ArticleFrontiers in oncology2020
Radiomics Approach for Prediction of Recurrence in Non-Functioning Pituitary Macroadenomas.
Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
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Who cites it
27 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
- Association between preoperative MRI radiomic features and methylation-defined phenotypes in non-functioning pituitary adenomas.Pituitary · 2026Article
- MRI radiomics-based approach to predict pituitary neuroendocrine tumor invasiveness.European radiology experimental · 2026Article
- Radiomics in Pituitary Adenomas: A Systematic Review of Clinical Applications and Predictive Models.Journal of clinical medicine · 2025Review
- MRI based radiomics nomogram for predict recurrence of non functioning pituitary macroadenomas post surgery.Scientific reports · 2025Article
- Artificial intelligence-driven approaches in pituitary neuroendocrine tumors: integrating endocrine-metabolic profiling for enhanced diagnostics and therapeutics.Frontiers in endocrinology · 2025Review
- Pairwise machine learning-based automatic diagnostic platform utilizing CT images and clinical information for predicting radiotherapy locoregional recurrence in elderly esophageal cancer patients.Abdominal radiology (New York) · 2024Article
- CT-based deep learning radiomics biomarker for programmed cell death ligand 1 expression in non-small cell lung cancer.BMC medical imaging · 2024Article
- Current status of artificial intelligence technologies in pituitary adenoma surgery: a scoping review.Pituitary · 2024Article
- Radiomic Analysis in Pituitary Tumors: Current Knowledge and Future Perspectives.Journal of clinical medicine · 2024Review
- Applications and Integration of Radiomics for Skull Base Oncology.Advances in experimental medicine and biology · 2024Review
- The current state of MRI-based radiomics in pituitary adenoma: promising but challenging.Frontiers in endocrinology · 2024Review
- Role of Artificial Intelligence in Surgical Decision-Making: A Comprehensive Review.Galen medical journal · 2024Review
- A Novel Magnetic Resonance Imaging-Based Radiomics and Clinical Predictive Model for the Regrowth of Postoperative Residual Tumor in Non-Functioning Pituitary Neuroendocrine Tumor.Medicina (Kaunas, Lithuania) · 2023Article
- Using 2-dimensional hand photographs to predict postoperative biochemical remission in acromegaly patients: a transfer learning approach.Quantitative imaging in medicine and surgery · 2023Article
- The Party Wall: Redefining the Indications of Transcranial Approaches for Giant Pituitary Adenomas in Endoscopic Era.Cancers · 2023Review
- Machine Learning Models to Forecast Outcomes of Pituitary Surgery: A Systematic Review in Quality of Reporting and Current Evidence.Brain sciences · 2023Review
- Multi-phase-combined CECT radiomics models for Fuhrman grade prediction of clear cell renal cell carcinoma.Frontiers in oncology · 2023Article
- Clinical and radiomic factors for predicting invasiveness in pulmonary ground‑glass opacity.Experimental and therapeutic medicine · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectivesA subset of non-functioning pituitary macroadenomas (NFPAs) may exhibit early progression/recurrence (P/R) after surgical resection. The purpose of this study was to apply radiomics in predicting P/R in NFPAs.
methodsOnly patients who had undergone preoperative MRI and postoperative MRI follow-ups for more than 1 year were included in this study. From September 2010 to December 2017, 50 eligible patients diagnosed with pathologically confirmed NFPAs were identified. Preoperative coronal T2WI and contrast-enhanced (CE) T1WI imaging were analyzed by computer algorithms. For each imaging sequence, 32 first-order features and 75 texture features were extracted. Support vector machine (SVM) classifier was utilized to evaluate the importance of extracted parameters, and the most significant three parameters were used to build the prediction model. The SVM score was calculated based on the three selected features.
resultsTwenty-eight patients exhibited P/R (28/50, 56%) after surgery. The median follow-up time was 38 months, and the median time to P/R was 20 months. Visual disturbance, hypopituitarism, extrasellar extension, compression of the third ventricle, large tumor height and volume, failed optic chiasmatic decompression, and high SVM score were more frequently encountered in the P/R group (
conclusionsOur preliminary results showed that objective and quantitative MR radiomic features can be extracted from NFPAs. Pending more studies and evidence to support the findings, radiomics analysis of preoperative MRI may have the potential to offer valuable information in treatment planning for NFPAs.
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