Presacral myelolipoma, case report and literature review

Introduction: Myelolipomas are very rare benign tumours consisting of hematopoietic cells and mature adipose tissues. They are most commonly found in the adrenal glands. However, there have been several reported cases of extra-adrenal myelolipomas, most commonly in the presacral region. Nearly all presacral lesions are small and asymptomatic; thus, most are discovered incidentally on imaging studies.Presentation of case: We report two cases of presacral myelolipomas. The first is a 48-year-old female presenting with atypical back pain, found to have a mass in her presacral region with a size of 3,3 cm. The second case is a 59-year-old female, who presented for evaluation of a hip fracture, found to have a 4,7 cm presacral lesion. Both presacral myelolipomas were discovered incidentally and were confirmed by percutaneous guided fine-needle aspiration biopsy. Both were treated conservatively.Discussion: Accepted indications for the surgical excision of myelolipomas are symptomatic tumour, size > 4 cm, metabolically active tumour, and a suspicion of malignancy on an imaging study. However, previous reports have documented that nearly half of the conservatively managed myelolipomas with a mean initial size of 5,1 cm, has increased in size or became symptomatic over a 3-years period.Conclusion: We conclude that symptomatic presacral myelolipomas or lesions larger than 4 cm should be en-bloc resected, and we present an intuitive decision-making algorithm.Surgical oncolog

Diagnostic accuracy of grayscale, power Doppler and contrast-enhanced ultrasound compared with contrast-enhanced MRI in the visualization of synovitis in knee osteoarthritis

Purpose: To assess the diagnostic accuracy of grayscale (GSUS), power Doppler (PDUS) and contrast-enhanced ultrasound (CEUS) for detecting synovitis in knee osteoarthritis (OA). Method: Patients with different degrees of radiographic knee OA were included prospectively. All underwent GSUS, PDUS, CEUS, and contrast-enhanced magnetic resonance imaging (CE-MRI), on which synovitis was assessed semi-quantitatively. Correlations of synovitis severity on ultrasound based techniques with CE-MRI were determined. Receiver operating characteristic (ROC) analysis was performed to assess diagnostic performance of GSUS, PDUS, and CEUS, for detecting synovitis, using CE-MRI as reference-standard

Differential diagnosis and mutation stratification of desmoid-type fibromatosis on MRI using radiomics

Purpose: Diagnosing desmoid-type fibromatosis (DTF) requires an invasive tissue biopsy with β-catenin staining and CTNNB1 mutational analysis, and is challenging due to its rarity. The aim of this study was to evaluate radiomics for distinguishing DTF from soft tissue sarcomas (STS), and in DTF, for predicting the CTNNB1 mutation types. Methods: Patients with histologically confirmed extremity STS (non-DTF) or DTF and at least a pretreatment T1- weighted (T1w) MRI scan were retrospectively included. Tumors were semi-automatically annotated on the T1w scans, from which 411 features were extracted. Prediction models were created using a combination of various machine learning approaches. Evaluation was performed through a 100x random-split cross-validation. The model for DTF vs. non-DTF was compared to classification by two radiologists on a location matched subset. Results: The data included 203 patients (72 DTF, 131 STS). The T1w radiomics model showed a mean AUC of 0.79 on the full dataset. Addition of T2w or T1w post-contrast scans did not improve the performance. On the location matched cohort, the T1w model had a mean AUC of 0.88 while the radiologists had an AUC of 0.80 and 0.88, respectively. For the prediction of the CTNNB1 mutation types (S45 F, T41A and wild-type), the T1w model showed an AUC of 0.61, 0.56, and 0.74. Conclusions: Our radiomics model was able to distinguish DTF from STS with high accuracy similar to two radiologists, but was not able to predict the CTNNB1 mutation status

Severity classification of Tenosynovial Giant Cell Tumours on MR imaging

Contains fulltext : 195626.pdf (publisher's version ) (Open Access