Preoperative prediction of pituitary neuroendocrine tumor invasion using multiparametric MRI radiomics
ObjectiveThe 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) r...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-01-01
|
| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2024.1475950/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841553884441477120 |
|---|---|
| author | Qiuyuan Yang Tengfei Ke Jialei Wu Yubo Wang Jiageng Li Yimin He Jianxian Yang Nan Xu Bin Yang |
| author_facet | Qiuyuan Yang Tengfei Ke Jialei Wu Yubo Wang Jiageng Li Yimin He Jianxian Yang Nan Xu Bin Yang |
| author_sort | Qiuyuan Yang |
| collection | DOAJ |
| description | ObjectiveThe 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 methodsThe 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).ResultsA 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.ConclusionThe 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. |
| format | Article |
| id | doaj-art-4da3de67cb704281aaf89d241abe39c6 |
| institution | Kabale University |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-4da3de67cb704281aaf89d241abe39c62025-01-09T06:10:39ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-01-011410.3389/fonc.2024.14759501475950Preoperative prediction of pituitary neuroendocrine tumor invasion using multiparametric MRI radiomicsQiuyuan Yang0Tengfei Ke1Jialei Wu2Yubo Wang3Jiageng Li4Yimin He5Jianxian Yang6Nan Xu7Bin Yang8Department of Medical Imaging, The Second People’s Hospital of Dali Prefecture, Dali, ChinaDepartment of Medical Imaging, Yunnan Cancer Hospital, Kunming, ChinaMedical Imaging Center, The First Hospital of Kunming, Kunming, ChinaMedical Imaging Center, The First Hospital of Kunming, Kunming, ChinaMedical Imaging Center, The First Hospital of Kunming, Kunming, ChinaDepartment of Medical Imaging, The Second People’s Hospital of Dali Prefecture, Dali, ChinaDepartment of Medical Imaging, The Second People’s Hospital of Dali Prefecture, Dali, ChinaDepartment of Radiology, Air Force Medical Center, Air Force Medical University, Beijing, ChinaMedical Imaging Center, The First Hospital of Kunming, Kunming, ChinaObjectiveThe 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 methodsThe 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).ResultsA 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.ConclusionThe 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.https://www.frontiersin.org/articles/10.3389/fonc.2024.1475950/fullpituitary neuroendocrine tumorradiomicsmagnetic resonance imagingcavernous sinusprognosis |
| spellingShingle | Qiuyuan Yang Tengfei Ke Jialei Wu Yubo Wang Jiageng Li Yimin He Jianxian Yang Nan Xu Bin Yang Preoperative prediction of pituitary neuroendocrine tumor invasion using multiparametric MRI radiomics Frontiers in Oncology pituitary neuroendocrine tumor radiomics magnetic resonance imaging cavernous sinus prognosis |
| title | Preoperative prediction of pituitary neuroendocrine tumor invasion using multiparametric MRI radiomics |
| title_full | Preoperative prediction of pituitary neuroendocrine tumor invasion using multiparametric MRI radiomics |
| title_fullStr | Preoperative prediction of pituitary neuroendocrine tumor invasion using multiparametric MRI radiomics |
| title_full_unstemmed | Preoperative prediction of pituitary neuroendocrine tumor invasion using multiparametric MRI radiomics |
| title_short | Preoperative prediction of pituitary neuroendocrine tumor invasion using multiparametric MRI radiomics |
| title_sort | preoperative prediction of pituitary neuroendocrine tumor invasion using multiparametric mri radiomics |
| topic | pituitary neuroendocrine tumor radiomics magnetic resonance imaging cavernous sinus prognosis |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2024.1475950/full |
| work_keys_str_mv | AT qiuyuanyang preoperativepredictionofpituitaryneuroendocrinetumorinvasionusingmultiparametricmriradiomics AT tengfeike preoperativepredictionofpituitaryneuroendocrinetumorinvasionusingmultiparametricmriradiomics AT jialeiwu preoperativepredictionofpituitaryneuroendocrinetumorinvasionusingmultiparametricmriradiomics AT yubowang preoperativepredictionofpituitaryneuroendocrinetumorinvasionusingmultiparametricmriradiomics AT jiagengli preoperativepredictionofpituitaryneuroendocrinetumorinvasionusingmultiparametricmriradiomics AT yiminhe preoperativepredictionofpituitaryneuroendocrinetumorinvasionusingmultiparametricmriradiomics AT jianxianyang preoperativepredictionofpituitaryneuroendocrinetumorinvasionusingmultiparametricmriradiomics AT nanxu preoperativepredictionofpituitaryneuroendocrinetumorinvasionusingmultiparametricmriradiomics AT binyang preoperativepredictionofpituitaryneuroendocrinetumorinvasionusingmultiparametricmriradiomics |