Deep Learning-Based Automatic Detection and Segmentation of Gross Tumor for Stereotactic Ablative Radiotherapy in Small-Volume Brain Metastases

Abstract

<h3>Purpose/Objective(s)</h3> Brain metastases (BM) are the most common intracranial tumors in adults. Benefiting from efficient local control, stereotactic ablative radiotherapy (SABR) is becoming popular in the treatment of BM. Detection and manual delineation of BM are labor-intense processes, and BM are occasionally mistaken for normal brain, especially if volumes are very small. Recently, several efforts have been made to develop automatic detection and segmentation for BM using advanced deep learning (DL) algorithms. In this study, we investigated the efficacy and accuracy of a DL model for the detection and segmentation of BM. <h3>Materials/Methods</h3> To train and validate the DL model, magnetic resonance imaging (MRI) for 65 patients and 603 BM were collected and analyzed. Manual contouring for BM was done by a radiation oncologist. We utilized 2.5-dimensional training using a two-dimensional U-Net as the DL architecture to avoid inefficiencies of the three-dimensional architecture. Because of MRI characteristic, we applied N4 bias field correction and gamma correction as pre-processing techniques. Also, we utilized the overlapping patch technique and under-sampling technique to extract small-volume BM from the normal brain that occupies most of the slice. During the training, data augmentations (horizontal flip, vertical flip, random rotation, random blur) were applied on the fly randomly. The detection performance was measured with sensitivity and average false positive rate and the segmentation performance with the Dice coefficient with dilation (DWD) with dilation factor set to a radius of 4 pixels. BMs with volumes < 0.04 cc were considered small-volume BM. <h3>Results</h3> As a test-set, 12 patients with 58 BM were randomly selected to evaluate the performance of the model. In the test-set, 41.2% BM had a volume of < 0.04 cc, Max volume of 1.219 cc, min volume of 0.021 cc, median volume of 0.068 cc, and mean volume of 0.158 cc. The DL showed 100% detection sensitivity for BM with volumes ≥0.04 cc, and 85.7% sensitivity for BM with volumes < 0.04 cc volume, showing a sensitivity of 97%, and the average number of false positives was 2.08 per patient. Out of the 12 test-set patients, 9 (75%) had less than 10 BM and 3 (25%) had 10 or more BM. In patients with less than 10 BM, the detection sensitivity was 100%. In patients with 10 or more BM, the overall detection sensitivity was 93.5%. The detection sensitivity was 100% and 86% for BM with volumes ≥0.04 cc and < 0.04 cc, respectively. DWD showed a value of over 67% in BM with volumes > 0.04 cc and 49.7% in BM with volumes < 0.04 cc. For all BM, the DWD value was 63%. <h3>Conclusion</h3> Our DL model can detect and segment BM with good performance; it is especially effective even for small-volume BM, suggesting its considerable benefit for gross tumor volume detection and segmentation for stereotactic radiosurgery or SABR.