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Research status of application of artificial intelligence technology based on magnetic resonance imaging in pituitary adenomas
YANG Qiuyuan  KE Tengfei  YANG Bin 

Cite this article as: Yang QY, Ke TF, Yang B. Research status of application of artificial intelligence technology based on magnetic resonance imaging in pituitary adenomas[J]. Chin J Magn Reson Imaging, 2022, 13(7): 160-163. DOI:10.12015/issn.1674-8034.2022.07.032.

[Abstract] Pituitary adenoma is a common benign intracranial tumor, but it can show high invasiveness and recurrence rate, and the incidence rate is increasing year by year. Radiomics and deep learning are important research directions of artificial intelligence in the field of medical imaging. They are widely used in tumor imaging research, and play an important role in the heterogeneous diagnosis, efficacy evaluation, and prognosis prediction of pituitary adenomas. This article reviews the application and research progress of radiomics and deep learning in pituitary adenomas.
[Keywords] pituitary adenoma;magnetic resonance imaging;artificial intelligence;radiomics;deep learning;texture feature;preoperative evaluation;prognosis

YANG Qiuyuan1   KE Tengfei2   YANG Bin3*  

1 School of Clinical Medical, Dali University, Dali 671000, China

2 Department of Medical Imaging, Yunnan Cancer Hospital, Kunming 650018, China

3 Department of Medical Imaging, the First People's Hospital of Kunming, Kunming 650051, China

Yang B, E-mail:

Conflicts of interest   None.

Received  2022-03-29
Accepted  2022-07-05
DOI: 10.12015/issn.1674-8034.2022.07.032
Cite this article as: Yang QY, Ke TF, Yang B. Research status of application of artificial intelligence technology based on magnetic resonance imaging in pituitary adenomas[J]. Chin J Magn Reson Imaging, 2022, 13(7): 160-163.DOI:10.12015/issn.1674-8034.2022.07.032

Melmed S. Pathogenesis of pituitary tumors[J]. Endocrinol Metab Clin N Am, 1999, 28(1): 1-12. DOI: 10.1016/S0889-8529(05)70055-4.
Mayerhoefer ME, Materka A, Langs G, et al. Introduction to radiomics[J]. J Nucl Med, 2020, 61(4): 488-495. DOI: 10.2967/jnumed.118.222893.
Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine[J]. Nat Rev Clin Oncol, 2017, 14(12): 749-762. DOI: 10.1038/nrclinonc.2017.141.
Thompson AC, Jammal AA, Medeiros FA. A Review of Deep Learning for Screening, Diagnosis, and Detection of Glaucoma Progression[J/OL]. Transl Vis Sci Technol, 2020 [2022-3-10]. DOI: 10.1167/tvst.9.2.42.
McBee MP, Awan OA, Colucci AT, et al. Deep learning in radiology[J]. Acad Radiol, 2018, 25(11): 1472-1480. DOI: 10.1016/j.acra.2018.02.018.
Moeskops P, Viergever MA, Mendrik AM, et al. Automatic segmentation of MR brain images with a convolutional neural network[J]. IEEE Trans Med Imaging, 2016, 35(5): 1252-1261. DOI: 10.1109/TMI.2016.2548501.
Zhao Z, Xiao D, Nie C, et al. Development of a Nomogram Based on Preoperative Bi-Parametric MRI and Blood Indices for the Differentiation Between Cystic-Solid Pituitary Adenoma and Craniopharyngioma[J/OL]. Front Oncol, 2021 [2022-3-10]. DOI: 10.3389/fonc.2021.709321.
Asa SL, Mete O, Perry A, et al. Overview of the 2022 WHO classification of pituitary tumors[J]. Endocr Pathol, 2022, 33(1): 6-26. DOI: 10.1007/s12022-022-09703-7.
Peng A, Dai H, Duan H, et al. A machine learning model to precisely immunohistochemically classify pituitary adenoma subtypes with radiomics based on preoperative magnetic resonance imaging[J/OL]. Eur J Radiol, 2020 [2022-3-10]. DOI: 10.1016/j.ejrad.2020.108892.
Zhang ST, Song GD, Zang YL, et al. Non-invasive radiomics approach potentially predicts non-functioning pituitary adenomas subtypes before surgery[J]. Eur Radiol, 2018, 28(9): 3692-3701. DOI: 10.1007/s00330-017-5180-6.
Smith KA, Leever JD, Chamoun RB. Prediction of consistency of pituitary adenomas by magnetic resonance imaging[J]. J Neurol Surg B Skull Base, 2015, 76(5): 340-343. DOI: 10.1055/s-0035-1549005.
Cuocolo R, Ugga L, Solari D, et al. Prediction of pituitary adenoma surgical consistency: radiomic data mining and machine learning on T2-weighted MRI[J]. Neuroradiology, 2020, 62(12): 1649-1656. DOI: 10.1007/s00234-020-02502-z.
Zeynalova A, Kocak B, Durmaz ES, et al. Preoperative evaluation of tumour consistency in pituitary macroadenomas: a machine learning-based histogram analysis on conventional T2-weighted MRI[J]. Neuroradiology, 2019, 61(7): 767-774. DOI: 10.1007/s00234-019-02211-2.
Wan T, Wu CX, Meng M, et al. Radiomic features on multiparametric MRI for preoperative evaluation of pituitary macroadenomas consistency: preliminary findings[J]. J Magn Reson Imaging, 2022, 55(5): 1491-1503. DOI: 10.1002/jmri.27930.
Wang H, Zhang WT, Li S, et al. Development and evaluation of deep learning-based automated segmentation of pituitary adenoma in clinical task[J]. J Clin Endocrinol Metab, 2021, 106(9): 2535-2546. DOI: 10.1210/clinem/dgab371.
Micko AS, Wöhrer A, Wolfsberger S, et al. Invasion of the cavernous sinus space in pituitary adenomas: endoscopic verification and its correlation with an MRI-based classification[J]. J Neurosurg, 2015, 122(4): 803-811. DOI: 10.3171/2014.12.JNS141083.
Niu JX, Zhang ST, Ma SC, et al. Preoperative prediction of cavernous sinus invasion by pituitary adenomas using a radiomics method based on magnetic resonance images[J]. Eur Radiol, 2019, 29(3): 1625-1634. DOI: 10.1007/s00330-018-5725-3.
Liu YQ, Gao BB, Dong B, et al. Preoperative vascular heterogeneity and aggressiveness assessment of pituitary macroadenoma based on dynamic contrast-enhanced MRI texture analysis[J/OL]. Eur J Radiol, 2020 [2022-3-10]. DOI: 10.1016/j.ejrad.2020.109125.
Lopes MBS. The 2017 World Health Organization classification of tumors of the pituitary gland: a summary[J]. Acta Neuropathol, 2017, 134(4): 521-535. DOI: 10.1007/s00401-017-1769-8.
Ugga L, Cuocolo R, Solari D, et al. Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning[J]. Neuroradiology, 2019, 61(12): 1365-1373. DOI: 10.1007/s00234-019-02266-1.
Fan Y, Chai Y, Li K, et al. Non-invasive and real-time proliferative activity estimation based on a quantitative radiomics approach for patients with acromegaly: a multicenter study[J]. J Endocrinol Invest, 2020, 43(6): 755-765. DOI: 10.1007/s40618-019-01159-7.
Katznelson L, Laws ER, Melmed S, et al. Acromegaly: an endocrine society clinical practice guideline[J]. J Clin Endocrinol Metab, 2014, 99(11): 3933-3951. DOI: 10.1210/jc.2014-2700.
Bhayana S, Booth GL, Asa SL, et al. The implication of somatotroph adenoma phenotype to somatostatin analog responsiveness in acromegaly[J]. J Clin Endocrinol Metab, 2005, 90(11): 6290-6295. DOI: 10.1210/jc.2005-0998.
Bakhtiar Y, Hirano H, Arita K, et al. Relationship between cytokeratin staining patterns and clinico-pathological features in somatotropinomae[J]. Eur J Endocrinol, 2010, 163(4): 531-539. DOI: 10.1530/EJE-10-0586.
Park YW, Kang YJ, Ahn SS, et al. Radiomics model predicts granulation pattern in growth hormone-secreting pituitary adenomas[J]. Pituitary, 2020, 23(6): 691-700. DOI: 10.1007/s11102-020-01077-5.
Liu CX, Heng LJ, Han Y, et al. Usefulness of the Texture Signatures Based on Multiparametric MRI in Predicting Growth Hormone Pituitary Adenoma Subtypes[J/OL]. Front Oncol, 2021 [2022-3-10]. DOI: 10.3389/fonc.2021.640375.
Fan Y, Liu Z, Hou B, et al. Development and validation of an MRI-based radiomic signature for the preoperative prediction of treatment response in patients with invasive functional pituitary adenoma[J/OL]. Eur J Radiol, 2019 [2022-03-10]. DOI: 10.1016/j.ejrad.2019.108647.
Staartjes VE, Serra C, Muscas G, et al. Utility of deep neural networks in predicting gross-total resection after transsphenoidal surgery for pituitary adenoma: a pilot study[J/OL]. Neurosurg Focus, 2018 [2022-03-10]. DOI: 10.3171/2018.8.FOCUS18243.
Park YW, Eom J, Kim S, et al. Radiomics With Ensemble Machine Learning Predicts Dopamine Agonist Response in Patients With Prolactinoma[J/OL]. J Clin Endocrinol Metab, 2021 [2022-03-10]. DOI: 10.1210/clinem/dgab159.
Fan Y, Jiang S, Hua M, et al. Machine Learning-Based Radiomics Predicts Radiotherapeutic Response in Patients With Acromegaly[J/OL]. Front Endocrinol (Lausanne), 2019 [2022-03-10]. DOI: 10.3389/fendo.2019.00588.
Machado LF, Elias PCL, Moreira AC, et al. MRI radiomics for the prediction of recurrence in patients with clinically non-functioning pituitary macroadenomas[J/OL]. Comput Biol Med, 2020 [2022-03-10]. DOI: 10.1016/j.compbiomed.2020.103966.
Hollon TC, Parikh A, Pandian B, et al. A machine learning approach to predict early outcomes after pituitary adenoma surgery[J/OL]. Neurosurg Focus, 2018 [2022-03-10]. DOI: 10.3171/2018.8.FOCUS18268.
Galm BP, Martinez-Salazar EL, Swearingen B, et al. MRI texture analysis as a predictor of tumor recurrence or progression in patients with clinically non-functioning pituitary adenomas[J]. Eur J Endocrinol, 2018, 179(3): 191-198. DOI: 10.1530/EJE-18-0291.
Zhou M, Leung A, Echegaray S, et al. Non-small cell lung cancer radiogenomics map identifies relationships between molecular and imaging phenotypes with prognostic implications[J]. Radiology, 2018, 286(1): 307-315. DOI: 10.1148/radiol.2017161845.
Ninomiya K, Arimura H, Chan WY, et al. Robust radiogenomics approach to the identification of EGFR mutations among patients with NSCLC from three different countries using topologically invariant Betti numbers[J/OL]. PLoS One, 2021 [2022-03-10]. DOI: 10.1371/journal.pone.0244354.
Shiri I, Amini M, Nazari M, et al. Impact of feature harmonization on radiogenomics analysis: Prediction of EGFR and KRAS mutations from non-small cell lung cancer PET/CT images[J/OL]. Comput Biol Med, 2022 [2022-03-10]. DOI: 10.1016/j.compbiomed.2022.105230.
Zwirner K, Hilke FJ, Demidov G, et al. Radiogenomics in head and neck cancer: correlation of radiomic heterogeneity and somatic mutations in TP53, FAT1 and KMT2D[J]. Strahlenther Onkol, 2019, 195(9): 771-779. DOI: 10.1007/s00066-019-01478-x.
Hu LS, Wang L, Hawkins-Daarud A, et al. Uncertainty quantifification in the radiogenomics modeling of EGFR amplifification in glioblastoma[J/OL]. Sci Rep, 2021 [2022-03-10]. DOI: 10.1038/s41598-021-83141-z.
Attiyeh MA, Chakraborty J, McIntyre CA, et al. CT radiomics associations with genotype and stromal content in pancreatic ductal adenocarcinoma[J]. Abdom Radiol (NY), 2019, 44(9): 3148-3157. DOI: 10.1007/s00261-019-02112-1.

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