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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1475950
This article is part of the Research Topic Advancing Cancer Imaging Technologies: Bridging the Gap from Research to Clinical Practice View all 8 articles

Preoperative Prediction of Pituitary Neuroendocrine Tumor Invasion Using Multiparametric MRI Radiomics

Provisionally accepted
Qiuyuan Yang Qiuyuan Yang 1Tengfei Ke Tengfei Ke 2Jialei Wu Jialei Wu 3Yubo Wang Yubo Wang 3Jiageng Li Jiageng Li 3Yimin He Yimin He 1Jianxian Yang Jianxian Yang 1Nan Xu Nan Xu 4*Bin Yang Bin Yang 3*
  • 1 Second People's Hospital of Dali City, Dali, China
  • 2 Department of Medical Imaging, Yunnan Cancer Hospital, Kunming, Yunnan Province, China
  • 3 Medical Imaging Center, Calmette Hospital & The First Hospital of Kunming, Kunming, Yunnan Province, China
  • 4 Department of Radiology, Air Force Medical Center, Air Force Medical University, Beijing, China, Beiijing, China

The final, formatted version of the article will be published soon.

    Objective: The invasiveness of pituitary neuroendocrine tumor is an important basis for formulating individualized treatment plans and improving the prognosis of patients. Radiomics can predict invasiveness preoperatively. To investigate the value of multiparameter magnetic resonance imaging (mpMRI) radiomics in predicting pituitary neuroendocrine tumor invasion into the cavernous sinus (CS) before surgery. Patients and Methods: The clinical data of 133 patients with pituitary neuroendocrine tumor ( 62 invasive and 71 non-invasive ) confirmed by surgery and pathology who underwent preoperative mpMRI examination were retrospectively analyzed. Data were divided into training set and testing set according to different field strength equipment. Radiomics features were extracted from the manually delineated regions of interest in T1WI, T2WI and CE-T1, and the best radiomics features were screened by LASSO algorithm. Single radiomics model ( T1WI, T2WI, CE-T1 ) and combined radiomics model (T1WI+T2WI+CE-T1) were constructed respectively. In addition, clinical features were screened to establish clinical model. Finally, the prediction model was evaluated by ROC curve, calibration curve and decision curve analysis (DCA).Results: A total of 10 radiomics features were selected from 306 primitive features. The combined radiomics model had the highest prediction efficiency. The area under curve ( AUC ) of the training set was 0.885 ( 95 % CI, 0.819-0.952 ), and the accuracy, sensitivity, and specificity were 0.951,0.826, and 0.725. The AUC of the testing set was 0.864 ( 95 % CI, 0.744-0.985 ), and the accuracy, sensitivity, and specificity were 0.829,0.952, and 0.700. DCA showed that the combined radiomics model had higher clinical net benefit.The combined radiomics model based on mpMRI can effectively and accurately predict the invasiveness of pituitary neuroendocrine tumor to CS preoperatively , and provide decision-making basis for clinical individualized treatment.

    Keywords: Pituitary neuroendocrine tumor, Radiomics, Magnetic Resonance Imaging, Cavernous Sinus, prognosis

    Received: 04 Aug 2024; Accepted: 03 Dec 2024.

    Copyright: © 2024 Yang, Ke, Wu, Wang, Li, He, Yang, Xu and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Nan Xu, Department of Radiology, Air Force Medical Center, Air Force Medical University, Beijing, China, Beiijing, China
    Bin Yang, Medical Imaging Center, Calmette Hospital & The First Hospital of Kunming, Kunming, Yunnan Province, China

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