Accurate treatment assessment for spinal metastases requires MRIbased delineation of post-ablative necrosis zones. Given the rising incidence of cancer and limited recent research in this area, there is still a need for improvements to address existing limitations and optimize outcomes. In this work, we evaluated the segmentation capabilities of four recent neural networks and explored the optimal imaging sequence combinations with regarding to their segmentation accuracy. The networks were trained using five-fold cross-validation on data from 28 patients with overall 35 necrosis zones. The segmentation performance yielded a Dice Similarity Coefficient of 83.3 ± 13.2% for nnU-Net, 83.2 ± 7.9% for TransUNet, 76.1 ± 11.6% for SwinUNETR and 75.8 ± 14.7% for SwinUNETRV2 when utilizing a combination of contrast-enhanced T1-, native T1-, and T2- weighted sequences. The achieved results represent the current state-of-the-art in spinal necrosis zone segmentation and are on par with the inter-rater-variability of clinical experts. Therefore, this work could play an important role in the assessment of ablative interventions.