Pott's Puffy Tumor: A Case Report on Diagnosis Through Imaging.

Pott's puffy tumor (PPT) is a rare but life-threatening complication of chronic sinusitis, although it can be secondary to other entities such as trauma or insect bites. It is characterized by circumscribed frontal swelling associated with a subperiosteal abscess. Imaging plays a crucial role in diagnosis and early identification of complications, some of which can be life-threatening, including intracerebral and intra-orbital complications. We present a case of a 14-year-old male with non-specific frontal pain and swelling, where the diagnosis of PTT was confirmed through imaging studies. Upon admission, the patient exhibited orbital and intracerebral complications, as shown in MRI and CT scans. Treatment involved a combination of antibiotics and sinus surgery, with close monitoring for orbital and intracranial complications.

Imaging Diagnosis of Desmoplastic Small Round Cell Tumor: A Report of Two Cases.

Desmoplastic small round cell tumor (DSRCT) is a rare multifocal peritoneal sarcoma, typically found in adolescent and young adult males. Symptoms are nonspecific and vary depending on tumor involvement. Diagnosis is primarily histopathological, although imaging results can assist in the diagnostic process. Although not pathognomonic, certain radiologic findings can help narrow down potential diagnoses and sometimes suggest the condition, as seen in our cases. Treatment options are not well-established or effective, and despite employing various therapeutic approaches, the prognosis remains poor. We present two cases of boys aged 11 and 10 with a final diagnosis of DSRCT, emphasizing the imaging findings.

Extramedullary Imaging Manifestations of Leukemia/Lymphoblastic Lymphoma in Pediatrics: A Case Series.

Leukemia is the most common childhood malignancy and often presents with nonspecific symptoms. Occasionally, it presents with extramedullary manifestations, which have been more frequent in cases of myeloid lineage or T cells. However, precursor B-cell leukemia/lymphoblastic lymphoma can also have extramedullary manifestations in some patients. Describing certain clinical features along with diagnostic imaging can establish a presentation pattern and suggest a diagnosis in the pediatric population. Herein, we present a series of four patients with extramedullary manifestations of B-cell lymphoblastic leukemia, describing their clinical imaging and histopathological characteristics.

Megacystis-Microcolon-Intestinal Hypoperistalsis Syndrome: A Case Report of an Uncommon Condition.

The megacystis-microcolon-intestinal hypoperistalsis syndrome (MMIHS), also known as Berdon syndrome, is a rare congenital condition that falls within the spectrum of visceral myopathies. It is characterized by the presence of megacystis, microcolon, and hypoperistalsis, which are secondary to gastrointestinal and urinary system dysmotility. It is frequently associated with other alterations in the gastrointestinal and genitourinary tracts. Although it is possible to make the diagnosis in the prenatal period, most cases are diagnosed after birth through genetic and imaging studies. Advances in treatment have led to a progressive increase in survival rates. We present the case of a newborn with congenital alterations described prenatally and with imaging findings characteristic of the syndrome.

Prenatal Diagnosis of Hemimegalencephaly Using Radiological Methods: A Case Report.

Hemimegalencephaly is a rare congenital anomaly characterized by an increase in the size and dysplastic involvement of one cerebral hemisphere, which can be partial or complete. It may also be associated with anomalies in the cerebellum and brainstem and, in some cases, be a part of different syndromes. The result of these abnormalities leads to intractable epilepsy and developmental delay. Diagnosis is typically made through imaging studies in the postnatal period, but it can also be done before birth. We present the case of a 23-week pregnant patient in whom a prenatal diagnosis of hemimegalencephaly was made, highlighting the need for fetal magnetic resonance imaging (MRI) to confirm the diagnosis.

Medical decision support system using weakly-labeled lung CT scans.

Purpose: Determination and development of an effective set of models leveraging Artificial Intelligence techniques to generate a system able to support clinical practitioners working with COVID-19 patients. It involves a pipeline including classification, lung and lesion segmentation, as well as lesion quantification of axial lung CT studies. Approach: A deep neural network architecture based on DenseNet is introduced for the classification of weakly-labeled, variable-sized (and possibly sparse) axial lung CT scans. The models are trained and tested on aggregated, publicly available data sets with over 10 categories. To further assess the models, a data set was collected from multiple medical institutions in Colombia, which includes healthy, COVID-19 and patients with other diseases. It is composed of 1,322 CT studies from a diverse set of CT machines and institutions that make over 550,000 slices. Each CT study was labeled based on a clinical test, and no per-slice annotation took place. This enabled a classification into Normal vs. Abnormal patients, and for those that were considered abnormal, an extra classification step into Abnormal (other diseases) vs. COVID-19. Additionally, the pipeline features a methodology to segment and quantify lesions of COVID-19 patients on the complete CT study, enabling easier localization and progress tracking. Moreover, multiple ablation studies were performed to appropriately assess the elements composing the classification pipeline. Results: The best performing lung CT study classification models achieved 0.83 accuracy, 0.79 sensitivity, 0.87 specificity, 0.82 F1 score and 0.85 precision for the Normal vs. Abnormal task. For the Abnormal vs COVID-19 task, the model obtained 0.86 accuracy, 0.81 sensitivity, 0.91 specificity, 0.84 F1 score and 0.88 precision. The ablation studies showed that using the complete CT study in the pipeline resulted in greater classification performance, restating that relevant COVID-19 patterns cannot be ignored towards the top and bottom of the lung volume. Discussion: The lung CT classification architecture introduced has shown that it can handle weakly-labeled, variable-sized and possibly sparse axial lung studies, reducing the need for expert annotations at a per-slice level. Conclusions: This work presents a working methodology that can guide the development of decision support systems for clinical reasoning in future interventionist or prospective studies.