About this Research Topic
Currently, prediction of clinical behavior following surgical resection is routinely performed through immunohistochemical investigation of a variety of markers such as Ki-67, p21, p27, p53 and EGFR. Availability of large genomics, transcriptomics, and proteomics data sets has recently provided the opportunity to apply machine learning (ML) methods for diagnosis, as well prediction of early outcomes following specific treatment. ML which is a subfield of artificial intelligence (AI) employs algorithms to allow computers to learn directly from the data and subsequently perform predictions.
Within this Research Topic we encourage the submission of manuscripts related to pituitary adenoma, novel Biomarkers that predict invasiveness, novel targeted therapies, Pathophysiology basis of resistance to conventional treatments, and personalized treatment strategies.
This Research Topic includes, but is not limited to, the following examples:
- Genomic, transcriptomic and proteomics characterization of pituitary adenoma
- Machine learning methods to predict early postsurgical and long-term outcomes
- Novel molecular biomarkers of tumor invasiveness
- Molecular mechanisms of resistance to conventional therapies
- Targeted therapy
Keywords: Molecular Investigation, Targeted Therapy, Pituitary Adenoma, Machine Learning, surgical resection, early outcomes, Metastatic behavior
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.