ArticleNeuroradiology2019
Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning.
Article in Neuroradiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 2 of them syntheses that pooled it.
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Who cites it
60 citing papers in PubMed, 2 syntheses or guidelines pooled it, 92 citations in OpenAlex.
- 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
- Integrating Molecular and Imaging Insights: Ryanodine Receptor 2 (RyR2) Expression, CK18 Status, and Conventional MRI Findings in Pituitary Neuroendocrine Tumors.International journal of molecular sciences · 2026Article
- Radiomics and artificial intelligence in pituitary adenomas (PitNETs): from imaging biomarkers to clinical translation.Reviews in endocrine & metabolic disorders · 2026Review
- Pituitary adenoma imaging as a determinant of acromegaly diagnosis and outcomes.The Journal of clinical endocrinology and metabolism · 2026Review
- Radiomics for Preoperative Assessment of Pituitary Adenoma Consistency with T2-Weighted MRI: A Multicenter Study.Journal of neurological surgery. Part B, Skull base · 2026Article
- MRI radiomics-based approach to predict pituitary neuroendocrine tumor invasiveness.European radiology experimental · 2026Article
- Development and validation of a machine learning model based on interpretable clinical characteristics for preoperative prediction of Ki-67 expression in pituitary adenomas.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Article
- Histogram analysis based on contrast-enhanced T1-weighted imaging in pituitary macroadenomas: relation to histological subtype and expression.Quantitative imaging in medicine and surgery · 2026Article
- An MRI radiomics approach using invasion-based weak supervision for identifying and evaluating aggressive PitNETs.NPJ digital medicine · 2025Article
- Preoperatively Predicting PIT1 Expression in Pituitary Adenomas Using Habitat, Intra-tumoral and Peri-tumoral Radiomics Based on MRI.Journal of imaging informatics in medicine · 2025Article
- 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
- Machine learning method based on radiomics help differentiate posterior pituitary tumors from pituitary neuroendocrine tumors and craniopharyngioma.Scientific reports · 2025Article
- MRI based radiomics nomogram for predict recurrence of non functioning pituitary macroadenomas post surgery.Scientific reports · 2025Article
- A semantic segmentation model for automatic precise identification of pituitary microadenomas with preoperative MRI.Neuroradiology · 2025Article
- Concomitant Prediction of the Ki67 and PIT-1 Expression in Pituitary Adenoma Using Different Radiomics Models.Journal of imaging informatics in medicine · 2025Article
- Combining clinical characteristics with CT radiomics to predict Ki67 expression level of small renal mass based on artificial intelligence algorithms.Frontiers in oncology · 2025Article
- Artificial intelligence-driven approaches in pituitary neuroendocrine tumors: integrating endocrine-metabolic profiling for enhanced diagnostics and therapeutics.Frontiers in endocrinology · 2025Review
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Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposePituitary adenomas are among the most frequent intracranial tumors. They may exhibit clinically aggressive behavior, with recurrent disease and resistance to multimodal therapy. The ki-67 labeling index represents a proliferative marker which correlates with pituitary adenoma aggressiveness. Aim of our study was to assess the accuracy of machine learning analysis of texture-derived parameters from pituitary adenomas preoperative MRI for the prediction of ki-67 proliferation index class.
methodsA total of 89 patients who underwent an endoscopic endonasal procedure for pituitary adenoma removal with available ki-67 labeling index were included. From T2w MR images, 1128 quantitative imaging features were extracted. To select the most informative features, different supervised feature selection methods were employed. Subsequently, a k-nearest neighbors (k-NN) classifier was employed to predict macroadenoma high or low proliferation index. Algorithm validation was performed with a train-test approach.
resultsOf the 12 subsets derived from feature selection, the best performing one was constituted by the 4 highest correlating parameters at Pearson's test. These all showed very good (ICC ≥ 0.85) inter-observer reproducibility. The overall accuracy of the k-NN in the test group was of 91.67% (33/36) of correctly classified patients.
conclusionsMachine learning analysis of texture-derived parameters from preoperative T2 MRI has proven to be effective for the prediction of pituitary macroadenomas ki-67 proliferation index class. This might aid the surgical strategy making a more accurate preoperative lesion classification and allow for a more focused and cost-effective follow-up and long-term management.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.