Metastasis in soft tissue sarcomas: prognostic criteria and treatment perspectives

Treatment and Outcomes of Radiation-Induced Soft Tissue Sarcomas of the Extremities and Trunk-A Systematic Review of the Literature

INTRODUCTION

Radiation-induced soft tissue sarcomas (RISs) are rare secondary malignancies with a dire prognosis. The literature on the management of these tumors remains scarce due to their low incidence. Our systematic review sought to assess the treatment alternatives and outcomes of patients with RIS.

METHODS

A systematic review was conducted following the PRISMA guidelines. Our study was registered in PROSPERO (ID: CRD42023438415). Quality assessment was performed using the STROBE checklist. Weighted means for both continuous and categorical values were calculated.

RESULTS

Twenty-one studies comprising 1371 patients with RIS were included. The mean latency period from radiation to RIS diagnosis was 14 years, and the mean radiation dose delivered to the primary malignancy was 29.2 Gy. The most common histological type was undifferentiated pleomorphic sarcoma (42.2%), and 64% of all tumors were high-grade. The trunk was the most common location (59%), followed by extremities (21%) and pelvis (11%). Surgery was performed in 68% of patients and, among those with an appendicular tumor, the majority (74%) underwent limb-salvage surgery. Negative margins were attained in 58% of patients. Chemotherapy and radiotherapy were administered in 29% and 15% of patients, respectively. The mean 5-year overall survival was 45%, and the local recurrence and metastasis rates were 39% and 27%, respectively.

CONCLUSIONS

In our study, the most common treatment was surgical resection, with RT and chemotherapy being administered in less than one third of patients. Patients with RIS exhibited poor oncologic outcomes. Future studies should compare RIS with de novo STS while controlling for confounders.

The Impact of Edema on MRI Radiomics for the Prediction of Lung Metastasis in Soft Tissue Sarcoma

INTRODUCTION

This study aimed to evaluate whether radiomic features extracted solely from the edema of soft tissue sarcomas (STS) could predict the occurrence of lung metastasis in comparison with features extracted solely from the tumoral mass.

MATERIALS AND METHODS

We retrospectively analyzed magnetic resonance imaging (MRI) scans of 32 STSs, including 14 with lung metastasis and 18 without. A segmentation of the tumor mass and edema was assessed for each MRI examination. A total of 107 radiomic features were extracted for each mass segmentation and 107 radiomic features for each edema segmentation. A two-step feature selection process was applied. Two predictive features for the development of lung metastasis were selected from the mass-related features, as well as two predictive features from the edema-related features. Two Random Forest models were created based on these selected features; 100 random subsampling runs were performed. Key performance metrics, including accuracy and area under the ROC curve (AUC), were calculated, and the resulting accuracies were compared.

RESULTS

The model based on mass-related features achieved a median accuracy of 0.83 and a median AUC of 0.88, while the model based on edema-related features achieved a median accuracy of 0.75 and a median AUC of 0.79. A statistical analysis comparing the accuracies of the two models revealed no significant difference.

CONCLUSION

Both models showed promise in predicting the occurrence of lung metastasis in soft tissue sarcomas. These findings suggest that radiomic analysis of edema features can provide valuable insights into the prediction of lung metastasis in soft tissue sarcomas.

Deep Learning Radiomics Nomogram to Predict Lung Metastasis in Soft-Tissue Sarcoma: A Multi-Center Study

Objectives

To build and evaluate a deep learning radiomics nomogram (DLRN) for preoperative prediction of lung metastasis (LM) status in patients with soft tissue sarcoma (STS).

Methods

In total, 242 patients with STS (training set, n=116; external validation set, n=126) who underwent magnetic resonance imaging were retrospectively enrolled in this study. We identified independent predictors for LM-status and evaluated their performance. The minimum redundancy maximum relevance (mRMR) method and least absolute shrinkage and selection operator (LASSO) algorithm were adopted to screen radiomics features. Logistic regression, decision tree, random forest, support vector machine (SVM), and adaptive boosting classifiers were compared for their ability to predict LM. To overcome the imbalanced distribution of the LM data, we retrained each machine-learning classifier using the synthetic minority over-sampling technique (SMOTE). A DLRN combining the independent clinical predictors with the best performing radiomics prediction signature (mRMR+LASSO+SVM+SMOTE) was established. Area under the receiver operating characteristics curve (AUC), calibration curves, and decision curve analysis (DCA) were used to assess the performance and clinical applicability of the models.

Result

Comparisons of the AUC values applied to the external validation set revealed that the DLRN model (AUC=0.833) showed better prediction performance than the clinical model (AUC=0.664) and radiomics model (AUC=0.799). The calibration curves indicated good calibration efficiency and the DCA showed the DLRN model to have greater clinical applicability than the other two models.

Conclusion

The DLRN was shown to be an accurate and efficient tool for LM-status prediction in STS.

Local tumour response to neoadjuvant therapy with 2-aminoethyl dihydrogen phosphate in dogs with soft tissue sarcoma

BACKGROUND

In cases of soft tissue sarcoma (STS), neoadjuvant therapy is indicated to downstage the tumour prior to surgery to achieve enhanced local tumour control. The antineoplastic phospholipid compound 2-aminoethyl dihydrogen phosphate (2-AEH2F) is an alkyl phosphate ester capable of inhibiting cell proliferation and inducing cell death by modifying the asymmetry of phospholipids in the cytoplasmic membrane OBJECTIVES: This clinical study was designed to investigate local antitumoural effects of neoadjuvant therapy with 2-AEH2F in dogs with naturally occurring STS MATERIAL AND METHODS: Dogs (n = 11) received four consecutive weekly intravenous injections of 2-AEH2F (70 mg/kg) prior to tumour resection. Tomographic (CT) and thermal (TE) images were used to investigate changes in tumour size and local temperature in response to treatment RESULTS: Comparative analysis of CT images (n = 9/11) failed to reveal complete or partial remission according to selected assessment criteria (RECIST, WHO and volumetric). Comparative analysis of TE images (n = 10/11) revealed significantly (p = 0.01416) lower temperatures in tumoural areas relative to surrounding tissues over the course of treatment CONCLUSIONS: 2-AEH2F had no cytoreductive effects when used at doses and intervals described in this study. However, significant drop in skin temperatures recorded in tumoural areas suggest induction of physiological changes.

Predicting distant metastases in soft-tissue sarcomas from PET-CT scans using constrained hierarchical multi-modality feature learning

Objective.Positron emission tomography-computed tomography (PET-CT) is regarded as the imaging modality of choice for the management of soft-tissue sarcomas (STSs). Distant metastases (DM) are the leading cause of death in STS patients and early detection is important to effectively manage tumors with surgery, radiotherapy and chemotherapy. In this study, we aim to early detect DM in patients with STS using their PET-CT data.Approach.We derive a new convolutional neural network method for early DM detection. The novelty of our method is the introduction of a constrained hierarchical multi-modality feature learning approach to integrate functional imaging (PET) features with anatomical imaging (CT) features. In addition, we removed the reliance on manual input, e.g. tumor delineation, for extracting imaging features.Main results.Our experimental results on a well-established benchmark PET-CT dataset show that our method achieved the highest accuracy (0.896) and AUC (0.903) scores when compared to the state-of-the-art methods (unpaired student's t-testp-value < 0.05).Significance.Our method could be an effective and supportive tool to aid physicians in tumor quantification and in identifying image biomarkers for cancer treatment.

Expression Patterns of TOP2A and SIRT1 Are Predictive of Survival in Patients with High-Risk Soft Tissue Sarcomas Treated with a Neoadjuvant Anthracycline-Based Chemotherapy

Molecular predictors of response to chemotherapy and survival have not been put into clinical practice in high-risk soft tissue sarcomas (HR-STS) by now. The expression of TOP2A and SIRT1 has implications for the mechanism of action of doxorubicin, which is the backbone of chemotherapy in HR-STS. Pre-treatment samples of 167 patients with HR-STS were collected. Protein expression levels of TOP2A and SIRT1 were evaluated with tissue microarrays and immunohistochemistry and correlated with clinicopathological parameters, including overall survival (OS). The expression of TOP2A and SIRT1 was seen in 47% and 60% of patients with HR-STS, respectively. TOP2A expression was associated with higher tumor grading and shorter 5-year OS. The expression of SIRT1 was correlated with a better 5- and 10-year OS. The combination of high SIRT1 and low TOP2A ("Top survivors") significantly predicted a better OS compared to other biomarker combinations. A multivariate analysis confirmed the expression of SIRT1 and the "Top survivor" biomarker combination as independent predictive factors of OS. This is the first study to associate SIRT1 overexpression with a statistically significant prolongation of OS in HR-STS. Both individual markers and their combination can be used as predictive indicators for HR-STS patients scheduled for neoadjuvant anthracycline-based chemotherapy.

Novel Insights into the Immunotherapy of Soft Tissue Sarcomas: Do We Need a Change of Perspective?

Soft tissue sarcomas (STSs) are rare mesenchymal tumors. With more than 80 histological subtypes of STSs, data regarding novel biomarkers of strong prognostic and therapeutic value are very limited. To date, the most important prognostic factor is the tumor grade, and approximately 50% of patients that are diagnosed with high-grade STSs die of metastatic disease within five years. Systemic chemotherapy represents the mainstay of metastatic STSs treatment for decades but induces response in only 15–35% of the patients, irrespective of the histological subtype. In the era of immunotherapy, deciphering the immune cell signatures within the STSs tumors may discriminate immunotherapy responders from non-responders and different immunotherapeutic approaches could be combined based on the predominant cell subpopulations infiltrating the STS tumors. Furthermore, understanding the immune diversity of the STS tumor microenvironment (TME) in different histological subtypes may provide a rationale for stratifying patients according to the TME immune parameters. In this review, we introduce the most important immune cell types infiltrating the STSs tumors and discuss different immunotherapies, as well as promising clinical trials, that would target these immune cells to enhance the antitumor immune responses and improve the prognosis of metastatic STSs patients.

Development and Validation of Novel Nomograms for Predicting Specific Distant Metastatic Sites and Overall Survival of Patients With Soft Tissue Sarcoma

Purpose:

The goal of this study is to construct nomograms to effectively predict the distant metastatic sites and overall survival (OS) of soft tissue sarcoma (STS) patients.

Methods:

STS case data between 2010 and 2015 for retrospective study were gathered from public databases. According to the chi-square and multivariate logistic regression analysis determined independent predictive factors of specific metastatic sites, the nomograms based on these factors were consturced. Subsequently, combined metastatic information a nomogram to predict 1-, 2-, and 3-year OS of STS patients was developed. The performance of models was validated by the area under the curve (AUC), calibration plots, and decision curve analyses (DCA).

Results:

A total of 7001 STS patients were included in this retrospective study, including 4901 cases in the training group and the remaining 2,100 patients in the validation group. Three nomograms were established to predict lung, liver and bone metastasis, and satisfactory results have been obtained by internal and external validation. The AUCs for predicting lung, liver, and bone metastases in the training cohort were 0.796, 0.799, and 0.766, respectively, and in the validation cohort were 0.807, 0.787, and 0.775, respectively, which means that the nomograms have good discrimination. The calibration curves showed that the models have high precision, and the DCA manifested that the nomograms have great clinical application prospects. Through univariate and multivariate COX regression analyses, 8 independent prognosis factors of age, grade, histological type, tumor size, surgery, chemotherapy, radiatiotherapy and lung metastasis were determined. A nomogram was then constructed to predict the 1-, 2-, and 3-years OS, which has a good performance in both internal and external validations.

Conclusion:

The nomograms for predicting specific metastatic sites and OS have good discrimination, accuracy and clinical applicability. The models could accurately predict the metastatic risk and survival information, and help clinical decision-making.

Nomogram for predicting the overall survival and cancer-specific survival of patients with extremity liposarcoma: a population-based study

BACKGROUND

Extremity liposarcoma represents 25% of extremity soft tissue sarcoma and has a better prognosis than liposarcoma occurring in other anatomic sites. The purpose of this study was to develop two nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) of patients with extremity liposarcoma.

METHODS

A total of 2170 patients diagnosed with primary extremity liposarcoma between 2004 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox analyses were performed to explore the independent prognostic factors and establish two nomograms. The area under the curve (AUC), C-index, calibration curve, decision curve analysis (DCA), Kaplan-Meier analysis, and subgroup analyses were used to evaluate the nomograms.

RESULTS

Six variables were identified as independent prognostic factors for both OS and CSS. In the training cohort, the AUCs of the OS nomogram were 0.842, 0.841, and 0.823 for predicting 3-, 5-, and 8-year OS, respectively, while the AUCs of the CSS nomogram were 0.889, 0.884, and 0.859 for predicting 3-, 5-, and 8-year CSS, respectively. Calibration plots and DCA revealed that the nomogram had a satisfactory ability to predict OS and CSS. The above results were also observed in the validation cohort. In addition, the C-indices of both nomograms were significantly higher than those of all independent prognostic factors in both the training and validation cohorts. Stratification of the patients into high- and low-risk groups highlighted the differences in prognosis between the two groups in the training and validation cohorts.

CONCLUSION

Age, sex, tumor size, grade, M stage, and surgery status were confirmed as independent prognostic variables for both OS and CSS in extremity liposarcoma patients. Two nomograms based on the above variables were established to provide more accurate individual survival predictions for extremity liposarcoma patients and to help physicians make appropriate clinical decisions.

Linear mixed-effects models for estimation of pulmonary metastasis growth rate: implications for CT surveillance in patients with sarcoma

OBJECTIVES

Sarcoma patients often undergo surveillance chest CT for detection of pulmonary metastases. No data exist on the optimal surveillance interval for chest CT. The aim of this study was to estimate pulmonary metastasis growth rate in sarcoma patients.

METHODS

This was a retrospective review of 95 patients with pulmonary metastases (43 patients with histologically confirmed metastases and 52 with clinically diagnosed metastases) from sarcoma treated at an academic tertiary-care center between 01 January 2000 and 01 June 2019. Age, sex, primary tumor size, grade, subtype, size and volume of the pulmonary metastasis over successive chest CT scans were recorded. Two metastases per patient were chosen if possible. Multivariate linear mixed-effects models with random effects for each pulmonary metastasis and each patient were used to estimate pulmonary metastasis growth rate, evaluating the impact of patient age, tumor size, tumor grade, chemotherapy and tumor subtype. We estimated the pulmonary metastasis volume doubling time using these analyses.

RESULTS

Maximal primary tumor size at diagnosis (LRT statistic = 2.58, df = 2, p = 0.275), tumor grade (LRT statistic = 1.13, df = 2, p = 0.567), tumor type (LRT statistic = 7.59, df = 6, p = 0.269), and patient age at diagnosis (LRT statistic = 0.735, df = 2, p = 0.736) were not statistically significant predictors of pulmonary nodule growth from baseline values. Chemotherapy decreased the rate of pulmonary nodule growth from baseline (LRT statistic = 7.96, df = 2, p = 0.0187). 95% of untreated pulmonary metastases are expected to grow less than 6 mm in 6.4 months. There was significant intrapatient and interpatient variation in pulmonary metastasis growth rate. Pulmonary metastasis volume growth rate was best fit with an exponential model in time. The volume doubling time for pulmonary metastases assuming an exponential model in time was 143 days (95% CI (104, 231) days).

CONCLUSIONS

Assuming a 2 mm nodule is the smallest reliably detectable nodule by CT, the data suggest that an untreated pulmonary metastasis is expected to grow to 8 mm in 8.4 months (95% CI (4.9, 10.2) months). Tumor size, grade and sarcoma subtype did not significantly alter pulmonary metastasis growth rate. However, chemotherapy slowed the pulmonary metastasis growth rate.

ADVANCES IN KNOWLEDGE

CT surveillance intervals for pulmonary metastases can be estimated based on metastasis growth rate. There was significant variation in the pulmonary metastasis growth rate between metastases within patient and between patients. Pulmonary nodule volume growth followed an exponential model, linear in time.

Impact of surgery in patients with metastatic soft tissue sarcoma: A monocentric retrospective analysis

Background and Objectives

The role of local surgical procedures in patients with metastatic soft tissue sarcoma is still undefined. Few retrospective studies have reported survival benefits for patients with pulmonary metastases after complete surgical resection. Treatment decisions are therefore mainly based on personal experiences rather than on reproducible knowledge.

Method

A total of 237 patients with metastatic sarcoma, treated between 1982 and 2015 at the University Hospital Tuebingen, Germany, were eligible for inclusion. Out of the 237 screened patients, 102 patients underwent at least one metastasectomy. Overall survival was defined as the primary endpoint in this study. For association of non‐linear relationship to the endpoint, significant prognostic factors were included into a recursive partitioning model. A subgroup analysis for long‐term survivors was also performed.

Results

The median overall survival was 64 months. The 3‐, 5‐, 10‐, and 20‐years overall survival rates were 70.7%, 50.3%, 24.7%, and 14.8%, respectively. The number of resections and the progression‐free intervals were independent prognostic factors in three statistical models.

Conclusion

Repeated resections of metastases from different localizations are a strong predictor for prolonged survival. We suggest that the progression‐free interval after metastasectomy should be considered as a predictive factor for benefit from further surgery.

Prognostic factors for soft tissue sarcoma patients with lung metastases only who are receiving first-line chemotherapy: An exploratory, retrospective analysis of the European Organization for Research and Treatment of Cancer-Soft Tissue and Bone Sarcoma Group (EORTC-STBSG)

The prognosis of adult soft tissue sarcoma (STS) patients with metastases is generally poor. As little is known about the impact of the involvement of different metastatic sites and the extent of pulmonary lesions on the outcome for patients receiving first-line chemotherapy, we aimed to establish prognostic factors for STS patients with lung metastases only. A retrospective, exploratory analysis was performed on 2,913 metastatic STS patients who received first-line chemotherapy. Detailed information from 580 patients who had lung metastases only, was used for prognostic factor analysis. Patients with lung metastases only were more often asymptomatic and had undergone complete primary tumor resection more frequently compared to patients with additional metastases outside the lung or without lung metastases. For extremity STS, the incidence of lung metastases only was much higher compared to non-extremity STS. Lung involvement only was an independent favorable prognostic factor for overall survival (OS) with regard to metastatic site. Within this subgroup, in a multivariate model, other factors associated with improved OS included: good performance status (PS), no progression at primary site, low histological grade, younger age, long interval between initial diagnosis and trial registration, and smaller diameter of the largest lung lesion. This unique analysis on prognostic factors in STS patients with lung metastases confirms well-known patient factors (such as age and PS), and tumor characteristics (including tumor grade, interval between primary diagnosis, and metastases), but also identifies diameter of the largest lung lesion as a new prognostic factor. Knowledge about these factors may support decision-making within multidisciplinary tumor boards.

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.

Post-metastasis survival in extremity soft tissue sarcoma: a recursive partitioning analysis of prognostic factors

BACKGROUND

Recursive partitioning analysis (RPA) enables grouping of patients into homogeneous prognostic groups in a visually intuitive form and has the capacity to account for complex interactions among prognostic variables. In this study, we employed RPA to generate a prognostic model for extremity soft tissue sarcoma (STS) patients with metastatic disease.

METHODS

A retrospective review was conducted on 135 patients with metastatic STS who had undergone surgical removal of their primary tumours. Patient and tumour variables along with the performance of metastasectomy were analysed for possible prognostic effect on post-metastatic survival. Significant prognostic factors on multivariate analysis were incorporated into RPA to build regression trees for the prediction of post-metastatic survival.

RESULTS

RPA identified six terminal nodes based on histological grade, performance of metastasectomy and disease-free interval (DFI). Based on the median survival time of the terminal nodes, four prognostic groups with significantly different post-metastatic survival were generated: (1) group A: low grade/metastasectomy; (2) group B: low grade/no metastasectomy/DFI ⩾ 12 months or high grade/metastasectomy; (3) group C: low grade/no metastasectomy/DFI < 12 months or high grade/no metastasectomy/DFI ⩾ 12 months; and (4) group D: high grade/no metastasectomy/DFI < 12 months. The 3-year survival rates for each group were: group A, 76.1 ± 9.6%; group B, 42.3 ± 10.3%; group C, 18.8 ± 8.0%; and group D, 0.0 ± 0.0%.

CONCLUSION

Our prognostic model using RPA successfully divides STS patients with metastasis into groups that can be easily implemented using standard clinical parameters.

Pulmonary metastasectomy from soft tissue sarcomas

INTRODUCTION

Isolated pulmonary metastases from soft tissue sarcomas occur in 20-50% of these(the issue is about metastases, not lung cancer )patients, and 70% of these patients will present disease limited only to the lungs. Surgical resection is well accepted as a standard approach to treat metastases from soft tissue sarcomas isolated in the lungs, and many studies investigating this technique have reported an overall 5-year survival ranging from 30-40%. The most consistent predictor of survival in these patients is complete resection. The aim of the present study was to determine the demographics and clinical treatment-related variables associated with long-term (90-month) overall survival in patients with lung metastases undergoing pulmonary metastasectomy from soft tissue sarcomas.

METHODS

We performed a retrospective review of patients admitted in the Thoracic Surgery Department with lung metastases who underwent thoracotomy for resection following treatment of the primary tumor. Data regarding primary tumor features, demographics, treatment, and outcome were collected.

RESULTS

One hundred twenty-two thoracotomies and 273 nodules were resected from 77 patients with previously treated soft tissue sarcomas. The median follow-up time of all patients was 36.7 months (range: 10-138 months). The postoperative complication rate was 9.1%, and the 30-day mortality rate was 0%. The 90-month overall survival rate for all patients was 34.7%. Multivariate analysis identified the following independent prognostic factors for overall survival: the number of metastases resected, the disease-free interval, and the number of complete resections.

CONCLUSION

These results confirm that lung metastasectomy is a safe and potentially curative procedure for patients with treated primary tumors. A select group of patients can achieve long-term survival after lung resection.

Early outcomes of soft tissue sarcomas presenting with metastases and treated with chemotherapy

OBJECTIVES

The purpose of this study is to describe outcomes for patients with metastatic soft tissue sarcoma treated with chemotherapy.

METHODS

We retrospectively reviewed the records of 383 soft tissue sarcoma patients treated at our institution from 1997 to 2006. Thirty-five patients met the inclusion criteria-metastatic disease on presentation; primary tumor outside the abdomen; treatment with chemotherapy; and minimum follow-up of 3 months. Treatments were individualized and determined by a multidisciplinary oncology team. Patient survival was calculated from the diagnostic biopsy to the date of death or last follow-up using the Kaplan-Meier method.

RESULTS

Mean age was 47 years (range 17-74 years). Most common tumors involved were MFH and leiomyosarcoma. Sixteen of 35 (46%) patients had died at last follow-up. Mean patient survival was 24 months (range 4-102). The 2-year and 5-year survival rates were 64% and 24%, respectively. Twenty-nine patients (83%) presented with pulmonary metastasis, 6 with bone metastases (17%), 4 with regional node metastases (11%), and 4 with soft tissue metastases (11%). Survival was the same for those with pulmonary and only extrapulmonary metastases. The 10 patients with complete resection or complete chemotherapy response of all detectable lesions had longer mean survival (34 months) than the 25 patients who had partial/no resection or response (20 months) (P = 0.02).

CONCLUSIONS

Soft tissue sarcoma patients presenting with metastatic disease have a low survival rate, but complete eradication of tumor correlated with longer survival. Longer-term studies especially those tracking the outcome of complete responders and those completely resected will help determine the efficacy of chemotherapy.

The insulin-like growth factor system and sarcomas

Sarcomas are a diverse group of malignant mesenchymal tumours arising from bone and soft tissues. The identification of critical cellular signalling pathways in sarcomas is an important issue for the development of new targeted therapies. This review highlights the experimental and clinical evidence supporting the role of the insulin-like growth factor (IGF) signalling system in the cellular transformation and progression of several types of sarcoma, including rhabdomyosarcoma, synovial sarcoma, leiomyosarcoma, Ewing's sarcoma and osteosarcoma. Preclinical data suggest that the IGF system could be a promising target for therapy in these sarcomas. Currently, therapies interrupting IGF signalling have been or are being developed. In recent phase 1 clinical studies with humanized monoclonal antibodies directed against IGF receptor type 1 (IGF-1R), objective tumour responses were observed in several patients with Ewing's sarcoma, encouraging further clinical testing in Ewing's sarcoma and other sarcoma (sub)types. Moreover, the occasional occurrence of paraneoplastic hypoglycaemia as a result of the secretion of incompletely processed forms of pro-IGF-II by sarcomas is discussed.

Multidisciplinary management of metastatic sarcoma

Soft tissue sarcomas comprise a heterogeneous group of malignancies of mesenchymal origin. Although sarcomas can arise virtually anywhere, the most common primary site is the extremity. The development of metastatic disease poses a major clinical problem because it is seldom amenable to a curative treatment. However, with careful and expert multidisciplinary team selection of patients with metastatic sarcoma-balancing probability of benefit with certain toxicity-a combined multimodality approach may provide hope to a select few for prolonged survival and even cure.

Combined vindesine and razoxane shows antimetastatic activity in advanced soft tissue sarcomas

BACKGROUND

Razoxane and vindesine were shown to suppress distant metastasis in animal systems. Both drugs affect main steps of the metastatic cascade. Therefore, a pilot study was performed to study the influence of these drugs on the dynamics of metastasis in advanced soft tissue sarcomas (STS). This study was now updated.

METHODS

Twenty-three patients with unresectable (n = 7) and oligometastatic STS (n = 16) received a basic treatment with razoxane and vindesine supported by radiotherapy and occasionally by surgery. Long-term treatment was intended in patients with metastatic disease. The cumulative number of new metastases after 6 and 9 months were determined. Thirty-six patients with comparable stages of STS treated with contemporary chemotherapy served as non-randomized, retrospective controls. The prognostic parameters of the groups were comparable.

RESULTS

In patients receiving razoxane and vindesine, the median number of new metastases after 6 months was 0 (range, 0-40) and after 9 months likewise 0 (0-70). The corresponding numbers in the control group were 4.5 (range, 0-40) and 9 (0->100) (P < 0.001). The progression-free survival at 6 months was 74% in the study group and 23% in the controls. The median survival time from the occurrence of the first metastasis or the time of unresectability was 20+ months (range, 8-120+) versus 9 months (range, 2-252) (P < 0.001). The combined treatment was associated with a low to moderate toxicity.

CONCLUSIONS

Trimodal treatment with razoxane, vindesine, and radiotherapy is feasible in patients with unresectable primaries and early metastatic STS. The combination inhibits the development of remote metastases in a majority of the patients and prolongs survival.

Epithelioid sarcoma: Still an only surgically curable disease

BACKGROUND

Epithelioid sarcoma is a rare soft tissue sarcoma with a known high propensity for locoregional recurrence and distant metastases. The clinical behavior and prognostic factors that influence the survival of patients with epithelioid sarcoma were studied.

METHODS

Twenty-three patients, including 16 men (70%) and 7 women (30%), who were treated for epithelioid sarcoma between 1979-2003 at the University Medical Center Groningen and Radboud University Nijmegen Medical Center, were reviewed retrospectively. The median age at diagnosis was 22 years (range, 1-54 years). At the time of diagnosis, 11 patients (48%) had metastases. Six patients with distant metastasis and 1 patient with an unresectable tumor received palliative treatment (30%). The remaining 16 patients underwent surgical treatment of local disease (11 patients) or locoregional disease (5 patients). Five patients in that group received isolated limb perfusion with tumor necrosis factor and melphalan.

RESULTS

The 5-year and 10-year disease-free survival rates for all patients were 34% and 17%, respectively; for the 16 patients who received curative treatment, both rates were 56%. In the latter group, 8 patients developed local recurrence (50%) after a median follow-up of 4 months (range, 1-14 months). Nine patients were disease free after a median follow-up of 50 months (range, 6-290 months). Tumor size >5 cm (P < .0026) at diagnosis and local recurrence (P < .0008) were significant predictors of survival.

CONCLUSIONS

The prognosis for patients with epithelioid sarcoma is poor, because a substantial number of patients present with extensive disease, lymph node metastases, and/or distant metastases. Treatment consists of radical surgical excision of the tumor and, if indicated, therapeutic lymph node dissection. In patients who have large tumors, isolated limb perfusion may be useful.

Soft tissue sarcoma: why not treated?

BACKGROUND

Soft tissue sarcomas (STS) are uncommon malignancies and elderly STS patients have been reported to receive less definitive treatment compared to young STS patients. The present study was performed to investigate whether withholding treatment was based on disease specific aspects, patients' general health condition, comorbidity or a combination of these.

METHODS

Patients with primary STS, registered by the Comprehensive Cancer Center North-Netherlands (CCCN) from 1989 to 1999, were analyzed retrospectively with regard to the inclusion-criteria: no primary anti-tumor treatment.

RESULTS

From 1989 to 1999, 620 patients (including 56 Kaposi sarcoma) were registered with primary STS. Seventy-six patients (13%) were registered as untreated. Nineteen patients were excluded. Records of 57 patients, median age 71 years (range 23-92, 40 patients > or =65 years, 17 patients < 65 years) were examined. The reasons for no treatment were irresectability of the sarcoma (65%), metastatic disease (11%), comorbidity (4%), poor general health (5%), death prior to therapy (7%) and refusal of therapy (3%) (motivation not documented in 5%).

CONCLUSIONS

Thirteen percent of all STS patients within the CCCN region were not treated, 70% of these patients were elderly. Withholding treatment was mostly disease-related (76%), e.g. irresectable retroperitoneal STS or metastatic disease; for 19% of the patients, it was related to their poor general health. The decision to refrain from cancer treatment was justifiable in all these STS patients.

The value of FDG-PET in the detection, grading and response to therapy of soft tissue and bone sarcomas; a systematic review and meta-analysis

BACKGROUND

Sarcomas represent a significant diagnostic and therapeutic challenge that requires techniques to provide better assessment of the disease than provided by traditional means. FDG-PET depicts the increased metabolism in abnormal tissues, enabling visualisation and quantification in vivo. The objective of this review was to assess the diagnostic value of FDG-PET in the detection, grading and therapy response of soft tissue and bone sarcomas.

METHODS

A systematic review and meta-analysis of clinical studies on FDG-PET and sarcomas was conducted. Databases of PubMed, Embase and Cochrane were searched for studies. Besides that, the references of identified studies were reviewed. Three reviewers independently assessed the methodological quality. Statistical pooling was possible for studies concerning detection and grading of studies with mixed sarcomas (soft tissue and bone) and studies with soft tissue sarcomas only.

RESULTS

Twenty-nine studies met the inclusion criteria. There was disagreement between the reviewers in 21.5% of the questions from the criteria list. The methodological quality of most of the included studies was poor. Pooled sensitivity, specificity and accuracy of PET for the detection of sarcomas were 0.91, 0.85 and 0.88, respectively. The difference between the mean Standard Uptake Value (SUV) in malignant and benign tumours for the studies concerning mixed and soft tissue sarcomas was statistically significant, as well as the difference in FDG uptake between low and high grade mixed sarcomas.

CONCLUSIONS

The meta-analysis in this study was limited by the fact that only a few studies had mutual comparable outcome parameters. Moreover, the methodological quality of the studies was generally poor. Nevertheless, our results indicate that FDG-PET can discriminate between sarcomas and benign tumours and low and high grade sarcomas based on the mean SUV. The diagnostic implications of these results have to be investigated, especially the discrimination between benign tumours and low grade sarcomas. Based on this meta-analysis, there is no indication to use FDG-PET in the standard treatment of sarcomas. In the future PET imaging in bone and soft tissue sarcomas should be directed to the clinical implication for the detection and grading of sarcomas and the treatment evaluation of locally advanced sarcomas.