In the last few years there have been dramatic increases in the range and quality of information available from non-invasive medical imaging methods, so that several potentially valuable imaging measurements are now available to quantitatively measure tumor growth, assess tumor status as well as anatomical or functional details. Using different methods such as the CT scan, magnetic resonance imaging (MRI), or positron emission tomography (PET), it is now possible to evaluate and define tumor status at different levels: physiological, molecular and cellular. These multimodal data help the decision process of oncologists in the definition of therapeutic protocols. At present, this decision process is mainly based on previously acquired statistical evidence and on the practitioner experience. The quality of the response to a treatment is decided according to the OMS criteria by estimating the length of the two main axis of the tumor in the largest cut. There are two blocking difficulties in this approach that we want to attack: i) previous statistical information is not patient specific; ii) there exist no quantitative mean of summarizing and using as predicting tools the multimodal patient-specific data presently available thanks to CT scans, MRI, PET scans and molecular biology data. The aim of this talk is to show how we can provide a simulation framework based on quantitative patient-specific data by using nonlinear model based on PDE. I will present how one can build such model in order to describe the may features of tumor growth and how one can expect to obtain a suitable parametrization of tumor growth by solving inverse problem. I will present some mathematical results on the models. The applications will concern lung and lever metastasis, meningiomas and brain tumor.