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Objective: To retrospectively analyze magnetic resonance imaging (MRI) features of various pathological subtypes of sinonasal rhabdomyosarcoma (RMS) and explore correlations between imaging features and pathological subtypes.
Methods: In total, 11 cases with embryonal, alveolar or pleomorphic sinonasal RMSs, confirmed by surgical pathology, were selected. Their characteristics and distinctive imaging features were analysed, and the correlation between pathology and imaging features was explored.
Results: Bone destruction was observed in all 11 cases with RMS. Expansive growth was predominant in three alveolar and three embryonal RMS cases, and creeping growth was predominant in two alveolar, two embryonal and one pleomorphic RMS cases. Signs of residual mucosa were observed in all 11 cases, and 10 cases showed involvement of multiple sinus cavities and orbital cavities. All cases exhibited mild-to-intermediate enhancement.
Conclusion: Sinonasal RMSs have the following characteristic MRI features: ethmoid sinuses and middle nasal conchae are the prevalent sites; lesions are mainly of mild enhancement; tumours exhibit signs of residual mucosa, mild-to-intermediate enhancement and frequent orbital involvement; bone invasion and bone destruction are frequently observed; and haematogenous metastasis is not as common as lymphatic metastasis. RMSs of various pathological subtypes were not significantly distinct by imaging.
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http://dx.doi.org/10.1259/dmfr.20210030 | DOI Listing |
Int J Surg Case Rep
September 2025
Department of Otorhinolaryngology, Al Mouwasat University Hospital, Damascus University, Damascus, Syria; Faculty of Medicine, Damascus University, Damascus, Syria.
Introduction: Antrochoanal polyps (ACPs) typically extend posteriorly into the choana and nasopharynx; orbital invasion is exceptionally rare. This report details an atypical ACP with orbital extension in a coagulopathic patient, highlighting diagnostic and surgical complexities.
Case Presentation: A 46-year-old woman with severe Factor V deficiency (0.
Int J Surg Pathol
September 2025
Department of Pathology, The Thirteenth People's Hospital of Chongqing, Chongqing, China.
Soft tissue sarcomas are a heterogeneous group of malignancies arising from mesenchymal cells. Recent advancements in genomic profiling have identified novel gene fusions in these tumors, offering new insights into their pathogenesis and potential therapeutic targets. Here, we describe a spindle cell sarcoma harboring a novel gene fusion.
View Article and Find Full Text PDFIEEE Trans Biomed Eng
September 2025
Objective: Diffusion magnetic resonance imaging (dMRI) often suffers from low spatial and angular resolution due to inherent limitations in imaging hardware and system noise, adversely affecting the accurate estimation of microstructural parameters with fine anatomical details. Deep learning-based super-resolution techniques have shown promise in enhancing dMRI resolution without increasing acquisition time. However, most existing methods are confined to either spatial or angular super-resolution, disrupting the information exchange between the two domains and limiting their effectiveness in capturing detailed microstructural features.
View Article and Find Full Text PDFJ Neurooncol
September 2025
Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China.
Rationale And Objectives: Double expression lymphoma (DEL) is an independent high-risk prognostic factor for primary CNS lymphoma (PCNSL), and its diagnosis currently relies on invasive methods. This study first integrates radiomics and habitat radiomics features to enhance preoperative DEL status prediction models via intratumoral heterogeneity analysis.
Materials And Methods: Clinical, pathological, and MRI imaging data of 139 PCNSL patients from two independent centers were collected.
Abdom Radiol (NY)
September 2025
Department of Radiology, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics and Gynecology and Pediatrics, Fujian Medical University, Fuzhou, China.
Background: We aimed to develop and validate a radiomics-based machine learning nomogram using multiparametric magnetic resonance imaging to preoperatively predict substantial lymphovascular space invasion in patients with endometrial cancer.
Methods: This retrospective dual-center study included patients with histologically confirmed endometrial cancer who underwent preoperative magnetic resonance imaging (MRI). The patients were divided into training and test sets.