Giant atypical lipoma

Mascarenhas, Marta Regina Machado; Mutti, Laís de Abreu; Paiva, João Marcos Góes de; Enokihara, Mílvia Maria Simões e Silva; Rosa, Ival Peres; Enokihara, Mauro Yoshiaki

doi:10.1590/abd1806-4841.20174447

Cited by 8 publications

(8 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…RPLs, sarcoma of mesenchymal origin, are the most common STS of the retroperitoneum ( 23 – 25 ). RPLs account for approximately 40% of cases of STS in the retroperitoneum ( 26 , 27 ). Several studies have suggested that the surgical management and histologic subtype of RPLs are associated with prognosis in patients with RPLs ( 28 – 32 ).…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Prognostic Model to Predict the Prognosis of Patients With Retroperitoneal Liposarcoma: A Large International Population-Based Cohort Study

Zhang

et al. 2022

Front. Oncol.

View full text Add to dashboard Cite

BackgroundRetroperitoneal liposarcomas (RPLs), sarcoma of mesenchymal origin, are the most common soft tissue sarcomas (STS) of the retroperitoneum. Given the rarity of RPLs, the prognostic values of clinicopathological features in the patients remain unclear. The nomogram can provide a visual interface to aid in calculating the predicted probability that a patient will achieve a particular clinical endpoint and communication with patients.MethodsWe included a total of 1,392 RPLs patients diagnosed between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. For nomogram construction and validation, patients in the SEER database were divided randomly into the training cohort and internal validation cohort at a ratio of 7:3, while 65 patients with RPLs from our center between 2010 and 2016 served as the external validation cohort. The OS curves were drawn using the Kaplan–Meier method and assessed using the log-rank test. Moreover, Fine and Gray’s competing-risk regression models were conducted to assess CSS. Univariate and multivariate analyses were performed to select the prognostic factors for survival time. We constructed a predictive nomogram based on the results of the multivariate analyses.ResultsThrough univariate and multivariate analyses, it is found that age, histological grade, classification, SEER stage, surgery constitute significant risk factors for OS, and age, classification, SEER stage, AJCC M stage, surgery, and tumor size constitute risk factors for CSS. We found that the nomogram provided a good assessment of OS and CSS at 1, 3, and 5 years in patients with RPLs (1-year OS: (training cohort: AUC = 0.755 (95% CI, 0.714, 0.796); internal validation cohort: AUC = 0.754 (95% CI, 0.681, 0.827); external validation cohort: AUC = 0.793 (95% CI, 0.651, 0.935)); 3-year OS: (training cohort: AUC = 0.782 (95% CI, 0.752, 0.811); internal validation cohort: AUC = 0.788 (95% CI, 0.736, 0.841); external validation cohort: AUC = 0.863 (95% CI, 0.773, 0.954)); 5-year OS: (training cohort: AUC = 0.780 (95% CI, 0.752, 0.808); internal validation cohort: AUC = 0.783 (95% CI, 0.732, 0.834); external validation cohort: AUC = 0.854 (95% CI, 0.762, 0.945)); 1-year CSS: (training cohort: AUC = 0.769 (95% CI, 0.717, 0.821); internal validation cohort: AUC = 0.753 (95% CI, 0.668, 0.838); external validation cohort: AUC = 0.799 (95% CI, 0.616, 0.981)); 3-year CSS: (training cohort: AUC = 0.777 (95% CI, 0.742, 0.811); internal validation cohort: AUC = 0.787 (95% CI, 0.726, 0.849); external validation cohort: AUC = 0.808 (95% CI, 0.673, 0.943)); 5-year CSS: (training cohort: AUC = 0.773 (95% CI, 0.741, 0.805); internal validation cohort: AUC = 0.768 (95% CI, 0.709, 0.827); external validation cohort: AUC = 0.829 (95% CI, 0.712, 0.945))). The calibration plots for the training, internal validation, and external validation cohorts at 1-, 3-, and 5-year OS and CSS indicated that the predicted survival rates closely correspond to the actual survival rates.ConclusionWe constructed and externally validated an unprecedented nomogram prognostic model for patients with RPLs. The nomogram can be used as a potential, objective, and supplementary tool for clinicians to predict the prognosis of RPLs patients around the world.

show abstract

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Prognostic Model to Predict the Prognosis of Patients With Retroperitoneal Liposarcoma: A Large International Population-Based Cohort Study

Zhang

et al. 2022

Front. Oncol.

View full text Add to dashboard Cite

show abstract

“…There is no clear evidence in the literature to support the need for histological biopsy of suspected retroperitoneal tumors because, with or without biopsy, such large tumors require surgical resection, which is the only curative option. Liposarcoma is histologically subdivided into well-differentiated, myxoid/round cell, pleomorphic and dedifferentiated subtypes ( 15 ). The 5-year survival rate of patients with well-differentiated retroperitoneal liposarcoma is ~90%.…”

Section: Discussionmentioning

confidence: 99%

Challenging surgical treatment of giant retroperitoneal liposarcoma: A case report

et al. 2022

View full text Add to dashboard Cite

Liposarcoma is a rare malignant tumor type and surgical resection is the gold standard treatment. The present study reported on the case of a 51-year-old woman who presented with a mass in the left upper abdomen. Computed tomography revealed a 32-cm giant retroperitoneal liposarcoma. Complete tumor resection was performed without the removal of other organs. Postoperative pathological examination indicated retroperitoneal well-differentiated liposarcoma and immunohistochemistry revealed S-100(-), MDM2(+), vimentin(+), CDK4(+), p16(+) and STAT6(+) results. The patient recovered well after the surgery. Complete tumor resection during the first surgery is key to cure liposarcoma. The present case report will be helpful for clinical oncologists to fully understand giant retroperitoneal liposarcoma and treat it accordingly.

show abstract

“…Unique giant cells with overlapping nuclei and edge arrangement could also be found in pleomorphic liposarcoma [20,21]. In immunohistochemistry, MDM2 and CDK4 are positive, which are helpful to distinguish benign lipoma from other lipomas [13,20,22]. CD34, B-cell lymphoma-2 (BCL-2), and CD99 are always strongly positive, while S-100 protein and signal transducer and activator of transcription 6 (STAT6) are usually negative [11,14,23,24].…”

Section: Discussionmentioning

confidence: 99%

Giant Pleomorphic Lipoma With Malignant Transformation in the Buttock: a Rare Case Report and Literature Review

Feng¹,

Xu²,

Xu³

et al. 2021

Preprint

View full text Add to dashboard Cite

Background: Pleomorphic lipoma is a benign tumor, which is often located in the neck, shoulder, and other subcutaneous tissues. Pleomorphic lipoma with malignant transformation in the buttock is rarely reported.Case Presentation: A 53-year-old male attended to our department with a large mass in his buttock that had been present for about one year, leading to difficulty with walking and defecation. Computed tomography (CT) showed a 10. 2 × 8. 3 cm mass with lumpy and mixed low to medium density shadows in the right side of the lower part of the tailbone. Color Doppler ultrasound showed that the skin and subcutaneous tissues of the right buttock were thickened with lymphedema. Pre-operative pathology revealed a large amount of adipose and fusiform fibrous tissues with obvious blood vessels. The patient underwent complete resection of the mass in the right buttock. Postoperative pathology indicated that the tumor was pleomorphic lipoma with malignant transformation. The patient recovered well and was discharged from the hospital at day 12 post-surgery. Follow up at 6 months did not find any symptoms of tumor recurrence.Conclusions: Pleomorphic lipoma with malignant transformation is rare. CT, magnetic resonance imaging (MRI) and histopathology are the main methods used to diagnose pleomorphic lipoma. Complete resection of the tumor is vital to treat giant pleomorphic lipoma successfully.

show abstract

Giant atypical lipoma

Cited by 8 publications

References 9 publications

Development and Validation of a Prognostic Model to Predict the Prognosis of Patients With Retroperitoneal Liposarcoma: A Large International Population-Based Cohort Study

Development and Validation of a Prognostic Model to Predict the Prognosis of Patients With Retroperitoneal Liposarcoma: A Large International Population-Based Cohort Study

Challenging surgical treatment of giant retroperitoneal liposarcoma: A case report

Giant Pleomorphic Lipoma With Malignant Transformation in the Buttock: a Rare Case Report and Literature Review

Contact Info

Product

Resources

About