Brain Tumor Models to Predict Clinical Outcome: Like the Phoenix?

2012 ◽  
pp. 3-20 ◽  
Author(s):  
Lois A. Lampson
Keyword(s):  
Brain Tumor ◽  
Clinical Outcome ◽  
Tumor Models ◽  
The Phoenix

Clinical outcome assessments of motor status in patients undergoing brain tumor surgery

2021 ◽  
Vol 201 ◽  
pp. 106420
Author(s):  
Mayla Santana Correia ◽  
Iuri Santana Neville ◽  
Cesar Cimonari de Almeida ◽  
Cintya Yukie Hayashi ◽  
Luana Talita Diniz Ferreira ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Clinical Outcome ◽  
Tumor Surgery ◽  
Brain Tumor Surgery ◽  
Outcome Assessments ◽  
Clinical Outcome Assessments

scholarly journals Opportunities and challenges of incorporating clinical outcome assessments in brain tumor clinical trials

2018 ◽  
Vol 6 (2) ◽  
pp. 81-92 ◽  
Author(s):  
Emanuela Molinari ◽  
Tito R Mendoza ◽  
Mark R Gilbert
Keyword(s):  
Clinical Trials ◽  
Brain Tumor ◽  
Clinical Outcome ◽  
Clinical Trial Design ◽  
Well Being ◽  
Successful Implementation ◽  
Outcome Assessments ◽  
Tumor Studies ◽  
Clinical Outcome Assessments ◽  
The Impact

Abstract Regulatory agencies have progressively emphasized the importance of assessing broader aspects of patient well-being to better define therapeutic gain. As a result, clinical outcome assessments (COAs) are increasingly used to evaluate the impact, both positive and negative, of cancer treatments and in some instances have played a major factor in the regulatory approval of drugs. Challenges remain, however, in the routine incorporation of these measures in cancer clinical trials, particularly in brain tumor studies. Factors unique to brain tumor patients such as cognitive decline and language dysfunction may hamper their successful implementation. Study designs often relegated these outcome measures to exploratory endpoints, further compromising data completion. New strategies are needed to maximize the complementary information that COAs could add to clinical trials alongside more traditional measures such as progression-free and overall survival. The routine incorporation of COAs as either primary or secondary objectives with attention to minimizing missing data should define a novel clinical trial design. We provide a review of the approaches, challenges, and opportunities for incorporating COAs into brain tumor clinical research, providing a perspective for integrating these measures into clinical trials.

scholarly journals Dendrimer size effects on the selective brain tumor targeting in orthotopic tumor models upon systemic administration

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Kevin Liaw ◽  
Fan Zhang ◽  
Antonella Mangraviti ◽  
Sujatha Kannan ◽  
Betty Tyler ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Size Effects ◽  
Tumor Targeting ◽  
Systemic Administration ◽  
Tumor Models ◽  
Orthotopic Tumor

Fundamental Solution via Invariant Approach for a Brain Tumor Model and its Extensions

2014 ◽  
Vol 69 (12) ◽  
pp. 725-732 ◽  
Author(s):  
Andrew G. Johnpillai ◽  
Fazal M. Mahomed ◽  
Saeid Abbasbandy
Keyword(s):  
Brain Tumor ◽  
Partial Differential Equations ◽  
Fundamental Solution ◽  
Differential Equations ◽  
Closed Form Solution ◽  
Form Solution ◽  
Tumor Models ◽  
Partial Differential ◽  
Equivalence Transformations ◽  
Extended Class

AbstractWe firstly show how one can use the invariant criteria for a scalar linear (1+1) parabolic partial differential equations to perform reduction under equivalence transformations to the first Lie canonical form for a class of brain tumor models. Fundamental solution for the underlying class of models via these transformations is thereby found by making use of the well-known fundamental solution of the classical heat equation. The closed-form solution of the Cauchy initial value problem of the model equations is then obtained as well. We also demonstrate the utility of the invariant method for the extended form of the class of brain tumor models and find in a simple and elegant way the possible forms of the arbitrary functions appearing in the extended class of partial differential equations. We also derive the equivalence transformations which completely classify the underlying extended class of partial differential equations into the Lie canonical forms. Examples are provided as illustration of the results.

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