Soft tissue sarcoma: can dynamic contrast-enhanced (DCE) MRI be used to predict the histological grade?

Predicting the Malignancy Grade of Soft Tissue Sarcomas on MRI Using Conventional Image Reading and Radiomics

Objectives: This study aims to investigate MRI features predicting the grade of STS malignancy using conventional image reading and radiomics. Methods: Pretherapeutic imaging data regarding size, tissue heterogeneity, peritumoral changes, necrosis, hemorrhage, and cystic degeneration were evaluated in conventional image reading. Furthermore, the tumors' apparent diffusion coefficient (ADC) values and radiomics features were extracted and analyzed. A random forest machine learning algorithm was trained and evaluated based on the extracted features. Results: A total of 139 STS cases were included in this study. The mean tumor ADC and the ratio between tumor ADC to healthy muscle ADC were significantly lower in high-grade tumors (p = 0.001 and 0.005, respectively). Peritumoral edema (p < 0.001) and peritumoral contrast enhancement (p < 0.001) were significantly more extensive in high-grade tumors. Tumor heterogeneity was significantly increased in high-grade sarcomas, particularly in T2w- and contrast-enhanced sequences using conventional image reading (p < 0.001) as well as in the radiomics analysis (p < 0.001). Our trained random forest machine learning model predicted high-grade status with an area under the curve (AUC) of 0.97 and an F1 score of 0.93. Biopsy-underestimated tumors exhibited differences in tumor heterogeneity and peritumoral changes. Conclusions: Tumor heterogeneity is a key characteristic of high-grade STSs, which is discernible through conventional imaging reading and radiomics analysis. Higher STS grades are also associated with low ADC values, peritumoral edema, and peritumoral contrast enhancement.

Inferring malignancy grade of soft tissue sarcomas from magnetic resonance imaging features: A systematic review

PURPOSE

Systematic reviews on the grading of STS using MRI are lacking. This review analyses the role of different MRI features in inferring the histological grade of STS.

MATERIALS AND METHODS

A systematic review was conducted and is reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) checklist. The electronic databases of PubMed/MEDLINE were systematically searched for literature addressing the correlation of MRI findings in soft tissue sarcoma with tumor grade. As keywords "MRI", "magnetic resonance imaging", "sarcoma", "grade", "grading", and "FNCLCC" have been selected.

RESULTS

14 studies have been included in this systematic review. Tumor size (p = 0.015 (51 patients) to p = 0.81 (36 patients)), tumor margin (p < 0.001 (95 patients) to 0.93 (36 patients)), necrosis (p = 0.004 (50 patients) to p = 0.65 (95 patients)), peritumoral edema (p = 0.002 (130 patients) to p = 0.337 (40 patients)), contrast enhancement (p < 0.01 (50 patients) to 0.019 (51 patients)) and polycyclic/multilobulated tumor configuration (p = 0.008 (71 patients)) were significantly associated with STS malignancy grade in most of the included studies. Heterogeneity in T2w images (p = 0.003 (130 patients) to 0.202 (40 patients)), signal intensity in T1w images/ hemorrhage (p = 0.02 (130 patients) to 0.5 (31 patients)), peritumoral contrast enhancement (p < 0.001 (95 patients) to 0.253 (51 patients)) and tumoral diffusion restriction (p = 0.01 (51 patients) to 0.53 (52 patients)) were regarded as significantly associated with FNCLCC grade in some of the studies which investigated these features. Most other MRI features were not significant.

CONCLUSION

Several MRI features, such as tumor size, necrosis, peritumoral edema, peritumoral contrast enhancement, intratumoral contrast enhancement, and polycyclic/multilobulated tumor configuration may indicate the malignancy grade of STS. However, further studies are needed to gain consensus.

Coefficient of variation of T2-weighted MRI may predict the prognosis of malignant peripheral nerve sheath tumor

BACKGROUND

We investigated whether non-enhancement MRI features, including measurement of the heterogeneity of the tumor with MR T2 imaging by calculating coefficient of variation (CV) values, were associated with the prognosis of non-metastatic malignant peripheral nerve sheath tumors (MPNST).

METHODS

This retrospective study included 42 patients with MPNST who had undergone surgical resection (mean age, 50 years ± 21; 20 male participants). Non-enhancement MR images were evaluated for signal intensity heterogeneity on T1- and T2-weighted imaging, tumor margin definition on T1- and T2-weighted imaging, peritumoral edema on T2-weight imaging, and CV. We measured the signal intensities of MR T2-weighted images and calculated the corresponding CV values. CV is defined as the ratio of the standard deviation to the mean. The associations between factors and overall survival (OS) were investigated via the Kaplan-Meier method with log-rank tests and the Cox proportional hazards model.

RESULTS

The mean CV value of MR T2 images was 0.2299 ± 0.1339 (standard deviation) (range, 0.0381-0.8053). Applying receiver operating characteristics analysis, the optimal cut-off level for CV value was 0.137. This cut-off CV value was used for its stratification into high and low CV values. At multivariate survival analysis, a high CV value (hazard ratio = 3.63; 95% confidence interval = 1.16-16.0; p = 0.047) was identified as an independent predictor of OS.

CONCLUSION

The CV value of the signal intensity of heterogenous MPNSTs MR T2-weighted images is an independent predictor of patients' OS.

Perfusion Imaging of the Musculoskeletal System

Perfusion imaging is the aspect of functional imaging, which is most applicable to the musculoskeletal system. In this review, the anatomy and physiology of bone perfusion is briefly outlined as are the methods of acquiring perfusion data on MR imaging. The current clinical indications of perfusion related to the assessment of soft tissue and bone tumors, synovitis, osteoarthritis, avascular necrosis, Keinbock's disease, diabetic foot, osteochondritis dissecans, and Paget's disease of bone are reviewed. Challenges and opportunities related to perfusion imaging of the musculoskeletal system are also briefly addressed.

Intravoxel Incoherent Motion and Dynamic Contrast-Enhanced Magnetic Resonance Imaging Can Differentiate Between Atypical Cartilaginous Tumors and High-Grade Chondrosarcoma: Correlation With Histological Vessel Characteristics

OBJECTIVE

To differentiate between atypical cartilaginous tumors and high-grade chondrosarcoma of the major long bones using intravoxel incoherent motion (IVIM) and Dynamic Contrast-Enhanced magnetic resonance imaging (DCE-MRI), and explore the correlation of quantitative parameters with hypoxia inducible factor-1α (HIF-1α), vascular endothelial growth factor (VEGF) and microvessel density (MVD).

METHOD

Between September 2016 and March 2022, 35 patients (17 atypical cartilaginous tumors, 18 high-grade chondrosarcoma) underwent MRI examination and pathological confirmation at our hospital. First, IVIM-derived parameters ( D , D* , and f ), and DCE-MRI parameters ( Ktrans , Kep , and V e ) were measured, and intraclass correlation efficient (ICC) and Mann-Whitney U test were performed. Second, receiver-operating characteristic curve analysis was performed to evaluate the diagnostic performance. Finally, Spearman's correlation analysis was performed between the quantitative parameters of IVIM-DWI and DCE-MRI and the immunohistochemical factors HIF-1α, VEGF, and MVD in chondrosarcoma tissue.

RESULTS

D in atypical cartilaginous tumors was significantly higher than that in high-grade chondrosarcoma ( P = 0.003), whereas D* , Ktrans , and K ep in atypical cartilaginous tumors were significantly lower than those in high-grade chondrosarcoma (all P < 0.001). Ktrans demonstrated the highest area under the curve (AUC) of 0.979. The D* , Ktrans , and K ep were positively correlated with HIF-1α, VEGF, and MVD (all P < 0.001), whereas D had no correlation with HIF-1α, VEGF, and MVD ( P = 0.113, 0.077, 0.058, respectively).

CONCLUSION

The IVIM-DWI quantitative parameters ( D , D* ) and DCE-MRI quantitative parameters ( Ktrans , Kep ) are helpful to differentiate between atypical cartilaginous tumors and high-grade chondrosarcoma and could be imaging biomarkers to reflect the expressions of HIF-1α, VEGF, and angiogenesis of chondrosarcoma.

Prediction of high-grade soft-tissue sarcoma using a combined intratumoural and peritumoural MRI-based radiomics nomogram

AIM

To develop an intratumoural and peritumoural magnetic resonance imaging (MRI)-based radiomics nomogram for predicting tumour grade to improve clinical treatment and long-term prognosis.

MATERIALS AND METHODS

MRI (3 T) features and T2-weighted imaging with fat-saturation (T2WI-FS)-based radiomics features of 57 patients with soft-tissue sarcoma (STS) were analysed retrospectively. Tumour size, ratio of width and length, relative depth to the peripheral fascia, peritumoural oedema, heterogeneity on T2WI, necrosis signal, enhancement model, and peritumoural enhancement were obtained. Independent risk factors were screened to construct an MRI feature nomogram. Radiomics features were obtained from intratumoural and peritumoural images on T2WI-FS. The optimal radiomics model was selected by the four-step dimensionality reduction method of minimum and maximum normalisation, optimal feature selection, selection based on support vector machine with L1-norm regularisation model, and iterative feature selection. MRI features and optimal radiomics features were used to construct a radiomics nomogram. The MRI feature nomogram model, the radiomics model, and the radiomics nomogram model were assessed by receiver operating characteristic (ROC) curves and calibration curves of the training and validation sets.

RESULTS

Heterogeneity on T2WI and peritumoural enhancement were independent risk factors for predicting high-grade STS. The areas under the curves of the training set and verification set of the three models were as follows: MRI feature nomogram, 0.86 and 0.83, respectively; intratumoural and peritumoural combined radiomics model, 0.99 and 0.86, respectively; and radiomics nomogram model, 0.98 and 0.96, respectively.

CONCLUSION

The radiomics nomogram model based on MRI features and combined intratumoural and peritumoural radiomic features was best able to predict high-grade STS.

Fluorescence guidance improves the accuracy of radiological imaging-guided surgical navigation

BACKGROUND

Imaging-based navigation technologies require static referencing between the target anatomy and the optical sensors. Imaging-based navigation is therefore well suited to operations involving bony anatomy; however, these technologies have not translated to soft-tissue surgery. We sought to determine if fluorescence imaging complement conventional, radiological imaging-based navigation to guide the dissection of soft-tissue phantom tumors.

METHODS

Using a human tissue-simulating model, we created tumor phantoms with physiologically accurate optical density and contrast concentrations. Phantoms were dissected using all possible combinations of computed tomography (CT), magnetic resonance, and fluorescence imaging; controls were included. The data were margin accuracy, margin status, tumor spatial alignment, and dissection duration.

RESULTS

Margin accuracy was higher for combined navigation modalities compared to individual navigation modalities, and accuracy was highest with combined CT and fluorescence navigation (p = 0.045). Margin status improved with combined CT and fluorescence imaging.

CONCLUSIONS

At present, imaging-based navigation has limited application in guiding soft-tissue tumor operations due to its inability to compensate for positional changes during surgery. This study indicates that fluorescence guidance enhances the accuracy of imaging-based navigation and may be best viewed as a synergistic technology, rather than a competing one.

Predictive model based on DCE-MRI and clinical features for the evaluation of pain response after stereotactic body radiotherapy in patients with spinal metastases

OBJECTIVE

To investigate the correlation of conventional MRI, DCE-MRI and clinical features with pain response after stereotactic body radiotherapy (SBRT) in patients with spinal metastases and establish a pain response prediction model.

METHODS

Patients with spinal metastases who received SBRT in our hospital from July 2018 to April 2022 consecutively were enrolled. All patients underwent conventional MRI and DCE-MRI before treatment. Pain was assessed before treatment and in the third month after treatment, and the patients were divided into pain-response and no-pain-response groups. A multivariate logistic regression model was constructed to obtain the odds ratio and 95% confidence interval (CI) for each variable. C-index was used to evaluate the model's discrimination performance.

RESULTS

Overall, 112 independent spinal lesions in 89 patients were included. There were 73 (65.2%) and 39 (34.8%) lesions in the pain-response and no-pain-response groups, respectively. Multivariate analysis showed that the number of treated lesions, pretreatment pain score, Karnofsky performance status score, Bilsky grade, and the DCE-MRI quantitative parameter K^trans were independent predictors of post-SBRT pain response in patients with spinal metastases. The discrimination performance of the prediction model was good; the C index was 0.806 (95% CI: 0.721-0.891), and the corrected C-index was 0.754.

CONCLUSION

Some imaging and clinical features correlated with post-SBRT pain response in patients with spinal metastases. The model based on these characteristics has a good predictive value and can provide valuable information for clinical decision-making.

KEY POINTS

• SBRT can accurately irradiate spinal metastases with ablative doses. • Predicting the post-SBRT pain response has important clinical implications. • The prediction models established based on clinical and MRI features have good performance.

Role of Imaging in Initial Prognostication of Locally Advanced Soft Tissue Sarcomas

BACKGROUND

Although imaging is central in the initial staging of patients with soft tissue sarcomas (STS), it remains underused and few radiological features are currently used in practice for prognostication and to help guide the best therapeutic strategy. Yet, several prognostic qualitative and quantitative characteristics from magnetic resonance imaging (MRI) and positron emission tomography (PET) have been identified over these last decades.

OBJECTIVE

After an overview of the current validated prognostic features based on baseline imaging and their integration into prognostic tools, such as nomograms used by clinicians, the aim of this review is to summarize more complex and innovative MRI, PET, and radiomics features, and to highlight their role to predict indirectly (through histologic grade) or directly the patients' outcomes.

Soft-tissue sarcoma in adults: Imaging appearances, pitfalls and diagnostic algorithms

This article provides an overview of the current knowledge regarding diagnostic imaging of patients with soft-tissue sarcomas, which is a heterogeneous group of rare mesenchymal malignancies. After an initial contextualization, diagnostic flow-chart based on initial radiological findings of soft-tissue masses (with specific focus on adipocytic soft-tissue tumors [STTs], hemorragic STTs and retroperitoneal STTs) are provided considering relevant results from novel researches, guidelines, and experts' viewpoints, with the aim to help radiologists and clinicians in their practice. Particularly, the central place of sarcoma reference centers in the diagnostic and therapeutic management is highlighted, as well as the pivotal role that radiologists should play to correctly identify patients with soft-tissue sarcoma at the initial stage of the disease. Indications and methods for performing imaging-guided biopsies are also discussed, as well as clues to improve soft-tissue sarcoma grading with conventional and quantitative imaging.

Diagnostic Performance of Dynamic Contrast-Enhanced MRI and 18F-FDG PET/CT for Evaluation of Soft Tissue Tumors and Correlation with Pathology Parameters

RATIONALE AND OBJECTIVES

To assess the diagnostic performance of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and fluorine-18-fluorodeoxyglucose (¹⁸F-FDG) positron-emission tomography/computed tomography (PET/CT) parameters in evaluating the biological behavior of soft tissue tumors.

MATERIALS AND METHODS

We retrospectively analyzed DCE-MRI and ¹⁸F-FDG PET/CT parameters in 78 patients with pathology-confirmed soft tissue tumors. A total of 78 patients had undergone DCE-MRI examination, while 24 patients with malignant soft tissue tumor had undergone ¹⁸F-FDG PET/CT examination. Microvessel density (MVD) and the Ki-67 labeling index (LI) were detected using immunohistochemistry. Differences in parameters (Ktrans, Kep, Ve, MVD, and Ki-67 LI) between benign and malignant tumors were compared. Differences in parameters (Ktrans, Kep, Ve, MVD, and SUV_max) between high- and low-proliferation malignant tumors (grouped by Ki-67 LI) were compared. Correlation of the DCE-MRI and ¹⁸F-FDG PET/CT parameters with MVD and Ki-67 LI was analyzed.

RESULTS

Only the Ktrans, Kep, MVD, and Ki-67 LI differed significantly between the benign and malignant soft tissue tumors (all p < 0.001). Only Kep (p = 0.033) and SUV_max (p = 0.001) differed significantly between high- and low-proliferation malignant soft tissue tumors. Ktrans, Kep, and SUV_max correlated positively with MVD (r = 0.805, 0.778, 0.730, respectively; all p < 0.001), and with Ki-67 LI (r = 0.721, 0.685, 0.655, respectively; all p < 0.001).

CONCLUSION

DCE-MRI and ¹⁸F-FDG PET/CT parameters indicate soft tissue tumor biological behavior and can be used to differentiate between benign and malignant soft tissue tumors and between high- and low-proliferation malignant soft tissue tumors.

Tumor cellularity beyond the visible in soft tissue sarcomas: Results of an ADC-based, single center, and preliminary radiomics study

PURPOSE

Soft tissue sarcomas represent approximately 1% of all malignancies, and diagnostic radiology plays a significant role in the overall management of this rare group of tumors. Recently, quantitative imaging and, in particular, radiomics demonstrated to provide significant novel information, for instance, in terms of prognosis and grading. The aim of this study was to evaluate the prognostic role of radiomic variables extracted from apparent diffusion coefficient (ADC) maps collected at diagnosis in patients with soft tissue sarcomas in terms of overall survival and metastatic spread as well as to assess the relationship between radiomics and the tumor grade.

METHODS

Patients with histologically proven soft tissue sarcomas treated in our tertiary center from 2016 to 2019 who underwent an Magnetic Resonance (MR) scan at diagnosis including diffusion-weighted imaging were included in this retrospective institution review board-approved study. Each primary lesion was segmented using the b50 images; the volumetric region of interest was then applied on the ADC map. A total of 33 radiomic features were extracted, and highly correlating features were selected by factor analysis. In the case of feature/s showing statistically significant results, the diagnostic accuracy was computed. The Spearman correlation coefficient was used to evaluate the relationship between the tumor grade and radiomic features selected by factor analysis. All analyses were performed applying p<0.05 as a significant level.

RESULTS

A total of 36 patients matched the inclusion criteria (15 women; mean age 58.9 ± 15 years old). The most frequent histotype was myxofibrosarcoma (16.6%), and most of the patients were affected by high-grade lesions (77.7%). Seven patients had pulmonary metastases, and, altogether, eight were deceased. Only the feature Imc1 turned out to be a predictor of metastatic spread (p=0.045 after Bonferroni correction) with 76.7% accuracy. The value -0.16 showed 73.3% sensitivity and 71.4% specificity, and patients with metastases showed lower values (mean Imc1 of metastatic patients -0.31). None of the examined variables was a predictor of the overall outcome (p>0.05, each). A moderate statistically significant correlation emerged only between Imc1 and the tumor grade (r=0.457, p=0.005).

CONCLUSIONS

In conclusion, the radiomic feature Imc1 acts as a predictor of metastatic spread in patients with soft tissue sarcomas and correlates with the tumor grade.

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.

Soft Tissue Sarcomas: The Role of Quantitative MRI in Treatment Response Evaluation

BACKGROUND

Although curative surgery remains the cornerstone of the therapeutic strategy in patients with soft tissue sarcomas (STS), neoadjuvant radiotherapy and chemotherapy (NART and NACT, respectively) are increasingly used to improve operability, surgical margins and patient outcome. The best imaging modality for locoregional assessment of STS is MRI but these tumors are mostly evaluated in a qualitative manner.

OBJECTIVE

After an overview of the current standard of care regarding treatment for patients with locally advanced STS, this review aims to summarize the principles and limitations of (i) the current methods used to evaluate response to neoadjuvant treatment in clinical practice and clinical trials in STS (RECIST 1.1 and modified Choi criteria), (ii) quantitative MRI sequences (i.e., diffusion weighted imaging and dynamic contrast enhanced MRI), and (iii) texture analyses and (delta-) radiomics.

What morphological MRI features enable differentiation of low-grade from high-grade soft tissue sarcoma?

Objective:

To assess the diagnostic performance of morphological MRI features separately and in combination for distinguishing low- from high-grade soft tissue sarcoma (STS).

Methods and materials:

We retrospectively analysed pre-treatment MRI examinations with T1, T2 with and without fat suppression (FS) and contrast-enhanced T1 obtained in 64 patients with STS categorized histologically as low (n = 21) versus high grade (n = 43). Two musculoskeletal radiologists blinded to histology evaluated MRI features. Diagnostic performance was calculated for each reader and for MRI features showing significant association with histology (p < 0.05). Logistic regression analysis was performed to develop a diagnostic model to identify high-grade STS.

Results:

Among all evaluated MRI features, only six features had adequate interobserver reproducibility (kappa>0.5). Multivariate logistic regression analysis revealed a significant association with tumour grade for lesion heterogeneity on FS images, intratumoural enhancement≥51% of tumour volume and peritumoural enhancement for both readers (p < 0.05). For both readers, the presence of each of the three features yielded odds ratios for high grade versus low grade from 4.4 to 9.1 (p < 0.05). The sum of the positive features for each reader independent of reader expertise yielded areas under the curve (AUCs) > 0.8. The presence of ≥2 positive features indicated a high risk for high-grade sarcoma, whereas ≤1 positive feature indicated a low-to-moderate risk

Conclusion:

A diagnostic MRI score based on tumour heterogeneity, intratumoural and peritumoural enhancement enables identification of lesions that are likely to be high-grade as opposed to low-grade STS.

Advances in knowledge:

Tumour heterogeneity in Fat Suppression sequence, intratumoural and peritumoural enhancement is identified as signs of high-grade sarcoma.

Update on MR Imaging of Soft Tissue Tumors of Head and Neck

This article reviews soft tissue tumors of the head and neck following the 2020 revision of WHO Classification of Soft Tissue and Bone Tumours. Common soft tissue tumors in the head and neck and tumors are discussed, along with newly added entities to the classification system. Salient clinical and imaging features that may allow for improved diagnostic accuracy or to narrow the imaging differential diagnosis are covered. Advanced imaging techniques are discussed, with a focus on diffusion-weighted and dynamic contrast imaging and their potential to help characterize soft tissue tumors and aid in distinguishing malignant from benign tumors.

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.