Application of Artificial Intelligence in Diagnosis of Craniopharyngioma

Craniopharyngioma is a congenital brain tumor with clinical characteristics of hypothalamic-pituitary dysfunction, increased intracranial pressure, and visual field disorder, among other injuries. Its clinical diagnosis mainly depends on radiological examinations (such as Computed Tomography, Magnetic Resonance Imaging). However, assessing numerous radiological images manually is a challenging task, and the experience of doctors has a great influence on the diagnosis result. The development of artificial intelligence has brought about a great transformation in the clinical diagnosis of craniopharyngioma. This study reviewed the application of artificial intelligence technology in the clinical diagnosis of craniopharyngioma from the aspects of differential classification, prediction of tissue invasion and gene mutation, prognosis prediction, and so on. Based on the reviews, the technical route of intelligent diagnosis based on the traditional machine learning model and deep learning model were further proposed. Additionally, in terms of the limitations and possibilities of the development of artificial intelligence in craniopharyngioma diagnosis, this study discussed the attentions required in future research, including few-shot learning, imbalanced data set, semi-supervised models, and multi-omics fusion.

Molecular profiling of congenital glioblastoma using a next-generation sequencing panel

Background Congenital glioblastoma (cGBM), presenting prenatally or within the first months of life, is among the rarest type of congenital brain tumor, with approximately 120 cases reported. Due to its infrequent occurrence, few studies have focused on the molecular and genetic aspects of this tumor, and the mutational events involved in the pathogenesis and progression of cGBM still remain poorly understood. This study aimed to investigate molecular alterations, with a potential prognostic marker and therapeutic target in cGBM using the next-generation sequencing (NGS) strategy. Methods We selected seven tumor samples from patients diagnosed with cGBM and treated at Pediatric Oncology Institute-GRAACC/UNIFESP. NGS was performed to identify somatic genetic variants in tumor samples using the Oncomine Childhood Cancer Research Assay panel, from ThermoFisher Scientific, designed specifically for pediatric neoplasms. Results Of all seven patients analyzed, three patients exhibited tumors with genetic variants, which include two pathogenic variants in NF1 and SUZ12 genes that have not been reported in cGBM yet, an increase in the number of copies of ALK gene, and two gene fusions, PPP1CB-ALK and TPM3-NTRK1. Also, none of the cases showed variants in H3F3A, TP53 and ATRX genes, alterations which are frequently seen in pediatric and adolescent GBM. Conclusions Our results suggest that cGBM may comprise a unique tumor entity and alterations in ALK and NTRK genes provide a potential target for therapy. Therefore, identification of genetic variants in cGBM is highly relevant in order to define prognosis and therapeutic strategies.

Molecular profiling of congenital glioblastoma using a next-generation sequencing panel

Background Congenital GBM (cGBM), presenting prenatally or within the first months of life, is among the rarest type of congenital brain tumor, with approximately 120 cases reported. Due to its infrequent occurrence, few studies have focused on the molecular and genetic aspects of this tumor, and the mutational events involved in the pathogenesis and progression of cGBM still remain poorly understood. This study aimed to investigate molecular alterations, with a potential prognostic marker and therapeutic target in cGBM using the next-generation sequencing (NGS) strategy. Methods We selected seven tumor samples from patients diagnosed with cGBM and treated at Pediatric Oncology Institute-GRAACC/UNIFESP. NGS was performed to identify somatic genetic variants in tumor samples using the Oncomine Childhood Cancer Research Assay panel, from ThermoFisher Scientific, designed specifically for pediatric neoplasms. Results Of all seven patients analyzed, three patients exhibited tumors with genetic variants, which include two pathogenic variants in NF1 and SUZ12 genes that have not been reported in cGBM yet, an increase in the number of copies of ALK gene, and two gene fusions, PPP1CB-ALK and TPM3-NTRK1. Also, none of the cases showed variants in H3F3A, TP53 and ATRX genes, alterations which are frequently seen in pediatric and adolescent GBM. Conclusions Our results suggest that cGBM may comprise a unique tumor entity and alterations in ALK and NTRK genes provide a potential target for therapy. Therefore, identification of genetic variants in cGBM is highly relevant in order to define prognosis and therapeutic strategies.

Molecular profiling of congenital glioblastoma using a next-generation sequencing panel

Background Congenital GBM (cGBM), presenting prenatally or within the first months of life, is among the rarest type of congenital brain tumor, with approximately 120 cases reported. Due to its infrequent occurrence, few studies have focused on the molecular and genetic aspects of this tumor, and the mutational events involved in the pathogenesis and progression of cGBM still remains poorly understood. This study aimed to investigate molecular alterations, with a potential prognostic marker and therapeutic target in cGBM using the next-generation sequencing (NGS) strategy. Methods We selected seven tumor samples from patients diagnosed with cGBM and treated at Pediatric Oncology Institute-GRAACC/UNIFESP. NGS was performed to identify somatic genetic variants in tumor samples using the Oncomine Childhood Cancer Research Assay panel, from ThermoFisher Scientific, designed specifically for pediatric neoplasms. Results Of all seven patients analyzed, three patients exhibited tumors with genetic variants, which include two pathogenic variants in NF1 and SUZ12 genes that have not been reported in cGBM yet, an increase in the number of copies of ALK gene, and two gene fusions, PPP1CB-ALK and TPM3-NTRK1. Also, none of the cases showed variants in H3F3A, TP53 and ATRX genes, alterations which are frequently seen in pediatric and adolescent GBM. Conclusions Our results suggest that cGBM may comprise a unique tumor entity and alterations in ALK and NTRK genes provide a potential target for therapy. Therefore, identification of genetic variants in cGBM is highly relevant in order to define prognosis and therapeutic strategies.

Molecular profiling of congenital glioblastoma using a next-generation sequencing panel

Background Congenital GBM (cGBM), presenting prenatally or within the first months of life, is among the rarest type of congenital brain tumor, with approximately 120 cases reported. Due to its infrequent occurrence, few studies have focused on the molecular and genetic aspects of this tumor, and the mutational events involved in the pathogenesis and progression of cGBM still remains poorly understood. This study aimed to investigate molecular alterations, with a potential prognostic marker and therapeutic target in cGBM using the next-generation sequencing (NGS) strategy. Methods We selected seven tumor samples from patients diagnosed with cGBM and treated at Pediatric Oncology Institute-GRAACC/UNIFESP. NGS was performed to identify somatic genetic variants in tumor samples using the Oncomine Childhood Cancer Research Assay panel, from Thermo Fisher, designed specifically for pediatric neoplasms. Results Of all seven patients analyzed, three patients exhibited tumors with genetic variants, which include two pathogenic variants in NF1 and SUZ12 genes that have not been reported in cGBM yet, an increase in the number of copies of ALK gene, and two gene fusions, PPP1CB-ALK and TPM3-NTRK1. Also, none of the cases showed variants in H3F3A, TP53 and ATRX genes, alterations which are frequently seen in pediatric and adolescent GBM. Conclusions Our results suggest that cGBM comprise a unique tumor entity and alterations in ALK and NTRK genes provide a potential target for therapy. Therefore, identification of genetic variants in cGBM is highly relevant in order to define prognosis and therapeutic strategies.