CASE REPORT: A 30-year-old lady presented with left breast pain and lumpiness for over one year. She has had several breast ultrasounds (US) and was treated for acute mastitis and abscess. Subsequently, in view of the rapid growth of the lump and worsening pain, she was re-investigated with US, elastography, digital breast tomosynthesis (DBT) and MRI. MRI raised the suspicion of angiosarcoma. The diagnosis was confirmed after biopsy and she underwent mastectomy.
DISCUSSION: Literature review on imaging findings of breast angiosarcoma, especially on MRI, is discussed. MRI features showed heterogeneous low signal intensity on T1 and high signal intensity on T2. Dynamic contrast enhancement (DCE) features included either early enhancement with or without washout in the delayed phase, and some reported central areas of non-enhancement.
CONCLUSION: This case report emphasises on the importance of MRI in clinching the diagnosis of breast angiosarcoma, and hence, should be offered sooner to prevent diagnostic delay.
INTRODUCTION: Magnetic resonance imaging is a useful technique to visualize soft tissues within the knee joint. Cartilage delineation in magnetic resonance (MR) images helps in understanding the disease progressions. Convolutional neural networks (CNNs) have shown promising results in computer vision tasks, and various encoder-decoder-based segmentation neural networks are introduced in the last few years. However, the performances of such networks are unknown in the context of cartilage delineation.
METHODS: This study trained and compared 10 encoder-decoder-based CNNs in performing cartilage delineation from knee MR images. The knee MR images are obtained from the Osteoarthritis Initiative (OAI). The benchmarking process is to compare various CNNs based on physical specifications and segmentation performances.
RESULTS: LadderNet has the least trainable parameters with the model size of 5 MB. UNetVanilla crowned the best performances by having 0.8369, 0.9108, and 0.9097 on JSC, DSC, and MCC.
CONCLUSION: UNetVanilla can be served as a benchmark for cartilage delineation in knee MR images, while LadderNet served as an alternative if there are hardware limitations during production.
OBJECTIVE: Our study aimed to examine and contrast the clinical and radiological characteristics of TDL, high-grade gliomas (HGG) and primary CNS lymphoma (CNSL).
METHOD: This was a retrospective review of 66 patients (23 TDL, 31 HGG and 12 CNSL). Clinical and laboratory data were obtained. MRI brain at presentation were analyzed by two independent, blinded neuroradiologists.
RESULTS: Patients with TDLs were younger and predominantly female. Sensorimotor deficits and ataxia were more common amongst TDL whereas headaches and altered mental status were associated with HGG and CNSL. Compared to HGG and CNSL, MRI characteristics supporting TDL included relatively smaller size, lack of or mild mass effect, incomplete peripheral rim enhancement, absence of central enhancement or restricted diffusion, lack of cortical involvement, and presence of remote white matter lesions on the index scan. Paradoxically, some TDLs may present atypically or radiologically mimic CNS lymphomas.
CONCLUSION: Careful evaluation of clinical and radiological features helps in differentiating TDLs at first presentation from CNS neoplasms.