![]() ![]() Cox proportional hazard regression on each parameter in close and distant levels demonstrated that capsule thickness, number of lobules and percent of reactive follicles were significantly associated with time to death from disease. Limited data from level V and regression analyses inferred that the values in level IV represented the worst changes for most patients. Data were presented by maximum scores of each parameter in I-III (close) and in IV-V (distant) levels. ![]() These were compared to 328 LN0 selected from neck dissections with metastases (pN+) after exclusion of metastatic LNs. ![]() A total of 435 LN0 selected from pN0 neck dissections (up to three nodes in each level) were scored for histopathological parameters of LN areas, capsule thickness, subcapsular and medullary sinus ectasia, lobular architecture and percent of cortical reactive follicles. We evaluated the relationship between clinical outcomes and different histopathological changes in tumor-negative LNs (LN0) selected from neck dissections without metastatic disease (pN0). Regional lymph node (LN) metastasis in oral cancer patients is the most significant grave prognostic factor. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. Radiogenomics analysis reveals that a prognostic radiomic signature, capturing intratumour heterogeneity, is associated with underlying gene-expression patterns. We find that a large number of radiomic features have prognostic power in independent data sets of lung and head-and-neck cancer patients, many of which were not identified as significant before. Here we present a radiomic analysis of 440 features quantifying tumour image intensity, shape and texture, which are extracted from computed tomography data of 1,019 patients with lung or head-and-neck cancer. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. Human cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Such methods may comprise a method for assessing attributes of tumor biology in vivo and for predicting the response of such tumors to various drugs and interventions. Using this methodology, we describe the presence of macroscopic organizational features in patients with head and neck cancers, specifically depicting regional differences between the geometrically coherent periphery and incoherent core region. This approach employs varying magnetic field gradients and diffusion sensitivities to yield voxel-scale probability distribution functions of proton diffusivity, and then maps multi-voxel cellular alignment with tractography. We present a novel methodology for probing the micro-structural constituents of tumors in vivo utilizing generalized Q-space MRI. Recognizing that tumors are composed of heterogeneous arrays of cells and their environment, there is a compelling rationale to explore the macroscopic organization of tumor tissue. Current approaches for studying tumor activity in patients involve molecular characterization in excised tissue or biopsied samples. ![]()
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