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Background Context: Secure institutional large language models (LLM) could reduce the burden of non-interpretative tasks for radiologists., Purpose: Assess the utility of a secure institutional LLM for MRI spine request form enhancement and auto-protocoling., Study Design/setting: Retrospective study conducted from December 2023 to February 2024, including patients with clinical entries accessible on the electronic medical record (EMR)., Patient Sample: Overall, 250 spine MRI request forms were analyzed from 218 patients (mean age = 55.9 years ± 18.9 [SD]; 108 women) across the cervical (n=56/250, 22.4%), thoracic (n=13/250, 5.2%), lumbar (n=166/250, 66.4%), and whole (n=15/250, 6.0%) spine. Of these, 60/250 (24.0%) required contrast and 41/250 (16.4%) had prior spine surgery/instrumentation., Outcome Measures: Primary-Adequacy of clinical information on clinician and LLM-augmented request forms were rated using a four-point scale. Secondary-Correct MRI protocol suggestion by LLM and first-year board-certified radiologists (Rad4 and Rad5) compared to a consensus reference standard., Methods: A secured institutional LLM (Claude 2.0) used a majority decision prompt (out of six runs) to enhance clinical information on clinician request forms using the EMR, and suggest the appropriate MRI protocol. The adequacy of clinical information on the clinician and LLM-augmented request forms was rated by three musculoskeletal radiologists independently (Rad1:10-years-experience; Rad2:12-years-experience; Rad3:10-years-experience). The same radiologists provided a consensus reference standard for the correct protocol, which was compared to the protocol suggested by the LLM and two first-year board-certified radiologists (Rad4 and Rad5). Overall agreement (Fleiss kappas for inter-rater agreement or % agreement with the reference standard and respective 95%CIs) were provided where appropriate., Results: LLM-augmented forms were rated by Rads 1-3 as having adequate clinical information in 93.6-96.0% of cases compared to 46.8-58.8% of the clinician request forms (p<0.01). Substantial interobserver agreement was observed with kappas of 0.71 (95%CI:0.67-0.76) for original forms and 0.66 (95%CI:0.61-0.72) for LLM-enhanced requests. Rads 1-3 showed almost perfect agreement on protocol decisions, with kappas of 0.99 (95%CI:0.94-1.0) for spine region selection, 0.93 (95%CI:0.86-1.0) for contrast necessity, and 0.93 (95%CI:0.86-0.99) for recognition of prior spine surgery. Compared to the consensus reference standard, the LLM suggested the correct protocol in 78.4% (196/250, p<0.01) of cases, albeit inferior to Rad4 (90.0%, p<0.01) and Rad5 (89.2%, p<0.01). The secure LLM did best in identifying spinal instrumentation in 39/41 (95.1%) cases, improved compared to Rad4 (61.0%) and Rad5 (41.5%) (both p<0.01). The secure LLM had high consistency with 227/250 cases (90.8%) having 100% (6/6 runs) agreement., Conclusions: Enhancing spine MRI request forms with a secure institutional LLM improved the adequacy of clinical information. The LLM also accurately suggested the correct protocol in 78.4% of cases which could optimize the MRI workflow., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

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., Competing Interests: Declaration of competing interest The authors declare no conflicts of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

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., (Copyright © 2024 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

Background Context: A deep learning (DL) model for degenerative cervical spondylosis on MRI could enhance reporting consistency and efficiency, addressing a significant global health issue., Purpose: Create a DL model to detect and classify cervical cord signal abnormalities, spinal canal and neural foraminal stenosis., Study Design/setting: Retrospective study conducted from January 2013 to July 2021, excluding cases with instrumentation., Patient Sample: Overall, 504 MRI cervical spines were analyzed (504 patients, mean=58 years±13.7[SD]; 202 women) with 454 for training (90%) and 50 (10%) for internal testing. In addition, 100 MRI cervical spines were available for external testing (100 patients, mean=60 years±13.0[SD];26 women)., Outcome Measures: Automated detection and classification of spinal canal stenosis, neural foraminal stenosis, and cord signal abnormality using the DL model. Recall(%), inter-rater agreement (Gwet's kappa), sensitivity, and specificity were calculated., Methods: Utilizing axial T2-weighted gradient echo and sagittal T2-weighted images, a transformer-based DL model was trained on data labeled by an experienced musculoskeletal radiologist (12 years of experience). Internal testing involved data labeled in consensus by 2 musculoskeletal radiologists (reference standard, both with 12-years-experience), 2 subspecialist radiologists, and 2 in-training radiologists. External testing was performed., Results: The DL model exhibited substantial agreement surpassing all readers in all classes for spinal canal (κ=0.78, p<.001 vs. κ range=0.57-0.70 for readers) and neural foraminal stenosis (κ=0.80, p<.001 vs. κ range=0.63-0.69 for readers) classification. The DL model's recall for cord signal abnormality (92.3%) was similar to all readers (range: 92.3-100.0%). Nearly perfect agreement was demonstrated for binary classification (grades 0/1 vs. 2/3) (κ=0.95, p<.001 for spinal canal; κ=0.90, p<.001 for neural foramina). External testing showed substantial agreement using all classes (κ=0.76, p<.001 for spinal canal; κ=0.66, p<.001 for neural foramina) and high recall for cord signal abnormality (91.9%). The DL model demonstrated high sensitivities (range:83.7%-92.4%) and specificities (range:87.8%-98.3%) on both internal and external datasets for spinal canal and neural foramina classification., Conclusions: Our DL model for degenerative cervical spondylosis on MRI showed good performance, demonstrating substantial agreement with the reference standard. This tool could assist radiologists in improving the efficiency and consistency of MRI cervical spondylosis assessments in clinical practice., Competing Interests: Declaration of competing interest One or more of the authors declare financial or professional relationships on ICMJE-TSJ disclosure forms., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Diagnostic imaging, particularly MRI, plays a key role in the evaluation of many spine pathologies. Recent progress in artificial intelligence and its subset, machine learning, has led to many applications within spine MRI, which we sought to examine in this review. A literature search of the major databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The search yielded 1226 results, of which 50 studies were selected for inclusion. Key data from these studies were extracted. Studies were categorized thematically into the following: Image Acquisition and Processing, Segmentation, Diagnosis and Treatment Planning, and Patient Selection and Prognostication. Gaps in the literature and the proposed areas of future research are discussed. Current research demonstrates the ability of artificial intelligence to improve various aspects of this field, from image acquisition to analysis and clinical care. We also acknowledge the limitations of current technology. Future work will require collaborative efforts in order to fully exploit new technologies while addressing the practical challenges of generalizability and implementation. In particular, the use of foundation models and large-language models in spine MRI is a promising area, warranting further research. Studies assessing model performance in real-world clinical settings will also help uncover unintended consequences and maximize the benefits for patient care.

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.

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.

Competing Interests: Financial disclosure statements have been obtained, and no conflicts of interest have been reported by the authors or by any individuals in control of the content of this article.

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., Competing Interests: The authors declare no conflict of interest.

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 (R 2 <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., Competing Interests: Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

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.

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Tan, Hallinan, Lee, Chan, Tan, Ang, Tan, Tan, Sin, Hey, Lau, Thambiah, Wong, Liu and Kumar.)

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., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Purpose: To develop a novel 3D printable polyether ether ketone (PEEK)-hydroxyapatite (HA)-magnesium orthosilicate (Mg 2 SiO 4 ) 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 % Mg 2 SiO 4 ; composite B: 70 weight% PEEK, 25 weight % HA, 5 weight % Mg 2 SiO 4 ; and composite C: 65 weight % PEEK, 30 weight % HA, 5 weight % Mg 2 SiO 4 . 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., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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.

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Hallinan, Zhu, Zhang, Ge, Muhamat Nor, Ong, Eide, Cheng, Kuah, Lim, Low, Yeong, AlMuhaish, Alsooreti, Kumarakulasinghe, Teo, Yap, Chan, Lin, Tan, Kumar, Vellayappan, Ooi, Quek and Makmur.)

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., Competing Interests: None

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.

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.

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., (Copyright © 2021 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

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.

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.

Large datasets with high-quality labels required to train deep neural networks are challenging to obtain in the radiology domain. This work investigates the effect of training dataset size on the performance of deep learning classifiers, focusing on chest radiograph pneumothorax detection as a proxy visual task in the radiology domain. Two open-source datasets (ChestX-ray14 and CheXpert) comprising 291,454 images were merged and convolutional neural networks trained with stepwise increase in training dataset sizes. Model iterations at each dataset volume were evaluated on an external test set of 525 emergency department chest radiographs. Learning curve analysis was performed to fit the observed AUCs for all models generated. For all three network architectures tested, model AUCs and accuracy increased rapidly from 2 × 10 3 to 20 × 10 3 training samples, with more gradual increase until the maximum training dataset size of 291 × 10 3 images. AUCs for models trained with the maximum tested dataset size of 291 × 10 3 images were significantly higher than models trained with 20 × 10 3 images: ResNet-50: AUC 20k  = 0.86, AUC 291k  = 0.95, p < 0.001; DenseNet-121 AUC 20k  = 0.85, AUC 291k  = 0.93, p < 0.001; EfficientNet AUC 20k  = 0.92, AUC 291 k  = 0.98, p < 0.001. Our study established learning curves describing the relationship between dataset training size and model performance of deep learning convolutional neural networks applied to a typical radiology binary classification task. These curves suggest a point of diminishing performance returns for increasing training data volumes, which algorithm developers should consider given the high costs of obtaining and labelling radiology data., (© 2022. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

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.

Background: Metastatic epidural spinal cord compression (MESCC) is a disastrous complication of advanced malignancy. Deep learning (DL) models for automatic MESCC classification on staging CT were developed to aid earlier diagnosis. Methods: This retrospective study included 444 CT staging studies from 185 patients with suspected MESCC who underwent MRI spine studies within 60 days of the CT studies. The DL model training/validation dataset consisted of 316/358 (88%) and the test set of 42/358 (12%) CT studies. Training/validation and test datasets were labeled in consensus by two subspecialized radiologists (6 and 11-years-experience) using the MRI studies as the reference standard. Test sets were labeled by the developed DL models and four radiologists (2−7 years of experience) for comparison. Results: DL models showed almost-perfect interobserver agreement for classification of CT spine images into normal, low, and high-grade MESCC, with kappas ranging from 0.873−0.911 (p < 0.001). The DL models (lowest κ = 0.873, 95% CI 0.858−0.887) also showed superior interobserver agreement compared to two of the four radiologists for three-class classification, including a specialist (κ = 0.820, 95% CI 0.803−0.837) and general radiologist (κ = 0.726, 95% CI 0.706−0.747), both p < 0.001. Conclusion: DL models for the MESCC classification on a CT showed comparable to superior interobserver agreement to radiologists and could be used to aid earlier diagnosis.

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Hallinan, Zhu, Zhang, Lim, Baskar, Low, Yeong, Teo, Kumarakulasinghe, Yap, Chan, Lin, Tan, Kumar, Vellayappan, Ooi, Quek and Makmur.)

"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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Competing Interests: The authors declare no conflict of interest.

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.

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., Competing Interests: Disclosures of Conflicts of Interest: Y.L.T. disclosed no relevant relationships. D.N. disclosed no relevant relationships. J.T.P.D.H. disclosed no relevant relationships. P.J. disclosed no relevant relationships. S.Y.S. disclosed no relevant relationships. C.H.T. disclosed no relevant relationships. Y.H.T. disclosed no relevant relationships. P.L.K. disclosed no relevant relationships. G.G.P. disclosed no relevant relationships. V.T.Y.T. disclosed no relevant relationships. S.T.Q. disclosed no relevant relationships. M.F. Activities related to the present article: National University of Singapore. Activities not related to the present article: disclosed no relevant relationships. Other relationships: disclosed no relevant relationships., (2021 by the Radiological Society of North America, Inc.)

Competing Interests: The authors declare no conflict of interest.

Competing Interests: The authors declare no conflict of interest.

Competing Interests: The authors declare no conflict of interest.

We compared the somatostatin analog radioligand, DOTANOC, with FDG, to determine whether there was increased detection of active or sub-clinical lesions in pulmonary tuberculosis (TB) with DOTANOC. Three groups were recruited: (1) active pulmonary TB; (2) IGRA-positive household TB contacts; (3) pneumonia (non-TB). DOTANOC PET/MRI followed by FDG PET/MRI was performed in active TB and pneumonia groups. TB contacts underwent FDG PET/MRI, then DOTANOC PET/MRI if abnormalities were detected. Quantitative and qualitative analyses were performed for total lung and individual lesions. Eight active TB participants, three TB contacts and three pneumonia patients had paired PET/MRI scans. In the active TB group, median SUVmax [FDG] for parenchymal lesions was 7.69 (range 3.00-15.88); median SUVmax [DOTANOC] was 2.59 (1.48-6.40). Regions of tracer uptake were fairly similar for both radioligands, albeit more diffusely distributed in the FDG scans. In TB contacts, two PET/MRIs had parenchymal lesions detected with FDG (SUVmax 5.50 and 1.82), with corresponding DOTANOC uptake < 1. FDG and DOTANOC uptake was similar in pneumonia patients (SUVmax [FDG] 4.17-6.18; SUVmax [DOTANOC] 2.92-4.78). DOTANOC can detect pulmonary TB lesions, but FDG is more sensitive for both active and sub-clinical lesions. FDG remains the preferred ligand for clinical studies, although DOTANOC may provide additional value for pathogenesis studies.