Abstract
Background: Prediction of tumor consistency before surgery is of vital importance to determine individualized therapeutic schemes for patients with acromegaly. The present study was performed to noninvasively predict tumor consistency based on magnetic resonance imaging and radiomics analysis.Methods: In total, 158 patients with acromegaly were randomized into the primary cohort (n = 100) and validation cohort (n = 58). The consistency of the tumor was classified as soft or firm according to the neurosurgeon's evaluation. The critical radiomics features were determined using the elastic net feature selection algorithm, and the radiomics signature was constructed. The most valuable clinical characteristics were then selected based on the multivariable logistic regression analysis. Next, a radiomics model was developed using the radiomics signature and clinical characteristics, and 30 patients with acromegaly were recruited for multicenter validation of the radiomics model. The model's performance was evaluated based on the receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), accuracy, and other associated classification measures. Its calibration, discriminating capacity, and clinical usefulness were also evaluated.Results: The radiomics signature established according to four radiomics features screened in the primary cohort exhibited excellent discriminatory capacity in the validation cohort. The radiomics model, which incorporated both the radiomics signature and Knosp grade, displayed favorable discriminatory capacity and calibration, and the AUC was 0.83 (95% confidence interval, 0.81–0.85) and 0.81 (95% confidence interval, 0.78–0.83) in the primary and validation cohorts, respectively. Furthermore, compared with the clinical characteristics, the as-constructed radiomics model is more effective in prediction of the tumor consistency in patients with acromegaly. Moreover, the multicenter validation and decision curve analysis suggested that the radiomics model was clinically useful.Conclusions: This radiomics model can assist neurosurgeons in predicting tumor consistency in patients with acromegaly before surgery and facilitates the determination of individualized therapeutic schemes.
Highlights
Pituitary adenoma (PA), one of the most frequently seen benign pituitary gland neoplasms, constitutes about 10–25% of all primary intracranial tumors [1, 2]; the prevalence of PA is showing an increasing trend [3]
Significant interclass differences in the tumor volume and Knosp classification were found in both cohorts, which might be ascribed to the correlations of these parameters with tumor consistency
The results suggested that the constructed radiomics signature had favorable performance in predicting tumor consistency, with an area under the curve (AUC) of 0.84 [95% confidence interval (CI), 0.81–0.86] and 0.76 in the primary and validation cohorts, respectively
Summary
Pituitary adenoma (PA), one of the most frequently seen benign pituitary gland neoplasms, constitutes about 10–25% of all primary intracranial tumors [1, 2]; the prevalence of PA is showing an increasing trend [3]. Tumor consistency remains a major factor that affects the surgical resection rate because of the complicated anatomic structure in the sella region and the limited operative field of view, which is true for macroadenomas (frequently seen among patients with acromegaly) [9, 10]. It is of vital importance to develop a noninvasive preoperative technique to enable precise prediction of the tumor consistency and to achieve a successful operative outcome and to establish an individualized therapeutic scheme, such as drug treatment [12]. Prediction of tumor consistency before surgery is of vital importance to determine individualized therapeutic schemes for patients with acromegaly. The present study was performed to noninvasively predict tumor consistency based on magnetic resonance imaging and radiomics analysis
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