Utility of artificial intelligence in radiosurgery for pituitary adenoma: a deep learning-based automated segmentation model and evaluation of its clinical applicability.

Abstract

Objective: The objective of this study was to develop a deep learning model for automated pituitary adenoma segmentation in MRI scans for stereotactic radiosurgery planning and to assess its accuracy and efficiency in clinical settings.

Methods: An nnU-Net-based model was trained on MRI scans with expert segmentations of 582 patients treated with Leksell Gamma Knife over the course of 12 years. The accuracy of the model was evaluated by a human expert on a separate dataset of 146 previously unseen patients. The primary outcome was the comparison of expert ratings between the predicted segmentations and a control group consisting of original manual segmentations. Secondary outcomes were the influence of tumor volume, previous surgery, previous stereotactic radiosurgery (SRS), and endocrinological status on expert ratings, performance in a subgroup of nonfunctioning macroadenomas (measuring 1000-4000 mm3) without previous surgery and/or radiosurgery, and influence of using additional MRI modalities as model input and time cost reduction.

Results: The model achieved Dice similarity coefficients of 82.3%, 63.9%, and 79.6% for tumor, normal gland, and optic nerve, respectively. A human expert rated 20.6% of the segmentations as applicable in treatment planning without any modifications, 52.7% as applicable with minor manual modifications, and 26.7% as inapplicable. The ratings for predicted segmentations were lower than for the control group of original segmentations (p < 0.001). Larger tumor volume, history of a previous radiosurgery, and nonfunctioning pituitary adenoma were associated with better expert ratings (p = 0.005, p = 0.007, and p < 0.001, respectively). In the subgroup without previous surgery, although expert ratings were more favorable, the association did not reach statistical significance (p = 0.074). In the subgroup of noncomplex cases (n = 9), 55.6% of the segmentations were rated as applicable without any manual modifications and no segmentations were rated as inapplicable. Manually improving inaccurate segmentations instead of creating them from scratch led to 53.6% reduction of the time cost (p < 0.001).

Conclusions: The results were applicable for treatment planning with either no or minor manual modifications, demonstrating a significant increase in the efficiency of the planning process. The predicted segmentations can be loaded into the planning software used in clinical practice for treatment planning. The authors discuss some considerations of the clinical utility of the automated segmentation models, as well as their integration within established clinical workflows, and outline directions for future research.