Research progress of MRI machine learning in predicting the prognosis of pituitary neuroendocrine tumors

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• Review •

CHEN Chunhui LUO Pan DONG Wenjie HAN Tao SUN Jiachen ZHOU Junlin

Cite this article as: CHEN C H, LUO P, DONG W J, et al. Research progress of MRI machine learning in predicting the prognosis of pituitary neuroendocrine tumors[J]. Chin J Magn Reson Imaging, 2025, 16(2): 154-158. DOI:10.12015/issn.1674-8034.2025.02.025.

Abstract

[Abstract] Pituitary neuroendocrine tumors (PitNETs) are mostly benign tumors, but pituitary dysfunction, tumor invasiveness, and the occurrence of various complications can significantly affect the quality of life of PitNETs patients, therefore, the non-invasive assessment of tumor prognosis is of great significance in clinical decision-making. MRI is the most commonly used examination method for PitNETs, and MRI machine learning have played an important role in the prognosis assessment of PitNETs. This review summarizes the research progress of MRI machine learning in predicting the chemotherapy prognosis, postoperative recurrence/remission, postoperative complications, and radiotherapy prognosis of PitNETs, with the aim of providing clinical guidance for individualized prognosis assessment and guiding future research.

[Keywords] pituitary neuroendocrine tumors；magnetic resonance imaging；machine learning；deep learning；radiomics；prognosis assessment

Contributor Information

CHEN Chunhui^{1,
2,
3,
4}   LUO Pan^{1,
2,
3,
4}   DONG Wenjie^{1,
2,
3,
4}   HAN Tao^{1,
2,
3,
4}   SUN Jiachen^{1,
2,
3,
4}   ZHOU Junlin^{1,
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: ZHOU J L, E-mail: lzuzjl601@163.com

Conflicts of interest None.

Publication Information

Received 2024-11-19

Accepted 2025-02-10

DOI: 10.12015/issn.1674-8034.2025.02.025

Text

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