Radiomics with Ensemble Machine Learning Predicts Dopamine Agonist Response in Patients with Prolactinoma
Journal of Clinical Endocrinology & Metabolism
Yae Won Park, Jihwan Eom, Sooyon Kim, Hwiyoung Kim, Sung Soo Ahn, Cheol Ryong Ku, Eui Hyun Kim, Eun Jig Lee, Sun Ho Kim, Seung-Koo Lee
Data-driven Clinical outcome Prediction
Mar, 2021
Context: Early identification of the response of prolactinoma patients to dopamine agonists (DA) is crucial in treatment planning.

Objective: To develop a radiomics model using an ensemble machine learning classifier with conventional magnetic resonance images (MRIs) to predict the DA response in prolactinoma patients.

Design: Retrospective study.

Setting: Severance Hospital.

Patients: A total of 177 prolactinoma patients who underwent baseline MRI (109 DA responders and 68 DA non-responders) were allocated to the training (n = 141) and test (n = 36) sets. Radiomic features (n = 107) were extracted from coronal T2-weighed MRIs. After feature selection, single models (random forest, light gradient boosting machine, extra-trees, quadratic discrimination analysis, and linear discrimination analysis) with oversampling methods were trained to predict the DA response. A soft voting ensemble classifier was used to achieve the final performance. The performance of the classifier was validated in the test set.

Results: The ensemble classifier showed an area under the curve (AUC) of 0.81 (95 % confidence interval [CI], 0.74-0.87) in the training set. In the test set, the ensemble classifier showed an AUC, accuracy, sensitivity, and specificity of 0.81 (95 % CI, 0.67-0.96), 77.8 %, 78.6 %, and 77.3 %, respectively. The ensemble classifier achieved the highest performance among all the individual models in the test set.

Conclusions: Radiomic features may be useful biomarkers to predict the DA response in prolactinoma patients.

Keywords: Machine learning; Magnetic resonance imaging; Pituitary neoplasms; Prolactionoma; Radiomics.