MRI radiomics-based approach to predict pituitary neuroendocrine tumor invasiveness.
Authors
Affiliations (7)
Affiliations (7)
- Dipartimento di Diagnostica per Immagini e Radioterapia Oncologica, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy.
- Radiomics GSTeP Core Research Facility, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy.
- Radiomics GSTeP Core Research Facility, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy. [email protected].
- Università Cattolica del Sacro Cuore, Rome, Italy.
- Neurosurgery, Dipartimento di neuroscienze, Organi di Senso e Torace, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy.
- Endocrinology, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy.
- Pathology, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy.
Abstract
To assess the diagnostic potential of magnetic resonance imaging (MRI) radiomics and machine learning models using T2-weighted and contrast-enhanced (CE)-T1-weighted images, individually and combined, to predict the invasiveness of pituitary neuroendocrine tumors (PitNETs). Patients with macro-PitNETs were retrospectively enrolled from 2019 to 2022. Radiomic features were extracted from manually segmented lesions on preoperative T2-weighted and CE-T1-weighted images and, after a feature selection step, used to assess invasiveness, defined following Trouillas' classification. Five machine learning models (logistic regression, random forest, gradient boosting, AdaBoost, XGBoost) were trained using CE-T1-weighted, T2-weighted, and CE-T1-weighted plus T2-weighted features. Performance was evaluated on a test set using the area under the receiver operating characteristic curve (AUC). Two hundred patients were included in the study: 95 PitNETs were noninvasive (74 grade 1a; 21 grade 1b) and 105 invasive (70 grade 2a; 35 grade 2b). A total of 102 radiomic features were extracted per sequence. The best-performing model was the XGBoost, using five combined CE-T1-weighted and T2-weighted features, with an AUC of 0.85 (95% confidence interval: 0.75‒0.95). Lower AUC values were obtained for logistic regression using CE-T1-weighted images (0.80) and AdaBoost using T2-weighted images (0.78). The XGBoost model, incorporating tumor shape, texture, and first-order features extracted from both CE-T1-weighted and T2-weighted MRI, showed high performance in predicting PitNETs invasiveness. This radiomic model might help identify tumors with a higher risk of disease persistence, recurrence, or progression. The radiomic model based on contrast-enhanced T1-weighted and T2-weighted MRI demonstrated high discriminative ability in predicting invasiveness of pituitary neuroendocrine tumors and could aid in identifying tumors that may be at higher risk for recurrence or progression, ultimately improving patient outcomes through personalized treatment strategies. Pituitary neuroendocrine tumors (PitNETs) represent a significant challenge in clinical practice. Accurate preoperative prediction of PitNET invasiveness is crucial for surgery and prognosis. Contrast-enhanced T1-weighted and T2-weighted MRI-based radiomic model effectively predicts PitNET invasiveness. The developed radiomic model could help optimize individualized treatment decisions before surgery.