Sentence examples for cancer dynamic from inspiring English sources

To bridge the gap between chronic inflammation and cancer, dynamic participants in the tumour microenvironment have been identified, including tumour-associated macrophages (TAMs) and regulatory T cells (Tregs).

Frameworks that associate cancer dynamic disease progression models with parametric survival models for clinical outcome have recently been proposed to support decision making in early clinical development.

In particular, for patients presenting with lung cancer, dynamic lung MRI is a quick and reliable method to estimate tumour motion and chest wall or mediastinal adhesion and invasion.

In patients with breast cancer, dynamic contrast-enhanced MR parameters, such as maximal tumour enhancement within the first minute of contrast injection and maximal rate of enhancement, have been observed to be superior to traditional prognostic parameters (such as tumour size and nodal metastasis) in the prediction of disease-free and overall survival.

Consequently, our study underscores the uniqueness of individual cancers, dynamic tracking of cancer progression and new therapy strategies targeting the entire cell system.

Emerging potential solutions to this challenge include the exploration of cancer genome dynamic evolution in time and space.

To this end, eight patients with non-small cell lung cancer received dynamic PET scans at three different time points (0, 120, and 240 min) after injection of 426 ± 72 MBq [18F]HX4, each lasting 30 min.

The progression of lung cancer involves dynamic changes in the genome and a complex network of interactions between cancer cells with multiple, distinct cell types that form tumors. Combination therapy using different pharmaceuticals has been proven highly effective due to the ability to affect multiple cellular pathways involved in the disease progression.

Little has been published on assessing response to treatment of breast cancer using dynamic contrast-enhanced methods.

In cancer patients, dynamic contrast-enhanced MRI studies are used for lesion detection and characterization, to detect early relapse within treated tissues, to predict the success of therapy and to monitor the effects of treatments.

The time-intensity profile for the classification of breast cancer on dynamic magnetic resonance images through an artificial neural network was used by the radiologist to achieve a better diagnostic accuracy [ 42].