Lipomatous neoplasms are a rare entity in the pediatric population, comprising less than 10% of soft tissue tumors in the first two decades of life. Some characteristics of pediatric adipocytic tumors are analogous to their adult counterparts, some pediatric lipomatous lesions however harbor unique features. In recent years, there have been significant advances in the understanding of the pathogenesis and hence in the classification and treatment of pediatric adipocytic tumors. This literature-based article will provide a review of the presently known clinicopathological, immunohistochemical and molecular features of pediatric lipomatous lesions.

In recent years, it has become clear that artificial intelligence (AI) models can achieve high accuracy in specific pathology‐related tasks. An example is our deep‐learning model, designed to automatically detect serous tubal intraepithelial carcinoma (STIC), the precursor lesion to high‐grade serous ovarian carcinoma, found in the fallopian tube. However, the standalone performance of a model is insufficient to determine its value in the diagnostic setting. To evaluate the impact of the use of this model on pathologists' performance, we set up a fully crossed multireader, multicase study, in which 26 participants, from 11 countries, reviewed 100 digitalized H&E‐stained slides of fallopian tubes (30 cases/70 controls) with and without AI assistance, with a washout period between the sessions. We evaluated the effect of the deep‐learning model on accuracy, slide review time and (subjectively perceived) diagnostic certainty, using mixed‐models analysis. With AI assistance, we found a significant increase in accuracy (p < 0.01) whereby the average sensitivity increased from 82% to 93%. Further, there was a significant 44 s (32%) reduction in slide review time (p < 0.01). The level of certainty that the participants felt versus their own assessment also significantly increased, by 0.24 on a 10‐point scale (p < 0.01). In conclusion, we found that, in a diverse group of pathologists and pathology residents, AI support resulted in a significant improvement in the accuracy of STIC diagnosis and was coupled with a substantial reduction in slide review time. This model has the potential to provide meaningful support to pathologists in the diagnosis of STIC, ultimately streamlining and optimizing the overall diagnostic process.

Paired-like homeobox 2B ( PHOX2B ) is a gene essential in the development of the autonomic nervous system. PHOX2B mutations are associated with neurocristopathies—Hirschsprung disease (HSCR) and congenital central hypoventilation syndrome (CCHS)—and peripheral neuroblastic tumours. PHOXB2 plays an important role in the diagnostics of these conditions. Genotyping of a PHOX2B pathogenic variant is required to establish a diagnosis of CCHS. In HSCR patients, PHOX2B immunohistochemical staining has proven to be a valuable tool in identifying this disease. Furthermore, PHOXB2 is a predisposition gene for neuroblastoma, in which PHOX2B immunohistochemical staining can be used as a highly sensitive and specific diagnostic marker. The utility of PHOX2B immunohistochemistry in pheochromocytoma and paraganglioma has also been studied but yields conflicting results. In this review, an overview is given of PHOX2B , its associated diseases and the usefulness of PHOX2B immunohistochemistry as a diagnostic tool.