Abstract
Objective
To predict silent corticotroph adenomas (SCAs) among non-functioning pituitary adenomas preoperatively using noninvasive radiomics.
Methods
A total of 302 patients including 146 patients diagnosed with SCAs and 156 patients with non-SCAs were enrolled (training set: n = 242; test set: n = 60). Tumor segmentation was manually generated using ITK-SNAP. From T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and contrast-enhanced T1WI, 2550 radiomics features were extracted using Pyradiomics. Pearson’s correlation coefficient values were calculated to exclude redundant features. Several machine learning algorithms were developed to predict SCAs incorporating the radiomics and semantic features including clinical, laboratory, and radiology-associated features. The performance of models was evaluated by AUC.
Results
Patients in the SCA group were younger (49.5 vs 55.2 years old) and more female (85.6% vs 37.2%) than those in the non-SCA group (p < 0.001). More invasiveness (p = 0.011) and cystic and microcystic change (p < 0.001) were observed in patients with SCAs. The ensemble algorithm presented the largest AUC of 0.927 among all the algorithms trained in the test set, and the accuracy, specificity, and sensitivity of predicting SCAs were all 0.867 (at cut-off 0.5). The overall model performed better than that only using semantic features available in the clinic. Radiomics prediction was the most important feature, with gender ranking second and age ranking third. Radiomics features on T2WI were superior to those on other MR modalities in SCA prediction.
Conclusion
Our ensemble learning model outperformed current clinical practice in differentiating patients with SCAs and non-SCAs using radiomics, which might help make appropriate treatment strategies.
Key Points
• Radiomics might improve the preoperative diagnosis of SCAs by MR images.
• T2WI was superior to T1WI and CE-T1WI in the preoperative diagnosis of SCAs.
• The ensemble machine learning model outperformed current clinical practice in SCAs diagnosis and treatment decision-making could be more individualised using the nomogram.
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Abbreviations
- ACTH:
-
Adrenocorticotropic hormones
- AUC:
-
Area under the curve
- CE:
-
Contrast-enhanced
- MRI:
-
Magnetic resonance imaging
- NFPAs:
-
Non-functioning pituitary adenomas
- SCAs:
-
Silent corticotroph adenomas
- T1WI:
-
T1-weighted imaging
- T2WI:
-
T2-weighted imaging
- VOI:
-
Volume of interest
- WHO:
-
World Health Organization
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Funding
This study has received funding from the National Natural Science Foundation of China (no. 82073640), National Project in promoting the diagnosis and treatment of major diseases by MDT, and CAMS Innovation Fund for Medical Sciences (2021-I2M-C&T-A-025).
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The scientific guarantor of this publication is Zhenwei Yao.
Conflict of interest
The authors of this manuscript declare relationships with the following companies: The author Mr. Yong Zhang, PhD, employed by GE Healthcare, MR Research, Shanghai, China, was responsible for the MRI technical support in our research.
Other authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.
Statistics and biometry
One of the authors (N.Q.) has significant statistical expertise (graduated from Clinical Investigation of Harvard Medical School).
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Written informed consent was obtained from all subjects (patients) in this study.
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Institutional Review Board approval was obtained.
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• retrospective
• diagnostic or prognostic study
• performed at one institution
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Wenting Rui and Nidan Qiao contributed equally to this work as co-first authors.
Zhenwei Yao and Yao Zhao contributed equally to this work as co-corresponding authors.
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Rui, W., Qiao, N., Wu, Y. et al. Radiomics analysis allows for precise prediction of silent corticotroph adenoma among non-functioning pituitary adenomas. Eur Radiol 32, 1570–1578 (2022). https://doi.org/10.1007/s00330-021-08361-3
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DOI: https://doi.org/10.1007/s00330-021-08361-3