Deep learning for osteoporosis screening using an anteroposterior hip radiograph image.

PURPOSE: Osteoporosis is a common bone disorder characterized by decreased bone mineral density (BMD) and increased bone fragility, which can lead to fractures and eventually cause morbidity and mortality. It is of great concern that the one-year mortality rate for osteoporotic hip fractures could be as high as 22%, regardless of the treatment. Currently, BMD measurement is the standard method for osteoporosis diagnosis, but it is costly and requires special equipment. While a plain radiograph can be obtained more simply and inexpensively, it is not used for diagnosis. Deep learning technologies had been applied to various medical contexts, yet few to osteoporosis unless they were trained on the advanced investigative images, such as computed tomography. The purpose of this study was to develop a deep learning model using the anteroposterior hip radiograph images and measure its diagnostic accuracy for osteoporosis. METHODS: We retrospectively collected all anteroposterior hip radiograph images of patients from 2013 to 2021 at a tertiary care hospital. The BMD measurements of the included patients were reviewed, and the radiograph images that had a time interval of more than two years from the measurements were excluded. All images were randomized using a computer-generated unequal allocation into two datasets, i.e., 80% of images were used for the training dataset and the remaining 20% for the test dataset. The T score of BMD obtained from the ipsilateral femoral neck of the same patient closest to the date of the performed radiograph was chosen. The T score cutoff value of - 2.5 was used to diagnose osteoporosis. Five deep learning models were trained on the training dataset, and their diagnostic performances were evaluated using the test dataset. Finally, the best model was determined by the area under the curves (AUC). RESULTS: A total of 363 anteroposterior hip radiograph images were identified. The average time interval between the performed radiograph and the BMD measurement was 6.6 months. Two-hundred-thirteen images were labeled as non-osteoporosis (T score > - 2.5), and the other 150 images as osteoporosis (T score ≤ - 2.5). The best-selected deep learning model achieved an AUC of 0.91 and accuracy of 0.82. CONCLUSIONS: This study demonstrates the potential of deep learning for osteoporosis screening using anteroposterior hip radiographs. The results suggest that the deep learning model might potentially be used as a screening tool to find patients at risk for osteoporosis to perform further BMD measurement.

Leukoaraiosis and stroke severity scores in post-rtPA intracerebral haemorrhage.

Introduction: Post thrombolytic symptomatic intracerebral haemorrhage (sICH) is a major concern in patients who had acute ischaemic stroke. Leukoaraiosis (LA) is reported to be related with sICH after intravenous thrombolytic treatment. However, the influence of LA and stroke neurological and imaging severity scores is still debated. Objective: To evaluate if LA or severity scores are related with sICH in patients who had acute ischaemic stroke who received thrombolytic therapy. And, predictors for sICH were also studied with adjustment of baseline severity scores. Methods: This was a retrospective, analytical study. The inclusion criteria were adult patients diagnosed as acute ischaemic stroke who received the recombinant tissue plasminogen activator (rtPA) treatment within 4.5 hours. The study period was between May 2007 and November 2016. Predictors for sICH were determined using logistic regression analysis. Results: During the study period, there were 504 eligible patients. Of those, 45 patients (8.92%) had sICH. Among nine factors in the final model for predicting sICH, there were four independent factors including previous antiplatelet therapy, previous anticoagulant therapy, presence of LA and hyperdense artery sign. The highest adjusted OR was previous anticoagulant therapy (5.08 with 95% CI of 1.18 to 11.83), while the LA factor had adjusted OR (95% CI) of 2.52 (1.01 to 6.30). Conclusions: LA, hyperdense artery sign, previous antiplatelet therapy and previous anticoagulant therapy were associated with post-rtPA sICH. Further studies are required to confirm the results of this study.

The diagnostic accuracy of mammography and ultrasonography for recurrent breast cancer after breast conserving treatment.

Objective: To evaluate the performance of mammography and breast ultrasonography to diagnose tumor recurrence in patients after breast conserving therapy. Material and Methods: Imaging findings of 130 breast cancer patients treated by breast conserving therapy (BCT) who have followed up with mammography and ultrasonography at our center between 1 st January 2010 and 1st January 2016 were interpreted by two radiologists. The information of recurrent tumor and baseline data were blinded. Imaging interpretation followed the ACR Breast imaging-reporting and data system (BI-RADS) 5th edition guideline. Findings of mammography, breast ultrasonography, demographic data and histological data were recorded and analyzed. Results: The presence of mass in mammography (P-value=0.025) and internal vascularity in mass in ultrasonography (P-value<0.001) were associated with recurrent tumor at the surgical bed. All the recurrent tumors were interpreted as BI-RADS 4 (71 patients) with sensitivity= 100%, specificity= 89.5%. BIRADS4 is significant in the diagnosis of recurrent breast cancer in BCT patients (AUC of the ROC curve = 0.742 and 95% CI=(0.7-0.79)). Conclusion: The presence of mass in mammography and internal vascularity in the mass in ultrasonography are the imaging findings which were significantly related to recurrent tumor at surgical bed in patient with breast conserving treatment.

BiTNet: Hybrid deep convolutional model for ultrasound image analysis of human biliary tract and its applications.

Certain life-threatening abnormalities, such as cholangiocarcinoma, in the human biliary tract are curable if detected at an early stage, and ultrasonography has been proven to be an effective tool for identifying them. However, the diagnosis often requires a second opinion from experienced radiologists, who are usually overwhelmed by many cases. Therefore, we propose a deep convolutional neural network model, named biliary tract network (BiTNet), developed to solve problems in the current screening system and to avoid overconfidence issues of traditional deep convolutional neural networks. Additionally, we present an ultrasound image dataset for the human biliary tract and demonstrate two artificial intelligence (AI) applications: auto-prescreening and assisting tools. The proposed model is the first AI model to automatically screen and diagnose upper-abdominal abnormalities from ultrasound images in real-world healthcare scenarios. Our experiments suggest that prediction probability has an impact on both applications, and our modifications to EfficientNet solve the overconfidence problem, thereby improving the performance of both applications and of healthcare professionals. The proposed BiTNet can reduce the workload of radiologists by 35% while keeping the false negatives to as low as 1 out of every 455 images. Our experiments involving 11 healthcare professionals with four different levels of experience reveal that BiTNet improves the diagnostic performance of participants of all levels. The mean accuracy and precision of the participants with BiTNet as an assisting tool (0.74 and 0.61, respectively) are statistically higher than those of participants without the assisting tool (0.50 and 0.46, respectively (p<0.001)). These experimental results demonstrate the high potential of BiTNet for use in clinical settings.

Artificial intelligence (AI) vs. human in hip fracture detection.

Objective: This study aimed to assess the diagnostic accuracy and sensitivity of a YOLOv4-tiny AI model for detecting and classifying hip fractures types. Materials and methods: In this retrospective study, a dataset of 1000 hip and pelvic radiographs was divided into a training set consisting of 450 fracture and 450 normal images (900 images total) and a testing set consisting of 50 fracture and 50 normal images (100 images total). The training set images were each manually augmented with a bounding box drawn around each hip, and each bounding box was manually labeled either (1) normal, (2) femoral neck fracture, (3) intertrochanteric fracture, or (4) subtrochanteric fracture. Next, a deep convolutional neural network YOLOv4-tiny AI model was trained using the augmented training set images, and then model performance was evaluated with the testing set images. Human doctors then evaluated the same testing set images, and the performances of the model and doctors were compared. The testing set contained no crossover data. Results: The resulting output images revealed that the AI model produced bounding boxes around each hip region and classified the fracture and normal hip regions with a sensitivity of 96.2%, specificity of 94.6%, and an accuracy of 95%. The human doctors performed with a sensitivity ranging from 69.2 to 96.2%. Compared with human doctors, the detection rate sensitivity of the model was significantly better than a general practitioner and first-year residents and equivalent to specialist doctors. Conclusions: This model showed hip fracture detection sensitivity comparable to well-trained radiologists and orthopedists and classified hip fractures highly accurately.

Diagnostic accuracy of deep learning for evaluation of C-spine injury from lateral neck radiographs.

Background: Traumatic spinal cord injury (TSI) is a leading cause of morbidity and mortality worldwide, with the cervical spine being the most affected. Delayed diagnosis carries a risk of morbidity and mortality. However, cervical spine CT scans are time-consuming, costly, and not always available in general care. In this study, deep learning was used to assess and improve the detection of cervical spine injuries on lateral radiographs, the most widely used screening method to help physicians triage patients quickly and avoid unnecessary CT scans. Materials and methods: Lateral neck or lateral cervical spine radiographs were obtained for patients who underwent CT scan of cervical spine. Ground truth was determined based on CT reports. CiRA CORE, a codeless deep learning program, was used as a training and testing platform. YOLO network models, including V2, V3, and V4, were trained to detect cervical spine injury. The diagnostic accuracy, sensitivity, and specificity of the model were calculated. Results: A total of 229 radiographs (129 negative and 100 positive) were selected for inclusion in our study from a list of 625 patients with cervical spine CT scans, 181 (28.9%) of whom had cervical spine injury. The YOLO V4 model performed better than the V2 or V3 (AUC = 0.743), with sensitivity, specificity, and accuracy of 80%, 72% and 75% respectively. Conclusion: Deep learning can improve the accuracy of lateral c-spine or neck radiographs. We anticipate that this will assist clinicians in quickly triaging patients and help to minimize the number of unnecessary CT scans.

Prevalence of Sclerotic Pterygoid Plate in Pretreatment Nasopharyngeal Carcinoma.

OBJECTIVES: The objective of our study was to determine the prevalence of sclerotic pterygoid plate in pretreatment CT of nasopharyngeal carcinoma compared with the control group. MATERIALS AND METHODS: A total of 51 nasopharyngeal carcinoma patients (37 men, 14 women) with a mean age of 51.94±13 years, and 51 controls (30 men, 21 women) with a mean age, 49.31±15 years were included in this study in a retrospective fashion. All computed tomographic (CT) images were evaluated by two neuroradiologists. Sclerosis of pterygoid plate and other findings included pterygoid plate erosion, adjacent tumor enhancement, and parapharyngeal extension which were assessed. MRI findings were also recorded. The prevalence of pterygoid plate sclerosis was compared using Chi-square statistical tests. Imaging findings were analyzed by binary logistic regression analyses. RESULTS: The prevalence of pterygoid plate sclerosis in nasopharyngeal carcinoma was 53.9% compared to the control group (16.7%) and the difference was statistically significant (P-value< 0.001). In nasopharyngeal carcinoma, the prevalence of tumor adjacent to the pterygoid plate, parapharyngeal extension and pterygoid plate erosion were 69.6%, 81.4%, 38.2%, respectively. No erosion of pterygoid plate was detected in the control group. The odds of adjacent tumor enhancement and pterygoid plate erosion was 7.29 and 20.56 times higher in the sclerotic pterygoid plate (p-values of 0.019 and 0.000, respectively). MRI was available for four nasopharyngeal carcinoma cases with five sclerotic pterygoid plates, where two showed enhancements. All non-sclerotic pterygoid plates showed no enhancement on MRI. CONCLUSION: The prevalence of sclerotic pterygoid plate is significantly higher in patients with nasopharyngeal carcinoma with a considerably higher chance of adjacent tumor enhancement and pterygoid plate erosion.

The Turtle Neck Sign: Identification of Severe Retracted Distal Biceps Tendon Rupture.

BACKGROUND: Chronic tendon retraction subsequent to distal biceps tendon rupture significantly increases repair difficulty and potential for tendon grafting. Biceps tendons that appear short or absent with magnetic resonance imaging (MRI) or that cannot be readily identified at surgery may erroneously be classified as irreparable. These apparent "absent" biceps tendons may actually be retracted and curled up inside the muscle, visually resembling the head-neck of a turtle retracted inside its shell (the "turtle neck sign"). When located, these tendons could be unfolded and repaired primarily. This type of tendon retraction seems to be associated with high-degree ruptures and larcertus fibrosus tears. PURPOSE: To test the hypothesis that tendon retractions with a turtle neck sign on MRI are more associated with high-degree ruptures and larcertus fibrosus tears versus tendon tears with simple linear retraction. STUDY DESIGN: Cross-sectional study; Level of evidence, 3. METHODS: Retracted distal biceps tendon ruptures on sagittal MRI were categorized as linear retraction or curled-up (turtle neck) retraction. Retraction length, injury severity, and lacertus fibrosus tears were analyzed. RESULTS: The authors retrospectively analyzed the patient records of 85 consecutive traumatic distal biceps tendon ruptures from 2003 to 2019; the final study cohort was 37 patients. Injury-to-surgery timing was as follows: <3 weeks, 43% (16 cases); 3 weeks to 3 months, 32% (12 cases); and >3 months, 24% (9 cases). Overall, 19 patients had linear retraction <7 cm (mean, 3.3 ± 1.9 cm) and 18 patients had a turtle neck retraction ≥7 cm (mean, 9.1 ± 1.6 cm). The injury-to-surgery time (median [± interquartile range]) was 27 days (±90 days) in the linear retraction group and 23 days (±65 days) in the turtle neck retraction group. The turtle neck retraction group had a significantly higher occurrence of abnormal hook test findings, complete distal biceps tendon rupture, and lacertus fibrosus tears compared with the linear retraction group (100% vs 58%, 100% vs 68%, and 100% vs 37%, respectively; P ≤ .02). However, significant repairability differences were not found. CONCLUSION: Highly retracted distal biceps turtle neck sign tendon ruptures occur frequently in association with high-degree ruptures and lacertus fibrosus tears. The presence of a turtle neck retraction did not affect reparability. Surgeons should be aware of this curled-up retraction to avoid mistaking it for an absent tendon or a muscle-tendon disruption.

Thyroid Nodule Size as a Predictor of Malignancy in Follicular and Hurthle Neoplasms.

INTRODUCTION: The management of follicular (FN) and Hurthle cell neoplasms (HCN) is often difficult because of the uncertainty of malignancy risk. We aimed to assess characteristics of benign and malignant follicular and Hurthle neoplasms based on their shape and size. MATERIALS AND METHODS: Patients with Follicular adenoma (FA) or carcinoma (FC) and Hurthle Cell adenoma (HCA) or carcinoma (HCC) who had preoperative ultrasonography were included. Demographic data were retrieved. Size and shape of the nodules were measured. Logistic regression analyses and odds ratios were performed. RESULTS: A total of 115 nodules with 57 carcinomas and 58 adenomas were included. Logistic regression analysis shows that the nodule height and the patient age are predictors of malignancy (p-values = 0.001 and 0.042). A cutoff value of nodule height ≥ 4 cm. produces an odds ratio of 4.5 (p-value = 0.006). An age ≥ 55 year-old demonstrates an odds ratio of 2.4-3.6 (p-value = 0.03). Taller-than-wide shape was not statistically significant (p-value = 0.613). CONCLUSION: FC and HCC are larger than FA and HCA in size, with a cutoff at 4 cm. Increasing age increases the odds of malignancy with a cutoff at 55 year-old. Taller-than-wide shape is not a predictor of malignancy.

Evaluation of Perienhancing Area in Differentiation between Glioblastoma and Solitary Brain Metastasis.

PURPOSE: Accurate differential diagnosis between glioblastoma and brain metastasis is important. We aimed to differentiate these tumors by evaluation of the perienhancing area. MATERIALS AND METHODS: Thirty patients with glioblastoma and solitary brain metastasis were included. The diameters of perienhancing and enhancing areas were measured, and the percentage of enhancing area was calculated. We measured Apparent diffusion coefficient (ADC) of perienhancing and enhancing areas. Intratumoral necrotic areas were measured. RESULTS: The enhancing area of glioblastoma was 56.61% and metastasis was 42.55% (p = 0.08). The ADC values of the perienhancing part of GBM was 0.7 and metastasis was 0.79 (p = 0.052). The ADC value of the enhancing part of the GBM was 0.82 and metastasis was 0.8 (p-value = 0.72).  The intratumoral necrotic area of glioblastoma (152.25 mm3) was higher than in metastasis (0 mm3) (p-value = 0.003) with a cutoff area of 11.8 mm2. CONCLUSION: The ADC values of the perienhancing area were lower in glioblastoma with a near-significant p-value. Other perienhancing parameters demonstrated no significant difference between both tumors. The intratumoral necrotic area of glioblastoma is larger than metastasis.
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Complete abdomen and pelvis segmentation using U-net variant architecture.

PURPOSE: Organ segmentation of computed tomography (CT) imaging is essential for radiotherapy treatment planning. Treatment planning requires segmentation not only of the affected tissue, but nearby healthy organs-at-risk, which is laborious and time-consuming. We present a fully automated segmentation method based on the three-dimensional (3D) U-Net convolutional neural network (CNN) capable of whole abdomen and pelvis segmentation into 33 unique organ and tissue structures, including tissues that may be overlooked by other automated segmentation approaches such as adipose tissue, skeletal muscle, and connective tissue and vessels. Whole abdomen segmentation is capable of quantifying exposure beyond a handful of organs-at-risk to all tissues within the abdomen. METHODS: Sixty-six (66) CT examinations of 64 individuals were included in the training and validation sets and 18 CT examinations from 16 individuals were included in the test set. All pixels in each examination were segmented by image analysts (with physician correction) and assigned one of 33 labels. Segmentation was performed with a 3D U-Net variant architecture which included residual blocks, and model performance was quantified on 18 test cases. Human interobserver variability (using semiautomated segmentation) was also reported on two scans, and manual interobserver variability of three individuals was reported on one scan. Model performance was also compared to several of the best models reported in the literature for multiple organ segmentation. RESULTS: The accuracy of the 3D U-Net model ranges from a Dice coefficient of 0.95 in the liver, 0.93 in the kidneys, 0.79 in the pancreas, 0.69 in the adrenals, and 0.51 in the renal arteries. Model accuracy is within 5% of human segmentation in eight of 19 organs and within 10% accuracy in 13 of 19 organs. CONCLUSIONS: The CNN approaches the accuracy of human tracers and on certain complex organs displays more consistent prediction than human tracers. Fully automated deep learning-based segmentation of CT abdomen has the potential to improve both the speed and accuracy of radiotherapy dose prediction for organs-at-risk.

Fully Automated Segmentation of Head CT Neuroanatomy Using Deep Learning.

PURPOSE: To develop a deep learning model that segments intracranial structures on head CT scans. MATERIALS AND METHODS: In this retrospective study, a primary dataset containing 62 normal noncontrast head CT scans from 62 patients (mean age, 73 years; age range, 27-95 years) acquired between August and December 2018 was used for model development. Eleven intracranial structures were manually annotated on the axial oblique series. The dataset was split into 40 scans for training, 10 for validation, and 12 for testing. After initial training, eight model configurations were evaluated on the validation dataset and the highest performing model was evaluated on the test dataset. Interobserver variability was reported using multirater consensus labels obtained from the test dataset. To ensure that the model learned generalizable features, it was further evaluated on two secondary datasets containing 12 volumes with idiopathic normal pressure hydrocephalus (iNPH) and 30 normal volumes from a publicly available source. Statistical significance was determined using categorical linear regression with P < .05. RESULTS: Overall Dice coefficient on the primary test dataset was 0.84 ± 0.05 (standard deviation). Performance ranged from 0.96 ± 0.01 (brainstem and cerebrum) to 0.74 ± 0.06 (internal capsule). Dice coefficients were comparable to expert annotations and exceeded those of existing segmentation methods. The model remained robust on external CT scans and scans demonstrating ventricular enlargement. The use of within-network normalization and class weighting facilitated learning of underrepresented classes. CONCLUSION: Automated segmentation of CT neuroanatomy is feasible with a high degree of accuracy. The model generalized to external CT scans as well as scans demonstrating iNPH.Supplemental material is available for this article.© RSNA, 2020.

A Survey of Deep-Learning Applications in Ultrasound: Artificial Intelligence-Powered Ultrasound for Improving Clinical Workflow.

Ultrasound is the most commonly used imaging modality in clinical practice because it is a nonionizing, low-cost, and portable point-of-care imaging tool that provides real-time images. Artificial intelligence (AI)-powered ultrasound is becoming more mature and getting closer to routine clinical applications in recent times because of an increased need for efficient and objective acquisition and evaluation of ultrasound images. Because ultrasound images involve operator-, patient-, and scanner-dependent variations, the adaptation of classical machine learning methods to clinical applications becomes challenging. With their self-learning ability, deep-learning (DL) methods are able to harness exponentially growing graphics processing unit computing power to identify abstract and complex imaging features. This has given rise to tremendous opportunities such as providing robust and generalizable AI models for improving image acquisition, real-time assessment of image quality, objective diagnosis and detection of diseases, and optimizing ultrasound clinical workflow. In this report, the authors review current DL approaches and research directions in rapidly advancing ultrasound technology and present their outlook on future directions and trends for DL techniques to further improve diagnosis, reduce health care cost, and optimize ultrasound clinical workflow.

RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with Deep Learning.

Deep-learning algorithms typically fall within the domain of supervised artificial intelligence and are designed to "learn" from annotated data. Deep-learning models require large, diverse training datasets for optimal model convergence. The effort to curate these datasets is widely regarded as a barrier to the development of deep-learning systems. We developed RIL-Contour to accelerate medical image annotation for and with deep-learning. A major goal driving the development of the software was to create an environment which enables clinically oriented users to utilize deep-learning models to rapidly annotate medical imaging. RIL-Contour supports using fully automated deep-learning methods, semi-automated methods, and manual methods to annotate medical imaging with voxel and/or text annotations. To reduce annotation error, RIL-Contour promotes the standardization of image annotations across a dataset. RIL-Contour accelerates medical imaging annotation through the process of annotation by iterative deep learning (AID). The underlying concept of AID is to iteratively annotate, train, and utilize deep-learning models during the process of dataset annotation and model development. To enable this, RIL-Contour supports workflows in which multiple-image analysts annotate medical images, radiologists approve the annotations, and data scientists utilize these annotations to train deep-learning models. To automate the feedback loop between data scientists and image analysts, RIL-Contour provides mechanisms to enable data scientists to push deep newly trained deep-learning models to other users of the software. RIL-Contour and the AID methodology accelerate dataset annotation and model development by facilitating rapid collaboration between analysts, radiologists, and engineers.

Interrater Reliability of Various Thyroid Imaging Reporting and Data System (TIRADS) Classifications for Differentiating Benign from Malignant Thyroid Nodules

Background: Thyroid ultrasound(US) is used as the first diagnostic tool to assess the management of disease but is operator dependent. There have been few reports evaluating interrater variability in US assessment. Therefore, we evaluated interrater reliability in US assessment of thyroid nodules and estimated its diagnostic accuracy for various TIRADS systems. Methods: This retrospective study included 24 malignant nodules and 84 benign nodules from January 2015 to October 2017. Two blinded observers independently reviewed stored US images by using TIRADS. All analyses followed guidelines proposed by ACR-TR, Siriraj-TR and EU-TR systems. Interrater reliability was calculated using Cohen's Kappa statistics. Diagnostic accuracy were also calculated. Results: Interobserver agreement showed substantial agreement for composition (K=0.616); echogenicity and echogenic foci showed fair agreement (K=0.327 and 0.288, respectively); margin showed slight agreement (K=0.143). Interrater reliability for the final assessment; moderate agreement for ACR-TIRADS system (K=0.500); fair agreement for EU-TIRADS system (K=0.209) and slight agreement (K=0.114) for Siriraj-TIRADS system. The diagnostic performance from the two observers; ACRTIRADS system; sensitivities were 75% and 79.2%, specificities were 58.3% and 56%, positive predictive value (PPV) were 34% and 33.9% and negative predictive value (NPV) were 89.1% and 90.4%. For the Siriraj-TIRADS system, sensitivities were 41.7% and 25%, specificities were 84.5% and 89.3%, positive predictive value (PPV) were 43.5% and 40% and negative predictive value (NPV) were 83.5% and 80.6%. For the EU-TIRADS system, sensitivities were 45.8% and 66.7%, specificities were 79.8% and 72.6%, positive predictive value (PPV) were 39.3% and 41% and negative predictive value (NPV) were 83.8% and 88.4%. Conclusion: The ACR-TIRADS had highest interobserver agreement, a trend to have highest sensitivity and negative predictive value for diagnosis of malignant thyroid nodules. Siriraj-TIRADS had higher specificity and accuracy, but lower interobserver agreement.

Preoperative Ultrasound Evaluation of the Semitendinosus Tendon for Anterior Cruciate Ligament Reconstruction.

BACKGROUND: Adequate graft size and length are crucial factors that correlate with improved outcomes after anterior cruciate ligament reconstruction with a semitendinosus (ST) tendon autograft alone. Anthropometric parameters could be used as predictors of graft measurements but they have shown imprecise correlation in some patients. PURPOSE: To evaluate the accuracy of ultrasound (US) for the preoperative evaluation of ST graft size and length. STUDY DESIGN: Cohort study (diagnosis); Level of evidence, 3. METHODS: A total of 40 patients were included in this study. Patient sex, age, anthropometric parameters, and preoperative US measurements were recorded. After the ST tendons were harvested, their lengths as well as the diameters of the 4-strand ST grafts were recorded. Correlations between patient US measurements were analyzed. Inadequate ST graft length was defined at <28 cm, and inadequate 4-strand ST graft diameter was defined at <8 mm. RESULTS: The prevalence of patients with an ST graft length <28 cm was 47.5%, and the prevalence of patients with a 4-strand ST graft diameter <8 mm was 42.5%. US measurements had a strong, significant correlation with the ST graft length (P < .001) and a moderate correlation with the 4-strand ST graft diameter (P < .001). Absolute agreement between the preoperative US measurement of ST graft length and the intraoperative ST graft length showed good reliability (ICC2,1 = 0.825). The cross-sectional area (CSA) of the ST tendon at the knee joint level by US showed a weak correlation (r = 0.207) with the 4-strand ST graft diameter (P = .200). A CSA of 16 mm2 measured by US could be used to predict a 4-strand ST graft diameter ≥8 mm, with a sensitivity of 73.9% and specificity of 76.5%. CONCLUSION: Preoperative US measurements of ST tendons had a strong correlation with intraoperative ST graft length and provided good sensitivity to detect a 4-strand ST graft diameter ≥8 mm. All other anthropometric parameters showed a weak to moderate correlation with ST graft length and size.

Tuberculous Distal Biceps Tendon Rupture: Case Report and Review of the Literature.

Tuberculous distal biceps tendon rupture is a rare condition in orthopedics. Musculoskeletal tuberculosis usually presents with bursitis, synovitis, myositis, and osteomyelitis, conditions which demonstrate an excellent response to antituberculosis chemotherapy. Tendon rupture is often associated with delayed diagnosis and treatment. We report a rare manifestation of musculoskeletal tuberculosis in the distal biceps tendon with delayed diagnosis.

Reduced visualization of cerebral infarction on diffusion-weighted images with short diffusion times.

PURPOSE: Diffusion-weighted imaging (DWI) is a useful tool for early detection of cerebral infarction. However, recent reports have demonstrated that DWI with short effective diffusion time (∆eff) can obscure visualization of infarction. METHODS: We report three cases, including four acute-to-subacute infarctions, that demonstrated reduced visualization of the infarctions on DWI with shorter ∆eff. RESULTS: DWI was performed with different ∆eff: short and long ∆eff, using oscillating gradient spin-echo (OGSE) DWI, and intermediate ∆eff, using pulsed gradient spin-echo (PGSE) DWI. Different apparent diffusion coefficient values (due to different ∆eff) were also observed; these were considered to be the underlying causes of the under-evaluation of infarctions on DWI. CONCLUSION: The DWI with shorter ∆eff may obscure infarction. High-performance magnetic resonance imaging scanners with higher maximal gradient strength (Gmax) can perform DWI with shorter ∆eff than scanners with lower Gmax. Therefore, the appropriate ∆eff should be set for the detection of restricted diffusion.

Preoperative MRI evaluation of meningioma consistency: A focus on detailed architectures.

OBJECTIVES: Meningioma is one of the most common primary intracranial tumors. Diagnosis by imaging is not difficult. However evaluation of tumor consistency is an important factor affecting the surgical outcomes. The purpose of our study is to discover the relationship of different findings on pre-operative MRI, with a focus on detailed architectures, and different degrees of intra-operative stiffness of meningioma. Consistency of meningioma is also analyzed in compression to semi-quantitative pathological grading of fibrosis. PATIENTS AND METHODS: Sixty patients who underwent pre-operative MRI and primary surgery at our hospital were included in prospective fashion. Pre-operative MRI parameters, including general data and detailed internal architectures, were recorded. Intra-operative grading of tumor consistency was performed by the neurosurgeon. Pathological report according to WHO 2007 was performed with additional semi-quantitative grading of fibrosis. This study is focused on correlation of operative grade and MRI findings. RESULTS: Meningioma with hard consistency shows significant correlation with several features including en plaque appearance (p = 0.0427), higher ADC value (p = 0.0046) and ratio (p = 0.0016), absent of prominent enhanced rim (p = 0.0306), absent of enostotic spur (p = 0.0040) and absent of vascular core (p = 0.0133) in univariate analysis but no significant correlation is found in multivariate analysis in all except ADC ratio. Higher ADC ratio increase relative risk of hard consistency of meningioma by a factor of 41.22 (ORs = 41.22; 95%CI = 1.19-1426.24, P = 0.04). Good to very good inter-rater agreements are found. No significant correlation between tumor consistency and WHO grading was shown (p = 0.606). However, near significant p-value (p = 0.055) is found with increase degree of fibrosis in pathology as increase degree of tumor consistency. CONCLUSION: We found that en plaque appearance, higher ADC value and ADC ratio, absent of prominent capsular enhancement and absent of vascular core were suggestive of hard consistency in univariate analysis but not independent factors. Additionally, semi-quantitative pathological grading of fibrosis showed near significant correlation with tumor consistent.

Acromioclavicular joint dislocation: a Dog Bone button fixation alone versus Dog Bone button fixation augmented with acromioclavicular repair-a finite element analysis study.

BACKGROUND: Suspension suture button fixation was frequently used to treat acromioclavicular joint (ACJ) dislocation. However, there were many studies reporting about complications and residual horizontal instability after fixation. Our study compared the stability of ACJ after fixation between coracoclavicular (CC) fixation alone and CC fixation combined with ACJ repair by using finite element analysis (FEA). MATERIALS AND METHODS: A finite element model was created by using CT images from the normal shoulder. The model 1 was CC fixation with suture button alone, and the model 2 was CC fixation with suture button combined with ACJ repair. Three different forces (50, 100, 200 N) applied to the model in three planes; inferior, anterior and posterior direction load to the acromion. The von Mises stress of the implants and deformation at ACJs was recorded. RESULTS: The ACJ repair in the model 2 could reduce the peak stress on the implant after applying the loading forces to the acromion which the ACJ repair could reduce the peak stress of the FiberWire at suture button about 90% when compared to model 1. And, the ACJ repair could reduce the deformation of the ACJ after applying the loading forces to the acromion in both vertical and horizontal planes. CONCLUSION: This FEA supports that the high-grade injuries of the ACJ should be treated with CC fixation combined with ACJ repair because this technique provides excellent stability in both vertical and horizontal planes and reduces stress to the suture button.