ArticlePituitary2020
A novel diagnostic method for pituitary adenoma based on magnetic resonance imaging using a convolutional neural network.
Article in Pituitary, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Radiomics of pituitary adenoma using computer vision: a review.Medical & biological engineering & computing · 2024Pooled it
- Application of artificial intelligence and radiomics in pituitary neuroendocrine and sellar tumors: a quantitative and qualitative synthesis.Neuroradiology · 2022Pooled it
- A stacking model for AI-assisted diagnosis of suspected pituitary microadenomas on non-contrast T1COR MRI: a multicenter reader study on bridging the experience gap.Neuroradiology · 2026Article
- Radiomic Applications in Skull Base Pathology: A Systematic Review of Potential Clinical Uses.Journal of neurological surgery. Part B, Skull base · 2025Article
- Radiomics in Pituitary Adenomas: A Systematic Review of Clinical Applications and Predictive Models.Journal of clinical medicine · 2025Review
- The Use of Machine Learning in the Diagnosis of Kidney Allograft Rejection: Current Knowledge and Applications.Diagnostics (Basel, Switzerland) · 2024Review
- The current state of MRI-based radiomics in pituitary adenoma: promising but challenging.Frontiers in endocrinology · 2024Review
- Applications and Integration of Radiomics for Skull Base Oncology.Advances in experimental medicine and biology · 2024Review
- Using 2-dimensional hand photographs to predict postoperative biochemical remission in acromegaly patients: a transfer learning approach.Quantitative imaging in medicine and surgery · 2023Article
- Prediction of intraoperative cerebrospinal fluid leaks in endoscopic endonasal transsphenoidal pituitary surgery based on a deep neural network model trained with MRI images: a pilot study.Frontiers in neuroscience · 2023Article
- Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review.Cancers · 2022Review
- Machine intelligence in non-invasive endocrine cancer diagnostics.Nature reviews. Endocrinology · 2022Review
- Deep Learning for Prediction of Progression and Recurrence in Nonfunctioning Pituitary Macroadenomas: Combination of Clinical and MRI Features.Frontiers in oncology · 2022Article
- A narrative review of artificial intelligence-assisted histopathologic diagnosis and decision-making for non-small cell lung cancer: achievements and limitations.Journal of thoracic disease · 2021Review
- Three-Dimensional Semantic Segmentation of Pituitary Adenomas Based on the Deep Learning Framework-nnU-Net: A Clinical Perspective.Micromachines · 2021Article
- Image-driven classification of functioning and nonfunctioning pituitary adenoma by deep convolutional neural networks.Computational and structural biotechnology journal · 2021Article
- Development and Validation of a Deep Learning Algorithm to Automatic Detection of Pituitary Microadenoma From MRI.Frontiers in medicine · 2021Article
- Assessing the risk of dengue severity using demographic information and laboratory test results with machine learning.PLoS neglected tropical diseases · 2020Article
- CT-Based Deep Learning Model for Invasiveness Classification and Micropapillary Pattern Prediction Within Lung Adenocarcinoma.Frontiers in oncology · 2020Article
- Clinical application of artificial intelligence algorithms in detecting clival remodeling in the setting of pituitary neuroendocrine tumors/pituitary adenomas.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study was designed to develop a computer-aided diagnosis (CAD) system based on a convolutional neural network (CNN) to diagnose patients with pituitary tumors.
methodsWe included adult patients clinically diagnosed with pituitary adenoma (pituitary adenoma group), or adult individuals without pituitary adenoma (control group). After pre-processing, all the MRI data were randomly divided into training or testing datasets in a ratio of 8:2 to create or evaluate the CNN model. Multiple CNNs with the same structure were applied for different types of MR images respectively, and a comprehensive diagnosis was performed based on the classification results of different types of MR images using an equal-weighted majority voting strategy. Finally, we assessed the diagnostic performance of the CAD system by accuracy, sensitivity, specificity, positive predictive value, and F1 score.
resultsWe enrolled 149 participants with 796 MR images and adopted the data augmentation technology to create 7960 new images. The proposed CAD method showed remarkable diagnostic performance with an overall accuracy of 91.02%, sensitivity of 92.27%, specificity of 75.70%, positive predictive value of 93.45%, and F1-score of 92.67% in separate MRI type. In the comprehensive diagnosis, the CAD achieved better performance with accuracy, sensitivity, and specificity of 96.97%, 94.44%, and 100%, respectively.
conclusionThe CAD system could accurately diagnose patients with pituitary tumors based on MR images. Further, we will improve this CAD system by augmenting the amount of dataset and evaluate its performance by external dataset.
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