Background: Craniopharyngioma (CP) is a challenging intracranial tumor due to its special hypothalamus-pituitary location. Each patient with CP should be evaluated and treated separately. Exploring novel methods of automatized analysis of data for gaining knowledge on any medical field is an encouraging task, particularly in such an extremely challenging tumor as CP. We aim to summary the situations, investigate the research trends and evaluate research hotspots using bibliometric analysis for the CP research. Methods: We extracted all the CP-related literatures from 2011 to 2020 from the Web of Science database. An Online analysis platform of literature metrology (Bibliometric), BICOMB, gCLUTO and CiteSpace softwares were used to do bibliometric analysis. As a supplement, we also analyzed the top 100 cited case reports with particular and certainly infrequent information to improve the analysis. Results: According to our retrieval strategy, we found a total of 1262 CP-related literatures. The United States has maintained a leading position in global CP research, followed by China and Germany. Among institutions, Capital Med Univ, St Jude Childrens Res Hosp and Southern Med Univ rank in the top 3 in terms of the number of articles published. “WORLD NEUROSURGERY” is the most popular journal for CP-related research. Moreover, MULLER HL, MERCHANT TE, QI ST and others have made great achievements in the study of CP. Finally, we did biclustering analysis on keywords and identified 4 CP research hotspot clusters. Conclusions: Our research provides a comprehensive analysis of the scientific progress of CP in the past 10 years, and insight into the development of CP research field, highlight research trends over time, and help identify valuable future directions.

Objective: Neuronavigation and classification of craniopharyngiomas can guide surgical approaches and prognostic information. The QST classification has been developed according to the origin of craniopharyngiomas; however, accurate preoperative automatic segmentation and the QST classification remain challenging. This study aimed to establish a method to automatically segment multiple structures in MRIs, detect craniopharyngiomas, and design a deep learning model and a diagnostic scale for automatic QST preoperative classification.Methods: We trained a deep learning network based on sagittal MRI to automatically segment six tissues, including tumors, pituitary gland, sphenoid sinus, brain, superior saddle cistern, and lateral ventricle. A deep learning model with multiple inputs was designed to perform preoperative QST classification. A scale was constructed by screening the images.Results: The results were calculated based on the fivefold cross-validation method. A total of 133 patients with craniopharyngioma were included, of whom 29 (21.8%) were diagnosed with type Q, 22 (16.5%) with type S and 82 (61.7%) with type T. The automatic segmentation model achieved a tumor segmentation Dice coefficient of 0.951 and a mean tissue segmentation Dice coefficient of 0.8668 for all classes. The automatic classification model and clinical scale achieved accuracies of 0.9098 and 0.8647, respectively, in predicting the QST classification.Conclusions: The automatic segmentation model can perform accurate multi-structure segmentation based on MRI, which is conducive to clearing tumor location and initiating intraoperative neuronavigation. The proposed automatic classification model and clinical scale based on automatic segmentation results achieve high accuracy in the QST classification, which is conducive to developing surgical plans and predicting patient prognosis.

Objective: Understanding the differences of suprasellar papillary and adamantinomatous craniopharyngiomas (PCPs/ACPs) is pivotal for target therapy, surgical strategy or postoperative management. Here, the clinical features, surgical nuances and postoperative hypothalamic outcomes of PCPs were systematically recapitulated. Methods: 24 PCPs and 52 ACPs underwent initial surgery were retrospectively reviewed. Clinical data, quantified third ventricle (3rd V) occupation and optic chiasm distortion were compared, as well as intra-operative findings, operating notes and prognosis. Moreover, analysis of tumor/3rd V relationship and hypothalamic outcomes were also performed. Results: Tumors were more likely to occupies the 3rd V cavity in PCPs. Chiasm distortion of "compressed forward" was the most common pattern (45.8 %) in PCPs, whereas "stretched forward" pattern accounted the highest (42.5 %) in ACPs. Besides, round-shaped with less calcification, duct-like recess, solid consistency, rare subdiaphragmatic invasion, visible lower stalk and improved postoperative visual outcome were more frequently observed in PCPs. The basal membranes of the tumor epithelium and the reactive gliosis were separated by a layer of collagen fibers in most PCPs, which differs from ACPs in the morphological examination of tumor/3rd V floor interface. In daytime sleepiness and memory difficulty, the PCPs showed significantly better outcomes than the ACPs groups, and PCPs suffered less postoperative weight gain (p < 0.05) than ACPs among adult-onset cases. Conclusion: PCPs are different from ACPs regards the clinical features, operative techniques and outcomes. If necessary, PCPs are suggested more amenable to total removal since its less invasiveness to the 3rd V floor and better hypothalamic outcomes.