in a cohort of breast reconstruction patients.

METHODS

Forty-four SSI were used for breast reconstruction in patients undergoing treatment for breast cancer. All patients were evaluated for magnetic field interactions, MRI-related heating and artifacts in a 1.5-T MRI unit using standard T1/T2-weighted sequences utilized in clinical assessment of breast tissue/implants.

RESULTS

Mean patient age was 41.5 years (27-53ys) and body mass index was 28+-6.44 kg/m². In 18/22 patients (81.8%), mastectomies were unilateral. No patients reported local heat/discomfort. All implants showed RFID-M-related artifacts with an estimated mean volume in T1 of 42.9cm³ (26.2-63.6cm³; SD±8.6 and 95% CI, 40.37-45.45) and in T2 of 60.5cm³ (35.4-97.2cm³; SD±14.7 and 95% CI, 56.29-65.01). Artifact volume was smaller in T1 than in T2, to a statistically significant degree (p <0.001). There were no statistically significant differences in artifact volume according to surgical indication, breast side or implant volume. There were 4/44 (9%) cases of minor rotation (<45°). In all cases, adequate analysis of the breast tissue was performed.

CONCLUSIONS

The results demonstrate that SSI with RFID-M technology presented an artifact volume of 42.9cm³ and 60.5cm³ in T1 and T2 images, respectively. Our findings provide detailed information on the quality and location of MRI artifacts in a reconstructed cohort which can help guide clinical decision-making for patients. To our knowledge, this is the first time RFID-M breast implants have been prospectively evaluated for clinical and MRI issues in a cohort of reconstructive patients.

LEVEL OF EVIDENCE IV

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Heavily T2-Weighted Magnetic Resonance Myelography as a Safe Cerebrospinal Fluid Leakage Detection Modality for Nontraumatic Subdural Hematoma

Objective

Nontraumatic subdural hematoma (SDH) is a common disease, and spinal cerebrospinal fluid (CSF) leakage is a possible etiology of unknown significance, which is commonly investigated by several invasive studies. This study demonstrates that heavily T2-weighted magnetic resonance myelography (HT2W-MRM) is a safe and clinically effective imaging modality for detecting CSF leakage in patients with nontraumatic SDH.

Methods

All patients who underwent HT2W-MRM for nontraumatic SDH workup at our institution were searched and enrolled in this study. Several parameters were measured and analyzed, including patient demographic data, initial modified Rankin Scale (mRS) score upon presentation, SDH bilaterality, hematoma thickness upon presentation, CSF leakage sites, treatment modalities, followup hematoma thickness, and follow-up mRS score.

Results

Forty patients were identified, of which 22 (55.0%) had CSF leakage at various spinal locations. Five patients (12.5%) showed no change in mRS score, whereas the remaining (87.5%) showed decreases in follow-up mRS scores. In terms of the overall hematoma thickness, four patients (10.0%) showed increased thickness, two (5.0%) showed no change, 32 (80.0%) showed decreased thickness, and two (5.0%) did not undergo follow-up imaging for hematoma thickness measurement.

Conclusion

HT2W-MRM is not only safe but also clinically effective as a primary diagnostic imaging modality to investigate CSF leakage in patients with nontraumatic SDH. Moreover, this study suggests that CSF leakage is a common etiology for nontraumatic SDH, which warrants changes in the diagnosis and treatment strategies.

Diagnostic Validity of Auditory Brainstem Response for the Initial Screening of Vestibular Schwannoma

Background and Objectives

: To investigate the diagnostic validity of auditory brainstem response (ABR) in the screening of vestibular schwannoma (VS).

Subjects and Methods

: Forty patients diagnosed with VS using magnetic resonance imaging who had undergone ABR before treatment between 2005 and 2015 were included. ABR results were considered positive when findings met at least one of the following criteria: 1) absent evoked response, 2) desynchronization of waves other than wave I, 3) interpeak latency (IPL) between waves I and III >2.5 ms, 4) IPL between waves I and V >4.4 ms, 5) wave V interaural latency difference >0.2 ms, and 6) interaural difference in IPL between waves I and V >0.2 ms.

Results

: The overall sensitivity of ABR was 85.0%. For tumors measuring <10 mm, the sensitivity of ABR was 66.7%, whereas it increased to 90.3% for tumors measuring >10 mm. The sensitivity of tumors confined to the internal acoustic canal was 73.3% compared with 100.0% for tumors confined to the cerebellopontine angle. In patients with serviceable hearing, the mean tumor size was 7.8±2.9 mm in patients with a normal ABR and 15.1±9.4 mm in patients with an abnormal ABR, indicating a significant difference (p<0.05).

Conclusions

: ABR alone is insufficient for the screening of VS, bearing the risk of false-negative outcomes when examining small, intracanalicular tumors. However, ABR can be inexpensively applied for the screening of VS measuring >10 mm in patients with serviceable hearing, supporting the need for further active diagnostic and treatment modalities in clinical practice.

Prediction of recurrence after surgery based on preoperative MRI features in patients with pancreatic neuroendocrine tumors

OBJECTIVES

To investigate useful MRI features in pancreatic neuroendocrine tumor (PNET) patients for predicting recurrence and its timing after surgery.

METHODS

A total of 99 patients with PNET who underwent MRI and surgery from 2000 to 2018 were enrolled. Two radiologists independently assessed MRI findings, including size, location, margin, T1 and T2 signal intensity, enhancement patterns, common bile duct (CBD) or main pancreatic duct (MPD) dilatation, vascular invasion, lymph node enlargement, DWI, and ADC value. Imaging findings associated with recurrence and disease-free survival (DFS) were assessed using logistic regression analysis and Cox proportional hazard regression analysis.

RESULTS

The median follow-up period was 40.4 months, and recurrence after surgery occurred in 12.1% (12/99). Among them, 6 patients experienced recurrence within 1 year, and 9 patients experienced recurrence within 2 years after surgery. In multivariate analysis, major venous invasion (OR 10.76 [1.14-101.85], p = 0.04) was associated with recurrence within 1 year, and portal phase iso- to hypoenhancement (OR 51.89 [1.73-1557.89], p = 0.02), CBD or MPD dilatation (OR 10.49 [1.35-81.64], p = 0.03) and larger size (OR 1.05 [1.00-1.10], p = 0.046) were associated with recurrence within 2 years. The mean DFS was 116.4 ± 18.5 months, and the 5-year DFS rate was 85.7%. In multivariate analysis, portal phase iso- to hypoenhancement (HR 21.36 [2.01-197.77], p = 0.01), ductal dilatation (HR 5.22 [1.46-18.68], p = 0.01), major arterial invasion (HR 42.90 [3.66-502.48], p = 0.003), and larger size (HR 1.04 [1.01-1.06], p = 0.01) showed a significant effect on poor DFS.

CONCLUSION

MRI features, including size, enhancement pattern, vascular invasion, and ductal dilatation, are useful in predicting recurrence and poor DFS after surgery in PNET. Key Points • MRI features are useful for predicting prognosis in patients with PNET after surgery. • PV or SMV invasion (OR 10.49 [1.35-81.64], p = 0.04) was significantly associated with 1-year recurrence. • Portal phase iso- to hypoenhancement (HR 21.36), CBD or MPD dilatation (HR 5.22), arterial invasion (HR 42.90), and larger size (HR 1.04) had significant effects on poor DFS (p < 0.05).