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February 2021 Medical Corner

 

Neural Network Modeling

weissThis month the PNA Medical Corner spotlights an article co-authored by Dr. Martin Weiss, chairman of neurosurgery at USC and a member of the PNA. The study used neural network modeling to look at the factors that lead to negative outcomes, which include the presence of a tumor that has recurred after surgery and visual deficits that did not improve after surgery. The authors analyzed the outcomes for 341 patients with acromegaly, Cushings or a mammosomatotroph adenoma, 81 of whom had sub-optimal outcomes. Their modeling was able to predict with 87.1% accuracy which patients are more at risk for a poor outcome.

Abstract:

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

Shane Shahrestani 1 2, Tyler Cardinal 3, Alexander Micko 3 4, Ben A Strickland 3, Dhiraj J Pangal 3, Guillaume Kugener 3, Martin H Weiss 3, John Carmichael 3, Gabriel Zada 3 Affiliations expand
• PMID: 33528731 DOI: 10.1007/s11102-021-01128-5

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.

Keywords:

Adenoma; Functional; Machine learning; Pituitary; Progression; Recurrence.

 

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