Current classification for solid tumors is based upon characteristics of their extent. Size of the primary tumor, presence of metastatic regional lymph nodes and/or of distant metastases are the key elements for their categorization. Treatment decision-making may depend upon defined extent of disease, but it requires the knowledge of several other factors. Furthermore, effective therapeutics is less dependent upon extent of disease, biological features being increasingly instrumental for treatment choice. A new classification that integrates both requisites is proposed. The scope of this proposal is to transform the current rigid and gross categorization into a more analytical and fine tuned listing including biological variables, making st...
This review describes the changes that have been implemented in the Tumor-Node-Metastasis (TNM)-base...
Breast cancer is a highly heterogeneous disease due to its diverse morphological features, the varia...
Background The aim of this study was to examine the effect of measurement bias in breast cancer and ...
The TNM UICC classification of breast cancer categorizes tumor size, regional...
The TNM(UICC) classification of breast cancer categorizes tumor size, regional lymph node involvemen...
Five decades ago, Denoix et al. proposed classification system (tumor node metastasis [TNM]) based o...
Staging systems for cancer, including the most universally used TNM classification system, have been...
The classification of breast tumors and differentiation of stages in men, as well as in women, is ne...
Expanded understanding of biologic factors that modulate the clinical course of malignant disease ha...
The 6th Edition of the TNM classification is considered with particular reference to the evidence ba...
BACKGROUND. The current study defined an optimal tumor size breakpoint to stratify localized renal c...
Isolated tumour cells and micrometastases represent two different staging categories and are often d...
Isolated tumour cells and micrometastases represent two different staging categories and are often d...
Disease burden is the most important determinant of survival in patients with cancer. This domain, r...
Background: Carrying out a correct anatomical classification of lung cancer is crucial to take clini...
This review describes the changes that have been implemented in the Tumor-Node-Metastasis (TNM)-base...
Breast cancer is a highly heterogeneous disease due to its diverse morphological features, the varia...
Background The aim of this study was to examine the effect of measurement bias in breast cancer and ...
The TNM UICC classification of breast cancer categorizes tumor size, regional...
The TNM(UICC) classification of breast cancer categorizes tumor size, regional lymph node involvemen...
Five decades ago, Denoix et al. proposed classification system (tumor node metastasis [TNM]) based o...
Staging systems for cancer, including the most universally used TNM classification system, have been...
The classification of breast tumors and differentiation of stages in men, as well as in women, is ne...
Expanded understanding of biologic factors that modulate the clinical course of malignant disease ha...
The 6th Edition of the TNM classification is considered with particular reference to the evidence ba...
BACKGROUND. The current study defined an optimal tumor size breakpoint to stratify localized renal c...
Isolated tumour cells and micrometastases represent two different staging categories and are often d...
Isolated tumour cells and micrometastases represent two different staging categories and are often d...
Disease burden is the most important determinant of survival in patients with cancer. This domain, r...
Background: Carrying out a correct anatomical classification of lung cancer is crucial to take clini...
This review describes the changes that have been implemented in the Tumor-Node-Metastasis (TNM)-base...
Breast cancer is a highly heterogeneous disease due to its diverse morphological features, the varia...
Background The aim of this study was to examine the effect of measurement bias in breast cancer and ...