2 resultados para Cone-beam CT, dose to organs, IGRT, cancer patients
em Universidade do Minho
Resumo:
Hyaluronan (HA) shows promise for detecting cancerous change in pleural effusion and urine. However, there is uncertainty about the localization of HA in tumor tissue and its relationship with different histological types and other components of the extracellular matrix, such as angiogenesis. We evaluated the association between HA and degree of malignancy through expression in lung tumor tissue and sputum. Tumoral tissue had significantly increased HA compared to normal tissue. Strong HA staining intensity associated with cancer cells was significant in squamous cell carcinoma compared to adenocarcinoma and large cell carcinoma. A significant direct association was found between tumors with a high percentage of HA and MVD (microvessel density) in tumoral stroma. Similarly significant was the direct association between N1 tumors and high levels of HA in cancer cells. Cox multivariate analysis showed significant association between better survival and low HA. HA increased in sputum from lung cancer patients compared to cancer-free and healthy volunteers and a significant correlation was found between HA in sputum and HA in cancer tissue. Localization of HA in tumor tissue was related to malignancy and reflected in sputum, making this an emerging factor for an important diagnostic procedure in patients suspected to have lung cancer. Further study in additional patients in a randomized prospective trial is required to finalize these results and to validate our quantitative assessment of HA, as well as to couple it to gold standard sputum cytology.
Resumo:
Allied to an epidemiological study of population of the Senology Unit of Braga’s Hospital that have been diagnosed with malignant breast cancer, we describe the progression in time of repeated measurements of tumor marker Carcinoembryonic antigen (CEA). Our main purpose is to describe the progression of this tumor marker as a function of possible risk factors and, hence, to understand how these risk factors influences that progression. The response variable, values of CEA, was analyzed making use of longitudinal models, testing for different correlation structures. The same covariates used in a previous survival analysis were considered in the longitudinal model. The reference time used was time from diagnose until death from breast cancer. For diagnostic of the models fitted we have used empirical and theoretical variograms. To evaluate the fixed term of the longitudinal model we have tested for a changing point on the effect of time on the tumor marker progression. A longitudinal model was also fitted only to the subset of patients that died from breast cancer, using the reference time as time from date of death until blood test.