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Published on: January 17, 2018
Radiomic Features on Multiparametric MRI for Preoperative Evaluation of Pituitary Macroadenomas Consistency:
Tao Wan1,2, Chunxue Wu3, Ming Meng3
1School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
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.
Insights
Radiomics models using multiparametric MRI accurately predict pituitary macroadenoma consistency before surgery. This aids surgical planning by assessing tumor hardness non-invasively.
Area of Science:
- Radiology
- Medical Imaging
- Oncology
Background:
- Preoperative assessment of pituitary macroadenoma (PMA) consistency is crucial for surgical planning.
- Tumor consistency can influence surgical approach and outcomes.
Purpose of the Study:
- To evaluate the diagnostic performance of radiomics models derived from multiparametric magnetic resonance imaging (mpMRI) for preoperatively assessing PMA consistency.
- To determine if radiomics can differentiate between soft and hard PMA.
Main Methods:
- A retrospective study included 156 PMA patients with tumor consistency determined during surgery.
- Radiomic features were extracted from T1-weighted imaging (T1WI), contrast-enhanced T1WI (T1CE), and T2-weighted imaging (T2WI) using a 3.0-T scanner.
- Random forest and support vector machine models were trained and tested to distinguish PMA consistency.
Main Results:
- Eleven mpMRI-based radiomic features showed significant differences between soft and hard PMA.
- The combined T1WI/T1CE/T2WI radiomics model achieved the highest performance in the test cohort.
- This model demonstrated an AUC of 0.90, accuracy of 0.87, sensitivity of 0.83, and specificity of 0.87.
Conclusions:
- Radiomic features extracted from mpMRI show strong potential for presurgical evaluation of PMA consistency.
- These findings suggest that radiomics can non-invasively predict tumor consistency, aiding surgical planning.
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