Computed Tomography and Magnetic Resonance Imaging of Bone Tumors

Bone tumors: state-of-the-art imaging

Imaging plays a central role in the management of patients with bone tumors. A number of imaging modalities are available, with different techniques having unique applications that render their use advantageous for various clinical purposes. Coupled with detailed clinical assessment, radiological imaging can assist clinicians in reaching a proper diagnosis, determining appropriate management, evaluating response to treatment, and monitoring for tumor recurrence. Although radiography is still the initial imaging test of choice for a patient presenting with a suspected bone tumor, technological innovations in the last decades have advanced the role of other imaging modalities for assessing bone tumors, including advances in computed tomography, magnetic resonance imaging, scintigraphy, and hybrid imaging techniques that combine two existing modalities, providing clinicians with diverse tools for bone tumor imaging applications. Determining the most suitable modality to use for a particular application requires familiarity with the modality in question, its advancements, and its limitations. This review highlights the various imaging techniques currently available and emphasizes the latest developments in imaging, offering a framework that can help guide the imaging of patients with bone tumors.

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.

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

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

Nanotechnology-Assisted Cell Tracking

The usefulness of nanoparticles (NPs) in the diagnostic and/or therapeutic sector is derived from their aptitude for navigating intra- and extracellular barriers successfully and to be spatiotemporally targeted. In this context, the optimization of NP delivery platforms is technologically related to the exploitation of the mechanisms involved in the NP–cell interaction. This review provides a detailed overview of the available technologies focusing on cell–NP interaction/detection by describing their applications in the fields of cancer and regenerative medicine. Specifically, a literature survey has been performed to analyze the key nanocarrier-impacting elements, such as NP typology and functionalization, the ability to tune cell interaction mechanisms under in vitro and in vivo conditions by framing, and at the same time, the imaging devices supporting NP delivery assessment, and consideration of their specificity and sensitivity. Although the large amount of literature information on the designs and applications of cell membrane-coated NPs has reached the extent at which it could be considered a mature branch of nanomedicine ready to be translated to the clinic, the technology applied to the biomimetic functionalization strategy of the design of NPs for directing cell labelling and intracellular retention appears less advanced. These approaches, if properly scaled up, will present diverse biomedical applications and make a positive impact on human health.

A CT-based radiomics nomogram for distinguishing between benign and malignant bone tumours

BACKGROUND

We sought to evaluate the performance of a computed tomography (CT)-based radiomics nomogram we devised in distinguishing benign from malignant bone tumours.

METHODS

Two hundred and six patients with bone tumours were spilt into two groups: a training set (n = 155) and a validation set (n = 51). A feature extraction process based on 3D Slicer software was used to extract the radiomics features from unenhanced CT images, and least absolute shrinkage and selection operator logistic regression was used to calculate the radiomic score to generate a radiomics signature. A clinical model comprised demographics and CT features. A radiomics nomogram combined with the clinical model and the radiomics signature was constructed. The performance of the three models was comprehensively evaluated from three aspects: identification ability, accuracy, and clinical value, allowing for generation of an optimal prediction model.

RESULTS

The radiomics nomogram comprised clinical and radiomics signature features. The nomogram model displayed good performance in training and validation sets with areas under the curve of 0.917 and 0.823, respectively. The areas under the curve, decision curve analysis, and net reclassification improvement showed that the radiomics nomogram model could obtain better diagnostic performance than the clinical model and achieve greater clinical net benefits than the clinical and radiomics signature models alone.

CONCLUSIONS

We constructed a combined nomogram comprising a clinical model and radiomics signature as a noninvasive preoperative prediction method to distinguish between benign and malignant bone tumours and assist treatment planning.

Cognitive map to support the diagnosis of solitary bone tumors in pediatric patients

Abstract