Metabolic activity measured on PET/CT correlates with clinical outcomes in patients with limb and girdle sarcomas

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.

Prognostic Value of Pre-Treatment [18F]FDG PET/CT Texture Analysis in Undifferentiated Soft-Tissue Sarcoma

BACKGROUND

Undifferentiated soft-tissue sarcomas (USTS) are one of the most common sarcoma histotypes in adults. The standard of care is surgical excision plus adjuvant radiotherapy, while the use of perioperative chemotherapy is still controversial. The aim of this study was to investigate the value of pre-treatment [18F]FDG PET/CT conventional metrics and textural features in predicting disease-free survival (DFS) and overall survival (OS) in patients with USTS of the limbs and trunk.

METHODS

[18F]FDG PET/CT scans of 51 consecutive patients with locally advanced USTS were retrospectively evaluated. Conventional and textural PET parameters were analysed and tested as predictive factors for DFS and OS.

RESULTS

During a median follow up of 50.7 months, 23 (45.1%) and 29 (56.9%) patients had death or disease progression, respectively. Univariate analysis revealed a significant association for perioperative treatment, PET volumetric parameters and the textural feature GLCM_correlation with DFS and OS. In multivariate analysis, perioperative treatment and GLCM_correlation were the only independent factors, allowing stratification of the population into three different prognostic classes.

CONCLUSION

GLCM_correlation can identify USTS at high risk of relapse and death, thus helping to optimize the perioperative treatment of patients.

Radiomics of Musculoskeletal Sarcomas: A Narrative Review

Bone and soft-tissue primary malignant tumors or sarcomas are a large, diverse group of mesenchymal-derived malignancies. They represent a model for intra- and intertumoral heterogeneities, making them particularly suitable for radiomics analyses. Radiomic features offer information on cancer phenotype as well as the tumor microenvironment which, combined with other pertinent data such as genomics and proteomics and correlated with outcomes data, can produce accurate, robust, evidence-based, clinical-decision support systems. Our purpose in this narrative review is to offer an overview of radiomics studies dealing with Magnetic Resonance Imaging (MRI)-based radiomics models of bone and soft-tissue sarcomas that could help distinguish different histotypes, low-grade from high-grade sarcomas, predict response to multimodality therapy, and thus better tailor patients’ treatments and finally improve their survivals. Although showing promising results, interobserver segmentation variability, feature reproducibility, and model validation are three main challenges of radiomics that need to be addressed in order to translate radiomics studies to clinical applications. These efforts, together with a better knowledge and application of the “Radiomics Quality Score” and Image Biomarker Standardization Initiative reporting guidelines, could improve the quality of sarcoma radiomics studies and facilitate radiomics towards clinical translation.

When SUV Matters: FDG PET/CT at Baseline Correlates with Survival in Soft Tissue and Ewing Sarcoma

Introduction: The role of positron-emission tomography/computed-tomography (PET/CT) in the management of sarcomas and as a prognostic tool has been studied. However, it remains unclear which metric is the most useful. We aimed to investigate if volume-based PET metrics (Tumor volume (TV) and total lesions glycolysis (TLG)) are superior to maximal standardized uptake value (SUVmax) and other metrics in predicting survival of patients with soft tissue and bone sarcomas. Materials and Methods: In this retrospective cohort study, we screened over 52′000 PET/CT scans to identify patients diagnosed with either soft tissue, bone or Ewing sarcoma and had a staging scan at our institution before initial therapy. We used a Wilcoxon signed-rank to assess which PET/CT metric was associated with survival in different patient subgroups. Receiver-Operating-Characteristic curve analysis was used to calculate cutoff values. Results: We identified a total of 88 patients with soft tissue (51), bone (26) or Ewing (11) sarcoma. Median age at presentation was 40 years (Range: 9–86 years). High SUVmax was most significantly associated with short survival (defined as <24 months) in soft tissue sarcoma (with a median and range of SUVmax 12.5 (8.8–16.0) in short (n = 18) and 5.5 (3.3–7.2) in long survival (≥24 months) (n = 31), with (p = 0.001). Similar results were seen in Ewing sarcoma (with a median and range of SUVmax 12.1 (7.6–14.7) in short (n = 6) and 3.7 (3.5–5.5) in long survival (n = 5), with (p = 0.017). However, no PET-specific metric but tumor-volume was significantly associated (p = 0.035) with survival in primary bone sarcomas (with a median and range of 217 cm³ (186–349) in short survival (n = 4) and 60 cm³ (22–104) in long survival (n = 19), with (p = 0.035). TLG was significantly inversely associated with long survival only in Ewing sarcoma (p = 0.03). Discussion: Our analysis shows that the outcome of soft tissue, bone and Ewing sarcomas is associated with different PET/CT metrics. We could not confirm the previously suggested superiority of volume-based metrics in soft tissue sarcomas, for which we found SUVmax to remain the best prognostic factor. However, bone sarcomas should probably be evaluated with tumor volume rather than FDG PET activity.

Prognosis prediction of extremity and trunk wall soft-tissue sarcomas treated with surgical resection with radiomic analysis based on random survival forest

Many researches have applied machine learning methods to find associations between radiomic features and clinical outcomes. Random survival forests (RSF), as an accurate classifier, sort all candidate variables as the rank of importance values. There was no study concerning on finding radiomic predictors in patients with extremity and trunk wall soft-tissue sarcomas using RSF. This study aimed to determine associations between radiomic features and overall survival (OS) by RSF analysis. To identify radiomic features with important values by RSF analysis, construct predictive models for OS incorporating clinical characteristics, and evaluate models' performance with different method. We collected clinical characteristics and radiomic features extracted from plain and contrast-enhanced computed tomography (CT) from 353 patients with extremity and trunk wall soft-tissue sarcomas treated with surgical resection. All radiomic features were analyzed by Cox proportional hazard (CPH) and followed RSF analysis. The association between radiomics-predicted risks and OS was assessed by Kaplan-Meier analysis. All clinical features were screened by CPH analysis. Prognostic clinical and radiomic parameters were fitted into RSF and CPH integrative models for OS in the training cohort, respectively. The concordance indexes (C-index) and Brier scores of both two models were evaluated in both training and testing cohorts. The model with better predictive performance was interpreted with nomogram and calibration plots. Among all 86 radiomic features, there were three variables selected with high importance values. The RSF on these three features distinguished patients with high predicted risks from patients with low predicted risks for OS in the training set (P < 0.001) using Kaplan-Meier analysis. Age, lymph node involvement and grade were incorporated into the combined models for OS (P < 0.05). The C-indexes in both two integrative models fluctuated above 0.80 whose Brier scores maintained less than 15.0 in the training and testing datasets. The RSF model performed little advantages over the CPH model that the calibration curve of the RSF model showed favorable agreement between predicted and actual survival probabilities for the 3-year and 5-year survival prediction. The multimodality RSF model including clinical and radiomic characteristics conducted high capacity in prediction of OS which might assist individualized therapeutic regimens. Level III, prognostic study.

Metabolic response as assessed by 18 F-fluorodeoxyglucose positron emission tomography-computed tomography does not predict outcome in patients with intermediate- or high-risk rhabdomyosarcoma: A report from the Children's Oncology Group Soft Tissue Sarcoma Committee

BACKGROUND

Strategies to optimize management in rhabdomyosarcoma (RMS) include risk stratification to assign therapy aiming to minimize treatment morbidity yet improve outcomes. This analysis evaluated the relationship between complete metabolic response (CMR) as assessed by ¹⁸ F-fluorodeoxyglucose positron emission tomography-computed tomography (FDG-PET) imaging and event-free survival (EFS) in intermediate-risk (IR) and high-risk (HR) RMS patients.

METHODS

FDG-PET imaging characteristics, including assessment of CMR and maximum standard uptake values (SUVmax) of the primary tumor, were evaluated by central review. Institutional reports of SUVmax were used when SUVmax values could not be determined by central review. One hundred and thirty IR and 105 HR patients had FDG-PET scans submitted for central review or had SUVmax data available from institutional report at any time point. A Cox proportional hazards regression model was used to evaluate the relationship between these parameters and EFS.

RESULTS

SUVmax at study entry did not correlate with EFS for IR (p = 0.32) or HR (p = 0.86) patients. Compared to patients who did not achieve a CMR, EFS was not superior for IR patients who achieved a CMR at weeks 4 (p = 0.66) or 15 (p = 0.46), nor for HR patients who achieved CMR at week 6 (p = 0.75) or 19 (p = 0.28). Change in SUVmax at week 4 (p = 0.21) or 15 (p = 0.91) for IR patients or at week 6 (p = 0.75) or 19 (p = 0.61) for HR patients did not correlate with EFS.

CONCLUSION

Based on these data, FDG-PET does not appear to predict EFS in IR or HR-RMS. It remains to be determined whether FDG-PET has a role in predicting survival outcomes in other RMS subpopulations.

Metastasis risk prediction model in osteosarcoma using metabolic imaging phenotypes: A multivariable radiomics model

BACKGROUND

Osteosarcoma (OS) is the most common primary bone tumor affecting humans and it has extreme heterogeneity. Despite modern therapy, it recurs in approximately 30-40% of patients initially diagnosed with no metastatic disease, with the long-term survival rates of patients with recurrent OS being generally 20%. Thus, early prediction of metastases in OS management plans is crucial for better-adapted treatments and survival rates. In this study, a radiomics model for metastasis risk prediction in OS was developed and evaluated using metabolic imaging phenotypes.

METHODS AND FINDINGS

The subjects were eighty-three patients with OS, and all were treated with surgery and chemotherapy for local control. All patients underwent a pretreatment 18F-FDG-PET scan. Forty-five features were extracted from the tumor region. The incorporation of features into multivariable models was performed using logistic regression. The multivariable modeling strategy involved cross validation in the following four steps leading to final prediction model construction: (1) feature set reduction and selection; (2) model coefficients computation through train and validation processing; and (3) prediction performance estimation. The multivariable logistic regression model was developed using two radiomics features, SUVmax and GLZLM-SZLGE. The trained and validated multivariable logistic model based on probability of endpoint (P) = 1/ (1+exp (-Z)) was Z = -1.23 + 1.53*SUVmax + 1.68*GLZLM-SZLGE with significant p-values (SUVmax: 0.0462 and GLZLM_SZLGE: 0.0154). The final multivariable logistic model achieved an area under the curve (AUC) receiver operating characteristics (ROC) curve of 0.80, a sensitivity of 0.66, and a specificity of 0.88 in cross validation.

CONCLUSIONS

The SUVmax and GLZLM-SZLGE from metabolic imaging phenotypes are independent predictors of metastasis risk assessment. They show the association between 18F-FDG-PET and metastatic colonization knowledge. The multivariable model developed using them could improve patient outcomes by allowing aggressive treatment in patients with high metastasis risk.

The role of 18F-FDG PET/CT in soft tissue sarcoma

INTRODUCTION

Soft tissue sarcomas (STS) are highly fluorine-18-fluorodeoxyglucose (F-FDG)-avid tumours. PET seems to be effective for the assessment of the extent of disease. However, the use of PET to stratify STS into different risk histotypes still remains controversial. Our aim was to evaluate F-FDG uptake in different STS types and to assess the prognostic value of the maximum standardized uptake value (SUVmax).

PATIENTS AND METHODS

We reviewed 50 adult patients with primary high-grade STS of the extremities with a preoperative PET. Overall survival and local recurrence were analysed.

RESULTS

The mean SUVmax was 12.9 (range: 2.2-33.4). All cases of myxoid liposarcoma and all cases of synovial sarcoma had SUVmax of less than 10.3. A better overall survival and local recurrence were observed in patients with SUVmax of less than 10.3 (P=0.005 and 0.046, respectively).

CONCLUSION

SUVmax seems to be specific among different STS histotypes. PET does not seem to be useful in myxoid liposarcoma as well as synovial sarcoma as these tumours seem to have a low uptake of glucose. SUVmax might also be included as a prognostic factor.

Maximum standardized uptake value on 18F-fluorodeoxyglucose positron emission tomography/computed tomography improves outcome prediction in retroperitoneal liposarcoma

While ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) has been investigated in extremity sarcomas, there is no evidence on its usefulness in retroperitoneal sarcoma. This study was designed to evaluate the usefulness of ¹⁸F-FDG PET/CT in predicting aggressiveness of retroperitoneal liposarcoma. Patients experienced surgery for retroperitoneal liposarcoma from November 2007 to February 2018 and underwent preoperative ¹⁸F-FDG PET/CT were included. Preoperative maximum standardized uptake value (SUV_max) was calculated. To evaluate the predictability of SUV_max for Fédération Nationale des Centres de Lutte Contre le Cancer (FNCLCC) grade 3, receiver operating characteristics (ROC) curve analysis was performed. To analyze whether SUV_max can be a risk factor for prognosis, multivariable Cox regression was performed including potential risk factors regarding operation and histopathology. A total of 133 patients were included. ROC curve showed area under the curve of 0.877 (P < 0.001), with a cut-off point of 4.5 SUV_max showing 85.7% sensitivity and 78.3% specificity. Cox analyses showed that SUV_max > 4.5 was a significant factor for recurrence-free survival (HR = 2.148, CI 1.301-3.546, P = 0.003) and overall survival (HR = 5.052, CI 1.854-13.766, P = 0.002). SUV_max is highly predictive of FNCLCC grade 3 and SUV_max > 4.5 can be used as a prognostic factor before obtaining the histopathology.

MRI Radiomic Features Are Independently Associated With Overall Survival in Soft Tissue Sarcoma

Purpose

Soft tissue sarcomas (STS) represent a heterogeneous group of diseases, and selection of individualized treatments remains a challenge. The goal of this study was to determine whether radiomic features extracted from magnetic resonance (MR) images are independently associated with overall survival (OS) in STS.

Methods and Materials

This study analyzed 2 independent cohorts of adult patients with stage II-III STS treated at center 1 (N = 165) and center 2 (N = 61). Thirty radiomic features were extracted from pretreatment T1-weighted contrast-enhanced MR images. Prognostic models for OS were derived on the center 1 cohort and validated on the center 2 cohort. Clinical-only (C), radiomics-only (R), and clinical and radiomics (C+R) penalized Cox models were constructed. Model performance was assessed using Harrell's concordance index.

Results

In the R model, tumor volume (hazard ratio [HR], 1.5) and 4 texture features (HR, 1.1-1.5) were selected. In the C+R model, both age (HR, 1.4) and grade (HR, 1.7) were selected along with 5 radiomic features. The adjusted c-indices of the 3 models ranged from 0.68 (C) to 0.74 (C+R) in the derivation cohort and 0.68 (R) to 0.78 (C+R) in the validation cohort. The radiomic features were independently associated with OS in the validation cohort after accounting for age and grade (HR, 2.4; P = .009).

Conclusions

This study found that radiomic features extracted from MR images are independently associated with OS when accounting for age and tumor grade. The overall predictive performance of 3-year OS using a model based on clinical and radiomic features was replicated in an independent cohort. Optimal models using clinical and radiomic features could improve personalized selection of therapy in patients with STS.

Investigating the role of functional imaging in the management of soft-tissue sarcomas of the extremities

Background and purpose

In this work, we validate a texture-based model computed from positron emission tomography (PET) and magnetic resonance imaging (MRI) for the prediction of lung metastases in soft-tissue sarcomas (STS). We explore functional imaging at different treatment time points and evaluate the feasibility of radiotherapy dose painting as a potential treatment strategy for patients with higher metastatic risk.

Materials and methods

We acquired fluorodeoxyglucose (FDG)-PET, fluoromisonidazole (FMISO)-PET, diffusion weighting (DW)-MRI and dynamic contrast enhanced (DCE)-MRI data for 18 patients with extremity STS before, during, and after pre-operative radiotherapy. We tested the lung metastases prediction model using pre-treatment images. We evaluated the feasibility of dose painting using volumetric arc therapy (VMAT) via treatment re-planning with a prescription of 50 Gy to the planning target volume (PTV_50Gy) and boost doses of 60 Gy to the FDG hypermetabolic gross tumour volume (GTV_60Gy) and 65 Gy to the low-perfusion DCE-MRI hypoxic GTV contained within the GTV_60Gy (GTV_65Gy).

Results

The texture-based model for lung metastases prediction reached an area under the curve (AUC), sensitivity, specificity and accuracy of 0.71, 0.75, 0.85 and 0.82, respectively. Dose painting resulted in adequate coverage and homogeneity in the re-planned treatments: D_95% to the PTV_50Gy, GTV_60Gy and GTV_65Gy were 50.0 Gy, 60.3 Gy and 65.4 Gy, respectively.

Conclusions

Textural biomarkers extracted from FDG-PET and MRI could be useful to identify STS patients that might benefit from dose escalation. The feasibility of treatment planning with double boost levels to intratumoural GTV functional sub-volumes was established.

Retrospective audit of 957 consecutive 18F-FDG PET-CT scans compared to CT and MRI in 493 patients with different histological subtypes of bone and soft tissue sarcoma

Background

The use of ¹⁸F-FDG PET–CT (PET–CT) is widespread in many cancer types compared to sarcoma. We report a large retrospective audit of PET–CT in bone and soft tissue sarcoma with varied grade in a single multi-disciplinary centre. We also sought to answer three questions. Firstly, the correlation between sarcoma sub-type and grade with ¹⁸FDG SUVmax, secondly, the practical uses of PET–CT in the clinical setting of staging (during initial diagnosis), restaging (new baseline prior to definitive intervention) and treatment response. Finally, we also attempted to evaluate the potential additional benefit of PET–CT over concurrent conventional CT and MRI.

Methods

A total of 957 consecutive PET–CT scans were performed in a single supra-regional centre in 493 sarcoma patients (excluding GIST) between 2007 and 2014. We compared, PET–CT SUVmax values in relation to histology and FNCCC grading. We compared PET–CT findings relative to concurrent conventional imaging (MRI and CT) in staging, restaging and treatment responses.

Results

High-grade (II/III) bone and soft tissue sarcoma correlated with high SUVmax, especially undifferentiated pleomorphic sarcoma, leiomyosarcoma, translocation induced sarcomas (Ewing, synovial, alveolar rhabdomyosarcoma), de-differentiated liposarcoma and osteosarcoma. Lower SUVmax values were observed in sarcomas of low histological grade (grade I), and in rare subtypes of intermediate grade soft tissue sarcoma (e.g. alveolar soft part sarcoma and solitary fibrous tumour). SUVmax variation was noted in malignant peripheral nerve sheath tumours, compared to the histologically benign plexiform neurofibroma, whereas PET–CT could clearly differentiate low from high-grade chondrosarcoma. We identified added utility of PET–CT in addition to MRI and CT in high-grade sarcoma of bone and soft tissues. An estimated 21% overall potential benefit was observed for PET–CT over CT/MRI, and in particular, in ‘upstaging’ of high-grade disease (from M0 to M1) where an additional 12% of cases were deemed M1 following PET–CT.

Conclusions

PET–CT in high-grade bone and soft tissue sarcoma can add significant benefit to routine CT/MRI staging. Further prospective and multi-centre evaluation of PET–CT is warranted to determine the actual predictive value and cost-effectiveness of PET–CT in directing clinical management of clinically complex and heterogeneous high-grade sarcomas.

Prognostic value of 18F-FDG PET-CT-based functional parameters in patients with soft tissue sarcoma: A meta-analysis

BACKGROUND

Considering the clinical importance of high 5-year mortality, we performed a meta-analysis of maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) from F-FDG PET-CT for overall survival (OS) and progression-free survival (PFS) in patients with soft tissue sarcoma.

METHODS

The search and selection of eligible articles was conducted on PubMed and EMBASE. We applied hazard ratio (HR) and odd ratio (OR) to measure the correlation between SUVmax, MTV, and TLG with PFS and OS. The SUVmax was analyzed through subgroup in terms of histological grade and HR of posttreatment SUVmax was also assessed.

RESULTS

Eleven studies with 582 patients were included. The pooled HRs of pretreatment SUVmax were 2.40 (95% CI: 1.38-4.17) for OS and 2.20 (95% CI: 1.47-3.30) for PFS. The HRs in terms of OS were 3.20 (95% CI: 1.71-5.98) based on MTV and 5.20 (95% CI: 2.34-11.56) based on TLG. Meanwhile, the predict results of pretreatment SUVmax on OR remained significant and the HRs of posttreatment SUVmax were 2.25 (95% CI: 1.33-3.80) for OS and 2.87 (95% CI: 1.81-4.55) for PFS.

CONCLUSIONS

The pretreatment SUVmax, MTV, and TLG of F-FDG PET-CT showed significant prognostic value for OS and the PET-CT can be used in identifying high-risk patients about progression and survival. The analysis for posttreatment SUVmax suggested PET-CT as a promising equipment in monitoring therapy response.

Positron emission tomography (18)F-fluorodeoxyglucose uptake and prognosis in patients with bone and soft tissue sarcoma: A meta-analysis

PURPOSE

To investigate the prognostic significance of (18)F-FDG PET imaging in patients with bone and soft tissue sarcoma, a meta-analysis was conducted.

METHODS

Comprehensive literature searches were performed in PubMed, Embase, Web of Science and Cochrane Library. Pooled hazard ratio (HR) values were calculated to assess the correlations of pre-chemotherapy SUV (SUV1), post-chemotherapy SUV (SUV2), SUV Ratio, total lesion glycolysis (TLG) and metabolic tumor volume (MTV) with event-free survival (EFS) and overall survival (OS).

RESULTS

Twenty-three studies with 1261 patients were identified. The combined HRs for EFS were 1.84 (95% CI: 1.54-2.20) for SUV1, 2.92 (95% CI: 2.15-3.97) for SUV2, 1.90 (95% CI: 1.43-2.52) for SUV Ratio, 3.01 (95% CI: 1.36-6.67) for TLG and 2.32 (95% CI: 1.44-3.75) for MTV. The pooled HRs for OS were 1.85 (95% CI: 1.49-2.30) for SUV1, 2.00 (95% CI: 1.39-2.88) for SUV2, 2.20 (95% CI: 1.18-4.10) for SUV Ratio, 6.19 (95% CI: 2.17-17.66) for TLG and 2.67 (95% CI: 1.52-4.68) for MTV. Besides, high SUV1 was found to be significantly associated with higher rate of metastasis (RR 5.55, 95% CI: 2.75-11.18) and local recurrence (RR 1.87 95% CI: 1.28-2.72).

CONCLUSION

(18)F-FDG PET parameters of SUV1, SUV2, SUV Ratio, TLG and MTV may have effective prognostic significance for patients with bone and soft tissue sarcoma. (18)F-FDG PET imaging may be a promising tool to help predict survival outcomes of these patients.

Prognostic significance of (18)F-FDG PET at diagnosis in patients with soft tissue sarcoma and bone sarcoma; systematic review and meta-analysis

PURPOSE

The usefulness of (18)F-fluorodeoxyglucose positron emission tomography ((18)F-FDG PET) for the survival prognosis in soft tissue sarcoma (STS) and bone sarcoma (BS) is controversial. The objective of this systematic review was to provide an up-to-date and unprecedented summary of the prognostic value of (18)F-FDG PET at diagnosis in STS and BS.

METHODS

Studies evaluating pre-treatment (18)F-FDG PET for overall survival of STS and BS were systematically searched for in MEDLINE, EMBASE, and Web of Science. Comparative analyses of the pooled hazard ratios (HR) of overall survival were performed between patients with high and low maximum standardised uptake value (SUVmax). The quality of study designs was evaluated using the Newcastle-Ottawa scale (NOS) for quality assessment of cohort studies. P < 0.05 was defined as statistically significant.

RESULTS

A total of six studies comprising 514 patients with STS and BS were considered for the meta-analysis. The pooled HR for overall survival was 1.22 (95% confidence interval: 1.03-1.46), suggesting that high SUVmax predicts a significantly shorter overall survival period than low SUVmax (P = 0.03). Additional subgroup analyses using patients with STS alone showed that high SUVmax might predict poorer overall survival than low SUVmax (P = 0.004), although only two studies consisting of 96 patients were included. The overall quality of the included studies evaluated by the NOS assessment was adequate.

CONCLUSION

(18)F-FDG PET at diagnosis provides a very useful predictive tool for patients with STS and BS.

Volume-Based F-18 FDG PET/CT Imaging Markers Provide Supplemental Prognostic Information to Histologic Grading in Patients With High-Grade Bone or Soft Tissue Sarcoma

The aim of the study is to assess the prognostic value of different volume-based calculations of tumor metabolic activity in the initial assessment of patients with high-grade bone sarcomas (BS) and soft tissue sarcomas (STS) using F-18 FDG PET/CT.A single-site, retrospective study from 2002 to 2012 including 92 patients with histologically verified high-grade BS (N = 37) or STS (N = 55). All patients underwent a pretreatment F-18 FDG PET/CT scan. Clinical data were registered. Measurements of the accuracy of metabolic tumor volume with a preset threshold of 40% of the maximum standardized uptake value of primary tumor (MTV40%) and total lesion glycolysis (TLG) as prognostic variables and identification of optimal discriminating cut-off values were performed through ROC curve analysis. Patients were grouped according to the cut-off values. All deaths were considered an event in survival analysis. Kaplan-Meier survival estimates and log-rank test were used to compare the degree of equality of survival distributions. Prognostic variables with related hazard ratios (HR) were assessed using Cox proportional hazards regression analysis.Forty-one of 92 patients died during follow-up (45%; 12 BS and 29 STS). Average survival for included patients was 6.5 years (95% CI 5.8-7.3 years) and probability of 5-year survival was 52%. There was a high-significant accuracy of TLG and MTV40% as prognostic variables when looking on all patients and during subgroup analysis. AUCs were higher for TLG than for MTV40%. TLG above optimal cut-off value was the only variable which was independently prognostic for survival throughout multivariate regression analysis of all included patients (P = 0.01, HR 4.78 [95% CI 1.45-15.87]) and subgroup analysis (BS: P = 0.04, HR 11.11 [95% CI 1.09-111.11]; STS: P < 0.05, HR 3.37 [95% CI 1.02-11.11]). No significant results were demonstrated for MTV40%.Volume-based F-18 FDG PET/CT imaging markers in terms of pretreatment estimation of TLG provide supplemental prognostic information to histologic grading, with significant independent properties for prediction of overall survival in patients with high-grade BS or STS.

Semi-Quantitative Calculations of Primary Tumor Metabolic Activity Using F-18 FDG PET/CT as a Predictor of Survival in 92 Patients With High-Grade Bone or Soft Tissue Sarcoma

To assess the prognostic value of primary tumor metabolic activity in patients with high-grade bone sarcomas (BS) or soft tissue sarcomas (STS) using F-18 FDG PET/CT. A single-site, retrospective study including 92 patients with high-grade BS or STS. Pretreatment F-18 FDG PET/CT scan was performed. Clinical data were registered. Accuracy of maximum standardized uptake value of primary tumor (SUVmax) and tumor-to-background (T/B) uptake ratio as prognostic variables and identification of cut-off values to group patients were determined. Kaplan-Meier survival estimates and log-rank test were used to compare survival distributions. Prognostic variables were assessed using Cox proportional hazards regression analysis. Forty-one of 92 patients died during follow-up (45%). Average survival was 6.5 years (95% CI 5.8-7.3 years) and probability of 5-year survival was 52%. Accuracy of SUVmax and T/B uptake ratio as prognostic variables in all patients and during subgroup analysis of patients with STS was significant. No significant results for AUCs were registered in patients with BS. Surgery was independently prognostic for survival throughout multivariate regression analysis of all patients (P = 0.001, HR 3.84) and subgroup analysis (BS: P = 0.02, HR 11.62; STS: P = 0.005, HR 4.13). SUVmax was significant as prognostic variable in all patients (P = 0.02, HR 3.66) and in patients with STS (P = 0.007, HR 3.75). No significant results were demonstrated for T/B uptake ratio. Estimation of primary tumor metabolic activity with pretherapeutic SUVmax using F-18 FDG PET/CT demonstrates independent properties beyond histologic grading for prediction of survival in patients with high-grade STS, but not with high-grade BS.

A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities

This study aims at developing a joint FDG-PET and MRI texture-based model for the early evaluation of lung metastasis risk in soft-tissue sarcomas (STSs). We investigate if the creation of new composite textures from the combination of FDG-PET and MR imaging information could better identify aggressive tumours. Towards this goal, a cohort of 51 patients with histologically proven STSs of the extremities was retrospectively evaluated. All patients had pre-treatment FDG-PET and MRI scans comprised of T1-weighted and T2-weighted fat-suppression sequences (T2FS). Nine non-texture features (SUV metrics and shape features) and forty-one texture features were extracted from the tumour region of separate (FDG-PET, T1 and T2FS) and fused (FDG-PET/T1 and FDG-PET/T2FS) scans. Volume fusion of the FDG-PET and MRI scans was implemented using the wavelet transform. The influence of six different extraction parameters on the predictive value of textures was investigated. The incorporation of features into multivariable models was performed using logistic regression. The multivariable modeling strategy involved imbalance-adjusted bootstrap resampling in the following four steps leading to final prediction model construction: (1) feature set reduction; (2) feature selection; (3) prediction performance estimation; and (4) computation of model coefficients. Univariate analysis showed that the isotropic voxel size at which texture features were extracted had the most impact on predictive value. In multivariable analysis, texture features extracted from fused scans significantly outperformed those from separate scans in terms of lung metastases prediction estimates. The best performance was obtained using a combination of four texture features extracted from FDG-PET/T1 and FDG-PET/T2FS scans. This model reached an area under the receiver-operating characteristic curve of 0.984 ± 0.002, a sensitivity of 0.955 ± 0.006, and a specificity of 0.926 ± 0.004 in bootstrapping evaluations. Ultimately, lung metastasis risk assessment at diagnosis of STSs could improve patient outcomes by allowing better treatment adaptation.