Four distinct ipsilateral vestibular schwannomas: A case of mosaic NF2-related schwannomatosis.

OBJECTIVES: Distinguishing between sporadic and germline/mosaic NF2-related schwannomatosis is important to ensure that patients have appropriate long-term care. With this report, we describe a unique case of a patient with 4 ipsilateral schwannomas and identify a combination of sequencing modalities that can accurately diagnose mosaic NF2-related schwannomatosis. METHODS: We present a 32-year-old woman with a familial history of vestibular schwannoma in her father and right-sided schwannomas involving the apical and basal turns of cochlea, lateral semicircular canal, and internal auditory canal (IAC). Genetic analysis of blood and frozen tissue from 2 tumors (intralabyrinthine and IAC tumors) was performed using next-generation sequencing (NGS), multiplex ligation-dependent probe amplification (MLPA), and optical genome mapping (OGM). RESULTS: Germline testing for NF2, LZTR1, and SMARCB1 was negative. Tumor genetic testing revealed a shared NF2 pathogenic variant between the 2 tumors ("first hit") but distinct "second hit" NF2 variants, including mosaic loss of chromosome 22 in the IAC tumor seen only with OGM, consistent with mosaic NF2-related schwannomatosis. CONCLUSIONS: Multimodality sequencing, including NGS, MLPA, and OGM, was required to ensure appropriate diagnosis of mosaic NF2-related schwannomatosis in this patient. A similar approach can be used for other patients with multiple ipsilateral tumors and suspected tumor predisposition.

Zebrafish-A Suitable Model for Rapid Translation of Effective Therapies for Pediatric Cancers.

Pediatric cancers are the leading cause of disease-related deaths in children and adolescents. Most of these tumors are difficult to treat and have poor overall survival. Concerns have also been raised about drug toxicity and long-term detrimental side effects of therapies. In this review, we discuss the advantages and unique attributes of zebrafish as pediatric cancer models and their importance in targeted drug discovery and toxicity assays. We have also placed a special focus on zebrafish models of pediatric brain cancers-the most common and difficult solid tumor to treat.

Kaposiform Lymphangiomatosis as a Cause of Vaginal Bleeding & Discharge: A Case Report.

BACKGROUND: Prepubertal vaginal bleeding is a common presentation for pediatric adolescent gynecologists with a broad differential diagnosis that historically may not have included complex lymphatic anomalies. However, given recent consensus criteria and imaging capabilities, this may be a condition that pediatric adolescent gynecologists see more frequently in the future. CASE: We present a case of a 5-year-old pre-pubertal girl whose only presenting symptoms of a rare complex lymphatic anomaly was copious vaginal bleeding. After three vaginoscopies, two hysteroscopies, two pelvic MRIs, and a percutaneous ultrasound guided core needle biopsy, this patient was eventually diagnosed with Kaposiform lymphangiomatosis at age 9 years-old, and she is now being treated medically with sirolimus, a mammalian target of rapamycin (mTOR) inhibitor, with improvement in her symptoms. SUMMARY AND CONCLUSION: Complex lymphatic anomalies should be considered after initial and secondary workups for pre-pubertal vaginal bleeding or copious vaginal discharge are negative. Furthermore, this case illustrates the value of pelvic MRI in the setting of unknown cause of vaginal bleeding when typical workup is negative.

Early prognostication of overall survival for pediatric diffuse midline gliomas using MRI radiomics and machine learning: A two-center study.

Background: Diffuse midline gliomas (DMG) are aggressive pediatric brain tumors that are diagnosed and monitored through MRI. We developed an automatic pipeline to segment subregions of DMG and select radiomic features that predict patient overall survival (OS). Methods: We acquired diagnostic and post-radiation therapy (RT) multisequence MRI (T1, T1ce, T2, and T2 FLAIR) and manual segmentations from 2 centers: 53 from 1 center formed the internal cohort and 16 from the other center formed the external cohort. We pretrained a deep learning model on a public adult brain tumor data set (BraTS 2021), and finetuned it to automatically segment tumor core (TC) and whole tumor (WT) volumes. PyRadiomics and sequential feature selection were used for feature extraction and selection based on the segmented volumes. Two machine learning models were trained on our internal cohort to predict patient 12-month survival from diagnosis. One model used only data obtained at diagnosis prior to any therapy (baseline study) and the other used data at both diagnosis and post-RT (post-RT study). Results: Overall survival prediction accuracy was 77% and 81% for the baseline study, and 85% and 78% for the post-RT study, for internal and external cohorts, respectively. Homogeneous WT intensity in baseline T2 FLAIR and larger post-RT TC/WT volume ratio indicate shorter OS. Conclusions: Machine learning analysis of MRI radiomics has potential to accurately and noninvasively predict which pediatric patients with DMG will survive less than 12 months from the time of diagnosis to provide patient stratification and guide therapy.