Deep learning (DL) is a widely applied mathematical modeling technique. Classically, DL models utilize large volumes of training data, which are not available in many healthcare contexts. For patients with brain tumors, non-invasive diagnosis would represent a substantial clinical advance, potentially sparing patients from the risks associated with surgical intervention on the brain. Such an approach will depend upon highly accurate models built using the limited datasets that are available. Herein, we present a novel genetic algorithm (GA) that identifies optimal architecture parameters using feature embeddings from state-of-the-art image classification networks to identify the pediatric brain tumor, adamantinomatous craniopharyngioma (ACP). We optimized classification models for preoperative Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and combined CT and MRI datasets with demonstrated test accuracies of 85.3%, 83.3%, and 87.8%, respectively. Notably, our GA improved baseline model performance by up to 38%. This work advances DL and its applications within healthcare by identifying optimized networks in small-scale data contexts. The proposed system is easily implementable and scalable for non-invasive computer-aided diagnosis, even for uncommon diseases.

BackgroundEwing sarcoma typically arises in bone and is unrelated to intraparenchymal small blue cell embryonal central nervous system (CNS) tumors previously designated primitive neuroectodermal tumors (PNETs). When the CNS is impacted, it is usually secondary to local extension from either the epidural space, skull, or intracranial or spinal metastases. Primary examples within the cranial vault are rare, usually dural-based, and are largely case reports in the literature. We detail four pediatric patients with solitary, primary intracranial Ewing sarcoma, all manifesting the archetypal EWRS1 gene rearrangement that confirms diagnosis.ProcedureNeurosurgical Department records, spanning 21 years (1995–2016), were reviewed to identify patients. Demographics, clinical history, pathological/genetic features, and clinical course were retrieved from the medical record and personal files of the authors.ResultsFour patients, one male and three females, age 5 to 16 years, were identified. One presented in extremis from a large lesion, two with soft tissue masses, and the fourth as an incidental finding after being involved in a motor vehicle collision. Three had clear bony involvement: a 10-year-old girl with a large left temporal lesion had clear origin in the skull, with spiculated calcified striations throughout the mass; a 9-year-old girl presented with a bony left petrous apex mass; and a 16-year-old girl presented with a left temporal mass with extension to the dura and underlying bone erosion. Only the 5-year-old boy had a large left frontoparietal mass traversing the falx with no bony contact. All four tumors manifested the diagnostic EWSR1 mutation and were treated with an Ewing sarcoma regimen. Outcomes were variable, with one patient showing progressive metastatic disease and death 3 years after presentation, one patient with disease-free survival 10.5 years after completion of therapy, and one alive and well at the completion of therapy 1 year after diagnosis. One patient completed therapy recently with post-therapy scans showing no evidence of disease.ConclusionTesting for the EWSR1 mutation confirms the diagnosis of Ewing sarcoma and excludes other types of embryonal CNS tumors. Long-term disease-free survival is possible with adherence to the appropriate therapeutic regimen after gross surgical resection.

Pediatric Adamantinomatous Craniopharyngiomas (ACPs) are histologically benign brain tumors that often follow an aggressive clinical course. Their suprasellar location leaves them in close proximity to critical neurological and vascular structures and often results in significant neuroendocrine morbidity. Current treatment paradigms, involving surgical resection and radiotherapy, confer significant morbidity to patients and there is an obvious need to discover effective and safe alternative treatments. Recent years have witnessed significant efforts to fully detail the genomic, transcriptomic and proteomic make-up of these tumors, in an attempt to identify potential therapeutic targets. These studies have resulted in ever mounting evidence that inflammatory processes and the immune response play a critical role in the pathogenesis of both the solid and cystic portion of ACPs. Several inflammatory and immune markers have been identified in both the cyst fluid and solid tumor tissue of ACP. Due to the existence of effective agents that target them, IL-6 and immune checkpoint inhibitors seem to present the most likely immediate candidates for clinical trials of targeted immune-related therapy in ACP. If effective, such agents may result in a paradigm shift in treatment that ultimately reduces morbidity and results in better outcomes for our patients.

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Adamantinomatous craniopharyngioma (ACP) is a biologically benign but clinically aggressive lesion that has a significant impact on quality of life. The incidence of the disease has a bimodal distribution, with peaks occurring in children and older adults. Our group previously published the results of a transcriptome analysis of pediatric ACPs that identified several genes that were consistently overexpressed relative to other pediatric brain tumors and normal tissue. We now present the results of a transcriptome analysis comparing pediatric to adult ACP to identify biological differences between these groups that may provide novel therapeutic insights or support the assertion that potential therapies identified through the study of pediatric ACP may also have a role in adult ACP. Using our compiled transcriptome dataset of 27 pediatric and 9 adult ACPs, obtained through the Advancing Treatment for Pediatric Craniopharyngioma Consortium, we interrogated potential age-related transcriptional differences using several rigorous mathematical analyses. These included: canonical differential expression analysis; divisive, agglomerative, and probabilistic based hierarchical clustering; information theory based characterizations; and the deep learning approach, HD Spot. Our work indicates that there is no therapeutically relevant difference in ACP gene expression based on age. As such, potential therapeutic targets identified in pediatric ACP are also likely to have relvance for adult patients.