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Selected Publication:
SHR Neuro Cancer Cardio Lipid Metab Microb
Shahrestani, S; Cardinal, T; Micko, A; Strickland, BA; Pangal, DJ; Kugener, G; Weiss, MH; Carmichael, J; Zada, G.
Neural network modeling for prediction of recurrence, progression, and hormonal non-remission in patients following resection of functional pituitary adenomas.
Pituitary. 2021; 24(4):523-529
Doi: 10.1007/s11102-021-01128-5
Web of Science
PubMed
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- Co-authors Med Uni Graz
- Micko Alexander
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- Abstract:
- PURPOSE: Functional 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. METHODS: Patients 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. RESULTS: 348 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. CONCLUSION: We 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.
- Find related publications in this database (using NLM MeSH Indexing)
- Acromegaly - administration & dosage
- Adenoma - surgery
- Adult - administration & dosage
- Humans - administration & dosage
- Neoplasm Recurrence, Local - administration & dosage
- Neural Networks, Computer - administration & dosage
- Pituitary ACTH Hypersecretion - administration & dosage
- Pituitary Neoplasms - surgery
- Retrospective Studies - administration & dosage
- Treatment Outcome - administration & dosage
- Find related publications in this database (Keywords)
- Functional
- Pituitary
- Adenoma
- Machine learning
- Recurrence
- Progression