Share:
Share this content in WeChat
X
Review
Research progress of MRI radiomics in pituitary neuroendocrine tumors
DONG Wenjie  ZHOU Junlin 

Cite this article as: DONG W J, ZHOU J L. Research progress of MRI radiomics in pituitary neuroendocrine tumors[J]. Chin J Magn Reson Imaging, 2024, 15(6): 179-184. DOI:10.12015/issn.1674-8034.2024.06.028.


[Abstract] Pituitary neuroendocrine tumors (PitNETs) are the most common tumors in the sella region. MRI, as the gold standard for the evaluation of pituitary neuroendocrine tumors, has made great contributions to the macroscopic evaluation of pituitary neuroendocrine tumors. In recent years, the continuous development of radiomics has provided new possibilities for the microscopic evaluation of tumors, and has shown good efficacy in the accurate/differential diagnosis, preoperative evaluation and prognosis prediction of pituitary neuroendocrine tumors. In this paper, the current research status of MRI radiomics in pituitary neuroendocrine tumors in recent years was reviewed, in order to provide reliable imaging basis for the formulation of clinical diagnosis and treatment protocols and comprehensive evaluation of tumors, and to guide future clinical practice and research direction.
[Keywords] pituitary neuroendocrine tumor;radiomics;magnetic resonance imaging;differential diagnosis;preoperative assessment;prognosis prediction

DONG Wenjie1, 2, 3, 4   ZHOU Junlin1, 2, 3, 4*  

1 Department of Radiology, the Second Hospital of Lanzhou University, Lanzhou 730000, China

2 Second Clinical School of Lanzhou University, Lanzhou 730000, China

3 Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730000, China

4 Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730000, China

Corresponding author: ZHUO J L, E-mail: lzuzjl601@163.com

Conflicts of interest   None.

Received  2024-02-28
Accepted  2024-06-03
DOI: 10.12015/issn.1674-8034.2024.06.028
Cite this article as: DONG W J, ZHOU J L. Research progress of MRI radiomics in pituitary neuroendocrine tumors[J]. Chin J Magn Reson Imaging, 2024, 15(6): 179-184. DOI:10.12015/issn.1674-8034.2024.06.028.

[1]
ASA S L, 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.
[2]
TRITOS N A, MILLER K K. Diagnosis and management of pituitary adenomas: A review[J]. JAMA, 2023, 329(16): 1386-1398. DOI: 10.1001/jama.2023.5444.
[3]
MACFARLANE J, BASHARI W A, SENANAYAKE R, et al. Advances in the imaging of pituitary tumors[J]. Endocrinol Metab Clin North Am, 2020, 49(3): 357-373. DOI: 10.1016/j.ecl.2020.06.002.
[4]
PEROSEVIC M, JONES P S, TRITOS N A. Magnetic resonance imaging of the hypothalamo-pituitary region[J]. Handb Clin Neurol, 2021, 179: 95-112. DOI: 10.1016/B978-0-12-819975-6.00004-2.
[5]
LAMBIN P, RIOS-VELAZQUEZ E, LEIJENAAR R, et al. Radiomics: extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48: 441-446. DOI: 10.1016/j.ejca.2011.11.036.
[6]
GUIOT J, VAIDYANATHAN A, DEPREZ L, et al. A review in radiomics: Making personalized medicine a reality via routine imaging[J]. Med Res Rev, 2022, 42(1): 426-440. DOI: 10.1002/med.21846.
[7]
TAHA B, BOLEY D, SUN J, et al. Potential and limitations of radiomics in neuro-oncology[J]. J Clin Neurosci, 2021, 90: 206-211. DOI: 10.1016/j.jocn.2021.05.015.
[8]
ZHANG X, ZHANG Y, ZHANG G, et al. Deep learning with radiomics for disease diagnosis and treatment: Challenges and potential[J/OL]. Front Oncol, 2022, 12: 773840 [2024-02-28]. https://doi.org/10.3389/fonc.2022.773840. DOI: 10.3389/fonc.2022.773840.
[9]
KOCHER M, RUGE M I, GALLDIKS N, et al. Applications of radiomics and machine learning for radiotherapy of malignant brain tumors[J]. Strahlenther Onkol, 2020, 196(10): 856-867. DOI: 10.1007/s00066-020-01626-8.
[10]
LIAO J, LI X, GAN Y, et al. Artificial intelligence assists precision medicine in cancer treatment[J/OL]. Front Oncol, 2023, 12: 998222 [2024-02-28]. https://doi.org/10.3389/fonc.2022.998222. DOI: 10.3389/fonc.2022.998222.
[11]
SUN Q, CHEN Y, LIANG C, et al. Biologic pathways underlying prognostic radiomics phenotypes from paired MRI and RNA sequencing in glioblastoma[J]. Radiology, 2021, 301(3): 654-663. DOI: 10.1148/radiol.2021203281.
[12]
KHALILI N, KAZEROONI A F, FAMILIAR A, et al. Radiomics for characterization of the glioma immune microenvironment[J/OL]. NPJ Precis Oncol, 2023, 7(1): 59 [2024-02-28]. https://doi.org/10.1038/s41698-023-00413-9. DOI: 10.1038/s41698-023-00413-9.
[13]
MAYERHOEFER M E, MATERKA A, LANGS G, et al. Introduction to radiomics[J]. J Nucl Med, 2020, 61(4): 488-495. DOI: 10.2967/jnumed.118.222893.
[14]
GU H, ZHANG X, DI RUSSO P, et al. The current state of radiomics for meningiomas: Promises and challenges[J/OL]. Front Oncol, 2020, 10: 567736 [2024-02-28]. https://doi.org/10.3389/fonc.2020.567736. DOI: 10.3389/fonc.2020.567736.
[15]
KALASAUSKAS D, KOSTERHON M, KERIC N, et al. Beyond glioma: The utility of radiomic analysis for non-glial intracranial tumors[J/OL]. Cancers (Basel), 2022, 14(3): 836 [2024-02-28]. https://doi.org/10.3390/cancers14030836. DOI: 10.3390/cancers14030836.
[16]
RONOT M, SOYER P. Can radiomics outperform pathology for tumor grading?[J]. Diagn Interv Imaging, 2024, 105(1): 3-4. DOI: 10.1016/j.diii.2023.09.001.
[17]
ROGERS W, THULASI SEETHA S, REFAEE T A G, et al. Radiomics: from qualitative to quantitative imaging[J/OL]. Br J Radiol, 2020, 93(1108): 20190948 [2024-02-28]. https://doi.org/10.1259/bjr.20190948. DOI: 10.1259/bjr.20190948.
[18]
LOPES M B S. 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.
[19]
XIE J, WU Z B. New pathologic classification and clinical implications of pituitary neuroendocrine tumors in 2022 edition of the World Health Organization[J]. Natl Med J China, 2022, 102(47): 3723-3726. DOI: 10.3760/cma.j.cn112137-20220417-00825.
[20]
LOUIS D N, PERRY A, WESSELING P, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary[J]. Neuro Oncol, 2021, 23(8): 1231-1251. DOI: 10.1093/neuonc/noab106.
[21]
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, 125: 108892 [2024-02-28]. https://doi.org/10.1016/j.ejrad.2020.108892. DOI: 10.1016/j.ejrad.2020.108892.
[22]
PARK Y W, KANG Y, AHN S S, 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.
[23]
RUI W, QIAO N, WU Y, et al. Radiomics analysis allows for precise prediction of silent corticotroph adenoma among non-functioning pituitary adenomas[J]. Eur Radiol, 2022, 32(3): 1570-1578. DOI: 10.1007/s00330-021-08361-3.
[24]
WANG H, CHANG J, ZHANG W, et al. Radiomics model and clinical scale for the preoperative diagnosis of silent corticotroph adenomas[J]. J Endocrinol Invest, 2023, 46(9): 1843-1854. DOI: 10.1007/s40618-023-02042-2.
[25]
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, 11: 709321 [2024-02-28]. https://doi.org/10.3389/fonc.2021.709321. DOI: 10.3389/fonc.2021.709321.
[26]
ZHANG Y, CHEN C, TIAN Z, et al. Discrimination between pituitary adenoma and craniopharyngioma using MRI-based image features and texture features[J]. Jpn J Radiol, 2020, 38(12): 1125-1134. DOI: 10.1007/s11604-020-01021-4.
[27]
ZHANG Y, SHANG L, CHEN C, et al. Machine-learning classifiers in discrimination of lesions located in the anterior skull base[J/OL]. Front Oncol, 2020, 10: 752 [2024-02-28]. https://doi.org/10.3389/fonc.2020.00752. DOI: 10.3389/fonc.2020.00752.
[28]
JOSHI M N, WHITELAW B C, CARROLL P V. Mechanisms in endocrinology: Hypophysitis: diagnosis and treatment[J/OL]. Eur J Endocrinol, 2018, 179(3): R151-R163 [2024-02-28]. https://pubmed.ncbi.nlm.nih.gov/29880706/. DOI: 10.1530/EJE-17-0009.
[29]
SAHIN S, YILDIZ G, OGUZ S H, et al. Discrimination between non-functioning pituitary adenomas and hypophysitis using machine learning methods based on magnetic resonance imaging-derived texture features[J]. Pituitary, 2022, 25(3): 474-479. DOI: 10.1007/s11102-022-01213-3.
[30]
XUE C, LIU S, DENG J, et al. Apparent diffusion coefficient histogram analysis for the preoperative evaluation of Ki-67 expression in pituitary macroadenoma[J]. Clin Neuroradiol, 2022, 32(1): 269-276. DOI: 10.1007/s00062-021-01134-x.
[31]
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.
[32]
WANG X, DAI Y, LIN H, et al. Shape and texture analyses based on conventional MRI for the preoperative prediction of the aggressiveness of pituitary adenomas[J]. Eur Radiol, 2023, 33(5): 3312-3321. DOI: 10.1007/s00330-023-09412-7.
[33]
DOAI M, TONAMI H, MATOBA M, et al. Pituitary macroadenoma: Accuracy of apparent diffusion coefficient magnetic resonance imaging in grading tumor aggressiveness[J]. Neuroradiol J, 2019, 32(2): 86-91. DOI: 10.1177/1971400919825696.
[34]
NIU J, ZHANG S, MA S, 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.
[35]
ČERNÝ M, SEDLÁK V, LESÁKOVÁ V, et al. Methods of preoperative prediction of pituitary adenoma consistency: a systematic review[J/OL]. Neurosurg Rev, 2022, 46(1): 11 [2024-02-28]. https://doi.org/10.1007/s10143-022-01909-x. DOI: 10.1007/s10143-022-01909-x.
[36]
SU C Q, ZHANG X, PAN T, et al. Texture analysis of high b-value diffusion-weighted imaging for evaluating consistency of pituitary macroadenomas[J]. J Magn Reson Imaging, 2020, 51(5): 1507-1513. DOI: 10.1002/jmri.26941.
[37]
DING W, HUANG Z, ZHOU G, et al. Diffusion-weighted imaging for predicting tumor consistency and extent of resection in patients with pituitary adenoma[J]. Neurosurg Rev, 2021, 44(5): 2933-2941. DOI: 10.1007/s10143-020-01469-y.
[38]
KAMIMURA K, NAKAJO M, BOHARA M, et al. Consistency of pituitary adenoma: Prediction by pharmacokinetic dynamic contrast-enhanced MRI and comparison with histologic collagen content[J/OL]. Cancers (Basel), 2021, 13(15): 3914 [2024-02-28]. https://doi.org/10.3390/cancers13153914. DOI: 10.3390/cancers13153914.
[39]
CHEN X Y, DING C Y, YOU H H, et al. Relationship between pituitary adenoma consistency and extent of resection based on tumor/cerebellar peduncle T2-weighted imaging intensity (TCTI) ratio of the point on preoperative magnetic resonance imaging (MRI) corresponding to the residual point on postoperative MRI[J/OL]. Med Sci Monit, 2020, 26: e919565 [2024-02-28]. https://doi.org/10.12659/MSM.919565. DOI: 10.12659/MSM.919565.
[40]
YUN J J, JOHANS S J, SHEPHERD D J, et al. The utility of using preoperative MRI as a predictor for intraoperative pituitary adenoma consistency and surgical resection technique[J]. J Neurol Surg B Skull Base, 2020, 81(6): 651-658. DOI: 10.1055/s-0039-1694049.
[41]
ROMANO A, COPPOLA V, LOMBARDI M, et al. Predictive role of dynamic contrast enhanced T1-weighted MR sequences in pre-surgical evaluation of macroadenomas consistency[J]. Pituitary, 2017, 20(2): 201-209. DOI: 10.1007/s11102-016-0760-z.
[42]
COHEN-COHEN S, HELAL A, YIN Z, et al. Predicting pituitary adenoma consistency with preoperative magnetic resonance elastography[J]. J Neurosurg, 2021, 136(5): 1356-1363. DOI: 10.3171/2021.6.JNS204425.
[43]
WANG M K. Application of magnetic resonance elastography and magnetic resonance fingerprint imaging in pathological classification and hardness of pituitary tumors[D]. Zhengzhou: Zhengzhou University, 2022. DOI: 10.27466/d.cnki.gzzdu.2021.004058.
[44]
RUTLAND J W, LOEWENSTERN J, RANTI D, et al. Analysis of 7-tesla diffusion-weighted imaging in the prediction of pituitary macroadenoma consistency[J]. J Neurosurg, 2020, 134(3): 771-779. DOI: 10.3171/2019.12.JNS192940.
[45]
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.
[46]
ZEYNALOVA A, KOCAK B, DURMAZ E S, 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.
[47]
CHEN J M, WAN Q, ZHU H Y, et al. The value of texture prediction of pituitary macroadenoma based on conventional magnetic resonance imaging model[J]. Natl Med J China, 2020, 100(45): 3626-3631. DOI: 10.3760/cma.j.cn112137-20200511-01511.
[48]
ZOU M H. Study on texture prediction of pituitary macroadenoma by imaging omics model based on MR texture parameters[D]. Changchun: Jilin University, 2022. DOI: 10.27162/d.cnki.gjlin.2021.004968.
[49]
ZHANG D D, XU Y. Feasibility analysis of preoperative evaluation of pituitary tumor texture based on MRI enhanced absolute gradient texture analysis[J]. Medical Information, 2019, 34(16): 91-94. DOI: 10.3969/j.issn.1006-1959.2021.16.024.
[50]
WAN T, ZHAO H, LI D Y, et al. Preoperative evaluation of pituitary tumor texture based on multi-parameter magnetic resonance imaging[J]. Chinese Journal of Biomedical Engineering, 2021, 40(5): 513-520. DOI: 10.3969/j.issn.0258-8021.2021.05.001.
[51]
RAVEROT G, BURMAN P, MCCORMACK A, et al. European society of endocrinology clinical practice guidelines for the management of aggressive pituitary tumours and carcinomas[J/OL]. Eur J Endocrinol, 2018, 178(1): G1-G24 [2024-02-28]. https://pubmed.ncbi.nlm.nih.gov/29046323/. DOI: 10.1530/EJE-17-0796.
[52]
YANG H A, ZHANG J R, WANG J X, et al. Study of Nomogram model for predicting pituitary tumor recurrence based on radiomic score and clinicopathological image parameters of MRI[J]. Radiol Prac, 2023, 38(7) : 853-862. DOI: 10.13609/j.cnki.1000-0313.2023.07.008.
[53]
MACHADO L F, ELIAS P C L, MOREIRA A C, et al. MRI radiomics for the prediction of recurrence in patients with clinically non-functioning pituitary macroadenomas[J/OL]. Comput Biol Med, 2020, 124: 103966 [2024-02-28]. https://doi.org/10.1016/j.compbiomed.2020.103966. DOI: 10.1016/j.compbiomed.2020.103966.
[54]
SHEN C, LIU X, JIN J, et al. A novel magnetic resonance imaging-based radiomics and clinical predictive model for the regrowth of postoperative residual tumor in non-functioning pituitary neuroendocrine tumor[J/OL]. Medicina (Kaunas), 2023, 59(9): 1525 [2024-02-28]. https://doi.org/10.3390/medicina59091525. DOI: 10.3390/medicina59091525.
[55]
TAGHVAEI M, SADREHOSSEINI S M, OSTADRAHIMI N, et al. Preoperative visual evoked potential in the prediction of visual outcome after pituitary macroadenomas surgery[J]. Pituitary, 2019, 22(4): 397-404. DOI: 10.1007/s11102-019-00969-5.
[56]
ZHANG Y, CHEN C, HUANG W, et al. Machine learning-based radiomics of the optic chiasm predict visual outcome following pituitary adenoma surgery[J/OL]. J Pers Med, 2021, 11(10): 991 [2024-02-28]. https://doi.org/10.3390/jpm11100991. DOI: 10.3390/jpm11100991.
[57]
ZHANG Y, ZHENG J, HUANG Z, et al. Predicting visual recovery in pituitary adenoma patients post-endoscopic endonasal transsphenoidal surgery: Harnessing delta-radiomics of the optic chiasm from MRI[J]. Eur Radiol, 2023, 33(11): 7482-7493. DOI: 10.1007/s00330-023-09963-9.
[58]
UVELIUS E, VALDEMARSSON S, BENGZON J, et al. Visual acuity in patients with non-functioning pituitary adenoma: Prognostic factors and long-term outcome after surgery[J/OL]. Brain Spine, 2023, 3: 102667 [2024-02-28]. https://doi.org/10.1016/j.bas.2023.102667. DOI: 10.1016/j.bas.2023.102667.
[59]
MOHAMADZADEH O, SADREHOSSEINI S M, TABARI A, et al. Can preoperative diffusion tensor imaging tractography predict the visual outcomes of patients with pituitary macroadenomas? A prospective pilot study[J/OL]. World Neurosurg, 2023, 172: e326-e334 [2024-02-28]. https://pubmed.ncbi.nlm.nih.gov/36640834/. DOI: 10.1016/j.wneu.2023.01.022.
[60]
ZHANG J, WANG Y, XU X, et al. Postoperative complications and quality of life in patients with pituitary adenoma[J]. Gland Surg, 2020, 9(5): 1521-1529. DOI: 10.21037/gs-20-690.
[61]
XU Y. Prediction of postoperative diabetes insipidus in patients with pituitary macroadenoma based on MRI imaging and clinical characteristics[D]. Nanchang: Nanchang University, 2023. DOI: 10.27232/d.cnki.gnchu.2023.000564.

PREV Progress in MRI in peritumoral brain zone of brain tumors
NEXT The principle of oscillating gradient spin echo in diffusion magnetic resonance imaging and its application in gliomas
  



Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn