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Neural network modeling for prediction of recurrence, progression, and hormonal non-remission in patients following resection of functional pituitary adenomas

Shahrestani, Shane and Cardinal, Tyler and Micko, Alexander and Strickland, Ben A. and Pangal, Dhiraj J. and Kugener, Guillaume and Weiss, Martin H. and Carmichael, John and Zada, Gabriel (2021) Neural network modeling for prediction of recurrence, progression, and hormonal non-remission in patients following resection of functional pituitary adenomas. Pituitary . ISSN 1386-341X. (In Press) https://resolver.caltech.edu/CaltechAUTHORS:20210210-101923765

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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.


Item Type:Article
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https://doi.org/10.1007/s11102-021-01128-5DOIArticle
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ORCID:
AuthorORCID
Shahrestani, Shane0000-0001-7561-4590
Micko, Alexander0000-0001-9105-3519
Zada, Gabriel0000-0001-5821-902X
Additional Information:© The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021. Accepted 16 January 2021; Published 02 February 2021.
Subject Keywords:Functional; Pituitary; Adenoma; Machine learning; Recurrence; Progression
Record Number:CaltechAUTHORS:20210210-101923765
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210210-101923765
Official Citation:Shahrestani, S., Cardinal, T., Micko, A. et al. Neural network modeling for prediction of recurrence, progression, and hormonal non-remission in patients following resection of functional pituitary adenomas. Pituitary (2021). https://doi.org/10.1007/s11102-021-01128-5
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:107984
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:10 Feb 2021 18:32
Last Modified:10 Feb 2021 18:32

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