Radiomic Features on Multiparametric MRI for Preoperative Evaluation of Pituitary Macroadenomas Consistency: Preliminary Findings

Background Preoperative assessment of the consistency of pituitary macroadenomas (PMA) might be needed for surgical planning. Purpose To investigate the diagnostic performance of radiomics models based on multiparametric magnetic resonance imaging (mpMRI) for preoperatively evaluating the tumor consistency of PMA. Study Type Retrospective. Population One hundred and fifty‐six PMA patients (soft consistency, N = 104 vs. hard consistency, N = 52), divided into training ( N = 108) and test ( N = 48) cohorts. The tumor consistency was determined on surgical findings. Field Strength/Sequence T1‐weighted imaging (T1WI), contrast‐enhanced T1WI (T1CE), and T2‐weighted imaging (T2WI) using spin‐echo sequences with a 3.0‐T scanner. Assessment An automated three‐dimensional (3D) segmentation was performed to generate the volume of interest (VOI) on T2WI, then T1WI/T1CE were coregistered to T2WI. A total of 388 radiomic features were extracted on each VOI of mpMRI. The top‐discriminative features were identified using the minimum‐redundancy maximum‐relevance method and 0.632+ bootstrapping. The radiomics models based on each sequence and their combinations were established via the random forest (RF) and support vector machine (SVM), and independently evaluated for their ability in distinguishing PMA consistency. Statistical Tests Mann–Whitney U ‐test and Chi‐square test were used for comparison analysis. The area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), and relative standard deviation (RSD) were calculated to evaluate each model's performance. ACC with P ‐value<0.05 was considered statistically significant. Results Eleven mpMRI‐based features exhibited statistically significant differences between soft and hard PMA in the training cohort. The radiomics model built on combined T1WI/T1CE/T2WI demonstrated the best performance among all the radiomics models with an AUC of 0.90 (95% confidence interval [CI]: 0.87–0.92), ACC of 0.87 (CI: 0.84–0.89), SEN of 0.83 (CI: 0.81–0.85), and SPE of 0.87 (CI: 0.85–0.99) in the test cohort. Data Conclusion Radiomic features based on mpMRI have good performance in the presurgical evaluation of PMA consistency. Level of Evidence 3 Technical Efficacy Stage 2