Pedicle Screw Pseudofracture on Computed Tomography Secondary to Metal Artifact Reduction

Metal artifact reduction (MAR) algorithms are commonly used in computed tomography (CT) scans where metal implants are involved. However, MAR algorithms also have the potential to create new artifacts in reconstructed images. We present a case of a screw pseudofracture due to MAR on CT.

Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography

Osteoporosis, marked by low bone mineral density (BMD) and a high fracture risk, is a major health issue. Recent progress in medical imaging, especially CT scans, offers new ways of diagnosing and assessing osteoporosis. This review examines the use of AI analysis of CT scans to stratify BMD and diagnose osteoporosis. By summarizing the relevant studies, we aimed to assess the effectiveness, constraints, and potential impact of AI-based osteoporosis classification (severity) via CT. A systematic search of electronic databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 39 articles were retrieved from the databases, and the key findings were compiled and summarized, including the regions analyzed, the type of CT imaging, and their efficacy in predicting BMD compared with conventional DXA studies. Important considerations and limitations are also discussed. The overall reported accuracy, sensitivity, and specificity of AI in classifying osteoporosis using CT images ranged from 61.8% to 99.4%, 41.0% to 100.0%, and 31.0% to 100.0% respectively, with areas under the curve (AUCs) ranging from 0.582 to 0.994. While additional research is necessary to validate the clinical efficacy and reproducibility of these AI tools before incorporating them into routine clinical practice, these studies demonstrate the promising potential of using CT to opportunistically predict and classify osteoporosis without the need for DEXA.

A deep learning-based technique for the diagnosis of epidural spinal cord compression on thoracolumbar CT

PURPOSE

To develop a deep learning (DL) model for epidural spinal cord compression (ESCC) on CT, which will aid earlier ESCC diagnosis for less experienced clinicians.

METHODS

We retrospectively collected CT and MRI data from adult patients with suspected ESCC at a tertiary referral institute from 2007 till 2020. A total of 183 patients were used for training/validation of the DL model. A separate test set of 40 patients was used for DL model evaluation and comprised 60 staging CT and matched MRI scans performed with an interval of up to 2 months. DL model performance was compared to eight readers: one musculoskeletal radiologist, two body radiologists, one spine surgeon, and four trainee spine surgeons. Diagnostic performance was evaluated using inter-rater agreement, sensitivity, specificity and AUC.

RESULTS

Overall, 3115 axial CT slices were assessed. The DL model showed high kappa of 0.872 for normal, low and high-grade ESCC (trichotomous), which was superior compared to a body radiologist (R4, κ = 0.667) and all four trainee spine surgeons (κ range = 0.625-0.838)(all p < 0.001). In addition, for dichotomous normal versus any grade of ESCC detection, the DL model showed high kappa (κ = 0.879), sensitivity (91.82), specificity (92.01) and AUC (0.919), with the latter AUC superior to all readers (AUC range = 0.732-0.859, all p < 0.001).

CONCLUSION

A deep learning model for the objective assessment of ESCC on CT had comparable or superior performance to radiologists and spine surgeons. Earlier diagnosis of ESCC on CT could reduce treatment delays, which are associated with poor outcomes, increased costs, and reduced survival.

Outcomes and Complications of Surgery for Symptomatic Spinal Metastases; a Comparison Between Patients Aged ≥ 70 and <70

STUDY DESIGN

Retrospective cohort study.

OBJECTIVE

Physicians may be deterred from operating on elderly patients due to fears of poorer outcomes and complications. We aimed to compare the outcomes of surgical treatment of spinal metastases patients aged ≥70-yrs and <70-yrs.

MATERIALS AND METHODS

This is a retrospective study of patients surgically treated for metastatic epidural spinal cord compression and spinal instability between January-2005 to December-2021. Follow-up was till death or minimum 1-year post-surgery. Outcomes included post-operative neurological status, ambulatory status, medical and surgical complications. Two Sample t-test/Mann Whitney U test were used for numerical variables and Pearson Chi-Squared or Fishers Exact test for categorical variables. Survival was presented with a Kaplan-Meier curve. P < .05 was significant.

RESULTS

We identified 412 patients of which 29 (7.1%) patients were excluded due to loss to follow-up and previous surgical treatment. 79 (20.6%) were ≥70-yrs. Age ≥70-yrs patients had poorer ECOG scores (P = .0017) and Charlson Comorbidity Index (P < .001). No significant difference in modified Tokuhashi score (P = .393) was observed with significantly more ≥ prostate (P < .001) and liver (P = .029) cancer in ≥70-yrs. Improved or maintained normal neurological function (P = .934), independent ambulatory status (P = .171), and survival at 6 months (P = .119) and 12 months (P = .659) was not significantly different between both groups. Medical (P = .528) or surgical (P = .466) complication rates and readmission rates (P = .800) were similar.

CONCLUSION

≥70-yrs patients have comparable outcomes to <70-yr old patients with no significant increase in complication rates. Age should not be a determining factor in deciding surgical management of spinal metastases.

Epithelioid Sarcoma of the Spine: A Review of Literature and Case Report

Epithelioid sarcoma is a rare malignant mesenchymal tumor that represents less than 1% of soft-tissue sarcomas. Despite its slow growth, the overall prognosis is poor with a high rate of local recurrence, lymph-node spread, and hematogenous metastasis. Primary epithelioid sarcoma arising from the spine is extremely rare, with limited data in the literature. We review the existing literature regarding spinal epithelioid sarcoma and report a case of epithelioid sarcoma arising from the spinal cord. A 54 year old male presented with a 1-month history of progressive left upper-limb weakness and numbness. Magnetic resonance imaging (MRI) of the spine showed an enhancing intramedullary mass at the level of T1 also involving the left T1 nerve root. Systemic radiological examination revealed no other lesion at presentation. Surgical excision of the mass was performed, and histology was consistent with epithelioid sarcoma of the spine. Despite adjuvant radiotherapy, there was aggressive local recurrence and development of intracranial metastatic spread. The patient died of the disease within 5 months from presentation. To the best of our knowledge, spinal epithelioid sarcoma arising from the spinal cord has not yet been reported. We review the challenges in diagnosis, surgical treatment, and oncologic outcome of this case.

Novel 3D printable PEEK-HA-Mg2SiO4 composite material for spine implants: biocompatibility and imaging compatibility assessments

PURPOSE

To develop a novel 3D printable polyether ether ketone (PEEK)-hydroxyapatite (HA)-magnesium orthosilicate (Mg2SiO4) composite material with enhanced properties for potential use in tumour, osteoporosis and other spinal conditions. We aim to evaluate biocompatibility and imaging compatibility of the material.

METHODS

Materials were prepared in three different compositions, namely composite A: 75 weight % PEEK, 20 weight % HA, 5 weight % Mg2SiO4; composite B: 70 weight% PEEK, 25 weight % HA, 5 weight % Mg2SiO4; and composite C: 65 weight % PEEK, 30 weight % HA, 5 weight % Mg2SiO4. The materials were processed to obtain 3D printable filament. Biomechanical properties were analysed as per ASTM standards and biocompatibility of the novel material was evaluated using indirect and direct cell cytotoxicity tests. Cell viability of the novel material was compared to PEEK and PEEK-HA materials. The novel material was used to 3D print a standard spine cage. Furthermore, the CT and MR imaging compatibility of the novel material cage vs PEEK and PEEK-HA cages were evaluated using a phantom setup.

RESULTS

Composite A resulted in optimal material processing to obtain a 3D printable filament, while composite B and C resulted in non-optimal processing. Composite A enhanced cell viability up to ~ 20% compared to PEEK and PEEK-HA materials. Composite A cage generated minimal/no artefacts on CT and MR imaging and the images were comparable to that of PEEK and PEEK-HA cages.

CONCLUSION

Composite A demonstrated superior bioactivity vs PEEK and PEEK-HA materials and comparable imaging compatibility vs PEEK and PEEK-HA. Therefore, our material displays an excellent potential to manufacture spine implants with enhanced mechanical and bioactive property.

Multiple Myeloma: Case of a "Moving" Sternal Wire

Multiple myeloma generally occurs in older adults, with the clonal proliferation of plasma cells and accumulation of monoclonal protein resulting in a broad range of clinical manifestations and complications, including hypercalcemia, renal dysfunction, anaemia, and bone destruction (termed CRAB features). A 64-year-old man with no history of malignancy presented with an enlarging precordial lump occurring three years post-sternotomy for uneventful coronary artery bypass grafting surgery. Initial investigations showed anaemia and impaired renal function. Multimodal imaging performed for further evaluation showcases the radio-pathological features which can be encountered in haematological malignancy. Subsequent percutaneous biopsy confirmed an underlying plasma cell neoplasm, and a diagnosis of multiple myeloma was achieved. The prompt resolution of the lesions upon the initiation of treatment highlights the importance of early diagnosis and treatment.

Incidental findings involving the temporomandibular joint on computed tomography and magnetic resonance imaging

The temporomandibular joint (TMJ) is frequently imaged in head and neck computed tomography (CT) and magnetic resonance imaging (MRI) studies. Depending on the indication for the study, an abnormality of the TMJ may be an incidental finding. These findings encompass both intra- and extra-articular disorders. They may also be related to local, regional or systemic conditions. Familiarity with these findings along with pertinent clinical information helps narrow the list of differential diagnoses. While definitive diagnosis may not be immediately apparent, a systematic approach contributes to improved discussions between clinicians and radiologists and better patient management.

Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review

An accurate diagnosis of bone tumours on imaging is crucial for appropriate and successful treatment. The advent of Artificial intelligence (AI) and machine learning methods to characterize and assess bone tumours on various imaging modalities may assist in the diagnostic workflow. The purpose of this review article is to summarise the most recent evidence for AI techniques using imaging for differentiating benign from malignant lesions, the characterization of various malignant bone lesions, and their potential clinical application. A systematic search through electronic databases (PubMed, MEDLINE, Web of Science, and clinicaltrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 34 articles were retrieved from the databases and the key findings were compiled and summarised. A total of 34 articles reported the use of AI techniques to distinguish between benign vs. malignant bone lesions, of which 12 (35.3%) focused on radiographs, 12 (35.3%) on MRI, 5 (14.7%) on CT and 5 (14.7%) on PET/CT. The overall reported accuracy, sensitivity, and specificity of AI in distinguishing between benign vs. malignant bone lesions ranges from 0.44–0.99, 0.63–1.00, and 0.73–0.96, respectively, with AUCs of 0.73–0.96. In conclusion, the use of AI to discriminate bone lesions on imaging has achieved a relatively good performance in various imaging modalities, with high sensitivity, specificity, and accuracy for distinguishing between benign vs. malignant lesions in several cohort studies. However, further research is necessary to test the clinical performance of these algorithms before they can be facilitated and integrated into routine clinical practice.

Deep learning assessment compared to radiologist reporting for metastatic spinal cord compression on CT

Introduction

Metastatic spinal cord compression (MSCC) is a disastrous complication of advanced malignancy. A deep learning (DL) algorithm for MSCC classification on CT could expedite timely diagnosis. In this study, we externally test a DL algorithm for MSCC classification on CT and compare with radiologist assessment.

Methods

Retrospective collection of CT and corresponding MRI from patients with suspected MSCC was conducted from September 2007 to September 2020. Exclusion criteria were scans with instrumentation, no intravenous contrast, motion artefacts and non-thoracic coverage. Internal CT dataset split was 84% for training/validation and 16% for testing. An external test set was also utilised. Internal training/validation sets were labelled by radiologists with spine imaging specialization (6 and 11-years post-board certification) and were used to further develop a DL algorithm for MSCC classification. The spine imaging specialist (11-years expertise) labelled the test sets (reference standard). For evaluation of DL algorithm performance, internal and external test data were independently reviewed by four radiologists: two spine specialists (Rad1 and Rad2, 7 and 5-years post-board certification, respectively) and two oncological imaging specialists (Rad3 and Rad4, 3 and 5-years post-board certification, respectively). DL model performance was also compared against the CT report issued by the radiologist in a real clinical setting. Inter-rater agreement (Gwet's kappa) and sensitivity/specificity/AUCs were calculated.

Results

Overall, 420 CT scans were evaluated (225 patients, mean age=60 ± 11.9[SD]); 354(84%) CTs for training/validation and 66(16%) CTs for internal testing. The DL algorithm showed high inter-rater agreement for three-class MSCC grading with kappas of 0.872 (p<0.001) and 0.844 (p<0.001) on internal and external testing, respectively. On internal testing DL algorithm inter-rater agreement (κ=0.872) was superior to Rad 2 (κ=0.795) and Rad 3 (κ=0.724) (both p<0.001). DL algorithm kappa of 0.844 on external testing was superior to Rad 3 (κ=0.721) (p<0.001). CT report classification of high-grade MSCC disease was poor with only slight inter-rater agreement (κ=0.027) and low sensitivity (44.0), relative to the DL algorithm with almost-perfect inter-rater agreement (κ=0.813) and high sensitivity (94.0) (p<0.001).

Conclusion

Deep learning algorithm for metastatic spinal cord compression on CT showed superior performance to the CT report issued by experienced radiologists and could aid earlier diagnosis.

Diagnostic Accuracy of CT for Metastatic Epidural Spinal Cord Compression

Background: Early diagnosis of metastatic epidural spinal cord compression (MESCC) is vital to expedite therapy and prevent paralysis. Staging CT is performed routinely in cancer patients and presents an opportunity for earlier diagnosis. Methods: This retrospective study included 123 CT scans from 101 patients who underwent spine MRI within 30 days, excluding 549 CT scans from 216 patients due to CT performed post-MRI, non-contrast CT, or a gap greater than 30 days between modalities. Reference standard MESCC gradings on CT were provided in consensus via two spine radiologists (11 and 7 years of experience) analyzing the MRI scans. CT scans were labeled using the original reports and by three radiologists (3, 13, and 14 years of experience) using dedicated CT windowing. Results: For normal/none versus low/high-grade MESCC per CT scan, all radiologists demonstrated almost perfect agreement with kappa values ranging from 0.866 (95% CI 0.787–0.945) to 0.947 (95% CI 0.899–0.995), compared to slight agreement for the reports (kappa = 0.095, 95%CI −0.098–0.287). Radiologists also showed high sensitivities ranging from 91.51 (95% CI 84.49–96.04) to 98.11 (95% CI 93.35–99.77), compared to 44.34 (95% CI 34.69–54.31) for the reports. Conclusion: Dedicated radiologist review for MESCC on CT showed high interobserver agreement and sensitivity compared to the current standard of care.

Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis

Spinal metastasis is the most common malignant disease of the spine. Recently, major advances in machine learning and artificial intelligence technology have led to their increased use in oncological imaging. The purpose of this study is to review and summarise the present evidence for artificial intelligence applications in the detection, classification and management of spinal metastasis, along with their potential integration into clinical practice. A systematic, detailed search of the main electronic medical databases was undertaken in concordance with the PRISMA guidelines. A total of 30 articles were retrieved from the database and reviewed. Key findings of current AI applications were compiled and summarised. The main clinical applications of AI techniques include image processing, diagnosis, decision support, treatment assistance and prognostic outcomes. In the realm of spinal oncology, artificial intelligence technologies have achieved relatively good performance and hold immense potential to aid clinicians, including enhancing work efficiency and reducing adverse events. Further research is required to validate the clinical performance of the AI tools and facilitate their integration into routine clinical practice.

State-of-the-Art Imaging Techniques in Metastatic Spinal Cord Compression

Metastatic Spinal Cord Compression (MSCC) is a debilitating complication in oncology patients. This narrative review discusses the strengths and limitations of various imaging modalities in diagnosing MSCC, the role of imaging in stereotactic body radiotherapy (SBRT) for MSCC treatment, and recent advances in deep learning (DL) tools for MSCC diagnosis. PubMed and Google Scholar databases were searched using targeted keywords. Studies were reviewed in consensus among the co-authors for their suitability before inclusion. MRI is the gold standard of imaging to diagnose MSCC with reported sensitivity and specificity of 93% and 97% respectively. CT Myelogram appears to have comparable sensitivity and specificity to contrast-enhanced MRI. Conventional CT has a lower diagnostic accuracy than MRI in MSCC diagnosis, but is helpful in emergent situations with limited access to MRI. Metal artifact reduction techniques for MRI and CT are continually being researched for patients with spinal implants. Imaging is crucial for SBRT treatment planning and three-dimensional positional verification of the treatment isocentre prior to SBRT delivery. Structural and functional MRI may be helpful in post-treatment surveillance. DL tools may improve detection of vertebral metastasis and reduce time to MSCC diagnosis. This enables earlier institution of definitive therapy for better outcomes.

Improved Productivity Using Deep Learning-assisted Reporting for Lumbar Spine MRI

Background Lumbar spine MRI studies are widely used for back pain assessment. Interpretation involves grading lumbar spinal stenosis, which is repetitive and time consuming. Deep learning (DL) could provide faster and more consistent interpretation. Purpose To assess the speed and interobserver agreement of radiologists for reporting lumbar spinal stenosis with and without DL assistance. Materials and Methods In this retrospective study, a DL model designed to assist radiologists in the interpretation of spinal canal, lateral recess, and neural foraminal stenoses on lumbar spine MRI scans was used. Randomly selected lumbar spine MRI studies obtained in patients with back pain who were 18 years and older over a 3-year period, from September 2015 to September 2018, were included in an internal test data set. Studies with instrumentation and scoliosis were excluded. Eight radiologists, each with 2-13 years of experience in spine MRI interpretation, reviewed studies with and without DL model assistance with a 1-month washout period. Time to diagnosis (in seconds) and interobserver agreement (using Gwet κ) were assessed for stenosis grading for each radiologist with and without the DL model and compared with test data set labels provided by an external musculoskeletal radiologist (with 32 years of experience) as the reference standard. Results Overall, 444 images in 25 patients (mean age, 51 years ± 20 [SD]; 14 women) were evaluated in a test data set. DL-assisted radiologists had a reduced interpretation time per spine MRI study, from a mean of 124-274 seconds (SD, 25-88 seconds) to 47-71 seconds (SD, 24-29 seconds) (P < .001). DL-assisted radiologists had either superior or equivalent interobserver agreement for all stenosis gradings compared with unassisted radiologists. DL-assisted general and in-training radiologists improved their interobserver agreement for four-class neural foraminal stenosis, with κ values of 0.71 and 0.70 (with DL) versus 0.39 and 0.39 (without DL), respectively (both P < .001). Conclusion Radiologists who were assisted by deep learning for interpretation of lumbar spinal stenosis on MRI scans showed a marked reduction in reporting time and superior or equivalent interobserver agreement for all stenosis gradings compared with radiologists who were unassisted by deep learning. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Hayashi in this issue.

Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI

Background

Metastatic epidural spinal cord compression (MESCC) is a devastating complication of advanced cancer. A deep learning (DL) model for automated MESCC classification on MRI could aid earlier diagnosis and referral.

Purpose

To develop a DL model for automated classification of MESCC on MRI.

Materials and Methods

Patients with known MESCC diagnosed on MRI between September 2007 and September 2017 were eligible. MRI studies with instrumentation, suboptimal image quality, and non-thoracic regions were excluded. Axial T2-weighted images were utilized. The internal dataset split was 82% and 18% for training/validation and test sets, respectively. External testing was also performed. Internal training/validation data were labeled using the Bilsky MESCC classification by a musculoskeletal radiologist (10-year experience) and a neuroradiologist (5-year experience). These labels were used to train a DL model utilizing a prototypical convolutional neural network. Internal and external test sets were labeled by the musculoskeletal radiologist as the reference standard. For assessment of DL model performance and interobserver variability, test sets were labeled independently by the neuroradiologist (5-year experience), a spine surgeon (5-year experience), and a radiation oncologist (11-year experience). Inter-rater agreement (Gwet’s kappa) and sensitivity/specificity were calculated.

Results

Overall, 215 MRI spine studies were analyzed [164 patients, mean age = 62 ± 12(SD)] with 177 (82%) for training/validation and 38 (18%) for internal testing. For internal testing, the DL model and specialists all showed almost perfect agreement (kappas = 0.92–0.98, p < 0.001) for dichotomous Bilsky classification (low versus high grade) compared to the reference standard. Similar performance was seen for external testing on a set of 32 MRI spines with the DL model and specialists all showing almost perfect agreement (kappas = 0.94–0.95, p < 0.001) compared to the reference standard.

Conclusion

A DL model showed comparable agreement to a subspecialist radiologist and clinical specialists for the classification of malignant epidural spinal cord compression and could optimize earlier diagnosis and surgical referral.

Detection of Pneumothorax with Deep Learning Models: Learning From Radiologist Labels vs Natural Language Processing Model Generated Labels

RATIONALE AND OBJECTIVES

To compare the performance of pneumothorax deep learning detection models trained with radiologist versus natural language processing (NLP) labels on the NIH ChestX-ray14 dataset.

MATERIALS AND METHODS

The ChestX-ray14 dataset consisted of 112,120 frontal chest radiographs with 5302 positive and 106, 818 negative labels for pneumothorax using NLP (dataset A). All 112,120 radiographs were also inspected by 4 radiologists leaving a visually confirmed set of 5,138 positive and 104,751 negative for pneumothorax (dataset B). Datasets A and B were used independently to train 3 convolutional neural network (CNN) architectures (ResNet-50, DenseNet-121 and EfficientNetB3). All models' area under the receiver operating characteristic curve (AUC) were evaluated with the official NIH test set and an external test set of 525 chest radiographs from our emergency department.

RESULTS

There were significantly higher AUCs on the NIH internal test set for CNN models trained with radiologist vs NLP labels across all architectures. AUCs for the NLP/radiologist-label models were 0.838 (95%CI:0.830, 0.846)/0.881 (95%CI:0.873,0.887) for ResNet-50 (p = 0.034), 0.839 (95%CI:0.831,0.847)/0.880 (95%CI:0.873,0.887) for DenseNet-121, and 0.869 (95%CI: 0.863,0.876)/0.943 (95%CI: 0.939,0.946) for EfficientNetB3 (p ≤0.001). Evaluation with the external test set also showed higher AUCs (p <0.001) for the CNN models trained with radiologist versus NLP labels across all architectures. The AUCs for the NLP/radiologist-label models were 0.686 (95%CI:0.632,0.740)/0.806 (95%CI:0.758,0.854) for ResNet-50, 0.736 (95%CI:0.686, 0.787)/0.871 (95%CI:0.830,0.912) for DenseNet-121, and 0.822 (95%CI: 0.775,0.868)/0.915 (95%CI: 0.882,0.948) for EfficientNetB3.

CONCLUSION

We demonstrated improved performance and generalizability of pneumothorax detection deep learning models trained with radiologist labels compared to models trained with NLP labels.

Evolution of materials for implants in metastatic spine disease till date - Have we found an ideal material?

"Metastatic Spine Disease" (MSD) often requires surgical intervention and instrumentation with spinal implants. Ti6Al4V is widely used in metastatic spine tumor surgery (MSTS) and is the current implant material of choice due to improved biocompatibility, mechanical properties, and compatibility with imaging modalities compared to stainless steel. However, it is still not the ideal implant material due to the following issues. Ti6Al4V implants cause stress-shielding as their Young's modulus (110 gigapascal [GPa]) is higher than cortical bone (17-21 GPa). Ti6Al4V also generates artifacts on CT and MRI, which interfere with the process of postoperative radiotherapy (RT), including treatment planning and delivery. Similarly, charged particle therapy is hindered in the presence of Ti6Al4V. In addition, artifacts on CT and MRI may result in delayed recognition of tumor recurrence and postoperative complications. In comparison, polyether-ether-ketone (PEEK) is a promising alternative. PEEK has a low Young's modulus (3.6 GPa), which results in optimal load-sharing and produces minimal artifacts on imaging with less hinderance on postoperative RT. However, PEEK is bioinert and unable to provide sufficient stability in the immediate postoperative period. This issue may possibly be mitigated by combining PEEK with other materials to form composites or through surface modification, although further research is required in these areas. With the increasing incidence of MSD, it is an opportune time for the development of spinal implants that possess all the ideal material properties for use in MSTS. Our review will explore whether there is a current ideal implant material, available alternatives and whether these require further investigation.

Deep Learning Systems for Pneumothorax Detection on Chest Radiographs: A Multicenter External Validation Study

PURPOSE

To assess the generalizability of a deep learning pneumothorax detection model on datasets from multiple external institutions and examine patient and acquisition factors that might influence performance.

MATERIALS AND METHODS

In this retrospective study, a deep learning model was trained for pneumothorax detection by merging two large open-source chest radiograph datasets: ChestX-ray14 and CheXpert. It was then tested on six external datasets from multiple independent institutions (labeled A-F) in a retrospective case-control design (data acquired between 2016 and 2019 from institutions A-E; institution F consisted of data from the MIMIC-CXR dataset). Performance on each dataset was evaluated by using area under the receiver operating characteristic curve (AUC) analysis, sensitivity, specificity, and positive and negative predictive values, with two radiologists in consensus being used as the reference standard. Patient and acquisition factors that influenced performance were analyzed.

RESULTS

The AUCs for pneumothorax detection for external institutions A-F were 0.91 (95% CI: 0.88, 0.94), 0.97 (95% CI: 0.94, 0.99), 0.91 (95% CI: 0.85, 0.97), 0.98 (95% CI: 0.96, 1.0), 0.97 (95% CI: 0.95, 0.99), and 0.92 (95% CI: 0.90, 0.95), respectively, compared with the internal test AUC of 0.93 (95% CI: 0.92, 0.93). The model had lower performance for small compared with large pneumothoraces (AUC, 0.88 [95% CI: 0.85, 0.91] vs AUC, 0.96 [95% CI: 0.95, 0.97]; P = .005). Model performance was not different when a chest tube was present or absent on the radiographs (AUC, 0.95 [95% CI: 0.92, 0.97] vs AUC, 0.94 [95% CI: 0.92, 0.05]; P > .99).

CONCLUSION

A deep learning model trained with a large volume of data on the task of pneumothorax detection was able to generalize well to multiple external datasets with patient demographics and technical parameters independent of the training data.Keywords: Thorax, Computer Applications-Detection/DiagnosisSee also commentary by Jacobson and Krupinski in this issue.Supplemental material is available for this article.©RSNA, 2021.

Deep Learning Model for Automated Detection and Classification of Central Canal, Lateral Recess, and Neural Foraminal Stenosis at Lumbar Spine MRI

Background Assessment of lumbar spinal stenosis at MRI is repetitive and time consuming. Deep learning (DL) could improve -productivity and the consistency of reporting. Purpose To develop a DL model for automated detection and classification of lumbar central canal, lateral recess, and neural -foraminal stenosis. Materials and Methods In this retrospective study, lumbar spine MRI scans obtained from September 2015 to September 2018 were included. Studies of patients with spinal instrumentation or studies with suboptimal image quality, as well as postgadolinium studies and studies of patients with scoliosis, were excluded. Axial T2-weighted and sagittal T1-weighted images were used. Studies were split into an internal training set (80%), validation set (9%), and test set (11%). Training data were labeled by four radiologists using predefined gradings (normal, mild, moderate, and severe). A two-component DL model was developed. First, a convolutional neural network (CNN) was trained to detect the region of interest (ROI), with a second CNN for classification. An internal test set was labeled by a musculoskeletal radiologist with 31 years of experience (reference standard) and two subspecialist radiologists (radiologist 1: A.M., 5 years of experience; radiologist 2: J.T.P.D.H., 9 years of experience). DL model performance on an external test set was evaluated. Detection recall (in percentage), interrater agreement (Gwet κ), sensitivity, and specificity were calculated. Results Overall, 446 MRI lumbar spine studies were analyzed (446 patients; mean age ± standard deviation, 52 years ± 19; 240 women), with 396 patients in the training (80%) and validation (9%) sets and 50 (11%) in the internal test set. For internal testing, DL model and radiologist central canal recall were greater than 99%, with reduced neural foramina recall for the DL model (84.5%) and radiologist 1 (83.9%) compared with radiologist 2 (97.1%) (P < .001). For internal testing, dichotomous classification (normal or mild vs moderate or severe) showed almost-perfect agreement for both radiologists and the DL model, with respective κ values of 0.98, 0.98, and 0.96 for the central canal; 0.92, 0.95, and 0.92 for lateral recesses; and 0.94, 0.95, and 0.89 for neural foramina (P < .001). External testing with 100 MRI scans of lumbar spines showed almost perfect agreement for the DL model for dichotomous classification of all ROIs (κ, 0.95-0.96; P < .001). Conclusion A deep learning model showed comparable agreement with subspecialist radiologists for detection and classification of central canal and lateral recess stenosis, with slightly lower agreement for neural foraminal stenosis at lumbar spine MRI. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Hayashi in this issue.

Shoulder Tumor/Tumor-Like Lesions: What to Look for

This article discusses the most common tumor and tumor-like lesions arising at the shoulder. Osseous tumors of the shoulder rank second in incidence to those at the knee joint and include benign osteochondromas and myeloma or primary malignant lesions, such as osteosarcoma or chondrosarcomas. Soft tissue tumors are overwhelmingly benign, with lipomas predominating, although malignant lesions, such as liposarcomas, can occur. Numerous tumor-like lesions may arise from the joints or bursae, due to either underlying arthropathy and synovitis (eg, rheumatoid arthritis and amyloid) or related to conditions, including tenosynovial giant cell tumor and synovial osteochondromatosis.

Unusual presentation of osteoblastoma as vertebra plana-a case report and review of literature

BACKGROUND

Osteoblastoma is rare and accounts for 3% of all benign tumors and 1% of all bone tumors. The spine is the most common site of occurrence, constituting 32% to 45% of all osteoblastomas. It has a strong predilection for the posterior elements, most often occurring in the lumbar spine.

METHOD

In this case report, we describe an unusual presentation of spinal osteoblastoma presenting as thoracic T9 vertebra plana in a 20-year-old female. She presented with discomfort over the midback with unsteadiness of gait. The patient underwent detailed investigations including computed tomography (CT), magnetic resonance imaging, and CT-guided biopsy. To our knowledge, this is the first case report of vertebra plana due to spinal osteoblastoma in the English literature.

RESULT

The patient successfully underwent posterior decompression of T9 with laminectomy followed by minimally invasive surgery posterior instrumentation from T7 to T11. Histopathology of the intraoperative specimen was consistent with osteoblastoma. The patient had an uneventful postoperative recovery and no evidence of tumor recurrence could be demonstrated on positron emission tomography scan at 15 months' follow-up.

CONCLUSION

In conclusion, the differential diagnosis for vertebra plana is extensive and we add spinal osteoblastoma as another etiology to the existing list. Diagnosis and treatment of vertebra plana involve multimodality radiological imaging, and careful histological and surgical evaluation to identify the underlying etiology.

Eye Globe Abnormalities on MR and CT in Adults: An Anatomical Approach

Eye globe abnormalities can be readily detected on dedicated and non-dedicated CT and MR studies. A primary understanding of the globe anatomy is key to characterising both traumatic and non-traumatic globe abnormalities. The globe consists of three primary layers: the sclera (outer), uvea (middle), and retina (inner layer). The various pathological processes involving these layers are highlighted using case examples with fundoscopic correlation where appropriate. In the emergent setting, trauma can result in hemorrhage, retinal/choroidal detachment and globe rupture. Neoplasms and inflammatory/infective processes predominantly occur in the vascular middle layer. The radiologist has an important role in primary diagnosis contributing to appropriate ophthalmology referral, thereby preventing devastating consequences such as vision loss.

The role of multidetector computed tomography versus digital subtraction angiography in triaging care and management in abdominopelvic trauma

INTRODUCTION

This study aimed to assess the ability of contrast-enhanced computed tomography (CECT) to detect active abdominopelvic haemorrhage in patients with blunt trauma, as compared to digital subtraction angiography (DSA).

METHODS

In this retrospective study, patients who underwent DSA within 24 hours following CECT for blunt abdominal and/or pelvic trauma were identified. The computed tomography (CT) trauma protocol consisted of a portal venous phase scan without CT angiography; delayed phase study was performed if appropriate. All selected CECT studies were independently reviewed for the presence of active extravasation of contrast by two radiologists, who were blinded to the DSA results. Fisher's exact test was used to correlate the presence of extravasation on CT with subsequent confirmed haemorrhage on DSA.

RESULTS

During the eight-year study period, 51 patients underwent CECT prior to emergent DSA for abdominal or pelvic trauma. Evidence of active extravasation of contrast on CECT was observed in 35 patients and active haemorrhage was confirmed on DSA in 31 of these patients; embolisation was performed in all 31 patients. Two patients who were negative for active extravasation of contrast on CECT but positive for active haemorrhage on DSA had extensive bilateral pelvic fractures and haematomas. The sensitivity, specificity, and positive and negative predictive values of CECT in detecting active abdominopelvic haemorrhage, as compared to DSA, were 93.9%, 77.8%, 88.6% and 87.5%, respectively.

CONCLUSION

When compared with DSA, dual-phase CECT without CT angiography shows high sensitivity and positive predictive value for the detection of active haemorrhage in patients with blunt abdominopelvic trauma.

Comparison of magnetic resonance elastography and diffusion-weighted imaging for differentiating benign and malignant liver lesions

OBJECTIVES

Comparison of magnetic resonance elastography (MRE) and diffusion-weighted imaging (DWI) for differentiating malignant and benign focal liver lesions (FLLs).

METHODS

Seventy-nine subjects with 124 FLLs (44 benign and 80 malignant) underwent both MRE and DWI. MRE was performed with a modified gradient-echo sequence and DWI with a free breathing technique (b = 0.500). Apparent diffusion coefficient (ADC) maps and stiffness maps were generated. FLL mean stiffness and ADC values were obtained by placing regions of interest over the FLLs on stiffness and ADC maps. The accuracy of MRE and DWI for differentiation of benign and malignant FLL was compared using receiver operating curve (ROC) analysis.

RESULTS

There was a significant negative correlation between stiffness and ADC (r = -0.54, p < 0.0001) of FLLs. Malignant FLLs had significantly higher mean stiffness (7.9kPa vs. 3.1kPa, p < 0.001) and lower mean ADC (129 vs. 200 × 10(-3)mm(2)/s, p < 0.001) than benign FLLs. The sensitivity/specificity/positive predictive value/negative predictive value for differentiating malignant from benign FLLs with MRE (cut-off, >4.54kPa) and DWI (cut-off, <151 × 10(-3)mm(2)/s) were 96.3/95.5/97.5/93.3% (p < 0.001) and 85/81.8/88.3/75% (p < 0.001), respectively. ROC analysis showed significantly higher accuracy for MRE than DWI (0.986 vs. 0.82, p = 0.0016).

CONCLUSION

MRE is significantly more accurate than DWI for differentiating benign and malignant FLLs.

KEY POINTS

• MRE is superior to DWI for differentiating benign and malignant focal liver lesions. • Benign lesions with large fibrous components may have higher stiffness with MRE. • Cholangiocarcinomas tend to have higher stiffness than hepatocellular carcinomas. • Hepatocellular adenomas tend to have lower stiffness than focal nodular hyperplasia. • MRE is superior to conventional MRI in differentiating benign and malignant liver lesions.

Multi-detector computed tomography in the diagnosis and management of acute aortic syndromes

Acute aortic syndrome (AAS) is a spectrum of conditions, which may ultimately progress to potentially life-threatening aortic rupture. This syndrome encompasses aortic dissection (AD), intramural haematoma, penetrating atherosclerotic ulcer and unstable thoracic aortic aneurysms. Multi-detector CT (MDCT) is crucial for the diagnosis of AAS, especially in the emergency setting due to its speed, accuracy and ready availability. This review attends to the value of appropriate imaging protocols in obtaining good quality images that can permit a confident diagnosis of AAS. AD is the most commonly encountered AAS and also the one with maximum potential to cause catastrophic outcome if not diagnosed and managed promptly. Hence, this review briefly addresses certain relevant clinical perspectives on this condition. Differentiating the false from the true lumen in AD is often essential; a spectrum of CT findings, e.g., “beak sign”, aortic “cobwebs” that allows such differentiation have been described with explicit illustrations. The value of non enhanced CT scans, especially useful in the diagnosis of an intramural hematoma has also been illustrated. Overlap in the clinical and imaging features of the various conditions presenting as AAS is not unusual. However, on most instances MDCT enables the right diagnosis. On select occasions MRI or trans-esophageal echocardiography may be required as a problem solving tool.

Gastric carcinoma: imaging diagnosis, staging and assessment of treatment response

Gastric carcinoma (GC) is one of the most common causes of cancer-related death worldwide. Surgical resection is the only cure available and is dependent on the GC stage at presentation, which incorporates depth of tumor invasion, extent of lymph node and distant metastases. Accurate preoperative staging is therefore essential for optimal surgical management with consideration of preoperative and/or postoperative chemotherapy. Multidetector computed tomography (MDCT) with its ability to assess tumor depth, nodal disease and metastases is the preferred technique for staging GC. Endoscopic ultrasonography is more accurate for assessing the depth of wall invasion in early cancer, but is limited in the assessment of advanced local or stenotic cancer and detection of distant metastases. Magnetic resonance imaging (MRI), although useful for staging, is not proven to be effective. Positron emission tomography (PET) is most useful for detecting and characterizing distant metastases. Both MDCT and PET are useful for assessment of treatment response following preoperative chemotherapy and for detection of recurrence after surgical resection. This review article discusses the usefulness of imaging modalities for detecting, staging and assessing treatment response for GC and the potential role of newer applications including CT volumetry, virtual gastroscopy and perfusion CT in the management of GC.