Systematic analysis of post-treatment soft-tissue edema and seroma on MRI in 177 sarcoma patients

Diagnostic Value of Magnetic Resonance Imaging Radiomics and Machine-learning in Grading Soft Tissue Sarcoma: A Mini-review on the Current State

Soft tissue sarcomas (STS) are a heterogeneous group of rare malignant tumors. Tumor grade might be underestimated in biopsy due to intratumoral heterogeneity. This mini-review aims to present the current state of predicting malignancy grades of STS through radiomics, machine learning, and deep learning on magnetic resonance imaging (MRI). Several studies investigated various machine-learning and deep-learning approaches in T2-weighted (w) images, contrast-enhanced (CE) T1w images, and DWI/ADC maps with promising results. Combining semantic imaging features, radiomics features, and deep-learning signatures in machine-learning models has demonstrated superior predictive performances compared to individual feature sources. Furthermore, incorporating features from both tumor volume and peritumor region is beneficial. Especially random forest and support vector machine classifiers, often combined with the least absolute shrinkage and selection operator (LASSO) and/or synthetic minority oversampling technique (SMOTE), did show high area under the curve (AUC) values and accuracies in existing studies.

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

PURPOSE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

Primary Chest Wall Ewing Sarcoma: Treatment and Long-Term Results

OBJECTIVE

The aim of the study is to evaluate early and long-term results of chest wall primary Ewing's sarcoma patients treated in the time period February 2000-February 2023 by a multidisciplinary approach.

METHODS

We retrospectively reviewed the medical records of patients who underwent chest wall resection for a primary tumor. Treatment approach, extent of resection, 30-day mortality, overall survival (OS), local recurrence-free survival (LRFS), and metastasis-free survival (MFS) were analyzed.

RESULTS

Overall, n = 15 consecutive patients were treated for chest wall primary Ewing's sarcoma. A median of n = 3 ribs was resected with a median of n = 2 ribs adjacent to the lesion. Resections were extended to the adjacent structures in n = 5 patients (33.3%). In all cases, we performed a prosthetic reconstruction, associated with muscle flap (n = 10, 66.6%) or with rigid titanium bars and muscle flap (n = 6, 40%). A radical resection was accomplished in n = 13 patients (84.6%). The median surgical time was 310 ± 120 min; median hospitalization was 7.8 ± 1.9 days. Post-operative mortality was zero. We recorded n = 4 (30.7%) post-operative complication. The median follow-up (FU) was 26 months. Moreover, 5-year overall and event-free survival were 52% and 48%, respectively.

CONCLUSIONS

This case series confirms the benefit of the multidisciplinary approach for Ewing sarcomas in early and long-term results.

The impact of radiomics in the management of soft tissue sarcoma

INTRODUCTION

Soft tissue sarcomas (STSs) are rare malignancies. Pre-therapeutic tumour grading and assessment are crucial in making treatment decisions. Radiomics is a high-throughput method for analysing imaging data, providing quantitative information beyond expert assessment. This review highlights the role of radiomic texture analysis in STSs evaluation.

MATERIALS AND METHODS

We conducted a systematic review according to the Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive search was conducted in PubMed/MEDLINE and Scopus using the search terms: 'radiomics [All Fields] AND ("soft tissue sarcoma" [All Fields] OR "soft tissue sarcomas" [All Fields])'. Only original articles, referring to humans, were included.

RESULTS

A preliminary search conducted on PubMed/MEDLINE and Scopus provided 74 and 93 studies respectively. Based on the previously described criteria, 49 papers were selected, with a publication range from July 2015 to June 2023. The main domains of interest were risk stratification, histological grading prediction, technical feasibility/reproductive aspects, treatment response.

CONCLUSIONS

With an increasing interest over the last years, the use of radiomics appears to have potential for assessing STSs from initial diagnosis to predicting treatment response. However, additional and extensive research is necessary to validate the effectiveness of radiomics parameters and to integrate them into a comprehensive decision support system.

Improved subcutaneous edema segmentation on abdominal CT using a generated adipose tissue density prior

PURPOSE

Edema, or swelling, is a common symptom of kidney, heart, and liver disease. Volumetric edema measurement is potentially clinically useful. Edema can occur in various tissues. This work focuses on segmentation and volume measurement of one common site, subcutaneous adipose tissue.

METHODS

The density distributions of edema and subcutaneous adipose tissue are represented as a two-class Gaussian mixture model (GMM). In previous work, edema regions were segmented by selecting voxels with density values within the edema density distribution. This work improves upon the prior work by generating an adipose tissue mask without edema through a conditional generative adversarial network. The density distribution of the generated mask was imported into a Chan-Vese level set framework. Edema and subcutaneous adipose tissue are separated by iteratively updating their respective density distributions.

RESULTS

Validation results on 25 patients with edema showed that the segmentation accuracy significantly improved. Compared to GMM, the average Dice Similarity Coefficient increased from 56.0 to 61.7% ([Formula: see text]) and the relative volume difference decreased from 36.5 to 30.2% ([Formula: see text]).

CONCLUSION

The generated adipose tissue density prior improved edema segmentation accuracy. Accurate edema volume measurement may prove clinically useful.

Explainable Model Using Shapley Additive Explanations Approach on Wound Infection after Wide Soft Tissue Sarcoma Resection: "Big Data" Analysis Based on Health Insurance Review and Assessment Service Hub

Background and Objectives: Soft tissue sarcomas represent a heterogeneous group of malignant mesenchymal tissues. Despite their low prevalence, soft tissue sarcomas present clinical challenges for orthopedic surgeons owing to their aggressive nature, and perioperative wound infections. However, the low prevalence of soft tissue sarcomas has hindered the availability of large-scale studies. This study aimed to analyze wound infections after wide resection in patients with soft tissue sarcomas by employing big data analytics from the Hub of the Health Insurance Review and Assessment Service (HIRA). Materials and Methods: Patients who underwent wide excision of soft tissue sarcomas between 2010 and 2021 were included. Data were collected from the HIRA database of approximately 50 million individuals' information in the Republic of Korea. The data collected included demographic information, diagnoses, prescribed medications, and surgical procedures. Random forest has been used to analyze the major associated determinants. A total of 10,906 observations with complete data were divided into training and validation sets in an 80:20 ratio (8773 vs. 2193 cases). Random forest permutation importance was employed to identify the major predictors of infection and Shapley Additive Explanations (SHAP) values were derived to analyze the directions of associations with predictors. Results: A total of 10,969 patients who underwent wide excision of soft tissue sarcomas were included. Among the study population, 886 (8.08%) patients had post-operative infections requiring surgery. The overall transfusion rate for wide excision was 20.67% (2267 patients). Risk factors among the comorbidities of each patient with wound infection were analyzed and dependence plots of individual features were visualized. The transfusion dependence plot reveals a distinctive pattern, with SHAP values displaying a negative trend for individuals without blood transfusions and a positive trend for those who received blood transfusions, emphasizing the substantial impact of blood transfusions on the likelihood of wound infection. Conclusions: Using the machine learning random forest model and the SHAP values, the perioperative transfusion, male sex, old age, and low SES were important features of wound infection in soft-tissue sarcoma patients.

Outcomes and complications of postoperative seroma cavities following soft-tissue sarcoma resection

Introduction

Seroma development is a known complication following extremity and trunk soft-tissue sarcoma (STS) resection. The purpose of this study is to evaluate and characterize seroma outcomes and the development of associated complications.

Methods

A retrospective review of 123 patients who developed postoperative seromas following STS resection at a single institution was performed. Various patient and surgical factors were analyzed to determine their effect on overall seroma outcomes.

Results

77/123 seromas (62.6%) were uncomplicated, 30/123 (24.4%) developed infection, and 16/123 (13.0%) were symptomatic and required aspiration or drainage for symptom relief at an average of 12.2 months postoperatively. 65/123 (52.8%) seromas resolved spontaneously at an average time of 12.41 months. Seromas in the lower extremity (p=0.028), surgical resection volume >864 cm3, (p=<0.001) and initial seroma volume >42 cm3 (p=<0.001) increased the likelihood of infection. 90% of infected seromas developed the infection within the first three months following initial resection. No seromas which were aspirated or drained ultimately developed an infection following these procedures, though 50% recurred.

Discussion

Most seromas following STS resection are uncomplicated and do not require intervention, though a large resection cavity >864 cm3 and a large seroma volume >42 cm3 are risk factors for complications.

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.

Does the primary soft-tissue sarcoma configuration predict configuration of recurrent tumors on magnetic resonance imaging?

BACKGROUND

Soft-tissue sarcomas (STS) are rare malignancies of the soft tissue.

PURPOSE

To assess whether the magnetic resonance imaging (MRI) configuration of primary STS can predict the configuration of a recurring tumor and whether the MRI configuration of multiple recurrences differs in one and the same patient.

MATERIAL AND METHODS

Thirty-nine patients with histologically proven recurrent STS were included in this retrospective study and underwent pre- and post-treatment MRI. Three main configurations of primary and recurrent tumors were identified: polycyclic/multilobulated; ovoid/nodular; and streaky.

RESULTS

Sixty recurrent lesions were detected: 34 ovoid/nodular; 15 polycyclic/multilobulated; and 11 streaky. Five recurrences were multifocal and eight were bifocal. Of 39 patients, 28 (71.8%) presented one recurrence within the MRI follow-up period (P = 0.006); in 10 patients (25.6%), up to three different configurations of recurring STS were identified in one patient. Recurrences of polycyclic/multilobulated primaries were mostly ovoid/nodular (48%; P = 0.003) or polycyclic/multilobulated (37%; P = 0.014), and recurring ovoid/nodular STS significantly most often showed the same configuration as the primary tumor (85%; P < 0.001). Primary STS with a streaky configuration recurred in all three configurations in roughly equal proportions. Homogeneity/heterogeneity and tumor borders are significantly associated with the configuration of recurrences.

CONCLUSION

Primary STS configuration may help predict recurrent tumor configuration when the primary STS had a polycyclic/multilobulated or ovoid/nodular configuration. However, recurrent STS configuration can also differ from primary STS configuration, especially when the primary STS had a streaky configuration, rendering recurrent STS difficult to predict. Different configurations of recurrent STS in one and the same patient are common.

High efficacy of tetra-PEG hydrogel sealants for sutureless dural closure

Advances in meticulous dural closure technique remain a great challenge for watertight dural closure in the aged society, because the cerebrospinal fluid (CSF) leakage after spinal surgery is often accompanied with the disgusting wound infection, meningitis and pseudomeningocele. Here, a tetra-poly (ethylene glycol) (PEG)-based hydrogel sealant is developed with collective advantages of facile operation, high safety, quick set time, easy injectability, favorable mechanical strength and powerful tissue adhesion for effective sutureless dural closure during the surgery procedure. Impressively, this tetra-PEG sealant can instantaneously adhere to the irregular tissue surfaces even in a liquid environment, and effectively prevent or block off the intraoperative CSF leakage for sutureless dural closure and dura regeneration. Together, this sutureless tetra-PEG adhesive can be utilized as a very promising alternative for high-efficient watertight dural closure of the clinical patients who incidentally or deliberately undergo the durotomy during the spinal surgery.

Long-term diagnostic value of MRI in detecting recurrent aggressive fibromatosis at two multidisciplinary sarcoma centers

PURPOSE

To assess the diagnostic accuracy of MRI in detecting recurrent aggressive fibromatosis (AF) during long-term follow-up at two multidisciplinary sarcoma centers.

METHODS

Seventy-nine patients from two sarcoma centers were included in this IRB-approved study and were examined postoperatively using 1.5-T MRI. MRI follow-up scans were reviewed for true-positive/-negative and false-positive/-negative results. Available pathological reports and MRI follow-ups were set as reference.

RESULTS

The median age of the patients was 38.1 ± 15.3 years. Of the patients 27.9 % showed recurrent AF lesions. The most common localizations of AF were the axilla/shoulder (n = 15) and the thigh (n = 11). From 498 postoperative MRI follow-ups, 24 true-positive, 16 false-positive, 6 false-negative, and 452 true-negative MRI follow-ups were identified. The overall sensitivity and specificity for detecting recurrent AF was 80 % and 97 %, respectively. There was no significant difference in the diagnostic accuracy at the two sarcoma centers. All false-negative results were found in small lesions. False-positive results mostly mimicked streaky (n = 10) and small ovoid/nodular (n = 5) lesions. The configuration of recurrent AF was significantly most often fascicular (50 %; p = 0.001-0.005).

CONCLUSION

MRI shows a high long-term diagnostic value in detecting AF recurrences. Nevertheless, radiologists should pay close attention when lesions are small, as they may remain undetected. Although the configuration of recurrent AF is most often fascicular, recurrences may also appear in different shapes.