Therapeutic response in musculoskeletal soft tissue sarcomas: evaluation by MRI

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.

Prediction of soft tissue sarcoma response to radiotherapy using longitudinal diffusion MRI and a deep neural network with generative adversarial network-based data augmentation

PURPOSE

The goal of this study was to predict soft tissue sarcoma response to radiotherapy (RT) using longitudinal diffusion-weighted MRI (DWI). A novel deep-learning prediction framework along with generative adversarial network (GAN)-based data augmentation was investigated for the response prediction.

METHODS

Thirty soft tissue sarcoma patients who were treated with five-fraction hypofractionated radiation therapy (RT, 6Gy×5) underwent diffusion-weighted MRI three times throughout the RT course using an MR-guided radiotherapy system. Pathologic treatment effect (TE) scores, ranging from 0-100%, were obtained from the post-RT surgical specimen as a surrogate of patient treatment response. Patients were divided into three classes based on the TE score (TE ≤ 20%, 20% < TE < 90%, TE ≥ 90%). Apparent diffusion coefficient (ADC) maps of the tumor from the three time points were combined as 3-channel images. An auxiliary classifier generative adversarial network (ACGAN) was trained on 20 patients to augment the data size. A total of 15,000 synthetic images were generated for each class. A prediction model based on a previously described VGG-19 network was trained using the synthesized data, validated on five unseen validation patients, and tested on the remaining five test patients. The entire process was repeated seven times, each time shuffling the training, validation, and testing datasets such that each patient was tested at least once during the independent test stage. Prediction performance for slice-based prediction and patient-based prediction was evaluated.

RESULTS

The average training and validation accuracies were 86.5% ± 1.6% and 84.8% ± 1.8%, respectively, indicating that the generated samples were good representations of the original patient data. Among the seven rounds of testing, slice by slice prediction accuracy ranged from 81.6% to 86.8%. The overall accuracy of the independent test sets was 83.3%. For patient-based prediction, 80% was achieved in one round and 100% was achieved in the remaining six rounds. The mean accuracy was 97.1%.

CONCLUSION

This study demonstrated the potential to use deep learning to predict the pathologic treatment effect from longitudinal DWI. Accuracies of 83.3% and 97.1% were achieved on independent test sets for slice-based and patient-based prediction respectively.

Configuration of soft-tissue sarcoma on MRI correlates with grade of malignancy

Background

The aim of the study was to assess whether the configuration of primary soft-tissue sarcoma (STS) on MRI correlates with the grade of malignancy.

Patients and methods

71 patients with histologically proven STS were included. Primary STS were examined for configuration, borders, and volume on MRI. The tumors were divided into high-grade (G3), intermediate-grade (G2) and low-grade (G1) STS according to the grading system of the French Federation of Cancer Centers Sarcoma Group (FNCLCC).

Results

30 high-grade, 22 intermediate-grade and 19 low-grade primary STS lesions were identified. High- and intermediate-grade (G3/2) STS significantly most often appeared as polycyclic/multilobulated tumors (p < 0.001 and p = 0.002, respectively). Low-grade (G1) STS mainly showed an ovoid/nodular or streaky configuration (p = 0.008), and well-defined borders. The appearance of high-, intermediate- and low-grade STS with an ovoid/nodular configuration were mainly the same on MRI. All streaky G3/2 sarcoma and 17 of 20 patients with polycyclic/multilobulated G3 sarcoma showed infiltrative borders. High-grade streaky and polycyclic/multilobulated STS are larger in volume, compared to intermediate- and low-grade STS.

Conclusions

Configuration of STS on MRI can indicate the grade of malignancy. Higher-grade (G2/3) STS most often show a polycyclic/multilobulated configuration, while low-grade STS are mainly ovoid/nodular or streaky. Infiltrative behavior might suggest higher-grade STS in streaky and polycyclic/multilobulated STS.

Treatment effect prediction for sarcoma patients treated with preoperative radiotherapy using radiomics features from longitudinal diffusion-weighted MRIs

The objective of this study was to explore radiomics features from longitudinal diffusion-weighted MRIs (DWIs) for pathologic treatment effect prediction in patients with localized soft tissue sarcoma (STS) undergoing hypofractionated preoperative radiotherapy (RT). Thirty patients with localized STS treated with preoperative hypofractionated RT were recruited to this longitudinal imaging study. DWIs were acquired at three time points using a 0.35 T MRI-guided radiotherapy system. Treatment effect score (TES) was obtained from the post-surgery pathology as a surrogate of treatment outcome. Patients were divided into two groups based on TES. Response prediction was first performed using a support vector machine (SVM) with only mean apparent diffusion coefficient (ADC) or delta ADC to serve as the benchmark. Radiomics features were then extracted from tumor ADC maps at each of the three time points. Logistic regression and SVM were constructed to predict the TES group using features selected by univariate analysis and sequential forward selection. Classification performance using SVM with features from different time points and with or without delta radiomics were evaluated. Prediction performance using only mean ADC or delta ADC was poor (area under the curve (AUC) < 0.7). For the radiomics study using features from all time points and corresponding delta radiomics, SVM significantly outperformed logistic regression (AUC of 0.91 ± 0.05 v.s. 0.85 ± 0.06). Prediction AUC values using single or multiple time points without delta radiomics were all below 0.74. Including delta radiomics of mid- or post-treatment relative to the baseline drastically boosted the prediction. In this work, an SVM model was built to predict the TES using radiomics features from longitudinal DWI. Based on this study, we found that use of mean ADC, delta ADC, or radiomics features alone was not sufficient for response prediction, and including delta radiomics features of mid- or post-treatment relative to the baseline can optimize the prediction of TES, a pathologic and clinical endpoint.

Radiomic analysis of soft tissues sarcomas can distinguish intermediate from high-grade lesions

PURPOSE

To assess the feasibility of grading soft tissue sarcomas (STSs) using MRI features (radiomics).

MATERIALS AND METHODS

MRI (echo planar SE, 1.5T) from 19 patients with STSs and a known histological grading, were retrospectively analyzed. The apparent diffusion coefficient (ADC) maps, obtained by diffusion-weighted imaging acquisitions, were analyzed through 65 radiomic features, intensity-based (first order statistics, FOS) and texture (gray level co-occurrence matrix, GLCM; and gray level run length matrix, GLRLM) features. Feature selection (sequential forward floating search) and classification (k-nearest neighbor classifier) were performed to distinguish intermediate- from high-grade STSs. Classification was performed using the three different sub-groups of features separately as well as all the features together. The entire dataset was divided in three subsets: the training, validation and test set, containing, respectively, 60, 30, and 10% of the data.

RESULTS

Intermediate-grade lesions had a higher and less disperse ADC values compared with high-grade ones: most of FOS related to intensity are higher for the intermediate-grade STSs, while FOS related to signal variability were higher in the high grade (e.g., the feature variance is 2.6*10⁵  ± 0.9*10⁵ versus 3.3*10⁵  ± 1.6*10⁵ , P = 0.3). The GLCM features related to entropy and dissimilarity were higher in the high-grade. When performing classification, the best accuracy is obtained with a maximum of three features for each subgroup, FOS features being those leading to the best classification (validation set: FOS accuracy 0.90 ± 0.11, area under the curve [AUC] 0.85 ± 0.16; test set: FOS accuracy 0.88 ± 0.25, AUC 0.87 ± 0.34).

CONCLUSION

Good accuracy and AUC could be obtained using only few Radiomic features, belonging to the FOS class.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:829-840.

Multiparametric voxel-based analyses of standardized uptake values and apparent diffusion coefficients of soft-tissue tumours with a positron emission tomography/magnetic resonance system: Preliminary results

OBJECTIVES

To investigate the usefulness of voxel-based analysis of standardized uptake values (SUVs) and apparent diffusion coefficients (ADCs) for evaluating soft-tissue tumour malignancy with a PET/MR system.

METHODS

Thirty-five subjects with either ten low/intermediate-grade tumours or 25 high-grade tumours were prospectively enrolled. Zoomed diffusion-weighted and fluorodeoxyglucose (¹⁸FDG)-PET images were acquired along with fat-suppressed T2-weighted images (FST2WIs). Regions of interest (ROIs) were drawn on FST2WIs including the tumour in all slices. ROIs were pasted onto PET and ADC-maps to measure SUVs and ADCs within tumour ROIs. Tumour volume, SUVmax, ADCminimum, the heterogeneity and the correlation coefficients of SUV and ADC were recorded. The parameters of high- and low/intermediate-grade groups were compared, and receiver operating characteristic (ROC) analysis was also performed.

RESULTS

The mean correlation coefficient for SUV and ADC in high-grade sarcomas was lower than that of low/intermediate-grade tumours (-0.41 ± 0.25 vs. -0.08 ± 0.34, P < 0.01). Other parameters did not differ significantly. ROC analysis demonstrated that correlation coefficient showed the best diagnostic performance for differentiating the two groups (AUC 0.79, sensitivity 96.0%, specificity 60%, accuracy 85.7%).

CONCLUSIONS

SUV and ADC determined via PET/MR may be useful for differentiating between high-grade and low/intermediate-grade soft tissue tumours.

KEY POINTS

• PET/MR allows voxel-based comparison of SUVs and ADCs in soft-tissue tumours. • A comprehensive assessment of internal heterogeneity was performed with scatter plots. • SUVmax or ADCminimum could not differentiate high-grade sarcoma from low/intermediate-grade tumours. • Only the correlation coefficient between SUV and ADC differentiated the two groups. • The correlation coefficient showed the best diagnostic performance by ROC analysis.

Diagnostic performance of conventional MRI parameters and apparent diffusion coefficient values in differentiating between benign and malignant soft-tissue tumours

AIM

To compare the abilities of conventional magnetic resonance imaging (MRI) and apparent diffusion coefficient (ADC) in differentiating between benign and malignant soft-tissue tumours (STT).

MATERIAL AND METHODS

A total of 123 patients with STT who underwent 3 T MRI, including diffusion-weighted imaging (DWI), were retrospectively analysed using variate conventional MRI parameters, ADC_mean and ADC_min.

RESULTS

For the all-STT group, the correlation between the malignant STT conventional MRI parameters, except deep compartment involvement, compared to those of benign STT were statistically significant with univariate analysis. Maximum diameter of the tumour (p=0.001; odds ratio [OR], 8.97) and ADC_mean (p=0.020; OR, 4.30) were independent factors with multivariate analysis. For the non-myxoid non-haemosiderin STT group, signal heterogeneity on axial T1-weighted imaging (T1WI; p=0.017), ADC_mean, and ADC_min (p=0.001, p=0.001), showed significant differences with univariate analysis between malignancy and benignity. Signal heterogeneity in axial T1WI (p=0.025; OR, 12.64) and ADC_mean (p=0.004; OR, 33.15) were independent factors with multivariate analysis.

CONCLUSION

ADC values as well as conventional MRI parameters were useful in differentiating between benign and malignant STT. The ADC_mean was the most powerful diagnostic parameter in non-myxoid non-haemosiderin STT.

Increased robustness in reference region model analysis of DCE MRI using two-step constrained approaches

PURPOSE

Reference region models (RRMs) can quantify tumor perfusion in dynamic contrast-enhanced MRI without an arterial input function. Inspection of the RRM reveals that one of the free parameters in the fit is uniquely linked to the reference region and is common to all voxels. A two-step approach is proposed that takes this constraint into account.

METHODS

Three constrained RRM (CRRM) approaches were devised and evaluated. Simulations were performed to compare their accuracy and precision over a range of noise and temporal resolutions. The CRRM was also applied on a virtual phantom that simulates different perfusion values. In vivo evaluation was performed on data from breast cancer and soft tissue sarcoma.

RESULTS

In simulations, the CRRM consistently improved precision and had better accuracy at low signal-to-noise ratio (SNR). In virtual phantom, the CRRMs were able to fit voxels that had similar kinetics to the reference tissue, whereas the unconstrained models failed to accurately fit these voxels. In the in vivo data, the constrained approaches produced parameter maps that had less variability and were in better agreement with the Tofts model.

CONCLUSION

These findings indicate that the two-step fitting approach of the CRRM can reduce the variability of perfusion estimates for quantifying perfusion with dynamic contrast-enhanced (DCE) MRI. Magn Reson Med 78:1547-1557, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Short-term follow-up MRI after unplanned resection of malignant soft-tissue tumours; quantitative measurements on dynamic contrast enhanced and diffusion-weighted MR images

OBJECTIVE

To determine the diagnostic availability of dynamic contrast-enhanced (DCE) and diffusion-weighted (DW) MR images for evaluating residual tumours at short-term follow-up after unplanned excision of malignant soft-tissue tumours.

METHODS

From January 2013 to September 2014, 38 patients underwent first follow-up MRI, including DCE and DW imaging (DWI), within 3 months of unplanned malignant soft-tissue tumour excision. The presence or absence of definite nodule formation, focal fluid/haematoma collection, oedema and fascial thickening around or at tumour beds were evaluated using conventional MR images. The volume transfer constant between blood plasma and extracellular/extravascular space (EES) (Ktrans), rate constant between EES and blood plasma (Kep), volume of EES space per unit volume of tissue and initial area under the concentration curve (iAUC) values with time-concentration curve (TCC) plots were obtained on DCE images, and apparent diffusion coefficient (ADC) values were measured on ADC maps. All data were statistically analyzed.

RESULTS

Of the 21 patients who underwent re-excision, 12 patients had a residual tumor and 9 did not. All conventional MRI variables, except definite nodule formation, were insignificantly related to the presence of residual tumour. However, ADC values were found to be significantly associated with the presence of residual tumour, as were the DCE MRI variables, Ktrans, Kep and iAUC. In particular, TCC pattern and Kep were most significantly associated with residual tumour.

CONCLUSION

Additional DCE images may be useful for determining the presence of residual tumours in tumour beds during short-term follow-up after inadequate malignant soft-tissue tumour excision.

ADVANCES IN KNOWLEDGE

The addition of DCE MRI and quantitative analysis of the images obtained might be useful for determining the presence of residual tumour in a tumour bed during short-term follow-up after inadequate excision of a malignant soft-tissue tumour, although DWI was also found to be helpful.

Musculoskeletal Sarcoma: Update on Imaging of the Post-treatment Patient

Post-treatment imaging of musculoskeletal sarcoma remains challenging, but newer imaging techniques are improving our ability to recognize both local and distant recurrence and accurately distinguish local recurrence from post-treatment change. We review recent advances in dynamic contrast-enhanced magnetic resonance imaging, diffusion-weighted magnetic resonance imaging with apparent diffusion coefficient mapping and positron emission tomography/computed tomography in the post-treatment follow-up of musculoskeletal sarcoma. We also describe our multidisciplinary sarcoma team approach to patient care and the essential role of the radiologist in the clinical follow-up scheme.

Relationship between diffusion parameters derived from intravoxel incoherent motion MRI and perfusion measured by dynamic contrast-enhanced MRI of soft tissue tumors

Our aim was to evaluate the link between diffusion parameters measured by intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) and the perfusion metrics obtained with dynamic contrast-enhanced (DCE) MRI in soft tissue tumors (STTs). Twenty-eight patients affected by histopathologically confirmed STT were included in a prospective study. All patients underwent both DCE MRI and IVIM DWI. The perfusion fraction f, diffusion coefficient D and perfusion-related diffusion coefficient D* were estimated using a bi-exponential function to fit the DWI data. DCE MRI was acquired with a temporal resolution of 3-5 s. Maps of the initial area under the gadolinium concentration curve (IAUGC), time to peak (TTP) and maximum slope of increase (MSI) were derived using commercial software. The relationships between the DCE MRI and IVIM DWI measurements were assessed by Spearman's test. To exclude false positive results under multiple testing, the false discovery rate (FDR) procedure was applied. The Mann-Whitney test was used to evaluate the differences between all variables in patients with non-myxoid and myxoid STT. No significant relationship was found between IVIM parameters and any DCE MRI parameters. Higher f and D*f values were found in non-myxoid tumors compared with myxoid tumors (p = 0.004 and p = 0.003, respectively). MSI was significantly higher in non-myxoid tumors than in myxoid tumors (p = 0.029). From the visual assessments of single clinical cases, both f and D*f maps were in satisfactory agreement with DCE maps in the extreme cases of an avascular mass and a highly vascularized mass, whereas, for tumors with slight vascularity or with a highly heterogeneous perfusion pattern, this association was not straightforward. Although IVIM DWI was demonstrated to be feasible in STT, our data did not support evident relationships between perfusion-related IVIM parameters and perfusion measured by DCE MRI.

Preoperative radiotherapy for extremity soft tissue sarcoma; past, present and future perspectives on dose fractionation regimens and combined modality strategies

INTRODUCTION

This critical review aims to summarize published data on limb sparing surgery for extremity soft tissue sarcoma in combination with pre-operative radiotherapy (RT).

METHODS

This review is based on peer-reviewed publications using a PubMed search on the MeSH headings "soft tissue sarcoma" AND "preoperative radiotherapy". Titles and abstracts screened for data including "fraction size AND/OR total dose AND/OR overall treatment time", "chemotherapy", "targeted agents AND/OR tyrosine kinase inhibitors", are collated. Reference lists from some articles have been studied to obtain other pertinent articles. Additional abstracts presented at international sarcoma meetings have been included as well as information on relevant clinical trials available at the ClinicalTrials.gov website.

RESULTS

Data are presented for the conventional regimen of 50-50.4Gy in 25-28 fractions in 5-6 of weeks preoperative external beam RT with respect to the regimen's local control probability compared to surgery alone, as well as acute and late toxicities. The rationale and outcome data for hypofractionated and/or reduced dose regimens are discussed. Finally, combination schedules with conventional chemotherapy and/or targeted agents are summarized.

CONCLUSION

Outside the setting of well-designed prospective clinical trials, the conventional 50Gy in 5-6week schedule should be considered as standard. However, current and future studies addressing alternative fraction size, total dose, overall treatment time and/or combination with chemotherapy or targeted agents may reveal regimens of equal or increased efficacy with reduced late morbidities.

Role of MR spectroscopy in musculoskeletal imaging

Magnetic resonance spectroscopy (MRS) is an imaging approach that allows for the noninvasive molecular characterization of a region of interest. By detecting signals of water, lipids, and other metabolites, MRS can provide metabolic information for lesion characterization and assessment of treatment response. Although MRS has been routinely used in the brain, clinical applications within the musculoskeletal system have only more recently emerged. The aim of this article is to review the technical considerations for performing MRS in the musculoskeletal system, focusing on proton MRS, and to discuss its potential roles in musculoskeletal tumor imaging and the assessment of muscle physiology and disease.

Hybrid PET/MR imaging in two sarcoma patients - clinical benefits and implications for future trials

PET/MRI is an evolving hybrid imaging modality which combines the inherent strengths of MRIs soft-tissue and contrast resolution and PETs functional metabolic capabilities. Bone and soft-tissue sarcoma are a relatively rare tumor entity, relying on MRI for local staging and often on PET/CT for lymph node involvement and metastatic spread evaluation. The purpose of this article is to demonstrate the successful use of PET/MRI in two sarcoma patients. We also use these patients as a starting point to discuss how PET/MRI might be of value in sarcoma. Among its potential benefits are: superior TNM staging than either modality alone, decreased radiation dose, more sensitive and specific follow-up and better assessment of treatment response. These potentials need to be investigated in future PET/MRI soft-tissue sarcoma trials.