Characterisation of musculoskeletal tumours by multivoxel proton MR spectroscopy

Virtual Biopsy in Soft Tissue Sarcoma. How Close Are We?

A shift in radiology to a data-driven specialty has been unlocked by synergistic developments in imaging biomarkers (IB) and computational science. This is advancing the capability to deliver “virtual biopsies” within oncology. The ability to non-invasively probe tumour biology both spatially and temporally would fulfil the potential of imaging to inform management of complex tumours; improving diagnostic accuracy, providing new insights into inter- and intra-tumoral heterogeneity and individualised treatment planning and monitoring. Soft tissue sarcomas (STS) are rare tumours of mesenchymal origin with over 150 histological subtypes and notorious heterogeneity. The combination of inter- and intra-tumoural heterogeneity and the rarity of the disease remain major barriers to effective treatments. We provide an overview of the process of successful IB development, the key imaging and computational advancements in STS including quantitative magnetic resonance imaging, radiomics and artificial intelligence, and the studies to date that have explored the potential biological surrogates to imaging metrics. We discuss the promising future directions of IBs in STS and illustrate how the routine clinical implementation of a virtual biopsy has the potential to revolutionise the management of this group of complex cancers and improve clinical outcomes.

An Update in Imaging Evaluation of Histopathological Grade of Soft Tissue Sarcomas Using Structural and Quantitative Imaging and Radiomics

Over the past two decades, considerable efforts have been made to develop non-invasive methods for determining tumor grade or surrogates for predicting the biological behavior, aiding early treatment decisions, and providing prognostic information. The development of new imaging tools, such as diffusion-weighted imaging, diffusion kurtosis imaging, perfusion imaging, and magnetic resonance spectroscopy have provided leverage in the diagnosis of soft tissue sarcomas. Artificial intelligence is a new technology used to study and simulate human thinking and abilities, which can extract and analyze advanced and quantitative image features from medical images with high throughput for an in-depth characterization of the spatial heterogeneity of tumor tissues. This article reviews the current imaging modalities used to predict the histopathological grade of soft tissue sarcomas and highlights the advantages and limitations of each modality. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

The value of bi-exponential and non-Gaussian distribution diffusion-weighted imaging in the differentiation of recurrent soft tissue neoplasms and post-surgical changes

Background

Many researches focused on the quantitative mono-exponential diffusion-weighted imaging (DWI) in the assessment of soft tissue neoplasms (STN), but few focused on the value of bi-exponential and non-Gaussian DWI in the application of Recurrent Soft Tissue Neoplasms (RSTN). This study aimed to explore the feasibility of bi-exponential decay and non-Gaussian distribution DWI in the differentiation of RSTN and Post-Surgery Changes (PSC), and compared with mono-exponential DWI.

Methods

The clinical, mono-exponential, bi-exponential [intravoxel incoherent motion (IVIM)] and non-Gaussian [diffusion kurtosis imaging (DKI)] DWI imaging of a cohort of 27 patients [15 RSTN (22 masses), and 12 PSC (12 lesions)] with 34 masses, from Nov 01 2017 to Sep 30 2018, were reviewed. The differences of apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudodiffusion coefficient (D*), perfusion fraction (f), mean diffusivity (MD), and mean kurtosis (MK) values were compared between RSTN and PSC groups. The mono-, bi-exponential, and non-Gaussian distribution based predictive models for RSTN and PSC were built and compared. ROC curves were generated and compared by the DeLong test.

Results

Intra-class correlation coefficient (ICC) of all IVIM/DKI parameters was high (≥0.841). There were significant differences in ADC, D, f, MD, and MK values between RSTN and PSC, but no difference in D* value. The ADC_IVIM, D, f and MD values of RSTN were lower than those of PSC, but with higher MK value. The ADC_IVIM and D values did better than f value in differentiating these two groups (P<0.05). While there was no significant difference in AUCs among ADC_DKI, MD, and MK values. Also, no significant difference was detected in AUCs between bi-exponential and mono-exponential (P=0.38), or between mono-exponential and non-Gaussian distribution based prediction models (P=0.09).

Conclusions

ADC, D, f, MD, and MK values can be used in the differentiation of RSTN and PSC.

Giant glomus tumor of the knee mimicking soft-tissue sarcoma

Glomangiomas (glomus tumors) are benign vascular tumors commonly located at the distal extremities, are usually subungual lesions, and account for 2% of all soft-tissue tumors. Patients with digital glomus tumors present with hypersensitivity to cold, paroxysmal severe pain, and point tenderness. These tumors are infrequent in the knee area, and when seen are superficial, usually have a diameter of less than 1 cm, which make their radiological diagnosis arduous. We report a noteworthy, unusual case of a large glomus tumor in the popliteal fossa showing biceps femoris infiltration, in a 51-year-old female patient who experienced severe intermittent posterior knee pain for the past 2 years. Magnetic resonance imaging revealed a large popliteal inhomogeneous soft-tissue lesion with irregular margins insinuating the posterolateral musculature mimicking soft-tissue sarcoma. Histopathology revealed a glomus tumor.

Proton MR spectroscopy in characterization of focal bone lesions of peripheral skeleton

Background

The aim of our study was to determine the value of single-voxel proton MR spectroscopy (1HMRS) in distinguishing benign from malignant focal bone lesions in the peripheral skeleton. MRI and 1HMRS was performed in 50 focal lesions (> 1 cm size) detected on radiographs of peripheral skeleton.1HMRS was performed at 1.5 T with TE of 144 ms with automatic shimming and water suppression. Qualitative analysis for a discrete choline peak at 3.2 ppm was done. Significance of the presence of choline peak on 1HMRS in distinguishing benign from malignant lesions was calculated using histopathology as a gold standard. Chi-square test was used and p value < 0.05 was considered significant.

Results

Forty-one benign and 9 malignant lesions were confirmed by histopathological results. Amongst malignant lesions, choline peak was positive in all but 1 case of low-grade lymphoma. MR spectra of 11 benign lesions showed the presence of choline peak. All 7 benign giant cell tumors (GCT) were positive for choline peak. The sensitivity, specificity, PPV, NPV of proton MR spectroscopy in differentiating benign from malignant lesions were 87.5%,71%,38.8%, and 96.4% respectively. p value was significant (< 0.05).

Conclusion

1HMRS in focal bone lesions can help in the differentiation of malignant from benign musculoskeletal tumors. Although some benign lesions may show false-positive result, absence of choline peak is a reliable reassurance against malignancy. GCT is an exception amongst benign bone tumors as it consistently shows the presence of choline peak on 1HMRS.

MR Imaging of Pediatric Musculoskeletal Tumors:: Recent Advances and Clinical Applications

Pediatric musculoskeletal tumors comprise approximately 10% of childhood neoplasms, and MR imaging has been used as the imaging evaluation standard for these tumors. The role of MR imaging in these cases includes identification of tumor origin, tissue characterization, and definition of tumor extent and relationship to adjacent structures as well as therapeutic response in posttreatment surveillance. Technical advances have enabled quantitative evaluation of biochemical changes in tumors. This article reviews recent updates to MR imaging of pediatric musculoskeletal tumors, focusing on advanced MR imaging techniques and providing information on the relevant physics of these techniques, clinical applications, and pitfalls.

Diffusion tensor imaging in the musculoskeletal and peripheral nerve systems: from experimental to clinical applications

Magnetic resonance imaging (MRI) is a well-established imaging modality which is used in all districts of the musculoskeletal and peripheral nerve systems. More recently, initial studies have applied multiparametric MRI to evaluate quantitatively different aspects of musculoskeletal and peripheral nerve diseases, thus providing not only images but also numbers and clinical data. Besides ¹H and ³¹P magnetic resonance spectroscopy, diffusion-weighted imaging (DWI) and blood oxygenation level-dependent imaging, diffusion tensor imaging (DTI) is a relatively new MRI-based technique relying on principles of DWI, which has traditionally been used mainly for evaluating the central nervous system to track fibre course. In the musculoskeletal and peripheral nerve systems, DTI has been mostly used in experimental settings, with still few indications in clinical practice. In this review, we describe the potential use of DTI to evaluate different musculoskeletal and peripheral nerve conditions, emphasising the translational aspects of this technique from the experimental to the clinical setting.