Sentence examples for prediction of therapy from inspiring English sources

For further assessing the effects of predicted RC on prediction of therapy outcome, linear models for the prediction of changes in VL or CD4+ T-cell count based on the following sets of input features were trained: (i) Treatment activity expressed in terms of TAS.

Differentiation of active inflammation from fibrosis is of significant clinical importance both for the prediction of therapy response and clinical outcome of ILD.

Frequency [4] and time-frequency analysis [5] of these f-waves can be used for the identification of underlying AF mechanisms and prediction of therapy efficacy.

Pre-therapeutic prediction of therapy response and clinical outcome is an important field in medicine and poses new challenges to statisticians.

Cleator and colleagues [17] showed, using gene expression microarray analysis, that the stromal component of breast carcinomas influenced the prediction of therapy response.

However, it is inferred that independent factors cannot provide a consistent prediction of therapy response [ 40].

At the single-cell level in conjunction with data-pattern analysis, high-content screening by image analysis or flow cytometry of clinical cell- or tissue-section samples provides differential molecular profiles for the personalized prediction of therapy-dependent disease progression in patients.

Thus, novel biomarkers are required to improve the prediction of recurrence, metastasis and, in particular, the prediction of therapy-related carcinogenesis.

Other nonlinear measures such as the spatial correlations, higher order moments of the spatial distribution and clustering coefficients can be used to characterize the metastatic potential of the tumours and can improve the predictions of therapy outcome.

Although many efforts have been made to identify biomarkers for therapy response [ 6], no clinical or single laboratory marker exists today that allows a prediction of TNFα therapy efficacy in the individual patient.

However, for prediction of immune therapy success, information from both tumor and surrounding cells has been valuable [ 45– 45].