CT guided versus non-image guided bone marrow aspiration and biopsy: Comparison of indications, specimen quality and cost.

PURPOSE: To compare the indications, specimen quality, and cost of CT versus non-image guided bone marrow aspirate and biopsy (BMAB). METHODS: All CT and non-image guided BMAB performed from January 2013-July 2022 were studied. Body-mass-index (BMI), skin-to-bone distance, aspirate, and core specimen quality, and core sample length were documented. Indications for CT guided BMAB were recorded. Categorical variables were compared using chi-squared test and continuous variables using Mann-Whitney test. Analysis of per-biopsy factors used linear mixed-effect models to adjust for clustering. Cost of CT and non-image guided BMAB was taken from patient billing data. RESULTS: There were 301 CT and 6535 non-image guided BMABs studied. All CT guided BMAB were studied. A subset of 317 non-image guided BMAB was selected randomly from the top ten CT BMAB referrers. BMI (kg/m2) and skin-to-bone distance (cm) was higher in the CT versus the non-image guided group; 34.4 v 26.8, p < 0.0001; 4.8 v 2.5, p < 0.0001, respectively. Aspirate and core sample quality were not different between groups, p = 0.21 and p = 0.12, respectively. CT guided core marrow samples were longer, p < 0.0001. The most common CT BMAB referral indications were large body habitus (47.7 %), failed attempt (18.8 %) and not stated (17.4 %). Cost of a CT guided BMAB with conscious sedation was $3945 USD versus $310 USD for non-image guided. CONCLUSION: CT guided BMAB are commonly performed in patients with large body habitus and failed attempt. However, the cost is 12.7 fold higher with no increase in specimen quality. These findings can help referrers be cost conscious.

Excellent accuracy of magnetic resonance imaging for diagnosis of discoid meniscus tears: A systematic review and meta-analysis.

Purpose: The discoid meniscus (DM) is distinguished by its thickened, disc-shaped formation, which extends over the tibial plateau. The likelihood of developing osteoarthritis escalates if a DM tear remains undiagnosed and untreated. While DM tears can be diagnosed through arthroscopy, the high cost, invasive nature and limited availability of this procedure highlight the need for a better diagnostic modality. This study aims to determine the accuracy of magnetic resonance imaging (MRI) in diagnosing DM tears. Methods: A systematic review was conducted to gather articles with at least 10 cases on the comparison of MRI and arthroscopy as the gold standard for DM tear diagnosis. Stata and MetaDisc were used to conduct the statistical analysis. The quality of the included studies was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Results: Five diagnostic performance studies, derived from four original research papers involving 305 patients, were evaluated. Based on the pooled data, the sensitivity, specificity, diagnostic odds ratio, positive limit of detection and negative limit of detection were found to be 0.87 (95% confidence interval [CI], 0.82-0.91) and 0.84 (95% CI, 0.75-0.90), 32.88 (95% CI, 5.81-186.02), 5.22 (95% CI, 1.71-15.92) and 0.18 (95% CI, 0.09-0.38), respectively. A hierarchical summary receiver operating characteristic curve with an area under the curve of 0.92 was generated. Conclusion: This meta-analysis demonstrates that MRI has excellent sensitivity and specificity for diagnosing DM tears. Despite its lower accuracy compared to arthroscopy, MRI can be used in symptomatic patients as a viable alternative to arthroscopy due to its inherent advantages. Level of Evidence: Level IV.

Detection and Localization of Spine Disorders from Plain Radiography.

Spine disorders can cause severe functional limitations, including back pain, decreased pulmonary function, and increased mortality risk. Plain radiography is the first-line imaging modality to diagnose suspected spine disorders. Nevertheless, radiographical appearance is not always sufficient due to highly variable patient and imaging parameters, which can lead to misdiagnosis or delayed diagnosis. Employing an accurate automated detection model can alleviate the workload of clinical experts, thereby reducing human errors, facilitating earlier detection, and improving diagnostic accuracy. To this end, deep learning-based computer-aided diagnosis (CAD) tools have significantly outperformed the accuracy of traditional CAD software. Motivated by these observations, we proposed a deep learning-based approach for end-to-end detection and localization of spine disorders from plain radiographs. In doing so, we took the first steps in employing state-of-the-art transformer networks to differentiate images of multiple spine disorders from healthy counterparts and localize the identified disorders, focusing on vertebral compression fractures (VCF) and spondylolisthesis due to their high prevalence and potential severity. The VCF dataset comprised 337 images, with VCFs collected from 138 subjects and 624 normal images collected from 337 subjects. The spondylolisthesis dataset comprised 413 images, with spondylolisthesis collected from 336 subjects and 782 normal images collected from 413 subjects. Transformer-based models exhibited 0.97 Area Under the Receiver Operating Characteristic Curve (AUC) in VCF detection and 0.95 AUC in spondylolisthesis detection. Further, transformers demonstrated significant performance improvements against existing end-to-end approaches by 4-14% AUC (p-values < 10-13) for VCF detection and by 14-20% AUC (p-values < 10-9) for spondylolisthesis detection.

An Automated Vertebrae Localization, Segmentation, and Osteoporotic Compression Fracture Detection Pipeline for Computed Tomographic Imaging.

Osteoporosis is the most common chronic metabolic bone disease worldwide. Vertebral compression fracture (VCF) is the most common type of osteoporotic fracture. Approximately 700,000 osteoporotic VCFs are diagnosed annually in the USA alone, resulting in an annual economic burden of ~$13.8B. With an aging population, the rate of osteoporotic VCFs and their associated burdens are expected to rise. Those burdens include pain, functional impairment, and increased medical expenditure. Therefore, it is of utmost importance to develop an analytical tool to aid in the identification of VCFs. Computed Tomography (CT) imaging is commonly used to detect occult injuries. Unlike the existing VCF detection approaches based on CT, the standard clinical criteria for determining VCF relies on the shape of vertebrae, such as loss of vertebral body height. We developed a novel automated vertebrae localization, segmentation, and osteoporotic VCF detection pipeline for CT scans using state-of-the-art deep learning models to bridge this gap. To do so, we employed a publicly available dataset of spine CT scans with 325 scans annotated for segmentation, 126 of which also graded for VCF (81 with VCFs and 45 without VCFs). Our approach attained 96% sensitivity and 81% specificity in detecting VCF at the vertebral-level, and 100% accuracy at the subject-level, outperforming deep learning counterparts tested for VCF detection without segmentation. Crucially, we showed that adding predicted vertebrae segments as inputs significantly improved VCF detection at both vertebral and subject levels by up to 14% Sensitivity and 20% Specificity (p-value = 0.028).

Proximal femur fracture detection on plain radiography via feature pyramid networks.

Hip fractures exceed 250,000 cases annually in the United States, with the worldwide incidence projected to increase by 240-310% by 2050. Hip fractures are predominantly diagnosed by radiologist review of radiographs. In this study, we developed a deep learning model by extending the VarifocalNet Feature Pyramid Network (FPN) for detection and localization of proximal femur fractures from plain radiography with clinically relevant metrics. We used a dataset of 823 hip radiographs of 150 subjects with proximal femur fractures and 362 controls to develop and evaluate the deep learning model. Our model attained 0.94 specificity and 0.95 sensitivity in fracture detection over the diverse imaging dataset. We compared the performance of our model against five benchmark FPN models, demonstrating 6-14% sensitivity and 1-9% accuracy improvement. In addition, we demonstrated that our model outperforms a state-of-the-art transformer model based on DINO network by 17% sensitivity and 5% accuracy, while taking half the time on average to process a radiograph. The developed model can aid radiologists and support on-premise integration with hospital cloud services to enable automatic, opportunistic screening for hip fractures.

Does the presence of macroscopic intralesional fat exclude malignancy? An analysis of 613 histologically proven malignant bone lesions.

OBJECTIVE: To determine if macroscopic intralesional fat detected in bone lesions on CT by Hounsfield unit (HU) measurement and on MRI by macroscopic assessment excludes malignancy. MATERIALS AND METHODS: All consecutive CT-guided core needle biopsies (CNB) of non-spinal bone lesions performed at a tertiary center between December 2005 and September 2021 were reviewed. Demographic and histopathology data were recorded. All cases with malignant histopathology were selected, and imaging studies were reviewed. Two independent readers performed CT HU measurements on all bone lesions using a circular region of interest (ROI) to quantitate intralesional fat density (mean HU < -30). MRI images were reviewed to qualitatively assess for macroscopic intralesional fat signal in a subset of patients. Inter-reader agreement was assessed with Cronbach's alpha and intraclass correlation coefficient. RESULTS: In 613 patients (mean age 62.9 years (range 19-95 years), 47.6% female), CT scans from the CNB of 613 malignant bone lesions were reviewed, and 212 cases had additional MRI images. Only 3 cases (0.5%) demonstrated macroscopic intralesional fat on either CT or MRI. One case demonstrated macroscopic intralesional fat density on CT in a case of metastatic prostate cancer. Two cases demonstrated macroscopic intralesional fat signal on MRI in cases of chondrosarcoma and osteosarcoma. Inter-reader agreement was excellent (Cronbach's alpha, 0.95-0.98; intraclass correlation coefficient, 0.90-0.97). CONCLUSION: Malignant lesions rarely contain macroscopic intralesional fat on CT or MRI. While CT is effective in detecting macroscopic intralesional fat in primarily lytic lesions, MRI may be better for the assessment of heterogenous and infiltrative lesions with mixed lytic and sclerotic components. CLINICAL RELEVANCE STATEMENT: Macroscopic intralesional fat is rarely seen in malignant bone tumors and its presence can help to guide the diagnostic workup of bone lesions. KEY POINTS: • Presence of macroscopic intralesional fat in bone lesions has been widely theorized as a sign of benignity, but there is limited supporting evidence in the literature. • CT and MRI are effective in evaluating for macroscopic intralesional fat in malignant bone lesions with excellent inter-reader agreement. • Macroscopic intralesional fat is rarely seen in malignant bone lesions.

Diagnostic Performance of Artificial Intelligence in Detection of Primary Malignant Bone Tumors: a Meta-Analysis.

We aim to conduct a meta-analysis on studies that evaluated the diagnostic performance of artificial intelligence (AI) algorithms in the detection of primary bone tumors, distinguishing them from other bone lesions, and comparing them with clinician assessment. A systematic search was conducted using a combination of keywords related to bone tumors and AI. After extracting contingency tables from all included studies, we performed a meta-analysis using random-effects model to determine the pooled sensitivity and specificity, accompanied by their respective 95% confidence intervals (CI). Quality assessment was evaluated using a modified version of Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) and Prediction Model Study Risk of Bias Assessment Tool (PROBAST). The pooled sensitivities for AI algorithms and clinicians on internal validation test sets for detecting bone neoplasms were 84% (95% CI: 79.88) and 76% (95% CI: 64.85), and pooled specificities were 86% (95% CI: 81.90) and 64% (95% CI: 55.72), respectively. At external validation, the pooled sensitivity and specificity for AI algorithms were 84% (95% CI: 75.90) and 91% (95% CI: 83.96), respectively. The same numbers for clinicians were 85% (95% CI: 73.92) and 94% (95% CI: 89.97), respectively. The sensitivity and specificity for clinicians with AI assistance were 95% (95% CI: 86.98) and 57% (95% CI: 48.66). Caution is needed when interpreting findings due to potential limitations. Further research is needed to bridge this gap in scientific understanding and promote effective implementation for medical practice advancement.

Bone Marrow Biopsies: Is CT, Fluoroscopy, or no Imaging Guidance the Most Cost-Effective Strategy?

RATIONALE AND OBJECTIVES: To determine the most cost-effective strategy for pelvic bone marrow biopsies. MATERIALS AND METHODS: A decision analytic model from the health care system perspective for patients with high clinical concern for multiple myeloma (MM) was used to evaluate the incremental cost-effectiveness of three bone marrow core biopsy techniques: computed tomography (CT) guided, and fluoroscopy guided, no-imaging (landmark-based). Model input data on utilities, costs, and probabilities were obtained from comprehensive literature review and expert opinion. Costs were estimated in 2023 U.S. dollars. Primary effectiveness outcome was quality adjusted life years (QALY). Willingness to pay threshold was $100,000 per QALY gained. RESULTS: No-imaging based biopsy was the most cost-effective strategy as it had the highest net monetary benefit ($4218) and lowest overall cost ($92.17). Fluoroscopy guided was excluded secondary to extended dominance. CT guided biopsies were less preferred as it had an incremental cost-effectiveness ratio ($334,043) greater than the willingness to pay threshold. Probabilistic sensitivity analysis found non-imaging based biopsy to be the most cost-effective in 100% of simulations and at all willingness to pay thresholds up to $200,000. CONCLUSION: No-imaging based biopsy appears to be the most cost-effective strategy for bone marrow core biopsy in patients suspected of MM. CLINICAL RELEVANCE: No imaging guidance is the preferred strategy, although image-guidance may be required for challenging anatomy. CT image interpretation may be helpful for planning biopsies. Establishing a non-imaging guided biopsy service with greater patient anxiety and pain support may be warranted.

MRI-based Neuropathy Score Reporting And Data System (NS-RADS): multi-institutional wider-experience usability study of peripheral neuropathy conditions among 32 radiology readers.

OBJECTIVE: To determine the inter-reader reliability and diagnostic performance of classification and severity scales of Neuropathy Score Reporting And Data System (NS-RADS) among readers of differing experience levels after limited teaching of the scoring system. METHODS: This is a multi-institutional, cross-sectional, retrospective study of MRI cases of proven peripheral neuropathy (PN) conditions. Thirty-two radiology readers with varying experience levels were recruited from different institutions. Each reader attended and received a structured presentation that described the NS-RADS classification system containing examples and reviewed published articles on this subject. The readers were then asked to perform NS-RADS scoring with recording of category, subcategory, and most likely diagnosis. Inter-reader agreements were evaluated by Conger's kappa and diagnostic accuracy was calculated for each reader as percent correct diagnosis. A linear mixed model was used to estimate and compare accuracy between trainees and attendings. RESULTS: Across all readers, agreement was good for NS-RADS category and moderate for subcategory. Inter-reader agreement of trainees was comparable to attendings (0.65 vs 0.65). Reader accuracy for attendings was 75% (95% CI 73%, 77%), slightly higher than for trainees (71% (69%, 72%), p = 0.0006) for nerves and comparable for muscles (attendings, 87.5% (95% CI 86.1-88.8%) and trainees, 86.6% (95% CI 85.2-87.9%), p = 0.4). NS-RADS accuracy was also higher than average accuracy for the most plausible diagnosis for attending radiologists at 67% (95% CI 63%, 71%) and for trainees at 65% (95% CI 60%, 69%) (p = 0.036). CONCLUSION: Non-expert radiologists interpreted PN conditions with good accuracy and moderate-to-good inter-reader reliability using the NS-RADS scoring system. CLINICAL RELEVANCE STATEMENT: The Neuropathy Score Reporting And Data System (NS-RADS) is an accurate and reliable MRI-based image scoring system for practical use for the diagnosis and grading of severity of peripheral neuromuscular disorders by both experienced and general radiologists. KEY POINTS: • The Neuropathy Score Reporting And Data System (NS-RADS) can be used effectively by non-expert radiologists to categorize peripheral neuropathy. • Across 32 different experience-level readers, the agreement was good for NS-RADS category and moderate for NS-RADS subcategory. • NS-RADS accuracy was higher than the average accuracy for the most plausible diagnosis for both attending radiologists and trainees (at 75%, 71% and 65%, 65%, respectively).

Current state and challenges in respiratory syncytial virus drug discovery and development.

Human respiratory syncytial virus (RSV) is a leading cause of lower respiratory tract infections (LRTI) in young children and elderly people worldwide. Recent significant progress in our understanding of the structure and function of RSV proteins has led to the discovery of several clinical candidates targeting RSV fusion and replication. These include both the development of novel small molecule interventions and the isolation of potent monoclonal antibodies. In this review, we summarize the state-of-the-art of RSV drug discovery, with a focus on the characteristics of the candidates that reached the clinical stage of development. We also discuss the lessons learned from failed and discontinued clinical developments and highlight the challenges that remain for development of RSV therapies.

The role of iodinated contrast media in computed tomography structured Reporting and Data Systems (RADS): a narrative review.

Background and Objective: In recent years, there has been a large-scale dissemination of guidelines in radiology in the form of Reporting & Data Systems (RADS). The use of iodinated contrast media (ICM) has a fundamental role in enhancing the diagnostic capabilities of computed tomography (CT) but poses certain risks. The scope of the present review is to summarize the current role of ICM only in clinical reporting guidelines for CT that have adopted the "RADS" approach, focusing on three specific questions per each RADS: (I) what is the scope of the scoring system; (II) how is ICM used in the scoring system; (III) what is the impact of ICM enhancement on the scoring. Methods: We analyzed the original articles for each of the latest versions of RADS that can be used in CT [PubMed articles between January, 2005 and March, 2023 in English and American College of Radiology (ACR) official website]. Key Content and Findings: We found 14 RADS suitable for use in CT out of 28 RADS described in the literature. Four RADS were validated by the ACR: Colonography-RADS (C-RADS), Liver Imaging-RADS (LI-RADS), Lung CT Screening-RADS (Lung-RADS), and Neck Imaging-RADS (NI-RADS). One RADS was validated by the ACR in collaboration with other cardiovascular scientific societies: Coronary Artery Disease-RADS 2.0 (CAD-RADS). Nine RADS were proposed by other scientific groups: Bone Tumor Imaging-RADS (BTI-RADS), Bone­RADS, Coronary Artery Calcium Data & Reporting System (CAC-DRS), Coronavirus Disease 2019 Imaging-RADS (COVID-RADS), COVID-19-RADS (CO-RADS), Interstitial Lung Fibrosis Imaging-RADS (ILF-RADS), Lung-RADS (LU-RADS), Node-RADS, and Viral Pneumonia Imaging-RADS (VP-RADS). Conclusions: This overview suggests that ICM is not strictly necessary for the study of bones and calcifications (CAC-DRS, BTI-RADS, Bone-RADS), lung parenchyma (Lung-RADS, LU-RADS, COVID-RADS, CO-RADS, VP-RADS and ILF-RADS), and in CT colonography (C-RADS). On the other hand, ICM plays a key role in CT angiography (CAD-RADS), in the study of liver parenchyma (LI-RADS), and in the evaluation of soft tissues and lymph nodes (NI-RADS, Node-RADS). Future studies are needed in order to evaluate the impact of the new iodinated and non-iodinate contrast media, artificial intelligence tools and dual energy CT in the assignment of RADS scores.

Noise driven phase transitions in eco-evolutionary systems.

In complex ecosystems such as microbial communities, there is constant ecological and evolutionary feedback between the residing species and the environment occurring on concurrent timescales. Species respond and adapt to their surroundings by modifying their phenotypic traits, which in turn alters their environment and the resources available. To study this interplay between ecological and evolutionary mechanisms, we develop a consumer-resource model that incorporates phenotypic mutations. In the absence of noise, we find that phase transitions require finely-tuned interaction kernels. Additionally, we quantify the effects of noise on frequency dependent selection by defining a time-integrated mutation current, which accounts for the rate at which mutations and speciation occurs. We find three distinct phases: homogeneous, patterned, and patterned traveling waves. The last phase represents one way in which co-evolution of species can happen in a fluctuating environment. Our results highlight the principal roles that noise and non-reciprocal interactions between resources and consumers play in phase transitions within eco-evolutionary systems.

Safety and efficacy of AK0529 in respiratory syncytial virus-infected infant patients: A phase 2 proof-of-concept trial.

Background: Respiratory syncytial virus (RSV) infection is a cause of substantial morbidity and mortality in young children. There is currently no effective therapy available. Methods: This was a Phase 2 study of the oral RSV fusion protein inhibitor AK0529 in infants aged 1-24 months, hospitalized with RSV infection. In Part 1, patients (n = 24) were randomized 2:1 to receive a single dose of AK0529 up to 4 mg/kg or placebo. In Part 2, patients (n = 48) were randomized 2:1 to receive AK0529 at 0.5, 1, or 2 mg/kg bid or placebo for 5 days. Sparse pharmacokinetic samples were assessed using population pharmacokinetics modelling. Safety, tolerability, viral load, and respiratory signs and symptoms were assessed daily during treatment. Results: No safety or tolerability signals were detected for AK0529: grade ≥3 treatment-emergent adverse events occurring in 4.1% of patients in AK0529 and 4.2% in placebo groups, respectively, and none led to death or withdrawal from the study. In Part 2, targeted drug exposure was reached with 2 mg/kg bid. A numerically greater reduction in median viral load with 2 mg/kg bid AK0529 than with placebo at 96 h was observed. A -4.0 (95% CI: -4.51, -2.03) median reduction in Wang Respiratory Score from baseline to 96 h was observed in the 2 mg/kg group compared with -2.0 (95% CI: -3.42, -1.82) in the placebo group. Conclusions: AK0529 was well tolerated in hospitalized RSV-infected infant patients. Treatment with AK0529 2 mg/kg bid was observed to reduce viral load and Wang Respiratory Score. Clinical Trials Registration: NCT02654171.

A scoping review of current and emerging techniques for evaluation of peripheral nerve health, degeneration and regeneration: part 2, non-invasive imaging.

Peripheral neuroregenerative research and therapeutic options are expanding exponentially. With this expansion comes an increasing need to reliably evaluate and quantify nerve health. Valid and responsive measures of the nerve status are essential for both clinical and research purposes for diagnosis, longitudinal follow-up, and monitoring the impact of any intervention. Furthermore, novel biomarkers can elucidate regenerative mechanisms and open new avenues for research. Without such measures, clinical decision-making is impaired, and research becomes more costly, time-consuming, and sometimes infeasible. Part 1 of this two-part scoping review focused on neurophysiology. In part 2, we identify and critically examine many current and emerging non-invasive imaging techniques that have the potential to evaluate peripheral nerve health, particularly from the perspective of regenerative therapies and research.

Peripheral Nerve Imaging: Magnetic Resonance and Ultrasound Correlation.

Magnetic resonance (MR) neurography and high-resolution ultrasound are complementary modalities for imaging peripheral nerves. Advances in imaging technology and optimized techniques allow for detailed assessment of nerve anatomy and nerve pathologic condition. Diagnostic accuracy of imaging modalities likely reflects local expertise and availability of the latest imaging technology.

What is the Value of Radiology Input During a Multidisciplinary Orthopaedic Oncology Conference?

BACKGROUND: Multidisciplinary orthopaedic oncology conferences are important in developing the treatment plan for patients with suspected orthopaedic bone and soft tissue tumors, involving physicians from several services. Past studies have shown the clinical value of these conferences; however, the impact of radiology input on the management plan and time cost for radiology to staff these conferences has not been fully studied. QUESTIONS/PURPOSES: (1) Does radiology input at multidisciplinary conference help guide clinical management and improve clinician confidence? (2) What is the time cost of radiology input for a multidisciplinary conference? METHODS: This prospective study was conducted from October 2020 to March 2022 at a tertiary academic center with a sarcoma center. A single data questionnaire for each patient was sent to one of three treating orthopaedic oncologists with 41, 19, and 5 years of experience after radiology discussion at a weekly multidisciplinary conference. A data questionnaire was completed by the treating orthopaedic oncologist for 48% (322 of 672) of patients, which refers to the proportion of those three oncologists' patients for which survey data were captured. A musculoskeletal radiology fellow and musculoskeletal fellowship-trained radiology attending physician provided radiology input at each multidisciplinary conference. The clinical plan (leave alone, follow-up imaging, follow-up clinically, recommend different imaging test, core needle biopsy, surgical excision or biopsy or fixation, or other) and change in clinical confidence before and after radiology input were documented. A second weekly data questionnaire was sent to the radiology fellow to estimate the time cost of radiology input for the multidisciplinary conference. RESULTS: In 29% (93 of 322) of patients, there was a change in the clinical plan after radiology input. Biopsy was canceled in 30% (24 of 80) of patients for whom biopsy was initially planned, and surgical excision was canceled in 24% (17 of 72) of patients in whom surgical excision was initially planned. In 21% (68 of 322) of patients, there were unreported imaging findings that affected clinical management; 13% (43 of 322) of patients had a missed finding, and 8% (25 of 322) of patients had imaging findings that were interpreted incorrectly. For confidence in the final treatment plan, 78% (251 of 322) of patients had an increase in clinical confidence by their treating orthopaedic oncologist after the multidisciplinary conference. Radiology fellows and attendings spent a mean of 4.2 and 1.5 hours, respectively, reviewing and presenting at a multidisciplinary conference each week. The annual combined prorated time cost for the radiology attending and fellow was estimated at USD 24,310 based on national median salary data for attendings and internal salary data for fellows. CONCLUSION: In a study taken at one tertiary-care oncology program, input from radiology attendings and fellows in the setting of a multidisciplinary conference helped to guide the final treatment plan, reduce procedures, and improve clinician confidence in the final treatment plan, at an annual time cost of USD 24,310. CLINICAL RELEVANCE: Multidisciplinary orthopaedic oncology conferences can lead to changes in management plans, and the time cost to the radiologists should be budgeted for by the radiology department or parent institution.

Automated Bone Tumor Segmentation and Classification as Benign or Malignant Using Computed Tomographic Imaging.

The purpose of this study was to pair computed tomography (CT) imaging and machine learning for automated bone tumor segmentation and classification to aid clinicians in determining the need for biopsy. In this retrospective study (March 2005-October 2020), a dataset of 84 femur CT scans (50 females and 34 males, 20 years and older) with definitive histologic confirmation of bone lesion (71% malignant) were leveraged to perform automated tumor segmentation and classification. Our method involves a deep learning architecture that receives a DICOM slice and predicts (i) a segmentation mask over the estimated tumor region, and (ii) a corresponding class as benign or malignant. Class prediction for each case is then determined via majority voting. Statistical analysis was conducted via fivefold cross validation, with results reported as averages along with 95% confidence intervals. Despite the imbalance between benign and malignant cases in our dataset, our approach attains similar classification performances in specificity (75%) and sensitivity (79%). Average segmentation performance attains 56% Dice score and reaches up to 80% for an image slice in each scan. The proposed approach establishes the first steps in developing an automated deep learning method on bone tumor segmentation and classification from CT imaging. Our approach attains comparable quantitative performance to existing deep learning models using other imaging modalities, including X-ray. Moreover, visual analysis of bone tumor segmentation indicates that our model is capable of learning typical tumor characteristics and provides a promising direction in aiding the clinical decision process for biopsy.