Stratified Assessment of a Commercial Deep Learning Algorithm for Automated Detection and Contouring of Metastatic Brain Tumors in Stereotactic Radiosurgery

Abstract

<h3>Purpose/Objective(s)</h3> Artificial intelligence-based tools can be leveraged to improve detection and segmentation of brain metastases for stereotactic radiosurgery (SRS). This study explores a deep learning algorithm with recent FDA clearance to assist in brain tumor contouring. In this study, we aimed to assess the performance of this tool by various demographic and clinical characteristics among patients with brain metastases treated with SRS. <h3>Materials/Methods</h3> We randomly selected 100 patients with brain metastases who underwent initial SRS on a frameless robotic radiosurgery system from 2017 to 2020 at a single institution. Cases with resection cavities were excluded from the analysis. Computed tomography (CT) and axial T1-weighted post-contrast magnetic resonance (MR) image data were extracted for each patient and uploaded to VBrain. A brain metastasis was considered "detected" when the VBrain "predicted" contours overlapped with the corresponding physician contours ("ground-truth" contours). We evaluated performance of VBrain against ground-truth contours using the following metrics: lesion-wise Dice similarity coefficient (DSC), lesion-wise average Hausdorff distance (AVD), false positive count (FP), and lesion-wise sensitivity (%). Wilcoxon rank-sum tests were performed to assess the relationships between patient characteristics including sex, race, primary histology, age, and size and number of brain metastases, and performance metrics such as DSC, AVD, FP, and sensitivity. <h3>Results</h3> We analyzed 100 patients with 422 intact brain metastases treated with SRS. Our cohort consisted of patients with a median number of 2 brain metastases (range: 1 to 47), median age of 79 (range: 19 to 91), and 50% male and 50% female patients. The primary site breakdown was 56% lung, 10% melanoma, 9% breast, 8% gynecological, 5% gastrointestinal, 2% prostate, and 5% other, while the race breakdown was 60% White, 18% Asian, 3% Black/African American, 2% Native Hawaiian or other Pacific Islander, and 17% other/unknown/not reported. The median tumor size was 0.118 c.c. (range: 0.003-28.838 c.c.). We found mean lesion-wise DSC to be 0.714, mean lesion-wise AVD to be 8.44% of lesion size (0.796 mm), mean FP count to be 0.17 tumors per case, and lesion-wise sensitivity to be 83.65% for all lesions. Moreover, mean sensitivity was found to be 98%, 91.93%, and 79.87% for lesions with diameter equal to and greater than 10 mm, 5 mm, and 3 mm, respectively. No other significant differences in performance metrics were observed across demographic or clinical characteristic groups. <h3>Conclusion</h3> A commercial deep learning algorithm showed promising results in segmenting brain metastases, with 98% sensitivity for metastases with diameters of 1 cm or higher. Performance declined for smaller lesions. As advances in imaging and radiosurgery enable the detection and treatment of smaller brain metastases, future work is ongoing to develop and improve these tools to assist in radiosurgical treatment planning.