Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
Pituitary
Published in: Pituitary 2/2024

06-01-2024 | Artificial Intelligence

Current status of artificial intelligence technologies in pituitary adenoma surgery: a scoping review

Authors: Seyed Farzad Maroufi, Yücel Doğruel, Ahmad Pour-Rashidi, Gurkirat S. Kohli, Colson Tomberlin Parker, Tatsuya Uchida, Mohamed Z. Asfour, Clara Martin, Mariagrazia Nizzola, Alessandro De Bonis, Mamdouh Tawfik-Helika, Amin Tavallai, Aaron A. Cohen-Gadol, Paolo Palmisciano

Published in: Pituitary | Issue 2/2024

Login to get access

Abstract

Purpose

Pituitary adenoma surgery is a complex procedure due to critical adjacent neurovascular structures, variations in size and extensions of the lesions, and potential hormonal imbalances. The integration of artificial intelligence (AI) and machine learning (ML) has demonstrated considerable potential in assisting neurosurgeons in decision-making, optimizing surgical outcomes, and providing real-time feedback. This scoping review comprehensively summarizes the current status of AI/ML technologies in pituitary adenoma surgery, highlighting their strengths and limitations.

Methods

PubMed, Embase, Web of Science, and Scopus were searched following the PRISMA-ScR guidelines. Studies discussing the use of AI/ML in pituitary adenoma surgery were included. Eligible studies were grouped to analyze the different outcomes of interest of current AI/ML technologies.

Results

Among the 2438 identified articles, 44 studies met the inclusion criteria, with a total of seventeen different algorithms utilized across all studies. Studies were divided into two groups based on their input type: clinicopathological and imaging input. The four main outcome variables evaluated in the studies included: outcome (remission, recurrence or progression, gross-total resection, vision improvement, and hormonal recovery), complications (CSF leak, readmission, hyponatremia, and hypopituitarism), cost, and adenoma-related factors (aggressiveness, consistency, and Ki-67 labeling) prediction. Three studies focusing on workflow analysis and real-time navigation were discussed separately.

Conclusion

AI/ML modeling holds promise for improving pituitary adenoma surgery by enhancing preoperative planning and optimizing surgical strategies. However, addressing challenges such as algorithm selection, performance evaluation, data heterogeneity, and ethics is essential to establish robust and reliable ML models that can revolutionize neurosurgical practice and benefit patients.
Appendix
Available only for authorised users
Literature
1.
Fang CH, Agarwal V, Liu JK, Eloy JA (2022) Overview of pituitary surgery. Otolaryngol Clin North Am 55(2):205–221PubMedCrossRef
2.
Wilkinson J, Arnold KF, Murray EJ, van Smeden M, Carr K, Sippy R et al (2020) Time to reality check the promises of machine learning-powered precision medicine. Lancet Digit Health 2(12):e677–e80PubMedPubMedCentralCrossRef
3.
Senders JT, Staples PC, Karhade AV, Zaki MM, Gormley WB, Broekman MLD et al (2018) Machine learning and neurosurgical outcome prediction: a systematic review. World Neurosurg 109:476–486PubMedCrossRef
4.
Senders JT, Arnaout O, Karhade AV, Dasenbrock HH, Gormley WB, Broekman ML et al (2018) Natural and artificial intelligence in neurosurgery: a systematic review. Neurosurgery 83(2):181–192PubMedCrossRef
5.
Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D et al (2018) PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med 169(7):467–473PubMedCrossRef
6.
Whiting PF, Rutjes AWS, Westwood ME, Mallett S, Deeks JJ, Reitsma JB et al (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155(8):529–536PubMedCrossRef
7.
Fan Y, Liu Z, Hou B, Li L, Liu X, Liu Z et al (2019) Development and validation of an MRI-based radiomic signature for the preoperative prediction of treatment response in patients with invasive functional pituitary adenoma. Eur J Radiol 121:108647PubMedCrossRef
8.
Dai C, Fan Y, Li Y, Bao X, Li Y, Su M et al (2020) Development and interpretation of multiple machine learning models for predicting postoperative delayed remission of acromegaly patients during long-term follow-up. Front Endocrinol 11:643CrossRef
9.
Fan Y, Li Y, Bao X, Zhu H, Lu L, Yao Y et al (2021) Development of machine learning models for predicting postoperative delayed remission in patients with Cushing’s disease. J Clin Endocrinol Metab 106(1):e217–e31PubMedCrossRef
10.
Zoli M, Staartjes VE, Guaraldi F, Friso F, Rustici A, Asioli S et al (2020) Machine learning-based prediction of outcomes of the endoscopic endonasal approach in Cushing disease: is the future coming? NeuroSurg Focus 48(6):E5PubMedCrossRef
11.
Sulu C, Bektaş AB, Şahin S, Durcan E, Kara Z, Demir AN et al (2022) Machine learning as a clinical decision support tool for patients with acromegaly. Pituitary 25(3):486–495PubMedCrossRef
12.
Fan Y, Li Y, Li Y, Feng S, Bao X, Feng M et al (2020) Development and assessment of machine learning algorithms for predicting remission after transsphenoidal surgery among patients with acromegaly. Endocrine 67(2):412–422PubMedCrossRef
13.
Zhang W, Sun M, Fan Y, Wang H, Feng M, Zhou S et al (2021) Machine learning in preoperative prediction of postoperative immediate remission of histology-positive Cushing’s disease. Front Endocrinol 12:635795CrossRef
14.
Shahrestani S, Cardinal T, Micko A, Strickland BA, Pangal DJ, Kugener G et al (2021) Neural network modeling for prediction of recurrence, progression, and hormonal non-remission in patients following resection of functional pituitary adenomas. Pituitary 24(4):523–529PubMedCrossRef
15.
Qiao N, Shen M, He W, He M, Zhang Z, Ye H et al (2021) Machine learning in predicting early remission in patients after surgical treatment of acromegaly: a multicenter study. Pituitary 24(1):53–61PubMedCrossRef
16.
Zanier O, Zoli M, Staartjes VE, Guaraldi F, Asioli S, Rustici A et al (2022) Machine learning-based clinical outcome prediction in surgery for acromegaly. Endocrine 75(2):508–515PubMedCrossRef
17.
Huber M, Luedi MM, Schubert GA, Musahl C, Tortora A, Frey J et al (2022) Machine learning for outcome prediction in first-line surgery of prolactinomas. Front Endocrinol 13:810219CrossRef
18.
Zhang Y, Luo Y, Kong X, Wan T, Long Y, Ma J (2021) A preoperative MRI-based radiomics-clinicopathological classifier to predict the recurrence of pituitary macroadenoma within 5 years. Front Neurol 12:780628PubMedCrossRef
19.
Machado LF, Elias PCL, Moreira AC, Dos Santos AC, Murta Junior LO (2020) MRI radiomics for the prediction of recurrence in patients with clinically non-functioning pituitary macroadenomas. Comput Biol Med 124:103966PubMedCrossRef
20.
Chen YJ, Hsieh HP, Hung KC, Shih YJ, Lim SW, Kuo YT et al (2022) Deep learning for prediction of progression and recurrence in nonfunctioning pituitary macroadenomas: combination of clinical and MRI features. Front Oncol 12:813806PubMedPubMedCentralCrossRef
21.
Liu Y, Liu X, Hong X, Liu P, Bao X, Yao Y et al (2019) Prediction of recurrence after transsphenoidal surgery for Cushing’s disease: the use of machine learning algorithms. Neuroendocrinology 108(3):201–210PubMedCrossRef
22.
Zhang Y, Ko CC, Chen JH, Chang KT, Chen TY, Lim SW et al (2020) Radiomics approach for prediction of recurrence in non-functioning pituitary macroadenomas. Front Oncol 10:590083PubMedPubMedCentralCrossRef
23.
Staartjes VE, Serra C, Muscas G, Maldaner N, Akeret K, van Niftrik CHB et al (2018) Utility of deep neural networks in predicting gross-total resection after transsphenoidal surgery for pituitary adenoma: a pilot study. NeuroSurg Focus 45(5):E12PubMedCrossRef
24.
Fang Y, Wang H, Feng M, Zhang W, Cao L, Ding C et al (2021) Machine-learning prediction of postoperative pituitary hormonal outcomes in nonfunctioning pituitary adenomas: a multicenter study. Front Endocrinol 12:748725CrossRef
25.
Zhang Y, Chen C, Huang W, Cheng Y, Teng Y, Zhang L et al (2021) Machine learning-based radiomics of the optic chiasm predict visual outcome following pituitary adenoma surgery. J Personalized Med 11(10):991CrossRef
26.
Qiao N, Ma Y, Chen X, Ye Z, Ye H, Zhang Z et al (2022) Machine learning prediction of visual outcome after surgical decompression of sellar region tumors. J Personalized Med 12(2):152CrossRef
27.
Hollon TC, Parikh A, Pandian B, Tarpeh J, Orringer DA, Barkan AL et al (2018) A machine learning approach to predict early outcomes after pituitary adenoma surgery. NeuroSurg Focus 45(5):E8PubMedCrossRef
28.
Crabb BT, Hamrick F, Campbell JM, Vignolles-Jeong J, Magill ST, Prevedello DM et al (2022) Machine learning-based analysis and prediction of unplanned 30-day readmissions after pituitary adenoma resection: a multi-institutional retrospective study with external validation. Neurosurgery 91(2):263–71PubMedCrossRef
29.
Voglis S, van Niftrik CHB, Staartjes VE, Brandi G, Tschopp O, Regli L et al (2020) Feasibility of machine learning based predictive modelling of postoperative hyponatremia after pituitary surgery. Pituitary 23(5):543–551PubMedCrossRef
30.
Muhlestein WE, Akagi DS, McManus AR, Chambless LB (2018) Machine learning ensemble models predict total charges and drivers of cost for transsphenoidal surgery for pituitary tumor. J Neurosurg 131(2):507–516PubMedCrossRef
31.
Mattogno PP, Caccavella VM, Giordano M, D’Alessandris QG, Chiloiro S, Tariciotti L et al (2022) Interpretable machine learning-based prediction of Intraoperative Cerebrospinal Fluid Leakage in Endoscopic Transsphenoidal Pituitary Surgery: a pilot study. J Neurol Surg Part B Skull base 83(5):485–495CrossRef
32.
Villalonga JF, Solari D, Cuocolo R, De Lucia V, Ugga L, Gragnaniello C et al (2022) Clinical application of the sellar barrier’s concept for predicting intraoperative CSF leak in endoscopic endonasal Surgery for pituitary adenomas with a machine learning analysis. Front Surg 9:934721PubMedPubMedCentralCrossRef
33.
34.
Staartjes VE, Zattra CM, Akeret K, Maldaner N, Muscas G, van Bas CH et al (2019) Neural network-based identification of patients at high risk for intraoperative cerebrospinal fluid leaks in endoscopic pituitary surgery. J Neurosurg 133:1–7
35.
Niu J, Zhang S, Ma S, Diao J, Zhou W, Tian J et al (2019) Preoperative prediction of cavernous sinus invasion by pituitary adenomas using a radiomics method based on magnetic resonance images. Eur Radiol 29(3):1625–1634PubMedCrossRef
36.
Fang Y, Wang H, Feng M, Chen H, Zhang W, Wei L et al (2022) Application of convolutional neural network in the diagnosis of cavernous sinus invasion in pituitary adenoma. Front Oncol 12:835047PubMedPubMedCentralCrossRef
37.
Feng T, Fang Y, Pei Z, Li Z, Chen H, Hou P et al (2022) A convolutional neural network model for detecting sellar floor destruction of pituitary adenoma on magnetic resonance imaging scans. Front NeuroSci 16:900519PubMedPubMedCentralCrossRef
38.
Zhang C, Heng X, Neng W, Chen H, Sun A, Li J et al (2022) Prediction of high infiltration levels in pituitary adenoma using MRI-based radiomics and machine learning. Chin Neurosurgical J 8(1):21CrossRef
39.
Zeynalova A, Kocak B, Durmaz ES, Comunoglu N, Ozcan K, Ozcan G et al (2019) Preoperative evaluation of tumour consistency in pituitary macroadenomas: a machine learning-based histogram analysis on conventional T2-weighted MRI. Neuroradiology 61(7):767–774PubMedCrossRef
40.
Cuocolo R, Ugga L, Solari D, Corvino S, D’Amico A, Russo D et al (2020) Prediction of pituitary adenoma surgical consistency: radiomic data mining and machine learning on T2-weighted MRI. Neuroradiology 62(12):1649–1656PubMedPubMedCentralCrossRef
41.
Zhu H, Fang Q, Huang Y, Xu K (2020) Semi-supervised method for image texture classification of pituitary tumors via CycleGAN and optimized feature extraction. BMC Med Inf Decis Mak 20(1):215CrossRef
42.
Wan T, Wu C, Meng M, Liu T, Li C, Ma J et al (2022) Radiomic features on multiparametric MRI for preoperative evaluation of pituitary macroadenomas consistency: preliminary findings. J Magn Reson Imaging: JMRI 55(5):1491–1503PubMedCrossRef
43.
Ugga L, Cuocolo R, Solari D, Guadagno E, D’Amico A, Somma T et al (2019) Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning. Neuroradiology 61(12):1365–1373PubMedCrossRef
44.
Shu XJ, Chang H, Wang Q, Chen WG, Zhao K, Li BY et al (2022) Deep learning model-based approach for preoperative prediction of Ki67 labeling index status in a noninvasive way using magnetic resonance images: a single-center study. Clin Neurol Neurosurg 219:107301PubMedCrossRef
45.
Zhang S, Song G, Zang Y, Jia J, Wang C, Li C et al (2018) Non-invasive radiomics approach potentially predicts non-functioning pituitary adenomas subtypes before surgery. Eur Radiol 28(9):3692–3701PubMedCrossRef
46.
Peng A, Dai H, Duan H, Chen Y, Huang J, Zhou L et al (2020) A machine learning model to precisely immunohistochemically classify pituitary adenoma subtypes with radiomics based on preoperative magnetic resonance imaging. Eur J Radiol 125:108892PubMedCrossRef
47.
Li H, Zhao Q, Zhang Y, Sai K, Xu L, Mou Y et al (2021) Image-driven classification of functioning and nonfunctioning pituitary adenoma by deep convolutional neural networks. Comput Struct Biotechnol J 19:3077–3086PubMedPubMedCentralCrossRef
48.
Neuner C, Coras R, Blümcke I, Popp A, Schlaffer SM, Wirries A et al (2022) A whole-slide image managing library based on fastai for deep learning in the context of histopathology. Two Use-Cases Explained 12(1):13
49.
Baysal B, Eser MB, Dogan MB, Kursun MA (2022) Multivariable diagnostic prediction model to detect hormone secretion profile from T2W MRI radiomics with artificial neural networks in pituitary adenomas. Medeniyet Med J 37(1):36–43
50.
Rui W, Qiao N, Wu Y, Zhang Y, Aili A, Zhang Z et al (2022) Radiomics analysis allows for precise prediction of silent corticotroph adenoma among non-functioning pituitary adenomas. Eur Radiol 32(3):1570–1578PubMedCrossRef
51.
Foster KR, Koprowski R, Skufca JD (2014) Machine learning, medical diagnosis, and biomedical engineering research—commentary. Biomed Eng Online 13:94PubMedPubMedCentralCrossRef
52.
Subramanian J, Simon R (2013) Overfitting in prediction models—is it a problem only in high. Contemp Clin Trials 36(2):636–641PubMedCrossRef
53.
Song H-S, Yoon H-S, Lee S, Hong C-K, Yi B-JJAS (2019) Surgical navigation system for transsphenoidal pituitary surgery applying U-net-based automatic segmentation and bendable devices. Appl Sci 9(24):5540CrossRef
54.
Staartjes VE, Volokitin A, Regli L, Konukoglu E, Serra CJON (2021) Machine vision for real-time intraoperative anatomic guidance: a proof-of-concept study in endoscopic pituitary surgery. Oper Neurosurg 21(4):242–247CrossRef
55.
Khan DZ, Luengo I, Barbarisi S, Addis C, Culshaw L, Dorward NL et al (2021) Automated operative workflow analysis of endoscopic pituitary surgery using machine learning: development and preclinical evaluation (IDEAL stage 0). J Neurosurg 137:1–8
Metadata
Title
Current status of artificial intelligence technologies in pituitary adenoma surgery: a scoping review
Authors
Seyed Farzad Maroufi
Yücel Doğruel
Ahmad Pour-Rashidi
Gurkirat S. Kohli
Colson Tomberlin Parker
Tatsuya Uchida
Mohamed Z. Asfour
Clara Martin
Mariagrazia Nizzola
Alessandro De Bonis
Mamdouh Tawfik-Helika
Amin Tavallai
Aaron A. Cohen-Gadol
Paolo Palmisciano
Publication date
06-01-2024
Publisher
Springer US
Published in
Pituitary / Issue 2/2024
Print ISSN: 1386-341X
Electronic ISSN: 1573-7403
DOI
https://doi.org/10.1007/s11102-023-01369-6

Other articles of this Issue 2/2024

Pituitary 2/2024 Go to the issue

OriginalPaper

Acromegaly increases depressive symptoms and reduces quality of life of cohabitants

OriginalPaper

Impulse control disorders in patients with dopamine agonist-treated pituitary adenomas: a cross-sectional multicenter study

OriginalPaper

Glucose intolerance in acromegaly is driven by low insulin secretion; results from an intravenous glucose tolerance test

OriginalPaper

Pituitary hypophysitis in granulomatosis with polyangiitis (GPA): a case series

OriginalPaper

Basal cortisol in relation to metyrapone confirmation in predicting adrenal insufficiency after pituitary surgery

Correction

Correction: Impulse control disorders in patients with dopamine agonist-treated pituitary adenomas: a cross-sectional multicenter study
Obesity Clinical Trial Summary

At a glance: The STEP trials

Read more

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.

Developed by: Springer Medicine

Highlights from the ACC 2024 Congress

Year in Review: Pediatric cardiology

Pediatric Cardiology Video

Watch Dr. Anne Marie Valente present the last year's highlights in pediatric and congenital heart disease in the official ACC.24 Year in Review session.

Year in Review: Pulmonary vascular disease

Cardiopulmonary Comorbidity Video

The last year's highlights in pulmonary vascular disease are presented by Dr. Jane Leopold in this official video from ACC.24.

Year in Review: Valvular heart disease

Valvular Heart Disease Video

Watch Prof. William Zoghbi present the last year's highlights in valvular heart disease from the official ACC.24 Year in Review session.

Year in Review: Heart failure and cardiomyopathies

Heart Failure Video

Watch this official video from ACC.24. Dr. Biykem Bozkurt discuss last year's major advances in heart failure and cardiomyopathies.

ACC 2024 Congress Coverage

Heart Failure Video

Year in Review: Heart failure and cardiomyopathies

Watch now

Watch this official video from ACC.24. Dr. Biykem Bozkurt discuss last year's major advances in heart failure and cardiomyopathies.

Semaglutide benefits people with type 2 diabetes and obesity-related heart failure

16-04-2024 Type 2 Diabetes News

Incentivised to exercise

11-04-2024 Lifestyle Modifications News

New intervention for STEMI-related cardiogenic shock

10-04-2024 Myocardial Infarction News

» View more highlights on the ACC 2024 congress hub page

Image Credits