Volume fractions of DCE-MRI parameter as early predictor of histologic response in soft tissue sarcoma: A feasibility study

Imaging of pediatric extremity soft tissue tumors: A COG Diagnostic Imaging Committee/SPR Oncology Committee White Paper

Pediatric soft tissue tumors of the extremity include rhabdomyosarcoma and nonrhabdomyosarcoma neoplasms. This manuscript provides consensus-based imaging recommendations for imaging evaluation at diagnosis, during treatment, and following completion of therapy for patients with a soft tissue tumor of the extremity.

Potential 18F-RGD PET/CT and DCE-MRI Imaging-Based Biomarkers for Postoperative Survival Prediction Among Patients With Newly Diagnosed Glioblastoma Treated With Bevacizumab and Chemoradiotherapy

Purpose

To investigate the ability of potential imaging biomarkers based on ¹⁸F-AlF-NOTA-PRGD2 positron emission tomography/computed tomography (¹⁸F-RGD PET/CT) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) imaging to predict the response to bevacizumab combined with conventional therapy in postoperative newly diagnosed glioblastoma.

Methods

Twenty patients with newly diagnosed with glioblastoma after surgery were prospectively enrolled to receive bevacizumab plus conventional concurrent radiotherapy and temozolomide (CCRT). ¹⁸F-RGD PET/CT and DCE-MRI were performed at baseline, week 3, and week 10 for each patient. Statistical methods included the analysis of variance (ANOVA), Kaplan–Meier method and Cox proportional hazard analysis.

Results

All patients completed CCRT plus bevacizumab therapy without interruption. The median follow-up time was 33.9 months (95% confidence interval [CI], 28.3-39.5 months). The median progression-free survival (PFS) and overall survival (OS) was 9.66 months (95% CI, 6.20-13.12 months) and 15.89 months (95% CI, 13.89-17.78), respectively. Treatment was generally well tolerated, and there were no Treatment emergent adverse events (TEAEs) with a toxicity grade equal to or exceeding 3 or that led to termination of treatment or patient death.Over the treatment interval of bevacizumab therapy from week 3 to week 10, patients with a large decrease of SUVmean was associated with a better PFS with a hazard ratio (HR) of 6.562, 95% CI (1.318-32.667), p=0.022. According to Kaplan-Meier analysis, patients with a decrease in the SUVmean of more than 0.115 on ¹⁸F-RGD PET/CT had a longer PFS than those with a decrease in the SUVmean of 0.115 or less (12.25 months vs.7.46 months, p=0.009). For OS, only a small decrease of Ktrans was also found to have certain prognostic value (HR=0.986, 95% CI (0.975-0.998), p=0.023). Patients with a decrease in Ktrans larger than 37.03 (min-1) on DCE-MRI had worse OS than those with a decrease in Ktrans of 37.03 (min-1) or less (15.93 months vs. 26.42 months, p=0.044).

Conclusion

¹⁸F-RGD PET/CT and DCE-MRI may be valuable in evaluating the response of glioblastoma to treatment with the combination of bevacizumab and CCRT, with a greater decrease in SUV_mean predicting better PFS as well as a small decrease in K^trans predicting improved OS.

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.

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.

Diffusion-weighted and dynamic contrast-enhanced magnetic resonance imaging after radiation therapy for bone metastases in patients with hepatocellular carcinoma

The objectives of this study were to assess changes in apparent diffusion coefficient (ADC) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) parameters after radiation therapy (RT) for bone metastases from hepatocellular carcinoma (HCC) and to evaluate their prognostic value. This prospective study was approved by the Institutional Review Board. Fourteen patients with HCC underwent RT (30 Gy in 10 fractions once daily) for bone metastases. The ADC and DCE-MRI parameters and the volume of the target lesions were measured before (baseline) and one month after RT (post-RT). The Wilcoxon signed-rank test was used to compare the parameters between the baseline and post-RT MRI. The parameters were compared between patients with or without disease progression in RT fields using the Mann–Whitney test. Intraclass correlation coefficients were used to evaluate the interobserver agreement. The medians of the ADC, rate constant [k_ep], and volume fraction of the extravascular extracellular matrix [v_e] in the baseline and post-RT MRI were 0.67 (range 0.61–0.72) and 0.75 (range 0.63–1.43) (× 10^–3 mm²/s) (P = 0.027), 836.33 (range 301.41–1082.32) and 335.80 (range 21.86–741.87) (× 10^–3/min) (P = 0.002), and 161.54 (range 128.38–410.13) and 273.99 (range 181.39–1216.95) (× 10^–3) (P = 0.027), respectively. The medians of the percent change in the ADC of post-RT MRI in patients with progressive disease and patients without progressive disease were − 1.35 (range − 6.16 to 6.79) and + 46.71 (range 7.71–112.81) (%) (P = 0.011), respectively. The interobserver agreements for all MRI parameters were excellent (intraclass correlation coefficients > 0.8). In conclusion, the ADC, k_ep, and v_e of bone metastases changed significantly after RT. The percentage change in the ADC was closely related to local tumor progression.

Feasibility of a multiparametric MRI protocol for imaging biomarkers associated with neoadjuvant radiotherapy for soft tissue sarcoma

Objective

Soft tissue sarcoma (STS) is a rare malignancy with a 5 year overall survival rate of 55%. Neoadjuvant radiotherapy is commonly used in preparation for surgery, but methods to assess early response are lacking despite pathological response at surgery being predictive of overall survival, local recurrence and distant metastasis. Multiparametric MR imaging (mpMRI) is used to assess response in a variety of tumours but lacks a robust, standardised method. The overall aim of this study was to develop a feasible imaging protocol to identify imaging biomarkers for further investigation.

Methods

15 patients with biopsy-confirmed STS suitable for pre-operative radiotherapy and radical surgery were imaged throughout treatment. The mpMRI protocol included anatomical, diffusion-weighted and dynamic contrast-enhanced imaging, giving estimates of apparent diffusion coefficient (ADC) and the area under the enhancement curve at 60 s (iAUC₆₀). Histological analysis of resected tumours included detection of CD31, Ki67, hypoxia inducible factor and calculation of a hypoxia score.

Results

There was a significant reduction in T1 at visit 2 and in ADC at visit 3. Significant associations were found between hypoxia and pre-treatment iAUC₆₀, pre-treatment ADC and mid-treatment iAUC₆₀. There was also statistically significant association between mid-treatment ADC and Ki67.

Conclusion

This work showed that mpMRI throughout treatment is feasible in patients with STS having neoadjuvant radiotherapy. The relationships between imaging parameters, tissue biomarkers and clinical outcomes warrant further investigation.

Advances in knowledge

mpMRI-based biomarkers have good correlation with STS tumour biology and are potentially of use for evaluation of radiotherapy response.

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

OBJECTIVE

To determine if dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) parameters reflect histological grade of soft tissue sarcoma (STS) MATERIALS AND METHODS: The medical records of 50 patients diagnosed with pathologically confirmed STS were retrospectively reviewed. Each STS was assessed with conventional contrast-enhanced MRI and DCE-MRI using a 3.0-T MRI system. The conventional MRI characteristics of low-grade (grade 1) and high-grade (grade 2 and grade 3) tumors were analyzed. Semi-quantitative parameters, including iAUC and TTP, and quantitative parameters, including K^trans, K_ep, and V_e, were derived from DCE-MRI. The diagnostic performances and optimal thresholds of various combinations of DCE-MRI parameters for predicting histological grades of STS were investigated using receiver operator characteristic (ROC) curves.

RESULTS

On conventional MRI, high-grade STSs were significantly larger (≥ 5 cm) and more likely to show a heterogeneous signal intensity on T₂WI (> 75%), peritumoral hyperintensity on T₂WI, or tumor necrosis (> 50%) compared with low-grade STS. On DCE-MRI, iAUC, TTP, K^trans, and K_ep were significant predictors of STS histological grade. K^trans had a high diagnostic value for differentiating between high-grade and low-grade STSs. The combination of iAUC, TTP, and K^trans yielded a higher AUC value (0.841) than the other models.

CONCLUSION

High-grade STSs were usually larger than low-grade STSs, had unclear boundaries, a heterogeneous signal intensity on T2-weighted image (T₂WI), and extensive necrosis. On DCE-MRI, iAUC, TTP, K^trans, and K_ep could differentiate between high-grade and low-grade STSs. The combination of iAUC, TTP, and K^trans had a high diagnostic performance for differentiating between STS histological grades.

High-Grade Soft-Tissue Sarcomas: Can Optimizing Dynamic Contrast-Enhanced MRI Postprocessing Improve Prognostic Radiomics Models?

BACKGROUND

Heterogeneity on pretreatment dynamic contrast-enhanced (DCE)-MRI of sarcomas may be prognostic, but the best technique to capture this characteristic remains unknown.

PURPOSE

To investigate the best method to extract prognostic data from baseline DCE-MRI.

STUDY TYPE

Retrospective, single-center.

POPULATION

Fifty consecutive uniformly-treated adults with nonmetastatic high-grade sarcomas.

FIELD STRENGTH/SEQUENCE

1.5T; T₂ -weighted-imaging, fat-suppressed fast spoiled gradient echo DCE-MRI.

ASSESSMENT

Ninety-two radiomics features (RFs) were extracted at each DCE-MRI phase (11, from t = 0-88 sec). Relative changes in RFs (rRFs) since the acquisition baseline were calculated (11 × 92 rRFs). Curves of rRF as function of time postinjection were integrated (92 integrated-rRFs [irRFs]). K^trans and area under the time-intensity curve at 88-sec parametric maps were computed and 2 × 92 parametric-RFs (pRFs) were extracted. Five DCE-MRI-based radiomics models were built on: an RFs subset (32 sec, 64 sec, 88 sec); all rRFs; all irRFs; and all pRFs. Two models were elaborated as reference, on: conventional radiological features; and T₂ -WI RFs.

STATISTICAL TESTS

A common machine-learning approach was applied to radiomics models. Features with P < 0.05 at univariate analysis were entered in a LASSO-penalized Cox regression including bootstrapped 10-fold cross-validation. The resulting radiomics scores (RScores) were dichotomized per their median and entered in multivariate Cox models for predicting metastatic relapse-free survival. Models were compared with integrative area under the curve (AUC) and concordance index.

RESULTS

Only dichotomized RScores from models based on rRFs subset, all rRFS and irRFS correlated with prognostic (P = 0.0107-0.0377). The models including all rRFs and irRFs had the highest c-index (0.83), followed by the radiological model. The radiological model had the highest integrative AUC (0.87), followed by models including all rRFs and irRFs. The radiological and full rRFs models were significantly better than the T₂ -based radiomics model (P = 0.02).

DATA CONCLUSION

The initial DCE-MRI of STS contains prognostic information. It seems more relevant to make predictions on rRFs instead of pRFs. Evidence Level: 3 Technical Efficacy: 3 J. Magn. Reson. Imaging 2020;52:282-297.

Diagnostic value of molybdenum target combined with DCE-MRI in different types of breast cancer

This study compared the diagnostic value of molybdenum target and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in different types of breast cancer. A retrospective analysis was performed on 120 female patients with breast cancer admitted to The First Affiliated Hospital of Zhengzhou University from February 2015 to October 2017. All of them underwent DCE-MRI and molybdenum target examination. Postoperatively, the pathological tissues were examined to determine their molecular typing. The sensitivity and diagnostic coincidence rates of Luminal-B breast cancer with different molecular types diagnosed by molybdenum target combined with DCE-MRI were significantly higher than those of molybdenum target or DCE-MRI alone. There were no statistical differences in sensitivity and diagnostic coincidence rates of breast cancer with different molecular types diagnosed by molybdenum target or DCE-MRI alone between the two groups (P>0.05). Molybdenum target combined with DCE-MRI in the diagnosis of different molecular types of breast cancer is better than the single imaging screening, which is of great clinical significance in the development of individualized comprehensive treatment for breast cancer patients and worthy of wide promotion in clinical practice.

Soft Tissue Sarcoma Stiffness and Perfusion Evaluation by MRE and DCE-MRI for Radiation Therapy Response Assessment: A Technical Feasibility Study

Soft tissue sarcomas are a rare and heterogeneous group of malignancies that present significant diagnostic and therapeutic challenges. Patient stratification based on tumor aggressiveness and early therapeutic response based on quantitative imaging may improve prediction of treatment response and the evaluation of new treatment strategies in clinical trials. The purpose of this pilot study was to determine the technical feasibility of magnetic resonance elastography (MRE) and dynamic contrast-enhanced (DCE) MRI for the evaluation of sarcoma stiffness and perfusion in 9 patients with histologically confirmed sarcoma. Additionally, we assessed the feasibility of utilizing MRE and DCE-MRI for the early evaluation of response to radiation therapy in 4 patients to determine the utility of further evaluation in a larger cohort study. Tumor size, stiffness, and perfusion parameters all decreased from baseline at the time of the pre-surgery or follow-up MRI, and results were compared to pathology or conventional imaging. MRE and DCE-MRI may be useful for the quantitative evaluation of tumor stiffness and perfusion, and therapy response assessment in soft tissue sarcomas.

Pathologic necrosis following neoadjuvant radiotherapy or chemoradiotherapy is prognostic of poor survival in soft tissue sarcoma

PURPOSE

Neoadjuvant radiotherapy ± chemotherapy and wide local excision is an accepted management of localized soft tissue sarcomas (STS). Necrosis is prognostic for survival in osteosarcomas, but the significance for STS is undetermined. This study aimed to determine if percent true necrosis, opposed to a combination of necrosis and fibrosis, leads to improved survival in extremity and trunk STS.

METHODS

From 2000 to 2015, 162 patients with STS were treated with neoadjuvant therapy and resection. Patient and tumor variables were reviewed, and resected specimens underwent pathological assessment. Necrosis was ratiometrically determined. Overall (OS), distant metastasis-free (DMFS), and progression-free survival (PFS) were calculated using Kaplan-Meier estimator. Survival was determined using the Fisher's exact test for univariate analysis (UVA) and logistic regression for multivariate analysis (MVA).

RESULTS

Median follow-up was 4.5 years and median necrosis was 24.97%. Necrosis predicted worse OS, DMFS, and PFS on UVA, and DMFS and PFS on MVA. Necrosis was positively correlated with size and grade. To mitigate the role of size, a sub-analysis of ≥ 10 cm tumors was performed revealing necrosis predicted decreased DMFS and PFS on UVA and MVA. In high-grade tumors, necrosis correlated with decreased DMFS and PFS on UVA. Necrosis did not predict OS in ≥ 10 cm or high-grade tumors.

CONCLUSIONS

Our data suggests necrosis may be an additional independent, prognostic variable with increased necrosis predicting a worse prognosis. Necrosis may not be a measure of treatment response and instead suggests more aggressive tumor biology as high-grade, large STS were associated with increased necrosis.