Showing papers by "Kristin R. Swanson published in 2007"

The evolution of mathematical modeling of glioma proliferation and invasion.

Abstract: Gliomas are well known for their potential for aggressive proliferation as well as their diffuse invasion of the normal-appearing parenchyma peripheral to the bulk lesion. This review presents a history of the use of mathematical modeling in the study of the proliferative-invasive growth of gliomas, illustrating the progress made in understanding the in vivo dynamics of invasion and proliferation of tumor cells. Mathematical modeling is based on a sequence of observation, speculation, development of hypotheses to be tested, and comparisons between theory and reality. These mathematical investigations, iteratively compared with experimental and clinical work, demonstrate the essential relationship between experimental and theoretical approaches. Together, these efforts have extended our knowledge and insight into in vivo brain tumor growth dynamics that should enhance current diagnoses and treatments.

Using mathematical modeling to predict survival of low‐grade gliomas

Abstract: Pallud and colleagues have presented some interesting data concerning the long-term follow-up of low-grade gliomas, but why did they not pursue the implications of the mathematical model of glioma behavior that they championed in their previous work. If they had, they would have seen that there can be no dichotomy, no distribution of velocities that “results in two groups,” but rather a continuous gradation of velocities of glioma expansion contributing to survival differences. The two independent variables that Pallud and colleagues found to be statistically significant (p 0.034), velocity and size, are exactly what the mathematical model predicts for survival. Actually, these two variables combine seamlessly to produce a predicted survival for each patient; but in the available space and without access to the original data, we can illustrate only a sampling of predictions from the data provided in the manuscript (Table). For example, a tumor 80 mm in diameter would require only 2.5 years to grow enough to kill if expanding at 11 mm/year but would not yet have reached a fatal size within 11 years if only expanding at 2–3 mm/year. A few questions require clarification. First, if “patients were excluded when an oncological treatment was administered,... or when anaplastic transformation occurred...histologically... or when a contrast enhancement appeared on MRI,” who was left to die? Were all 143 patients “pure” low-grade gliomas until they died or were last seen alive? Second, what was “time zero” in this study? That is, what is the relation between the time for “the median tumor volume (estimated on MRI before surgery)” and the time to the beginning of “the overall median duration of clinical follow-up since radiological diagnosis”? Were there not magnetic resonance images on some patients being followed before any surgery (and after a radiological diagnosis), and were these excluded? Third, rather than a p value of 0.619 for the “random histological review...in 40 cases (28%),” would it not have been more interesting to review and compare all 22 cases in the “high-rate” group (ie, velocity 8mm/yr) with the 16 cases in the lowest of the “low-rate” group (ie, velocity 1mm/yr)? Can not neuropathology do better than to lump velocities of greater than 10-fold differences together? Finally, we offer a suggestion: We prefer the phrase “velocity of radial expansion” (which is more euphonious and would be half that of “diametric expansion”) to “growth rate” because the latter ambiguously implies proliferation, perhaps forgetting the cell loss factor, perhaps even including diffusion, whereas the mathematical model clearly separates net proliferation ( ) and net dispersal or diffusion (D), the product of which relate to the velocity.

Using mathematical modeling to predict survival of low-grade gliomas. Authors' reply

Abstract: Pallud and colleagues have presented some interesting data concerning the long-term follow-up of low-grade gliomas, but why did they not pursue the implications of the mathematical model of glioma behavior that they championed in their previous work. If they had, they would have seen that there can be no dichotomy, no distribution of velocities that “results in two groups,” but rather a continuous gradation of velocities of glioma expansion contributing to survival differences. The two independent variables that Pallud and colleagues found to be statistically significant (p 0.034), velocity and size, are exactly what the mathematical model predicts for survival. Actually, these two variables combine seamlessly to produce a predicted survival for each patient; but in the available space and without access to the original data, we can illustrate only a sampling of predictions from the data provided in the manuscript (Table). For example, a tumor 80 mm in diameter would require only 2.5 years to grow enough to kill if expanding at 11 mm/year but would not yet have reached a fatal size within 11 years if only expanding at 2–3 mm/year. A few questions require clarification. First, if “patients were excluded when an oncological treatment was administered,... or when anaplastic transformation occurred...histologically... or when a contrast enhancement appeared on MRI,” who was left to die? Were all 143 patients “pure” low-grade gliomas until they died or were last seen alive? Second, what was “time zero” in this study? That is, what is the relation between the time for “the median tumor volume (estimated on MRI before surgery)” and the time to the beginning of “the overall median duration of clinical follow-up since radiological diagnosis”? Were there not magnetic resonance images on some patients being followed before any surgery (and after a radiological diagnosis), and were these excluded? Third, rather than a p value of 0.619 for the “random histological review...in 40 cases (28%),” would it not have been more interesting to review and compare all 22 cases in the “high-rate” group (ie, velocity 8mm/yr) with the 16 cases in the lowest of the “low-rate” group (ie, velocity 1mm/yr)? Can not neuropathology do better than to lump velocities of greater than 10-fold differences together? Finally, we offer a suggestion: We prefer the phrase “velocity of radial expansion” (which is more euphonious and would be half that of “diametric expansion”) to “growth rate” because the latter ambiguously implies proliferation, perhaps forgetting the cell loss factor, perhaps even including diffusion, whereas the mathematical model clearly separates net proliferation ( ) and net dispersal or diffusion (D), the product of which relate to the velocity.

Nuclear Imaging of Gliomas

Abstract: This chapter reviews nuclear imaging of gliomas, both high- and low-grade, with emphasis on results from positron emission tomography (PET) with [F-18]-2-fluoro-2-deoxyglucose (FDG) for assessing energy metabolism There are many additional advances beyond FDG-PET that are very exciting and potentially applicable in the management of gliomas Biosynthesis in tumors occurs along several important broad fronts for DNA, proteins, and membrane lipids Molecular imaging of these pathways is coming to the foreground Hypoxia, a significant resistance mechanism that compromises the efficacy of radiotherapy and chemotherapy, can now be regionally quantified in vivo with PET In the near future it is likely that the presence of mutant receptors, apoptosis, and angiogenesis will also be measurable with PET and new tracers