Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61\% Dice score, and the best classification performance was about 80\% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection.Comment: 13 pages, 4 figures, 4 table

Cancer Types: RNA Sequencing Values from Tumor Samples/Tissues

samples: 2086 genes: 971 categories: 5 (BRCA, KIRC, LUAD, LUSC, UCEC) . [data: 2086x972 double] - each row contains a specific sample - each column contains the RPKM RNA-Seq values of a specific gene - the last column contains the cancer categories encoded numerically: 1=BRCA , 2=KIRC, 3=LUAD, 4=LUSC, 5=UCEC . [geneIds: 1x971 cell] - each cell contains the name/gene ID of every gene stored in each column . [cancerTypes: 2086x1 cell] - each cell contains the category of every sample stored in each row . [1] BRCA: 878 [2] KIRC: 537 [3] LUAD: 162 [4] LUSC: 240 [5] UCEC: 269 (#) in total: 208

Self-organizing Hidden Markov model map (SOHMMM): Biological sequence clustering and cluster visualization

The present study devises mapping methodologies and projection techniques that visualize and demonstrate biological sequence data clustering results. The Sequence Data Density Display (SDDD) and Sequence Likelihood Projection (SLP) visualizations represent the input symbolical sequences in a lower-dimensional space in such a way that the clusters and relations of data elements are depicted graphically. Both operate in combination/synergy with the Self-Organizing Hidden Markov Model Map (SOHMMM). The resulting unified framework is in position to analyze automatically and directly raw sequence data. This analysis is carried out with little, or even complete absence of, prior information/domain knowledge. © Springer Science+Business Media LLC 2017