BackgroundFetus in fetu is a rare congenital anomaly and is defined as a monozygotic twin incorporated into the abdomen of its sibling during development. Fetus in fetu is often overlooked in the differential diagnosis of an abdominal mass. Unlike teratomas, fetus in fetu is a benign disorder.Case presentationWe describe the clinical characteristics of two patients, a thirty-months old boy who was found to have abdominal distension and a neonate who was diagnosed antenatally with abdominal mass. Computed tomography scan revealed the mass in which the contents favor a fetus in fetu rather than a teratoma. Surgical removal revealed that the anencephalic fetus have limb buds situated relative to a palpable vertebral column, supporting the diagnosis of fetus in fetu. In the present report, presentation, diagnosis, pathology, management, and recent literature are also reviewed.ConclusionFetus in fetu is a rare entity that typically presents in infancy and early childhood. It should be differentiated from a teratoma because of the teratoma’s malignant potential. Preoperative diagnosis is based on radiologic findings. The treatment of fetus in fetu is operative to relieve obstruction, prevent further compression and possible complications. Complete excision allows confirmation of the diagnosis and lowers the risk of recurrence.
A known value of 1 μg mL LPS might induce odontoblast-like MDPC-23 cells to generate odontoclast-like cells or to function as odontoclasts. The data might provide a new explanation for the precursors of odontoclasts and root resorption.
Objectives. To evaluate the utility of radiomics features in differentiating central lung cancers and atelectasis on contrast-enhanced computed tomography (CT) images. This study is retrospective. Materials and Methods. In this study, 36 patients with central pulmonary cancer and atelectasis between July 2013 and June 2018 were identified. A total of 1,653 2D and 2,327 3D radiomics features were extracted from segmented lung cancers and atelectasis on contrast-enhanced CT. The refined features were investigated for usefulness in classifying lung cancer and atelectasis according to the information gain, and 10 models were trained based on these features. The classification model is trained and tested at the region level and pixel level, respectively. Results. Among all the extracted features, 334 2D features and 1,507 3D features had an information gain (IG) greater than 0.1. The highest accuracy (AC) of the region classifiers was 0.9375. The best Dice score, Hausdorff distance, and voxel AC were 0.2076, 45.28, and 0.8675, respectively. Conclusions. Radiomics features derived from contrast-enhanced CT images can differentiate lung cancers and atelectasis at the regional and voxel levels.
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