Brain Tumor Segmentation for Multi-Modal MRI with Missing Information.

Deep convolutional neural networks (DCNNs) have shown promise in brain tumor segmentation from multi-modal MRI sequences, accommodating heterogeneity in tumor shape and appearance. The fusion of multiple MRI sequences allows networks to explore complementary tumor information for segmentation. However, developing a network that maintains clinical relevance in situations where certain MRI sequence(s) might be unavailable or unusual poses a significant challenge. While one solution is to train multiple models with different MRI sequence combinations, it is impractical to train every model from all possible sequence combinations. In this paper, we propose a DCNN-based brain tumor segmentation framework incorporating a novel sequence dropout technique in which networks are trained to be robust to missing MRI sequences while employing all other available sequences. Experiments were performed on the RSNA-ASNR-MICCAI BraTS 2021 Challenge dataset. When all MRI sequences were available, there were no significant differences in performance of the model with and without dropout for enhanced tumor (ET), tumor (TC), and whole tumor (WT) (p-values 1.000, 1.000, 0.799, respectively), demonstrating that the addition of dropout improves robustness without hindering overall performance. When key sequences were unavailable, the network with sequence dropout performed significantly better. For example, when tested on only T1, T2, and FLAIR sequences together, DSC for ET, TC, and WT increased from 0.143 to 0.486, 0.431 to 0.680, and 0.854 to 0.901, respectively. Sequence dropout represents a relatively simple yet effective approach for brain tumor segmentation with missing MRI sequences.

The use of stereotactic radiosurgery for the treatment of spinal axis tumors: a review.

As the prevalence of cancer in the general population increases, a greater proportion of patients will present with symptomatic metastatic lesions to the spine. While surgery has been historically considered the treatment of choice for spinal cord/nerve root compression, mechanical instability and intractable pain, radiation therapy - particularly stereotactic radiosurgery (SRS) - has been increasingly used as either a primary or adjuvant treatment modality. In this manuscript, the authors perform a review on the principles behind SRS and its use in the treatment of spinal tumors, specifically primary and secondary malignant tumors. In the last decades, numerous retrospective studies have shown the feasibility of SRS as both primary treatment for malignant tumors, as well as adjuvant treatment following surgical resection. Although local control rates may reach 90%, future studies are warranted to determine optimal doses, fractionation of therapy and the long-term implications of irradiation to neural structures.