ArticleAJNR. American journal of neuroradiology2022
Thin-Slice Pituitary MRI with Deep Learning-Based Reconstruction for Preoperative Prediction of Cavernous Sinus Invasion by Pituitary Adenoma: A Prospective Study.
Article in AJNR. American journal of neuroradiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04268251 (Prospective MRI Evaluation of Cavernous Sinus Invasion by Pituitary Adenoma Using Deep Learning Based Denoising), which is not on this map. Cited by 16 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Prospective MRI Evaluation of Cavernous Sinus Invasion by Pituitary Adenoma Using Deep Learning Based Denoising
Who cites it
16 citing papers in PubMed, 21 citations in OpenAlex.
- Deep learning reconstruction improves appendix visualization on pediatric magnetic resonance imaging (MRI): a single-center experience.Pediatric radiology · 2026Article
- Application of Deep Learning-Based Reconstruction to Magnetic Resonance Imaging of Canine Stifle Joint in Healthy Beagles: Achieving Enhanced Image Quality With Reduced Slice Thickness.Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association · 2026Article
- Preoperative Prediction of Non-functional Pituitary Neuroendocrine Tumors and Posterior Pituitary Tumors Based on MRI Radiomic Features.Journal of imaging informatics in medicine · 2026Article
- Automated segmentation of pituitary adenomas, pituitary gland, and internal carotid arteries on routine coronal contrast-enhanced T1-weighted MRI: a single-sequence feasibility study.Frontiers in endocrinology · 2026Article
- Radiomic Applications in Skull Base Pathology: A Systematic Review of Potential Clinical Uses.Journal of neurological surgery. Part B, Skull base · 2025Article
- Advances in Shoulder Pain Imaging: A Narrative Review of Current Practice and Emerging Trends.Current medical imaging · 2025Article
- Improved Assessment of Juxtacortical Lesions in Multiple Sclerosis Using Highly-accelerated High-resolution Double Inversion Recovery MR Imaging with Deep Learning-based Reconstruction.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Article
- Evaluation of high-resolution pituitary dynamic contrast-enhanced MRI using deep learning-based compressed sensing and super-resolution reconstruction.European radiology · 2025Article
- Identification of the Distal Dural Ring Using Three-dimensional Motion-sensitized Driven-equilibrium Prepared TMagnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Article
- Radiomics in Pituitary Adenomas: A Systematic Review of Clinical Applications and Predictive Models.Journal of clinical medicine · 2025Review
- Artificial intelligence-driven approaches in pituitary neuroendocrine tumors: integrating endocrine-metabolic profiling for enhanced diagnostics and therapeutics.Frontiers in endocrinology · 2025Review
- Multidisciplinary quantitative and qualitative assessment of IDH-mutant gliomas with full diagnostic deep learning image reconstruction.European journal of radiology open · 2024Article
- Usefulness of pituitary high-resolution 3D MRI with deep-learning-based reconstruction for perioperative evaluation of pituitary adenomas.Neuroradiology · 2024Article
- Deep learning-based reconstruction enhances image quality and improves diagnosis in magnetic resonance imaging of the shoulder joint.Quantitative imaging in medicine and surgery · 2024Article
- Improving Diagnostic Performance of MRI for Temporal Lobe Epilepsy With Deep Learning-Based Image Reconstruction in Patients With Suspected Focal Epilepsy.Korean journal of radiology · 2024Article
- Thin-slice Two-dimensional T2-weighted Imaging with Deep Learning-based Reconstruction: Improved Lesion Detection in the Brain of Patients with Multiple Sclerosis.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2024Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and purposeAccurate radiologic prediction of cavernous sinus invasion by pituitary adenoma remains challenging. We aimed to assess whether 1-mm-slice-thickness MRI with deep learning-based reconstruction can better predict cavernous sinus invasion by pituitary adenoma preoperatively and to estimate the depth of invasion and degree of contact in relation to the carotid artery, compared with 3-mm-slice-thickness MRI. MATERIALS AND
methodsThis single-institution, prospective study included 67 consecutive patients (mean age, 53 [SD, 12] years; 28 women), between January and August 2020, who underwent a combined contrast-enhanced T1-weighted imaging protocol of 1-mm-slice-thickness MRI + deep learning-based reconstruction and 3-mm-slice-thickness MRI. An expert neuroradiologist who was blinded to the imaging protocol determined cavernous sinus invasion using the modified Knosp classification on 1-mm-slice-thickness MRI + deep learning-based reconstruction and 3-mm-slice-thickness MRI, respectively. Reference standards were established by the consensus of radiologic, intraoperative, pathologic, and laboratory findings. The primary end point was the diagnostic performance of each imaging protocol, and the secondary end points included depth of invasion and degree of contact in relation to the carotid artery.
resultsThe diagnostic performance of 1-mm-slice-thickness MRI + deep learning-based reconstruction (area under the curve, 0.79; 95% CI, 0.69 - 0.89) in predicting cavernous sinus invasion by pituitary adenoma was higher than that of 3-mm-slice-thickness MRI (area under the curve, 0.61; 95% CI, 0.52-0.70;
conclusionsCompared with 3-mm-slice-thickness MRI, 1-mm-slice-thickness MRI + deep learning-based reconstruction showed a higher diagnostic performance in preoperatively predicting cavernous sinus invasion by pituitary adenomas and demonstrated a greater depth and degree of contact in relation to the carotid artery.
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Registered trials
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.