Patient-Specific Modeling for Critical Care

Neal, Maxwell Lewis

doi:10.1007/978-1-4419-6691-9_5

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…These models, including our 4D-anatomical model, require significant computational overhead to simulate and analyze, and interpretation of simulation results often requires expertise with the model. The few patient-specific modeling applications that have demonstrated clinical impact are based on simple models that do not necessitate the use of a computer (31, 32). Because our ultimate goal is to improve the outcomes of GBM patients by applying patient-specific modeling approaches, we sought to find a minimal model of GBM growth that can be readily computed and analyzed.…”

Section: Discussionmentioning

confidence: 99%

Response Classification Based on a Minimal Model of Glioblastoma Growth Is Prognostic for Clinical Outcomes and Distinguishes Progression from Pseudoprogression

et al. 2013

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Glioblastoma multiforme (GBM) is the most aggressive type of primary brain tumor. GBM growth dynamics vary widely across patients, making it difficult to accurately gauge their response to treatment. We developed a model-based metric of therapy response called Days Gained that accounts for this heterogeneity. Here we demonstrate in 63 newly diagnosed GBM patients that Days Gained scores from a simple GBM growth model computed at the time of the first post-radiation therapy MRI scan are prognostic for time to tumor recurrence and overall patient survival. After radiation treatment, Days Gained also distinguished patients with pseudoprogression from those with true progression. Since Days Gained scores can be easily computed with routinely available clinical imaging devices, it offers immediate potential to be used in ongoing prospective studies.

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Section: Discussionmentioning

confidence: 99%

Response Classification Based on a Minimal Model of Glioblastoma Growth Is Prognostic for Clinical Outcomes and Distinguishes Progression from Pseudoprogression

et al. 2013

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“…Computational models are mathematical representations of human anatomy, physiology, and/or pathophysiology. The computational models aim to assist the clinician and researcher in improving understanding of the interrelationship between various parameters or estimating unknown ones, aiding in the diagnosis, and experimenting with potential interventions and procedures in the model before introducing them to the patient [50].…”

Section: Computational Modeling From Research To Patient Carementioning

confidence: 99%