Imaging of Ganglioneuroma: A Literature Review and a Rare Case of Cystic Presentation in an Adolescent Girl.

Retroperitoneal ganglioneuroma is a rare neuroectodermal tumor with a benign nature. We performed a literature review among 338 studies. We included 9 studies, whose patients underwent CT and/or MRI to characterize a retroperitoneal mass, which was confirmed to be a ganglioneuroma by histologic exam. The most common features of ganglioneuroma are considered to be a solid nature, oval/lobulated shape, and regular margins. The ganglioneuroma shows a progressive late enhancement on CT. On MRI it appears as a hypointense mass in T1W images and with a heterogeneous high-intensity in T2W. The MRI-"whorled sign" is described in the reviewed studies in about 80% of patients. The MRI characterization of a primitive retroperitoneal cystic mass should not exclude a cystic evolution from solid masses, and in the case of paravertebral location, the differential diagnosis algorithm should include the hypothesis of ganglioneuroma. In our case, the MRI features could have oriented towards a neurogenic nature, however, the predominantly cystic-fluid aspect and the considerable longitudinal non-invasive extension between retroperitoneal structures, misled us to a lymphatic malformation. In the literature, it is reported that the cystic presentation can be due to a degeneration of a well-known solid form while maintaining a benign character: the distinguishing malignity character is the revelation of immature cells on histological examination.

Early Diagnosis of Liver Metastases from Colorectal Cancer through CT Radiomics and Formal Methods: A Pilot Study.

BACKGROUND: Liver metastases are a leading cause of cancer-associated deaths in patients affected by colorectal cancer (CRC). The multidisciplinary strategy to treat CRC is more effective when the radiological diagnosis is accurate and early. Despite the evolving technologies in radiological accuracy, the radiological diagnosis of Colorectal Cancer Liver Metastases (CRCLM) is still a key point. The aim of our study was to define a new patient representation different by Artificial Intelligence models, using Formal Methods (FMs), to help clinicians to predict the presence of liver metastasis when still undetectable using the standard protocols. METHODS: We retrospectively reviewed from 2013 to 2020 the CT scan of nine patients affected by CRC who would develop liver lesions within 4 months and 8 years. Seven patients developed liver metastases after primary staging before any liver surgery, and two patients were enrolled after R0 liver resection. Twenty-one patients were enrolled as the case control group (CCG). Regions of Interest (ROIs) were identified through manual segmentation on the medical images including only liver parenchyma and eventual benign lesions, avoiding major vessels and biliary ducts. Our predictive model was built based on formally verified radiomic features. RESULTS: The precision of our methods is 100%, scheduling patients as positive only if they will be affected by CRCLM, showing a 93.3% overall accuracy. Recall was 77.8%. CONCLUSION: FMs can provide an effective early detection of CRCLM before clinical diagnosis only through non-invasive radiomic features even in very heterogeneous and small clinical samples.