Use of Imaging Prior to Referral to a Musculoskeletal Oncologist

Canadian Association of Radiologists Musculoskeletal System Diagnostic Imaging Referral Guideline

The Canadian Association of Radiologists (CAR) Musculoskeletal System Expert Panel consists of musculoskeletal radiologists, a family physician, a sports and exercise medicine physician, emergency medicine physicians, a patient advisor, and an epidemiologist/guideline methodologist. After developing a list of 25 musculoskeletal clinical/diagnostic scenarios, a systematic rapid scoping review was undertaken to identify systematically produced referral guidelines that provide recommendations for 1 or more of these clinical/diagnostic scenarios. Recommendations from 41 guidelines (50 publications) and contextualization criteria in the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) for guidelines framework were used to develop 124 recommendation statements across the 25 scenarios related to the evaluation of the musculoskeletal system. This guideline presents the methods of development and the recommendations for imaging in the context of musculoskeletal pain, infection, tumors, arthropathies, metabolic bone disease, stress injuries, orthopedic hardware, avascular necrosis/bone infarction, and complex regional pain syndrome.

Recommender-based bone tumour classification with radiographs-a link to the past

OBJECTIVES

To develop an algorithm to link undiagnosed patients to previous patient histories based on radiographs, and simultaneous classification of multiple bone tumours to enable early and specific diagnosis.

MATERIALS AND METHODS

For this retrospective study, data from 2000 to 2021 were curated from our database by two orthopaedic surgeons, a radiologist and a data scientist. Patients with complete clinical and pre-therapy radiographic data were eligible. To ensure feasibility, the ten most frequent primary tumour entities, confirmed histologically or by tumour board decision, were included. We implemented a ResNet and transformer model to establish baseline results. Our method extracts image features using deep learning and then clusters the k most similar images to the target image using a hash-based nearest-neighbour recommender approach that performs simultaneous classification by majority voting. The results were evaluated with precision-at-k, accuracy, precision and recall. Discrete parameters were described by incidence and percentage ratios. For continuous parameters, based on a normality test, respective statistical measures were calculated.

RESULTS

Included were data from 809 patients (1792 radiographs; mean age 33.73 ± 18.65, range 3-89 years; 443 men), with Osteochondroma (28.31%) and Ewing sarcoma (1.11%) as the most and least common entities, respectively. The dataset was split into training (80%) and test subsets (20%). For k = 3, our model achieved the highest mean accuracy, precision and recall (92.86%, 92.86% and 34.08%), significantly outperforming state-of-the-art models (54.10%, 55.57%, 19.85% and 62.80%, 61.33%, 23.05%).

CONCLUSION

Our novel approach surpasses current models in tumour classification and links to past patient data, leveraging expert insights.

CLINICAL RELEVANCE STATEMENT

The proposed algorithm could serve as a vital support tool for clinicians and general practitioners with limited experience in bone tumour classification by identifying similar cases and classifying bone tumour entities.

KEY POINTS

• Addressed accurate bone tumour classification using radiographic features. • Model achieved 92.86%, 92.86% and 34.08% mean accuracy, precision and recall, respectively, significantly surpassing state-of-the-art models. • Enhanced diagnosis by integrating prior expert patient assessments.

Update in Imaging Evaluation of Bone and Soft Tissue Sarcomas

The evolution in imaging evaluation of musculoskeletal sarcomas contributed to a significant improvement in the prognosis and survival of patients with these neoplasms. The precise characterization of these lesions, using the most appropriate imaging modalities to each clinical condition presented, is of paramount importance in the design of the therapeutic approach to be instituted, with a direct impact on clinical outcomes. The present article seeks to update the reader regarding imaging methodologies in the context of local and systemic evaluation of bone sarcomas and soft tissues.

Imaging Hallmarks of Sarcoma Progression Via X-ray Computed Tomography: Beholding the Flower of Evil

Simple Summary

Sarcomas represent the largest group of rare solid tumors that arise from mesenchymal stem cells and are a leading cause of cancer death in individuals younger than 20 years of age. There is an immediate need for the development of an algorithm for the early accurate diagnosis of sarcomas due to the high rate of diagnostic inaccuracy, which reaches up to 30%. X-ray computed tomography is a non-invasive imaging technique used to obtain detailed internal images of the human or animal body in clinical practice and preclinical studies. We summarized the main imaging features of soft tissue and bone sarcomas, and noted the development of new molecular markers to reach tumor type-specific imaging. Also, we demonstrated the possibility of the use X-ray computed microtomography for non-destructive 3D visualization of sarcoma progression in preclinical studies. Finding correlations between X-ray computed tomography modalities and the results of the histopathological specimen examination may significantly increase the accuracy of diagnostics, which leads to the initiation of appropriate management in a timely manner and, consequently, to improved outcomes.

Abstract

Sarcomas are a leading cause of cancer death in individuals younger than 20 years of age and represent the largest group of rare solid tumors. To date, more than 100 morphological subtypes of sarcomas have been described, among which epidemiology, clinical features, management, and prognosis differ significantly. Delays and errors in the diagnosis of sarcomas limit the number of effective therapeutic modalities and catastrophically worsen the prognosis. Therefore, the development of an algorithm for the early accurate diagnosis of sarcomas seems to be as important as the development of novel therapeutic advances. This literature review aims to summarize the results of recent investigations regarding the imaging of sarcoma progression based on the use of X-ray computed tomography (CT) in preclinical studies and in current clinical practice through the lens of cancer hallmarks. We attempted to summarize the main CT imaging features of soft-tissue and bone sarcomas. We noted the development of new molecular markers with high specificity to antibodies and chemokines, which are expressed in particular sarcoma subtypes to reach tumor type-specific imaging. We demonstrate the possibility of the use of X-ray computed microtomography (micro-CT) for non-destructive 3D visualization of solid tumors by increasing the visibility of soft tissues with X-ray scattering agents. Based on the results of recent studies, we hypothesize that micro-CT enables the visualization of neovascularization and stroma formation in sarcomas at high-resolution in vivo and ex vivo, including the novel techniques of whole-block and whole-tissue imaging. Finding correlations between CT, PET/CT, and micro-CT imaging features, the results of the histopathological specimen examination and clinical outcomes may significantly increase the accuracy of soft-tissue and bone tumor diagnostics, which leads to the initiation of appropriate histotype-specific management in a timely manner and, consequently, to improved outcomes.

Establishment of a Novel Mouse Hepatocellular Carcinoma Model for Dynamic Monitoring of Tumor Development by Bioluminescence Imaging

In this study, a novel mouse model of hepatocellular carcinoma (HCC) was established by simultaneously knocking out Pten and p53 suppressor genes and overexpressing c-Met and △90-β-catenin proto-oncogenes in the livers of mice via hydrodynamic injection (HDI). The mutations were introduced using the CRISPR/Cas9 and Sleeping Beauty transposon systems. In this way, a primary liver cancer model was established within six weeks. In addition, macrophages expressing arginase-1(Arg1) promoter coupled with firefly luciferase were engineered for bioluminescence imaging (BLI) of the tumor microenvironment. This novel, rapidly-generated model of primary hepatocellular carcinoma can be monitored noninvasively, which can facilitate not only applications of the model, but also the development of new drugs and treatment strategies of HCC.

Imaging update on soft tissue sarcoma

Soft tissue sarcomas (STS) are rare tumours presenting as soft tissue lumps. Ultrasound is often the primary modality for the initial assessment, with MRI the mainstay for lesion characterisation. PET/CT along with other emerging MRI sequences are used in certain situations as an adjunct and problem solving tool in STS staging and assessment of disease recurrence. Recent advances include the promise of whole body MRI, hybrid PET/MRI, diffusion weighted imaging, dynamic contrast enhanced MRI and advances in artificial intelligence. This article discusses current concepts in extremity STS imaging and highlights recent advances.

The Educational Impact of a Fellowship-trained Orthopaedic Oncologist

INTRODUCTION

Musculoskeletal oncology is a subspecialty of orthopaedics with few fellowship-training locations. Although orthopaedic oncologists comprise a minority within the field of orthopaedic surgery, most work at academic centers and serve in leadership roles with notable impact on patients and the training of residents. This article investigates the objective impact orthopaedic oncologists have regarding resident operative case volume and performance on in-training examinations.

METHODS

The William Beaumont Army Medical Center and Texas Tech University Health Sciences Center of El Paso combined orthopaedic residency program's case logs and Orthopaedic In-Training Examination (OITE) scores between 2013 and 2018 were reviewed. This provided 3 academic years of data before and after an orthopaedic oncology faculty member arrived in 2016. The case volume and OITE examination performance before and after the addition of the orthopaedic oncology faculty member were compared.

RESULTS

After the addition of an orthopaedic oncology faculty member, a significant increase was observed in the program's OITE overall correctly answered questions (171.30 versus 181.03, P = 0.004) and oncology subsection percentile (56th to the 66th percentile, P = 0.038). An increase was also observed in resident oncology case volume from 29 oncology cases per year to 138 cases on average (P = 0.022).

DISCUSSION

The addition of a fellowship-trained orthopaedic oncologist results in increased exposure to orthopaedic oncology cases and improved resident performance on the OITE. This may correlate to improved American Board of Orthopaedic Surgeons Part I pass rates and improved overall resident satisfaction.

Histogram analysis with computed tomography angiography for discriminating soft tissue sarcoma from benign soft tissue tumor

To investigate the feasibility of histogram analysis with computed tomography angiography (CTA) in distinguishing between soft tissue sarcomas and benign soft tissue tumors. Fourty nine patients (23 men, mean age = 44.3 years, age range = 25-64) with pathologically-confirmed soft tissue sarcoma (n = 24) or benign soft tissue tumors (n = 25) in the lower extremities undergoing CTA for tumor evaluation were retrospectively analyzed. Two radiologists separately performed histogram analyses of CT density with CTA images by drawing a region of interest (ROI). The 10th (P10), 25th (P25), 50th (P50), 75th (P75), 90th percentiles (P90), mean, and standard deviations (SD) of measured tumor density were obtained along with measurements of the absolute value of kurtosis (AVK), absolute value of skewness (AVS), and inhomogeneity for each tumor. Intra-class correlation coefficients (ICC) were calculated to determine inter- and intra-reader variability in parameter measurements. The Mann-Whitney U test was used to compare histogram parameters between soft tissue sarcomas and benign soft tissue tumors. Receiver operator characteristic (ROC) curves were constructed to evaluate the accuracy of tumor discrimination. ICC was greater than 0.7 for AVS, AVK, and inhomogeneity, and >0.9 for mean, SD, and all percentile measures. There was no significant difference in P10, P25, P50, P75, P90, mean, or SD between soft tissue sarcomas and benign tumors (P > .05). AVS, AVK, and inhomogeneity were significantly higher in soft tissue sarcomas (P < .05). Areas under the curve (AUC) were 0.81, 0.83, and 0.84 for AVS, AVK, and inhomogeneity respectively. AUC were below 0.6 for mean, SD, and all percentiles.Skewness, kurtosis, and inhomogeneity measurements derived from histogram analysis from CTA distinguish between soft tissue sarcomas and benign soft tissue tumors.