Shape and texture analyses based on conventional MRI for the preoperative prediction of the aggressiveness of...

Pituitary adenomas can exhibit aggressive behavior, characterized by rapid growth, resistance to conventional treatment, and early recurrence. This study aims to evaluate the clinical value of shape-related features combined with textural features based on conventional MRI in evaluating the aggressiveness of pituitary adenomas and develop the best diagnostic model.

Two hundred forty-six pituitary adenoma patients (84 aggressive, 162 non-aggressive) who underwent preoperative MRI were retrospectively reviewed. The patients were divided into training (n = 193) and testing (n = 53) sets. Clinical information, shape-related, and textural features extracted from the tumor volume on contrast-enhanced T1-weighted images (CE-T1WI), were compared between aggressive and non-aggressive groups. Variables with significant differences were enrolled into Pearson's correlation analysis to weaken multicollinearity. Logistic regression models based on the selected features were constructed to predict tumor aggressiveness under fivefold cross-validation.

Sixty-five imaging features, including five shape-related and sixty textural features, were extracted from volumetric CE-T1WI. Forty-seven features were significantly different between aggressive and non-aggressive groups (all p values < 0.05). After feature selection, four features (SHAPE_Sphericity, SHAPE_Compacity, DISCRETIZED_Q3, and DISCRETIZED_Kurtosis) were put into logistic regression analysis. Based on the combination of these features and Knosp grade, the model yielded an area under the curve value of 0.935, with a sensitivity of 94.4% and a specificity of 82.9%, to discriminate between aggressive and non-aggressive pituitary adenomas in the testing set.

The radiomic model based on tumor shape and textural features study from CE-T1WI might potentially assist in the preoperative aggressiveness diagnosis of pituitary adenomas.

• Pituitary adenomas with aggressive behavior exhibit rapid growth, resistance to conventional treatment, and early recurrence despite gross resection and may require multiline treatments. • Shape-related features and texture features based on CE-T1WI were significantly correlated with the Ki-67 labeling index, mitotic count, and p53 expression, and the proposed model achieved a favorable prediction of the aggressiveness of PAs with an AUC value of 0.935. • The prediction model might provide valuable guidance for individualized treatment in patients with PAs.