Automated Detection, Segmentation, and Tracking of Brain Metastases in Repeated Courses of Stereotactic Radiosurgery Using Integrated Artificial Intelligence.

Abstract

Salvage stereotactic radiosurgery (SRS) for distant brain metastases has been demonstrated as a safe and effective approach for intracranial recurrences after initial SRS. However, accurate tumor detection and segmentation among responding tumors within the irradiated parenchyma can be challenging. The requirement for the registration and reference to the previous course of SRS is very time-consuming and suffers significant inter and intra-reader variability. Artificial intelligence (AI)-assisted system has been proven to improve the accuracy and efficiency in the clinical flow of de-novo SRS. We hypothesize that an integrated AI system can facilitate an automated tumor contouring process for repeated SRS. Three patients who underwent their third course of SRS to brain metastases were selected for the pioneering works. They have had two sessions of SRS with a mean lesion number of 4 and 3.7, respectively. VBrain, an FDA-approved brain tumor management AI platform, was used to co-registered serial MR scans and automatically identify, track, and contour brain metastases for each course of SRS. The AI also indicated new lesions and treated lesions for each course. Three radiation oncologists experienced in brain SRS contoured the gross tumor volumes (GTVs) of the third course of SRS in two reader modes (assisted then unassisted) with a memory washout period of one week between each section. The segmentation ground truth was established through consensus among the three experts. Lesion-wise sensitivity, contouring accuracy, and consuming time were compared between the two contouring modes. In each patient, there were 15, 11, and 9 metastases, with a median diameter of 4.72 (95% CI: 4.05, 6.91) mm. The mean lesion-wise sensitivity was 96.96±2.47% with AI assistance and 76.90 ± 7.10% without assistance. There were two false-positive lesions in the assisted read, resulting in a low average false-positive rate of 0.67 per patient, while no false positive for the unassisted mode. AI assistance improved contouring accuracy. The median Dice similarity coefficient (DSC) was 0.71 (95% CI: 0.55, 0.87) for assisted contouring and 0.65 (95% CI: 0.46, 0.85) for unassisted contouring. We also use average Hausdorff distance (HD) to measure segmentation results. The mean HD was 0.72± 0.13 mm versus 0.73±0.08 mm for the two contouring modes (p = 0.02) Furthermore, the median contouring time per case was significantly shorter with AI assistance than without assistance (20.8 minutes vs. 29.8 minutes; p < 0.001), corresponding to a 43.2% time-saving. Our results suggest that the integration of an AI-based system into repeated brain SRS can significantly improve the accuracy and efficiency of tumor detection and segmentation. This approach has the potential to streamline the treatment planning process for salvage SRS.