Glioblastoma multiforme (GBM) is the most common and lethal primary brain tumor in adults. We combined neuroimaging and DNA microarray analysis to create a multidimensional map of gene-expression patterns in GBM that provided clinically relevant insights into tumor biology. Tumor contrast enhancement and mass effect predicted activation of specific hypoxia and proliferation gene-expression programs, respectively. Overexpression of EGFR, a receptor tyrosine kinase and potential therapeutic target, was also directly inferred by neuroimaging and was validated in an independent set of tumors by immunohistochemistry. Furthermore, imaging provided insights into the intratumoral distribution of gene-expression patterns within GBM. Most notably, an ''infiltrative'' imaging phenotype was identified that predicted patient outcome. Patients with this imaging phenotype had a greater tendency toward having multiple tumor foci and demonstrated significantly shorter survival than their counterparts. Our findings provide an in vivo portrait of genome-wide gene expression in GBM and offer a potential strategy for noninvasively selecting patients who may be candidates for individualized therapies.cancer ͉ genomics ͉ glioblastoma multiforme ͉ radiogenomics R ecent advances in the molecular analysis of brain tumors have led to an improved understanding of gliablastoma multiforme (GBM) tumor biology and the genomic heterogeneity that typifies the disease (1-7). However, the diagnosis and treatment of GBM is still largely guided by histopathology and immunohistochemistry, approaches that group histologically similar tumors that can often demonstrate markedly distinct clinical behaviors. Overall survival remains poor, with most patients succumbing to their disease within 15 months of diagnosis. Methods that assess molecular differences between GBMs hold promise for improving outcome by potentially allowing for individualized patient management.Magnetic resonance imaging (MRI) is routinely used in the diagnosis, characterization, and clinical management of GBM (8). It is a powerful and noninvasive diagnostic imaging tool that allows global assessment of GBMs and their interaction with their local environment. In its ability to extract structural, compositional, physiological, and functional information, MRI captures multidimensional, in vivo portraits of GBMs. Interestingly, histologically similar tumors often demonstrate highly distinct imaging profiles on MRI (9). Recently, several studies have attempted to correlate imaging findings with molecular markers, but no consistent associations have emerged. and many of the imaging features that characterize tumors currently lack biological or molecular correlates (10-15). Much of the information encoded within neuroimaging studies therefore remains unaccounted for and incompletely characterized at the molecular level. We reasoned that the phenotypic diversity of GBM captured by neuroimaging reflects underlying inter-and intratumoral gene-expression differences and that these relationships ...

Large-scale quantum computation will only be achieved if experimentally implementable quantum error correction procedures are devised that can tolerate experimentally achievable error rates. We describe an improved decoding algorithm for the Kitaev surface code, which requires only a two-dimensional square lattice of qubits that can interact with their nearest neighbors, that raises the tolerable quantum gate error rate to over 1%. The precise maximum tolerable error rate depends on the error model, and we calculate values in the range 1.1-1.4% for various physically reasonable models. These values represent a very high threshold error rate calculated in a constrained setting.

Microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is an independent predictor of poor outcomes subsequent to surgical resection or liver transplantation (LT); however, MVI currently cannot be adequately determined preoperatively. Radiogenomic venous invasion (RVI) is a contrast-enhanced computed tomography (CECT) biomarker of MVI derived from a 91-gene HCC “venous invasion” gene expression signature. Preoperative CECTs of 157 HCC patients who underwent surgical resection (N = 72) or LT (N = 85) between 2000 and 2009 at three institutions were evaluated for the presence or absence of RVI. RVI was assessed for its ability to predict MVI and outcomes. Interobserver agreement for scoring RVI was substantial among five radiologists (κ = 0.705; P < 0.001). The diagnostic accuracy, sensitivity, and specificity of RVI in predicting MVI was 89%, 76%, and 94%, respectively. Positive RVI score was associated with lower overall survival (OS) than negative RVI score in the overall cohort (P < 0.001; 48 vs. >147 months), American Joint Committee on Cancer tumor-node-metastasis stage II (P < 0.001; 34 vs. >147 months), and in LT patients within Milan criteria (P < 0.001; 69 vs. >147 months). Positive RVI score also portended lower recurrence-free survival at 3 years versus negative RVI score (P = 0.001; 27% vs. 62%). Conclusion: RVI is a noninvasive radiogenomic biomarker that accurately predicts histological MVI in HCC surgical candidates. Its presence on preoperative CECT is associated with early disease recurrence and poor OS and may be useful for identifying patients less likely to derive a durable benefit from surgical treatment. (Hepatology 2015;62:792–800)