Dedifferentiated liposarcoma of the musculoskeletal system: expanded MR imaging spectrum from predominant fatty mass to non-fatty mass

Differences in MRI findings of superficial spindle cell lipoma and atypical lipomatous tumor/well-differentiated liposarcoma

OBJECTIVE

This study aimed to evaluate the efficacy of using MRI findings to differentiate superficial spindle cell lipomas (SCLs) from atypical lipomatous tumor/well-differentiated liposarcomas (ALT/WDLs).

METHODS

This study included 12 patients with histopathologically proven superficial SCLs and 11 with ALT/WDLs. MRI findings for both pathologies were retrospectively reviewed and compared between the two pathologies.

RESULTS

The neck, upper back, and shoulder regions were more frequent locations of SCLs than of ALT/WDLs (100% vs 55%, p < 0.05), whereas no significant differences were observed in age and sex. The median maximum diameter of the lesion was smaller in SCLs than in ALT/WDLs (44 mm [interquartile range (IQR): 35-63] vs 102 mm [IQR: 86-119], p < 0.05). On T 1 weighted images, non-fatty area was more frequently observed in SCLs than in ALT/WDLs (73% vs 25%, p < 0.05), and the median rate of non-fatty area was larger in SCLs than in ALT/WDLs (7.5% [IQR: 1.0-53] vs 0% [IQR: 0-0.2], p < 0.05). On fat-suppressed T 2 weighted images, a solid hyperintense area was more frequently observed in SCLs than in ALT/WDLs (83% vs 27%, p < 0.05).

CONCLUSION

The maximum diameter, non-fatty area on T 1 weighted images, and solid hyperintense area on fat-suppressed T 2 weighted images were useful imaging features for differentiating superficial SCLs from ALT/WDLs.

ADVANCES IN KNOWLEDGE

In superficial lipomatous tumors, small tumor size and non-fatty solid area were valuable findings for diagnosing SCLs.

The Characteristics of magnetic resonance imaging and immunohistochemical findings in de-differentiated liposarcoma

PURPOSE

Radiological imaging in Dedifferentiated liposarcoma (DDLPS) often shows the coexistence of fatty and non-fatty solid components; however, it has been shown that when fatty components were not identified on magnetic resonance imaging (MRI), the diagnosis of DDLPS would not have been diagnosed if immunohistochemical (IHC) staining had not been performed. The aim of this study was to investigate the pattern of MRI and relationship between MRI and IHC findings in DDLPS.

METHODS

We retrospectively reviewed the cases of 25 patients with DDLPS. To identify the MRI spectrum of DDLPS, tumors were classified into the following four categories based on MRI findings: I = a well-defined fatty mass and juxtaposed well-defined non-fatty mass, II = a non-fatty component within a predominantly fatty mass, III = a focal fatty component within a large non-fatty mass, and IV = a non-fatty mass with atypical MRI findings. IHC staining for CDK4, MDM2, and p16 were evaluated.

RESULTS

Category IV tumor was the most common tumor in this population. Of the 22 patients who underwent IHC staining, MDM2, CDK4, and p16 were positive in 21, 20, and 19 patients, respectively. MDM2 was positive in all 11 patients with category IV tumors; CDK4 and p 16 were positive in 10 and eight patients, respectively. There was no difference of survival between the patients with category I, II and III and category IV.

CONCLUSIONS

DDLPS without fatty components on MRI scans was mostly found. We recommend IHC staining to screen for DDLPS even if the tumors in STS cases have a non-fatty component.

MRI findings to differentiate musculoskeletal dedifferentiated liposarcoma from atypical lipomatous tumor

OBJECTIVE

This study aimed to assess the efficacy of using MRI findings for differentiating musculoskeletal dedifferentiated liposarcoma (DDLP) from atypical lipomatous tumor (ALT).

MATERIALS AND METHODS

This study included 22 patients with histopathologically proven DDLP and 35 with ALT in the musculoskeletal areas. All DDLPs were immunohistochemically positive for MDM2. MRI findings for both pathologies were retrospectively reviewed and compared.

RESULTS

The maximum lesion diameter was significantly lower in DDLPs than in ALTs (p < 0.01). Ill-defined margin, peritumoral edema, and tail sign were more frequently observed in DDLPs than in ALTs (p < 0.01, respectively). The fatty component was less frequently observed in DDLPs than in ALTs (27 vs. 100%; p < 0.01), whereas the non-fatty component was more frequently observed in DDLPs than in ALTs (100 vs. 11%; p < 0.01). The occupation rate by non-fatty components was significantly higher in DDLPs than in ALTs (p < 0.01). No significant differences were observed in imaging findings associated with fatty component; however, necrosis within the non-fatty component on the contrast-enhanced image was more frequently observed in DDLPs than in ALTs (72 vs. 0%, p < 0.05).

CONCLUSION

DDLPs always had a non-fatty component, whereas ALTs always had fatty component. Ill-defined margin, peritumoral edema, tail sign, and necrosis within non-fatty components were useful MRI features for differentiating musculoskeletal DDLP from ALT.

Radiomics approach to distinguish between well differentiated liposarcomas and lipomas on MRI

Background

Well differentiated liposarcoma (WDLPS) can be difficult to distinguish from lipoma. Currently, this distinction is made by testing for MDM2 amplification, which requires a biopsy. The aim of this study was to develop a noninvasive method to predict MDM2 amplification status using radiomics features derived from MRI.

Methods

Patients with an MDM2‐negative lipoma or MDM2‐positive WDLPS and a pretreatment T1‐weighted MRI scan who were referred to Erasmus MC between 2009 and 2018 were included. When available, other MRI sequences were included in the radiomics analysis. Features describing intensity, shape and texture were extracted from the tumour region. Classification was performed using various machine learning approaches. Evaluation was performed through a 100 times random‐split cross‐validation. The performance of the models was compared with the performance of three expert radiologists.

Results

The data set included 116 tumours (58 patients with lipoma, 58 with WDLPS) and originated from 41 different MRI scanners, resulting in wide heterogeneity in imaging hardware and acquisition protocols. The radiomics model based on T1 imaging features alone resulted in a mean area under the curve (AUC) of 0·83, sensitivity of 0·68 and specificity of 0·84. Adding the T2‐weighted imaging features in an explorative analysis improved the model to a mean AUC of 0·89, sensitivity of 0·74 and specificity of 0·88. The three radiologists scored an AUC of 0·74 and 0·72 and 0·61 respectively; a sensitivity of 0·74, 0·91 and 0·64; and a specificity of 0·55, 0·36 and 0·59.

Conclusion

Radiomics is a promising, non‐invasive method for differentiating between WDLPS and lipoma, outperforming the scores of the radiologists. Further optimization and validation is needed before introduction into clinical practice.