Characterizing the Transformation and Diagnosis of Atypical Lipomatous Tumor to Dedifferentiated Liposarcoma: Single Institutional Outcomes

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Abstract
Introduction: Atypical lipomatous tumor (ALT) in the extremities is a locally aggressive adipocytic tumor with the potential risk of transformation into dedifferentiated liposarcoma (DDLS). Studies seldom differentiate whether DDLS was diagnosed on initial biopsy, final resected specimen, or subsequent recurrence. Our study seeks to characterize how and when patients received their ALT or DDLS diagnoses to better understand the relationship between the two neoplasms. Methods: We performed a retrospective review of patients diagnosed with ALT or DDLS of the extremities. Clinical characteristics, including the method of diagnosis of an ALT or DDLS, time between diagnoses, and tumor recurrence was recorded. Univariate/multivariate analysis was performed to identify risk factors. Results: Forty‐five patients were diagnosed with ALT after core needle biopsy (CNB) and 41 of them received marginal en bloc excision. Three (7.3%) of these patients had a heterogeneous tumor on final resection, pathology revealed both ALT and DDLS. Four patients (8.2%) were diagnosed with DDLS from CNB and received negative margin en bloc excision. One of these tumors was identified as heterogeneous ALT/DDLS after resection. Fifty‐three patients received marginal en bloc resection without CNB after a benign lipomatous mass was suspected on CT/MRI. Among these, one (1.9%) had a tumor with a heterogeneous composition of both ALT and DDLS on pathology. There were 11 (11.7%) ALT recurrences and 1 (1.0%) DDLS recurrence after ALT resection. Conclusion: Obtaining a proper diagnosis whether ALT or DDLS is critical. Our cohort found that amongst those concerning lipomatous lesions biopsied, 7.84% will show biopsy proven DDLS. Additionally, 6.67% of the biopsies will be false negatives and show DDLS on final pathology. Furthermore, our local recurrence for ALT was 11.7% recurring as ALT and 1.0% recurring as DDLS.

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