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In spinal oncology, integrating deep learning with computed tomography (CT) imaging has shown promise in enhancing diagnostic accuracy, treatment planning, and patient outcomes. This systematic review synthesizes evidence on artificial intelligence (AI) applications in CT imaging for spinal tumors. A PRISMA-guided search identified 33 studies: 12 (36.4%) focused on detecting spinal malignancies, 11 (33.3%) on classification, 6 (18.2%) on prognostication, 3 (9.1%) on treatment planning, and 1 (3.0%) on both detection and classification. Of the classification studies, 7 (21.2%) used machine learning to distinguish between benign and malignant lesions, 3 (9.1%) evaluated tumor stage or grade, and 2 (6.1%) employed radiomics for biomarker classification. Prognostic studies included three (9.1%) that predicted complications such as pathological fractures and three (9.1%) that predicted treatment outcomes. AI's potential for improving workflow efficiency, aiding decision-making, and reducing complications is discussed, along with its limitations in generalizability, interpretability, and clinical integration. Future directions for AI in spinal oncology are also explored. In conclusion, while AI technologies in CT imaging are promising, further research is necessary to validate their clinical effectiveness and optimize their integration into routine practice.

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BACKGROUND: Blood loss is an important consideration in metastatic spine tumour surgery (MSTS). Allogeneic blood transfusion (ABT) is the current standard of blood replenishment for MSTS despite known complications. Salvaged blood transfusion (SBT) through intraoperative cell salvage addresses the majority of complications related to ABT. However, the use of SBT in MSTS still remains controversial. We aim to conduct a prospective propensity-score (PS) matched analysis to evaluate the long-term clinical outcomes of intraoperative cell salvage (IOCS) in MSTS. METHODS: Our study included 98 patients who underwent MSTS from 2014-2017. A PS matched cohort was created using the relevant and available predictors of treatment assignment and outcomes of interest. Clinical outcomes consisting of overall survival (OS), as well tumour progression (TP) that was evaluated using RECIST (v1.1) were compared in the matched cohort. RESULTS: Our study had a total of 98 patients with a mean age of 60 years old. A total of 33 patients received SBT. Overall median blood loss was 600 mL [interquartile range (IQR): 300-1,000 mL] and overall median blood transfusion (BT) was 620 mL (IQR: 110-1,600 mL). Group PS matching included 30 patients who received ABT and 28 patients who received SBT. There was also no significant difference between the OS of patients who underwent ABT or SBT (P=0.19). SBT did not show any significant increase in 4-year tumour progression [PS matched hazard ratio (HR) 3.659; 95% confidence interval (CI): 0.346-38.7; P=0.28]. CONCLUSIONS: SBT has been shown to have similar clinical outcomes to that of ABT in patients undergoing MSTS, with potential benefits of avoiding complications and costs of ABT. This will be the first long-term PS matched analysis to report on the clinical outcomes of SBT and affirms the clinical role of SBT in MSTS today.

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BACKGROUND: Delayed treatment in symptomatic metastatic epidural spinal cord compression (MESCC) is significantly associated with poorer functional outcomes. In this study, we aim to identify the patterns of treatment delay in patients and factors predictive of postoperative ambulatory function. METHODS: Retrospective review of patients with symptomatic MESCC treated surgically between January 2015 and January 2022. MESCC symptoms were categorized into symptoms suggesting cord compression requiring immediate referral and symptoms suggestive of spinal metastases. Multivariate analysis was performed to identify factors predictive of postoperative ambulatory function. Delays in treatment were identified and categorized into patient delay (onset of symptoms till initial medical consultation), diagnostic delay (medical consultation till radiological diagnosis of MESCC), referral delay (from diagnosis till spine surgeon review) and surgical delay (from spine surgeon review till surgery) and compared between patients. RESULTS: One hundred and seventy-eight patients were identified. In this cohort 92 (52.0%) patients were able to ambulate independently, and 86 (48.3%) patients were non independent. One hundred and thirty-nine (78.1%) of patients had symptoms of cord compression and 93 (52.3%) had neurological deficits on presentation. On multivariate analysis, pre-operative neurological deficits (P=0.01) and symptoms of cord compression (P=0.01) were significantly associated with post-operative ambulatory function. Mean total delay was 66 days, patient delay was 41 days, diagnostic delay was 16 days, referral delay was 3 days and surgical delay was 6 days. In patients with neurological deficits, there was a significant decrease in all forms of treatment delay (P<0.05). There was no significant decrease in patient delay, diagnostic delay and referral delay in patients with symptoms of cord compression. CONCLUSIONS: Both patients and physicians understand the need for urgent surgical treatment of MESCC with neurological deficits, however there is still a need for increased education and recognition of the symptoms of MESCC.

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BACKGROUND: Survival prognostication plays a key role in the decision-making process for the surgical treatment of patients with spinal metastases. In the past traditional scoring systems such as the modified Tokuhashi and Tomita scoring systems have been used extensively, however in recent years their accuracy has been called into question. This has led to the development of machine learning algorithms to predict survival. In this study, we aim to compare the accuracy of prognostic scoring systems in a surgically treated cohort of patients. METHODS: This is a retrospective review of 318 surgically treated spinal metastases patients between 2009 and 2021. The primary outcome measured was survival from the time of diagnosis. Predicted survival at 3 months, 6 months and 1 year based on the prognostic scoring system was compared to actual survival. Predictive values of each scoring system were measured via area under receiver operating characteristic curves (AUROC). The following scoring systems were compared, Modified Tokuhashi (MT), Tomita (T), Modified Bauer (MB), Van Den Linden (VDL), Oswestry (O), New England Spinal Metastases score (NESMS), Global Spine Study Tumor Group (GSTSG) and Skeletal Oncology Research Group (SORG) scoring systems. RESULTS: For predicting 3 months survival, the GSTSG 0.980 (0.949-1.0) and NESM 0.980 (0.949-1.0) had outstanding predictive value, while the SORG 0.837 (0.751-0.923) and O 0.837 (0.775-0.900) had excellent predictive value. While for 6 months survival, only the O 0.819 (0.758-0.880) had excellent predictive value and the GSTSG 0.791(0.725-0.857) had acceptable predictive value. For 1 year survival, the NESM 0.871 (0.822-0.919) had excellent predictive value and the O 0.722 (0.657-0.786) had acceptable predictive value. The MT, T and MB scores had an area under the curve (AUC) of <0.5 for 3-month, 6-month and 1-year survival. CONCLUSIONS: Increasingly, traditional scoring systems such as the MT, T and MB scoring systems have become less predictive. While newer scoring systems such as the GSTSG, NESM and SORG have outstanding to excellent predictive value, there is no one survival scoring system that is able to accurately prognosticate survival at all 3 time points. A multidisciplinary, personalised approach to survival prognostication is needed.

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OBJECTIVE: Develop structured, quality improvement interventions to achieve a 15%-point reduction in MRIs performed under sedation or general anesthesia (GA) delayed more than 15 min within a 6-month period. METHODS: A prospective audit of MRIs under sedation or GA from January 2022 to June 2023 was conducted. A multidisciplinary team performed process mapping and root cause analysis for delays. Interventions were developed and implemented over four Plan, Do, Study, Act (PDSA) cycles, targeting workflow standardization, preadmission patient counseling, reinforcing adherence to scheduled scan times and written consent respectively. Delay times (compared with Kruskal-Wallis and Dunn's tests), delays more than 15 min and delays of 60 min or more at baseline and after each PDSA cycle were recorded. RESULTS: In all, 627 MRIs under sedation or GA were analyzed, comprising 443 at baseline and 184 postimplementation. Of the 627, 556 (88.7%) scans were performed under sedation, 22 (3.5%) under monitored anesthesia care, and 49 (7.8%) under GA. At baseline, 71.6% (317 of 443) scans were delayed over 15 min and 28.2% (125 of 443) scans by 60 min or more, with a median delay of 30 min. Postimplementation, there was a 34.7%-point reduction in scans delayed more than 15 min, a 17.5%-point reduction in scans delayed by 60 min or more, and a reduction in median delay time by 15 min (P < .001). DISCUSSION: Structured interventions significantly reduced delays in MRIs under sedation and GA, potentially improving outcomes for both patients and providers. Key factors included a diversity of perspectives in the study team, continued stakeholder engagement and structured quality improvement tools including PDSA cycles.

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BACKGROUND: There is growing interest in the association of CT-assessed sarcopenia with adverse outcomes in non-oncological settings. PURPOSE: The aim of this systematic review is to summarize existing literature on the prognostic implications of CT-assessed sarcopenia in non-oncological patients. MATERIALS AND METHODS: Three independent authors searched Medline/PubMed, Embase and Cochrane Library up to 30 December 2023 for observational studies that reported the presence of sarcopenia defined on CT head and neck in association with mortality estimates and other adverse outcomes, in non-oncological patients. The quality of included studies were assessed using the Quality of Prognostic Studies tool. RESULTS: Overall, 15 studies (3829 participants) were included. Nine studies were at low risk of bias, and six were at moderate risk of bias. Patient populations included those admitted for trauma or treatment of intracranial aneurysms, ischemic stroke, transient ischemic attack, and intracranial stenosis. Sarcopenia was associated with increased 30-day to 2-year mortality in inpatients and patients undergoing carotid endarterectomy or mechanical thrombectomy for acute ischemic stroke. Sarcopenia was also associated with poorer neurological and functional outcomes, increased likelihood of admission to long-term care facilities, and longer duration of hospital stays. The observed associations of sarcopenia with adverse outcomes remained similar across different imaging modalities and methods for quantifying sarcopenia. CONCLUSION: CT-assessed sarcopenia was associated with increased mortality and poorer outcomes across diverse patient populations. Measurement and early identification of sarcopenia in vulnerable patients allows for enhanced prognostication, and focused allocation of resources to mitigate adverse outcomes.

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Osteoporosis is a complex endocrine disease characterized by a decline in bone mass and microstructural integrity. It constitutes a major global health problem. Recent progress in the field of artificial intelligence (AI) has opened new avenues for the effective diagnosis of osteoporosis via radiographs. This review investigates the application of AI classification of osteoporosis in radiographs. A comprehensive exploration of electronic repositories (ClinicalTrials.gov, Web of Science, PubMed, MEDLINE) was carried out in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 statement (PRISMA). A collection of 31 articles was extracted from these repositories and their significant outcomes were consolidated and outlined. This encompassed insights into anatomical regions, the specific machine learning methods employed, the effectiveness in predicting BMD, and categorizing osteoporosis. Through analyzing the respective studies, we evaluated the effectiveness and limitations of AI osteoporosis classification in radiographs. The pooled reported accuracy, sensitivity, and specificity of osteoporosis classification ranges from 66.1% to 97.9%, 67.4% to 100.0%, and 60.0% to 97.5% respectively. This review underscores the potential of AI osteoporosis classification and offers valuable insights for future research endeavors, which should focus on addressing the challenges in technical and clinical integration to facilitate practical implementation of this technology.

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INTRODUCTION: Modern magnetic resonance imaging (MRI) scanners utilise superconducting magnets that are permanently active. Patients and healthcare professionals have been known to unintentionally introduce ferromagnetic objects into the scanning room. In this study, we evaluated the projectile risk of Singapore coinage as well as some common healthcare equipment within a 3 T MRI scanner. METHODS: A rig termed 'Object eNtry Guidance and Linear Acceleration Instrument' (ONG LAI) was custom-built to facilitate safe trajectory of the putative ferromagnetic objects. A ballistic gel target was utilised as a human tissue surrogate to estimate tissue penetration. The point at which objects would self-propel towards the scanner was named 'Huge Unintended Acceleration Towards Actual Harm (HUAT AH)'. RESULTS: Singapore third-series coins (10-cent to 1-dollar coins) are highly ferromagnetic and would accelerate towards the MRI scanner from more than one metre away. Cannulas with their needles are ferromagnetic and would self-propel towards the scanner from a distance of 20 cm. Standard surgical masks are ferromagnetic and may lose their sealing efficacy when they are worn too close to the magnet. Among the tested objects, a can of pineapple drink (Lee Pineapple Juice) had the highest HUAT AH at a distance of more than 1.5 m. CONCLUSION: Some local coinage and commonly found objects within a healthcare setting demonstrate ferromagnetic activity with projectile potential from a distance of more than 1 m. Patients and healthcare professionals should be cognisant of the risk associated with introducing these objects into the MRI scanning room.

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The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically.

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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.

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Introduction: Surgical treatment is increasingly the treatment of choice in cancer patients with epidural spinal cord compression and spinal instability. There has also been an evolution in surgical treatment with the advent of minimally invasive surgical (MIS) techniques and separation surgery. This paper aims to investigate the changes in epidemiology, surgical technique, outcomes and complications in the last 17 years in a tertiary referral center in Singapore. Methods: This is a retrospective study of 383 patients with surgically treated spinal metastases treated between January 2005 to January 2022. Patients were divided into 3 groups, patients treated between 2005 - 2010, 2011-2016, and 2017- 2021. Demographic, oncological, surgical, patient outcome and survival data were collected. Statistical analysis with univariate analysis was performed to compare the groups. Results: There was an increase in surgical treatment (87 vs 105 vs 191). Lung, Breast and prostate cancer were the most common tumor types respectively. There was a significant increase in MIS(p<0.001) and Separation surgery (p<0.001). There was also a significant decrease in mean blood loss (1061ml vs 664 ml vs 594ml) (p<0.001) and total transfusion (562ml vs 349ml vs 239ml) (p<0.001). Group 3 patients were more likely to have improved or normal neurology (p=<0.001) and independent ambulatory status(p=0.012). There was no significant change in overall survival. Conclusion: There has been a significant change in our surgical practice with decreased blood loss, transfusion and improved neurological and functional outcomes. Patients should be managed in a multidisciplinary manner and surgical treatment should be recommended when indicated.

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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.

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OBJECTIVE: To investigate the behaviour of common healthcare related objects in a 3 tesla (T) MRI (magnetic resonance imaging) scanner, examining their ability to self-propel towards the scanner bore and their potential for tissue penetration. DESIGN: Prospective in situ experimental study. SETTING: Clinical 3 T MRI scanner. Customised rig designed and built to guide objects towards the scanner bore. PARTICIPANTS: 12 categories of objects commonly found in hospitals, or on patients or healthcare professionals, or near an MRI scanning room. Human tissue penetration simulated with ballistic gel (Federal Bureau of Investigation and North Atlantic Treaty Organisation graded). MAIN OUTCOME MEASURES: SANTA (site where applied newtonian mechanics triggers acceleration) measurements and depth of tissue penetration of the objects. RESULTS: SANTA measurements ranged from 0 cm for the 20 pence, 50 pence, and £2 coins to 152-161 cm for a knife and the biscuit tins. One penny, two pence, five pence, and 10 pence coins showed self-propulsion and acceleration towards the scanner bore at a distance >100 cm from the gantry entry point. Linear regression analysis showed no apparent correlation between the weight of the objects and their SANTA measurements (R2<0.1). Only five objects penetrated the ballistic gel (simulated human tissue). The deepest penetration was by the knife (5.5 cm), closely followed by the teaspoon (5.0 cm), fork (4.0 cm), spoon (3.5 cm), and a 10 pence coin (0.5 cm). Although the biscuit tins did not penetrate the simulated human tissue, they exerted substantial impact force which could potentially cause bone fractures. A smartphone, digital thermometer, metallic credit card, and pen torch remained fully functional after several passes into the MRI scanner. No discernible loss of image quality for the MRI scanner after the experiments was found. CONCLUSIONS: The study highlights the potential for harm (major tissue damage and bone fractures) when commonly found objects in a healthcare setting are unintentionally brought into the MRI scanner room. Patients and healthcare professionals need to be aware of the dangers associated with bringing ferromagnetic objects into the MRI environment.

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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.

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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.

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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.

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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.

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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.

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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.

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