Literature DB >> 30816555

Biophysical model-based parameters to classify tumor recurrence from radiation-induced necrosis for brain metastases.

Saramati Narasimhan1, Haley B Johnson2, Tanner M Nickles2, Michael I Miga1,3, Nitesh Rana4, Albert Attia4, Jared A Weis2,5.   

Abstract

PURPOSE: Stereotactic radiosurgery (SRS) is used for local control treatment of patients with intracranial metastases. As a result of SRS, some patients develop radiation-induced necrosis. Radiographically, radiation-induced necrosis can appear similar to tumor recurrence in magnetic resonance (MR) T1 -weighted contrast-enhanced imaging, T2 -weighted MR imaging, and Fluid-Attenuated Inversion Recovery (FLAIR) MR imaging. Radiographic ambiguities often necessitate invasive brain biopsies to determine lesion etiology or cause delayed subsequent therapy initiation. We use a biomechanically coupled tumor growth model to estimate patient-specific model parameters and model-derived measures to noninvasively classify etiology of enhancing lesions in this patient population.
METHODS: In this initial, preliminary retrospective study, we evaluated five patients with tumor recurrence and five with radiation-induced necrosis. Longitudinal patient-specific MR imaging data were used in conjunction with the model to parameterize tumor cell proliferation rate and tumor cell diffusion coefficient, and Dice correlation coefficients were used to quantify degree of correlation between model-estimated mechanical stress fields and edema visualized from MR imaging.
RESULTS: Results found four statistically relevant parameters which can differentiate tumor recurrence and radiation-induced necrosis.
CONCLUSIONS: This preliminary investigation suggests potential of this framework to noninvasively determine the etiology of enhancing lesions in patients who previously underwent SRS for intracranial metastases.
© 2019 American Association of Physicists in Medicine.

Entities:  

Keywords:  computational model; radiation-induced necrosis; recurrence; tumor

Mesh:

Year:  2019        PMID: 30816555      PMCID: PMC6510636          DOI: 10.1002/mp.13461

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


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