Prognostic Value of Invasion, Markers of Proliferation, and Classification of Giant Pituitary Tumors, in a Georeferred Cohort in Brazil of 50 Patients, with a Long-Term Postoperative Follow-Up.
Juliano Coelho de Oliveira ZakirLuiz Augusto CasulariJosé Wilson Corrêa RosaJoão Willy Corrêa RosaPaulo Andrade de MelloAlbino Verçosa de MagalhãesLuciana Ansaneli NavesPublished in: International journal of endocrinology (2016)
Although some pituitary adenomas may have an aggressive behavior, the vast majority are benign. There are still controversies about predictive factors regarding the biological behavior of these particular tumors. This study evaluated potential markers of invasion and proliferation compared to current classification patterns and epidemiogeographical parameters. The study included 50 patients, operated on for tumors greater than 30 mm, with a mean postoperative follow-up of 15.2 ± 4.8 years. Pituitary magnetic resonance was used to evaluate regrowth, invasion, and extension to adjacent tissue. Three tissue biomarkers were analyzed: p53, Ki-67, and c-erbB2. Tumors were classified according to a combination of histological and radiological features, ranging from noninvasive and nonproliferative (grade 1A) to invasive-proliferative (grade 2B). Tumors grades 2A and 2B represented 42% and 52%, respectively. Ki-67 (p = 0.23) and c-erbB2 (p = 0.71) had no significant relation to tumor progression status. P53 (p = 0.003), parasellar invasion (p = 0.03), and classification, grade 2B (p = 0.01), were associated with worse clinical outcome. Parasellar invasion prevails as strong predictive factor of tumor recurrence. Severe suprasellar extension should be considered as invasion parameter and could impact prognosis. No environmental factors or geographical cluster were associated with tumor behavior.
Keyphrases
- cell migration
- magnetic resonance
- machine learning
- deep learning
- patients undergoing
- end stage renal disease
- signaling pathway
- chronic kidney disease
- ejection fraction
- squamous cell carcinoma
- computed tomography
- risk assessment
- tyrosine kinase
- magnetic resonance imaging
- radiation therapy
- growth hormone
- long non coding rna