ArticleActa neurochirurgica2024
Development and validation of a prediction model for consistency of pituitary adenoma: the PiTCon score.
Article in Acta neurochirurgica, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 4 citations in OpenAlex.
- Article
- Machine Learning Prediction of Pituitary Macroadenoma Consistency: Utilizing Demographic Data and Brain MRI Parameters.Journal of imaging informatics in medicine · 2025Article
- Toward standardized brain tumor tissue processing protocols in neuro-oncology: a perspective for gliomas and beyond.Frontiers in oncology · 2024Article
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Authors and funding
5 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
purposePituitary adenomas (PAs) usually have a soft consistency, facilitating gross total resection. However, 5-13% of PAs with fibrous consistency are challenging to remove entirely and are accompanied by greater morbimortality. This study aims to identify the clinical and radiological characteristics that correlate with PA fibrous consistency preoperatively. A simple scoring system has been proposed to predict incidence of fibrous PAs. MATERIALS AND
methodsConsecutive interventions (226) were analyzed, all performed through an endoscopic endonasal transsphenoidal approach. Univariable and multivariable logistic regression analysis was performed. Hosmer-Lemeshow test and receiver operating characteristic (ROC) curves were assessed to evaluate the model. A point scoring system (PiTCon) was derived based on the multivariable regression model. Our study aimed to identify the clinical and radiological characteristics that correlate with fibrous tumor consistency preoperatively.
resultsThe best diagnostic accuracy for predicting PA consistency consisted of five predictive factors: age, compressive symptoms, panhypopituitarism, craniocaudal extension of the PA in mm, and prior surgery. The multivariable model achieved good discrimination with an area under the curve (AUC) of the ROC curve being 0.82 and the 95% CI 0.76 to 0.88. Internal validation yielded an optimism-adjusted C-statistic of 0.80 (95% CI 0.74 to 0.86). A point scoring system (PiTCon score) was designed using the best predictive model.
conclusionsPA consistency can be estimated preoperatively regarding clinical and radiological characteristics. We propose a point-based scoring system (PiTCon score) that can better guide neurosurgeons in clinical decision-making and surgical risk assessment and help establish and describe patient prognosis.
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