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

Sedaghat, Sam; Ravesh, Mona Salehi; Sedaghat, Maya; Both, Marcus; Jansen, Olav

doi:10.2478/raon-2021-0007

Cited by 12 publications

(10 citation statements)

References 28 publications

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“…The configuration of STS has been reported several times in recent years. Higher grade (G2/3) STS display a polycyclic/ multilobulated structure, and this configuration may help predict tumor recurrence (26,27). In the present study, the multilobulated structure was associated with high HIF-1α expression and might be a potential predictor of poor patient prognosis.…”

Section: Discussionsupporting

confidence: 51%

Soft tissue sarcoma: correlation of dynamic contrast-enhanced magnetic resonance imaging features with HIF-1α expression and patient outcomes

Li¹,

Xie²,

Hu³

et al. 2022

Quant Imaging Med Surg

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Background: To investigate the value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features for predicting hypoxia-inducible factor 1-alpha (HIF-1α) expression and patient outcomes in soft tissue sarcoma (STS).Methods: We enrolled 71 patients with STS who underwent 3.0 Tesla (3.0T) MRI, including conventional MRI and DCE-MRI sequencing. The location, maximum tumor diameter, envelope, T2-weighted tumor heterogeneity, peritumoral edema, peritumoral enhancement, necrosis, configuration, tail-like pattern, bone invasion, and vessel/nerve invasion and/or encasement of the STSs were determined using conventional MRI images. The DCE-MRI parameters, including the volume transfer constant (K trans ), reflux rate (K ep ), volume fraction of extravascular extracellular matrix (V e ), and time-signal intensity curve (TIC) type, of each lesion were independently analyzed by two observers. Independent samples t-test, chi-square test, and Mann-Whitney U-test were performed to evaluate the differences in the MRI features between the two groups. The relationships between the DCE-MRI parameters and HIF-1α expression were analyzed using Spearman's correlation analysis. The Cox proportional hazards model and Kaplan-Meier method were used for survival analysis.Results: Of the conventional MRI features, high heterogeneity, peritumoral enhancement, necrosis, and multilobulation of the T2-weighted tumor were prone to occur in the high-expression group. Of the DCE-MRI parameters, the high-expression group showed significantly higher K trans (0.311±0.091 vs. 0.210±0.058 min −1 ), and K ep values (0.896±0.656 vs. 0.444±0.300 min −1 ) than the low-expression group. No significant differences in TIC types and V e values were observed between the low-and high-expression groups (P>0.05). There were positive correlations between K trans and K ep values with P<0.001; r=0.123, P<0.001, respectively). Receiver operating characteristic (ROC) analysis indicated high specificity (93.9%) of the K trans value for predicting high expression of HIF-1α. The K ep value provided the best performance in diagnostic sensitivity (84.2%). Survival analyses revealed that more than 50% necrosis, multilobulation, and K trans values greater than 0.262 min −1 were strongly associated with a higher risk of death.Conclusions: Conventional MRI features and DCE-MRI parameters were significantly helpful in determining HIF-1α expression levels and predicting the overall survival (OS) of patients with STS. present the following article in accordance with the STARD reporting checklist (available at https://qims.amegroups. com/article/view/10.21037/qims-22-75/rc). MethodsThis prospective study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Health Sciences Institutional Review Board (HIRB) of Huashan Hospital, Fudan University, and informed consent was provided by all individual participants.

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Section: Discussionsupporting

confidence: 51%

Soft tissue sarcoma: correlation of dynamic contrast-enhanced magnetic resonance imaging features with HIF-1α expression and patient outcomes

Li¹,

Xie²,

Hu³

et al. 2022

Quant Imaging Med Surg

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“…Therefore, radiographic size is not always a reliable radiomic feature for diagnosis. Although ovoid/nodular configurations cannot distinguish the grade of malignancy (i.e., high-, intermediate, and low-grade soft tissue tumors) on MRI [ 43 ], it may differentiate IM lipomas from ALTs/WDLSs. Based on our results, ALTs/WDLSs had a more compact (sphere-like) shape than IM lipomas, as evidenced by the surface area-to-volume ratio.…”

Section: Discussionmentioning

confidence: 99%

“…Based on our results, ALTs/WDLSs had a more compact (sphere-like) shape than IM lipomas, as evidenced by the surface area-to-volume ratio. This radiomic feature may be correlated to ovoid/nodular configurations on MRI [ 43 , 44 ]. We can observe that this shape difference may also reflect excessive cell proliferation and/or non-lipomatous or nodular fibrous septa, which showed a fluffier consistency in ALTs/WDLSs [ 45 , 46 ].…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2023

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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.

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“…Sedaghat et al reported that the MRI configuration of soft-tissue sarcoma (STS) correlates with the grade of malignancy. 9 Higher-grade STS are multilobulated, whereas low-grade STS are ovoid or streaky. Infiltrative lesions suggest a higher grade.…”

Section: Limitations Of the Ultrasoundmentioning

confidence: 99%

“…Infiltrative lesions suggest a higher grade. 9 Additionally, MRI is a valuable tool for postsurgical follow-up of various soft tissue tumors [10][11][12][13] such as recurrent dermatofibrosarcoma protuberans (DFSP) which appear as nodular, homogeneous, and well-defined enhancing lesions. 10 Furthermore, MRI can assess the occurrence of post-treatment changes such as subcutaneous and muscle edema.…”

Section: Limitations Of the Ultrasoundmentioning

confidence: 99%

Recent Advances in Ultrasound of Soft Tissue Lesions

Pluetrattanabha¹,

Direksunthorn²

2023

IJGM

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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.

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

Cited by 12 publications

References 28 publications

Soft tissue sarcoma: correlation of dynamic contrast-enhanced magnetic resonance imaging features with HIF-1α expression and patient outcomes

Soft tissue sarcoma: correlation of dynamic contrast-enhanced magnetic resonance imaging features with HIF-1α expression and patient outcomes

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

Recent Advances in Ultrasound of Soft Tissue Lesions

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