Literature DB >> 33528731

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

Shane Shahrestani1,2, Tyler Cardinal3, Alexander Micko3,4, Ben A Strickland3, Dhiraj J Pangal3, Guillaume Kugener3, Martin H Weiss3, John Carmichael3, Gabriel Zada3.   

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.
© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.

Entities:  

Keywords:  Adenoma; Functional; Machine learning; Pituitary; Progression; Recurrence

Mesh:

Year:  2021        PMID: 33528731     DOI: 10.1007/s11102-021-01128-5

Source DB:  PubMed          Journal:  Pituitary        ISSN: 1386-341X            Impact factor:   4.107


  4 in total

Review 1.  Management of hormone-secreting pituitary adenomas.

Authors:  Gautam U Mehta; Russell R Lonser
Journal:  Neuro Oncol       Date:  2017-06-01       Impact factor: 12.300

2.  Long-term mortality after transsphenoidal surgery and adjunctive therapy for acromegaly.

Authors:  B Swearingen; F G Barker; L Katznelson; B M Biller; S Grinspoon; A Klibanski; N Moayeri; P M Black; N T Zervas
Journal:  J Clin Endocrinol Metab       Date:  1998-10       Impact factor: 5.958

3.  Transient Central Diabetes Insipidus after Discontinuation of Vasopressin.

Authors:  Nathaniel Carman; Carl Kay; Abigail Petersen; Maria Kravchenko; Joshua Tate
Journal:  Case Rep Endocrinol       Date:  2019-12-11
  4 in total
  5 in total

1.  A Prehospital Triage System to Detect Traumatic Intracranial Hemorrhage Using Machine Learning Algorithms.

Authors:  Daisu Abe; Motoki Inaji; Takeshi Hase; Shota Takahashi; Ryosuke Sakai; Fuga Ayabe; Yoji Tanaka; Yasuhiro Otomo; Taketoshi Maehara
Journal:  JAMA Netw Open       Date:  2022-06-01

Review 2.  Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review.

Authors:  Paul Windisch; Carole Koechli; Susanne Rogers; Christina Schröder; Robert Förster; Daniel R Zwahlen; Stephan Bodis
Journal:  Cancers (Basel)       Date:  2022-05-27       Impact factor: 6.575

3.  Deep Learning for Prediction of Progression and Recurrence in Nonfunctioning Pituitary Macroadenomas: Combination of Clinical and MRI Features.

Authors:  Yan-Jen Chen; Hsun-Ping Hsieh; Kuo-Chuan Hung; Yun-Ju Shih; Sher-Wei Lim; Yu-Ting Kuo; Jeon-Hor Chen; Ching-Chung Ko
Journal:  Front Oncol       Date:  2022-04-20       Impact factor: 5.738

4.  The influence of modifiable risk factors on short-term postoperative outcomes following cervical spine surgery: A retrospective propensity score matched analysis.

Authors:  Shane Shahrestani; Joshua Bakhsheshian; Xiao T Chen; Andy Ton; Alexander M Ballatori; Ben A Strickland; Djani M Robertson; Zorica Buser; Raymond Hah; Patrick C Hsieh; John C Liu; Jeffrey C Wang
Journal:  EClinicalMedicine       Date:  2021-05-15

Review 5.  Application of Artificial Intelligence in Diagnosis of Craniopharyngioma.

Authors:  Caijie Qin; Wenxing Hu; Xinsheng Wang; Xibo Ma
Journal:  Front Neurol       Date:  2022-01-06       Impact factor: 4.003

  5 in total

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