Malignant behavior of a well­differentiated digestive neuroendocrine tumor with peritoneal carcinomatosis: A case report.

Neuroendocrine neoplasms (NENs) are a heterogenous group of tumors, arising from enterochromaffin cells, with different biological and clinical characteristics. Well-differentiated Grade 1 (G1) small intestinal NENs are often characterized by a slow progression rate and a good prognosis. Peritoneal carcinomatosis of a G1 digestive NEN is not a very common finding, and thus there is little published evidence regarding its progression and management. The complex, multistage interplay between the peritoneum and the metastasizing neuroendocrine cells is not well understood, and a reliable predictive tool to identify these patients earlier in their disease course is lacking. The present study describes the case of a 68-year-old woman presenting with an oligosymptomatic, stage IV, small intestinal G1 NEN (pTxpN1pM1) with synchronous liver metastases, multifocal mesenteric tumor deposits and a low Ki67 labeling index (1%). Over a period of 15 months, the patient developed rapidly progressive peritoneal metastatic disease with repetitive self-limiting obstructive symptoms and eventually succumbed to her illness. The present case report discusses the potential relationship between low-grade NEN, location of the primary tumor and the metastatic site, and also speculates on the role of the underlying subcellular mechanisms, specific micro-environment, spreading modalities and therapeutic strategy.

Evaluation of Virtual Grid Processed Clinical Chest Radiographs.

OBJECTIVES: We evaluated the different Virtual Grid software ratios (Fujifilm, Tokyo, Japan) on gridless clinical chest radiographs with visual grading analysis (VGA). In addition, we investigated the 2 image quality assessment algorithms (IQAAs). MATERIALS AND METHODS: Gridless chest radiographs of 50 different intensive care unit patients were collected and afterward processed with Virtual Grid software. Different software (SW) grid ratios-6:1, 10:1, 13:1, 17:1, and 20:1-were applied to investigate the image quality (IQ) improvement. Image quality improvement was assessed by 4 radiologists in a relative VGA study where the reference image was processed with SW grid ratio of 10:1. One of the IQAAs used to analyze the radiographs was implemented in our department but was originally developed by the research group of the Duke University Medical Center. A general IQ score (IQS) was calculated based on contrast, detail, and noise. Another IQAA-NIQE (naturalness image quality evaluator)-available in Matlab (MATLAB Research R2019b; the MathWorks, Inc) was evaluated. Both methods were compared with VGA. RESULTS: Visual grading analysis scores of gridless radiographs are significantly lower ( P < 0.001). Image quality increases with increasing SW grid ratios, up to grid ratio of 17:1. However, some anatomical structures-spine and ribs-are negatively affected by the higher grid ratios. A correlation coefficient of 0.99 between the VGA and the IQS was observed. The correlation coefficient between VGA and NIQE was 1.00. CONCLUSIONS: Virtual Grid with SW grid ratio of 6:1 improves the IQ of gridless chest bedside radiographs. The grid ratios 17:1 and 20:1 should be considered carefully as the SW negatively affects parts of the ribs and spine. Therefore, grid ratios up to 13:1 can be advised. The IQAAs are promising and could be used to detect differences in IQ when different scatter correction SW settings are used.

Unusual Presentation of Pulmonary Hydatidosis Mimicking Thoracic Malignancy in a Paediatric South African Patient.

In this case report, an illustrative case of pulmonary hydatidosis in a young South African girl is presented. The acute symptomatology, rapidly worsening clinical condition and the extremely atypical appearance of the hydatid cysts on imaging were initially misleading and raised suspicion for malignant disease.