ArticlePituitary2021
Neural network modeling for prediction of recurrence, progression, and hormonal non-remission in patients following resection of functional pituitary adenomas.
Article in Pituitary, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Prediction of recurrence after surgery for pituitary adenoma using machine learning- based models: systematic review and meta-analysis.BMC endocrine disorders · 2025Pooled it
- Radiomic Applications in Skull Base Pathology: A Systematic Review of Potential Clinical Uses.Journal of neurological surgery. Part B, Skull base · 2025Article
- Identification of Prolactinoma in Pituitary Neuroendocrine Tumors Using Radiomics Analysis Based on Multiparameter MRI.Journal of imaging informatics in medicine · 2024Article
- Current status of artificial intelligence technologies in pituitary adenoma surgery: a scoping review.Pituitary · 2024Article
- Artificial Intelligence, Radiomics, and Computational Modeling in Skull Base Surgery.Advances in experimental medicine and biology · 2024Review
- The State of Machine Learning in Outcomes Prediction of Transsphenoidal Surgery: A Systematic Review.Journal of neurological surgery. Part B, Skull base · 2023Article
- An individualized approach to the management of Cushing disease.Nature reviews. Endocrinology · 2023Review
- Supervised machine learning to validate a novel scoring system for the prediction of disease remission of functional pituitary adenomas following transsphenoidal surgery.Scientific reports · 2023Article
- Machine learning models for differential diagnosis of Cushing's disease and ectopic ACTH secretion syndrome.Endocrine · 2023Article
- Machine Learning Models to Forecast Outcomes of Pituitary Surgery: A Systematic Review in Quality of Reporting and Current Evidence.Brain sciences · 2023Review
- Developing Predictive Models to Anticipate Shunt Complications in 33,248 Pediatric Patients with Shunted Hydrocephalus Utilizing Machine Learning.Pediatric neurosurgery · 2023Article
- A Prehospital Triage System to Detect Traumatic Intracranial Hemorrhage Using Machine Learning Algorithms.JAMA network open · 2022Article
- Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review.Cancers · 2022Review
- Deep Learning for Prediction of Progression and Recurrence in Nonfunctioning Pituitary Macroadenomas: Combination of Clinical and MRI Features.Frontiers in oncology · 2022Article
- The influence of modifiable risk factors on short-term postoperative outcomes following cervical spine surgery: A retrospective propensity score matched analysis.EClinicalMedicine · 2021Article
- Application of Artificial Intelligence in Diagnosis of Craniopharyngioma.Frontiers in neurology · 2021Review
- Effectiveness of transsphenoidal surgery on remission and recurrence rates of pituitary adenomas: A systematic review and meta-analysis.Tzu chi medical journalArticle
Corrections and comments
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Authors and funding
9 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeFunctional pituitary adenomas (FPAs) cause severe neuro-endocrinopathies including Cushing's disease (CD) and acromegaly. While many are effectively cured following FPA resection, some encounter disease recurrence/progression or hormonal non-remission requiring adjuvant treatment. Identification of risk factors for suboptimal postoperative outcomes may guide initiation of adjuvant multimodal therapies.
methodsPatients undergoing endonasal transsphenoidal resection for CD, acromegaly, and mammosomatotroph adenomas between 1992 and 2019 were identified. Good outcomes were defined as hormonal remission without imaging/biochemical evidence of disease recurrence/progression, while suboptimal outcomes were defined as hormonal non-remission or MRI evidence of recurrence/progression despite adjuvant treatment. Multivariate regression modeling and multilayered neural networks (NN) were implemented. The training sets randomly sampled 60% of all FPA patients, and validation/testing sets were 20% samples each.
results348 patients with mean age of 41.7 years were identified. Eighty-one patients (23.3%) reported suboptimal outcomes. Variables predictive of suboptimal outcomes included: Requirement for additional surgery in patients who previously had surgery and continue to have functionally active tumor (p = 0.0069; OR = 1.51, 95%CI 1.12-2.04), Preoperative visual deficit not improved after surgery (p = 0.0033; OR = 1.12, 95%CI 1.04-1.20), Transient diabetes insipidus (p = 0.013; OR = 1.27, 95%CI 1.05-1.52), Higher MIB-1/Ki-67 labeling index (p = 0.038; OR = 1.08, 95%CI 1.01-1.15), and preoperative low cortisol axis (p = 0.040; OR = 2.72, 95%CI 1.06-7.01). The NN had overall accuracy of 87.1%, sensitivity of 89.5%, specificity of 76.9%, positive predictive value of 94.4%, and negative predictive value of 62.5%. NNs for all FPAs were more robust than for CD or acromegaly/mammosomatotroph alone.
conclusionWe demonstrate capability of predicting suboptimal postoperative outcomes with high accuracy. NNs may aid in stratifying patients for risk of suboptimal outcomes, thereby guiding implementation of adjuvant treatment in high-risk patients.
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