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

Perfusion-weighted imaging with dynamic contrast enhancement (PWI/DCE) morphologic, qualitative, semiquantitative, and radiomics features predicting undifferentiated pleomorphic sarcoma (UPS) treatment response

Undifferentiated pleomorphic sarcoma (UPS) is the largest subgroup of soft tissue sarcomas. This study determined the value of perfusion-weighted imaging with dynamic-contrast-enhancement (PWI/DCE) morphologic, qualitative, and semiquantitative features for predicting UPS pathology-assessed treatment effect (PATE). This retrospective study included 33 surgically excised extremity UPS patients with pre-surgical MRI. Volumetric tumor segmentation from PWI/DCE was obtained at Baseline (BL), Post-Chemotherapy (PC), and Post-Radiation Therapy (PRT). The surgical specimens' PATE separated cases into Responders (R) (≥ 90%, 16 patients), Partial-Responders (PR) (89 - 31%, 10 patients), and Non-Responders (NR) (≤ 30%, seven patients). Seven semiquantitative kinetic parameters and maps were extracted from time-intensity curves (TICs), and 107 radiomic features were derived. Statistical analyses compared R vs. PR/NR. At PRT, 79% of R displayed a "Capsular" morphology (P = 1.49 × 10-7), and 100% demonstrated a TIC-type II (P = 8.32 × 10-7). 80% of PR showed "Unipolar" morphology (P = 1.03 × 10-5), and 60% expressed a TIC-type V (P = 0.06). Semiquantitative wash-in rate (WiR) was able to separate R vs. PR/NR (P = 0.0078). The WiR radiomics displayed significant differences in the first_order_10 percentile (P = 0.0178) comparing R vs. PR/NR at PRT. The PWI/DCE TIC-type II curve, low WiR, and "Capsular" enhancement represent PRT patterns typically observed in successfully treated UPS and demonstrate potential for UPS treatment response assessment.

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.

Myxofibrosarcoma of the Chest Wall Detected on 99mTc-MDP Whole-Body Bone Scan

Myxofibrosarcoma is a type of soft tissue sarcoma, predominantly characterized by a high propensity for local recurrence, albeit demonstrating a relatively diminished risk for distant metastasis. Its prevalence is notably higher in elderly patients. Here, we present a case of a 73-year-old woman diagnosed with Myxofibrosarcoma. She was subjected to a whole-body bone scan using 99mTc-methylene diphosphonate (MDP) to survey potential bony metastasis. It revealed marked MDP accumulation with peripheral soft tissue uptake in the right lateral chest region of this patient. This imaging phenotype could potentially be attributed to the augmented vascularity within the tumor, a finding that was prominently displayed in this particular case.

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.

Calculating Tumor Volume Using Three-Dimensional Models in Preoperative Soft-Tissue Sarcoma Surgical Planning: Does Size Matter?

This feasibility study aims to explore the use of three-dimensional virtual surgical planning to preoperatively determine the need for reconstructive surgery following resection of an extremity soft-tissue sarcoma. As flap reconstruction is performed more often in advanced disease, we hypothesized that tumor volume would be larger in the group of patients that had undergone flap reconstruction. All patients that were treated by surgical resection for an extremity soft-tissue sarcoma between 1 January 2016 and 1 October 2019 in the University Medical Center Groningen were included retrospectively. Three-dimensional models were created using the diagnostic magnetic resonance scan. Tumor volume was calculated for all patients. Three-dimensional tumor volume was 107.8 (349.1) mL in the group of patients that had undergone primary closure and 29.4 (47.4) mL in the group of patients in which a flap reconstruction was performed, p = 0.004. Three-dimensional tumor volume was 76.1 (295.3) mL in the group of patients with a complication following ESTS treatment, versus 57.0 (132.4) mL in patients with an uncomplicated course following ESTS treatment, p = 0.311. Patients who had undergone flap reconstruction had smaller tumor volumes compared to those in the group of patients treated by primary closure. Furthermore, a larger tumor volume did not result in complications for patients undergoing ESTS treatment. Therefore, tumor volume does not seem to influence the need for reconstruction. Despite the capability of three-dimensional virtual surgical planning to measure tumor volume, we do not recommend its utilization in the multidisciplinary extremity soft-tissue sarcoma treatment, considering the findings of the study.

Primary synovial sarcoma on MRI - a case series and review of the literature

Purpose

Simple MRI features are mandatory to facilitate the diagnostics of synovial sarcomas, especially for radiolo-gists outside multidisciplinary sarcoma centres. In this case-series and review, we investigate the main appearance of synovial sarcoma on MRI.

Material and methods

Fifteen histologically proven primary synovial sarcomas who underwent MRI at 2 different sarcoma centres were included in this case series. Patients and their primary synovial sarcomas were examined for age, localization, mean tumour size (in mm), histological grade (G - according to the Fédération Nationale des Centres de Lutte Contre Le Cancer [FNCLCC]), configuration, T2 signal intensity, presence/absence of "triple sign", heterogeneity/homogeneity, borders (well-defined or infiltrative), and intensity of contrast enhancement on MRI. Additionally, a comprehensive literature review to identify observational studies, reviews, and case-reports assessing MRI features of primary synovial sarcoma was performed.

Results

The mean age of the patients was 47.6 years (SD: 17.2). The mean size of primary synovial sarcoma was 59.3 mm (SD: 42). Primary synovial sarcomas were significantly most often multilobulated (n = 12, p < 0.01), heterogeneous (n = 10), and infiltrative (n = 9). Additionally, 3 other primary synovial sarcomas showed the following configurations: ovoid/nodular (n = 2) and fascicular (n = 1). Ovoid/nodular synovial sarcomas were solely depicted as homogeneous with well-defined borders. All tumours showed T2 hyperintense signal and presented with marked contrast enhancement.

Conclusions

Primary synovial sarcomas are mainly multilobulated, heterogeneous, and infiltrative tumours. In minor cases, ovoid/nodular or fascicular configurations occur. Knowing the main appearance of synovial sarcoma can help facilitate the diagnostics of primary synovial sarcomas.

Recent Advances in Ultrasound of Soft Tissue Lesions

The superficial soft tissue lesions are commonly discovered during routine clinical practice. However, their diagnosis can be challenging. Non-invasive imaging can differentiate the features of various superficial soft tissue lesions. Moreover, imaging-based evaluations can help guide treatment and surgical planning, evaluate tumor extension, and perform staging and follow-up. Novel imaging modalities and techniques have been developed to improve diagnostic performance and differentiate between benign and malignant lesions in vivo. The authors reviewed the literature to determine how ultrasound has been utilized to diagnose and treat superficial soft tissue lesions.

Tumor-to-bone distance and radiomic features on MRI distinguish intramuscular lipomas from well-differentiated liposarcomas

Background

To develop a machine learning model based on tumor-to-bone distance and radiomic features derived from preoperative MRI images to distinguish intramuscular (IM) lipomas and atypical lipomatous tumors/well-differentiated liposarcomas (ALTs/WDLSs) and compared with radiologists.

Methods

The study included patients with IM lipomas and ALTs/WDLSs diagnosed between 2010 and 2022, and with MRI scans (sequence/field strength: T1-weighted (T1W) imaging at 1.5 or 3.0 Tesla MRI). Manual segmentation of tumors based on the three-dimensional T1W images was performed by two observers to appraise the intra- and interobserver variability. After radiomic features and tumor-to-bone distance were extracted, it was used to train a machine learning model to distinguish IM lipomas and ALTs/WDLSs. Both feature selection and classification steps were performed using Least Absolute Shrinkage and Selection Operator logistic regression. The performance of the classification model was assessed using a tenfold cross-validation strategy and subsequently evaluated using the receiver operating characteristic curve (ROC) analysis. The classification agreement of two experienced musculoskeletal (MSK) radiologists was assessed using the kappa statistics. The diagnosis accuracy of each radiologist was evaluated using the final pathological results as the gold standard. Additionally, we compared the performance of the model and two radiologists in terms of the area under the receiver operator characteristic curves (AUCs) using the Delong’s test.

Results

There were 68 tumors (38 IM lipomas and 30 ALTs/WDLSs). The AUC of the machine learning model was 0.88 [95% CI 0.72–1] (sensitivity, 91.6%; specificity, 85.7%; and accuracy, 89.0%). For Radiologist 1, the AUC was 0.94 [95% CI 0.87–1] (sensitivity, 97.4%; specificity, 90.9%; and accuracy, 95.0%), and as to Radiologist 2, the AUC was 0.91 [95% CI 0.83–0.99] (sensitivity, 100%; specificity, 81.8%; and accuracy, 93.3%). The classification agreement of the radiologists was 0.89 of kappa value (95% CI 0.76–1). Although the AUC of the model was lower than of two experienced MSK radiologists, there was no statistically significant difference between the model and two radiologists (all P > 0.05).

Conclusions

The novel machine learning model based on tumor-to-bone distance and radiomic features is a noninvasive procedure that has the potential for distinguishing IM lipomas from ALTs/WDLSs. The predictive features that suggested malignancy were size, shape, depth, texture, histogram, and tumor-to-bone distance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13018-023-03718-4.

Musculoskeletal Soft-Tissue Sarcoma: Quality Assessment of Initial MRI Reports Shows Frequent Deviation from ESSR Guidelines

Soft-tissue sarcomas (STS) are a rare subtype of soft-tissue mass and are frequently misinterpreted as benign lesions. Magnetic resonance imaging (MRI) is the primary recommended type of diagnostics. To assess the quality of primary radiology reports, we investigated whether recommended MRI report elements were included in compliance with European Society of Musculoskeletal Radiology (ESSR) guidelines. A total of 1107 patients were evaluated retrospectively, and 126 radiological reports on patients with malignant STS were assessed for ESSR quality criteria. One or more required sequences or planes were missing in 67% of the reports. In all 126 cases, the report recognized the mass as anomalous (100%). Sixty-eight percent of the reports mentioned signs of malignancy. The majority of reports (n = 109, 87%) articulated a suspected diagnosis, 32 of which showed a mismatch with the final diagnosis (25%). Thirty-two percent of the reports had a misinterpretation of the masses as benign. Benign misinterpretations were more common in masses smaller than 5 cm (65% vs. 27%). Thirty percent of the reports suggested tissue biopsy and 6% recommended referral to a sarcoma center. MRI reports showed frequent deviations from ESSR guidelines, and protocol guidelines were not routinely met. Deviations from standard protocol and reporting guidelines could put patients at risk for inadequate therapy.