December 8, 2025, Alexandria, Virginia—Researchers at Thomas Jefferson University have developed a groundbreaking automated machine learning (AutoML) model that can accurately differentiate between two common types of brain tumors using preoperative MRI scans, potentially improving surgical planning and patient outcomes.
The study , published in the December 2025 issue of Otolaryngology–Head and Neck Surgery, represents the first application of AutoML technology specifically trained to classify pituitary macroadenomas and parasellar meningiomas—two benign but challenging-to-distinguish brain tumors that require different treatment approaches.
"Our automated machine learning model achieved over 97% accuracy in distinguishing between two common types of skull base tumors (pituitary macroadenomas and meningiomas of the parasellar region) using preoperative MRI scans. This work is significant because it demonstrates that automated machine learning can streamline model development for medical imaging classification, reducing barriers to implementing artificial intelligence-based diagnostic support in otolaryngology," said Gurston G. Nyquist, MD, Professor of Otolaryngology and Neurological Surgery, and Chief, Division of Rhinology and Skull Base Surgery at Thomas Jefferson University.
"While multi-institutional validation and careful integration into clinical workflows are warranted, this study represents an important step in the development of reliable tools that may improve skull base tumor diagnosis in both community and tertiary care settings," he continued.
Why This Matters for Patients
Accurate preoperative diagnosis is crucial because these tumors require significantly different surgical approaches and treatment strategies. Unlike many other tumors, brain masses are rarely biopsied before surgery, making accurate imaging interpretation essential. Misdiagnosis can lead to inadequate surgical preparation, prolonged procedures, or suboptimal outcomes.
According to the authors, the accuracy of MRI interpretation varies significantly—ranging from 82.6% to 96.7%—depending on clinician expertise and institutional experience; it can be difficult to differentiate these tumors because they share overlapping features on imaging.
Key Findings
The research team analyzed 1,628 MRI images from 116 patients and achieved remarkable results:
- Overall accuracy: 97.55% at standard confidence thresholds
- Pituitary macroadenomas: 97% sensitivity, 98.96% specificity
- Parasellar meningiomas: 98.41% sensitivity, 95.53% specificity
- External validation on 959 additional images confirmed the model's reliability
Clinical Implications
The model's ability to adjust confidence thresholds after development makes it particularly versatile for different clinical settings:
- High-sensitivity mode (99.39% sensitivity) could benefit community screening settings with limited specialist access
- High-specificity mode (99.31% specificity) may reduce false positives in high-volume tertiary care centers
The technology could serve multiple purposes:
- Assisting in preliminary evaluations and triage
- Expediting referrals to skull base specialists
- Improving preoperative surgical planning
- Providing educational support for residents and fellows
Looking Ahead
The research team plans to expand the model by incorporating additional imaging modalities, clinical metadata such as hormone levels, and multi-label classification to identify coexisting pathologies. They also envision applications beyond skull base surgery, including potential use in thyroid nodule assessment and laryngoscopic lesion evaluation.
About the Study
The research was conducted at Thomas Jefferson University's Department of Otolaryngology-Head and Neck Surgery in collaboration with the Department of Neurological Surgery. The study received exemption approval from the institutional review board and was presented at the AAO-HNSF 2025 Annual Meeting & OTO EXPO, October 11-14, in Indianapolis, Indiana.
Study Citation: Sina EM, Limage K, Anisman E, et al. Automated Machine Learning Differentiation of Pituitary Macroadenomas and Parasellar Meningiomas Using Preoperative Magnetic Resonance Imaging. Otolaryngology–Head and Neck Surgery. December 2025. DOI: 10.1002/ohn.70034
About Otolaryngology–Head and Neck Surgery
Otolaryngology–Head and Neck Surgery (OTO Journal) is the official peer-reviewed publication of the American Academy of Otolaryngology–Head and Neck Surgery Foundation. Our mission is to publish contemporary, ethical, clinically relevant information in otolaryngology, head and neck surgery (ear, nose, throat, head, and neck disorders) that can be used by otolaryngologists, clinicians, scientists, and specialists to improve patient care and public health.
The AAO-HNS/F is one of the world's largest organizations representing specialists who treat the ears, nose, throat, and related structures of the head and neck. Otolaryngologist-head and neck surgeons diagnose and treat medical disorders that are among the most common affecting patients of all ages in the United States and around the world. Those medical conditions include chronic ear disease, hearing and balance disorders, hearing loss, sinusitis, snoring and sleep apnea, allergies, swallowing disorders, nosebleeds, hoarseness, dizziness, and tumors of the head and neck as well as aesthetic and reconstructive surgery and intricate micro-surgical procedures of the head and neck. The Academy has approximately 13,000 members. The AAO-HNS Foundation works to advance the art, science, and ethical practice of otolaryngology-head and neck surgery through education, research, and quality measurement.
