Multispectral tissue characterization in a RIF‐1 tumor model: Monitoring the ADC and T₂ responses to single‐dose radiotherapy. Part II

Abstract

A multispectral (MS) approach that combines apparent diffusion coefficient (ADC) and T 2 parameter maps with k-means (KM) clustering was employed to distinguish multiple compartments within viable tumor tissue (V1 and V2) and necrosis (N1 and N2) following single-dose (1000 cGy) radiotherapy in a radiation-induced fibrosarcoma (RIF-1) tumor model. The contributions of cell kill and tumor growth kinetics to the radiotherapy-induced response were investigated. A larger pretreatment V1 volume was correlated with decreased tumor growth delay (TGD) (r ‫؍‬ 0.68) and cell kill (r ‫؍‬ 0.71). There was no correlation for the pretreatment V2 volume. These results suggest that V1 tissue is well oxygenated and radiosensitive, whereas V2 tissue is hypoxic and therefore radioresistant. The relationship between an early ADC response and vasogenic edema and formation of necrosis was investigated. A trend for increased ADC was observed prior to an increase in the necrotic fraction (NF). Because there were no changes in T 2 , these observations suggest that the early increase in ADC is more likely based on a slight reduction in cell density, rather than radiation-induced vasogenic edema. Quantitative assessments of individual tissue regions, tumor growth kinetics, and cell kill should provide a more accurate means of monitoring therapy in preclinical animal models because such assessments can minimize the issue of intertumor variability. Magn Reson Med 57:513-519, 2007.

Quantitative multiparametric PROPELLER MRI of diethylnitrosamine‐induced hepatocarcinogenesis in wister rat model

“…Benign liver lesions including cysts and hemangiomas demonstrate increased apparent diffusion coefficient (ADC) values compared to malignant lesions including HCC and metastases (19–21). Quantitative DWI measurements (22) and combined multi-parametric (ADC + T2 + proton density) MRI measurements (23–25) have demonstrated the potential to differentiate intratumoral tissue types as well as characterize therapy response.…”

Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats

“…[139][140][141] This leads to stochastic and cyclical changes in blood flow 142 that, in turn, have profound effects on the local environmental properties, and the resulting evolutionary selection pressure applied to cancer cells. [147][148][149][150] In fact, intratumoral spatial heterogeneity has been associated with resistance to therapy, 151 tumor growth, 152 and poor prognosis, 153 and therefore has direct clinical consequences. [143][144][145][146] Thus, these subregions, or habitats, are expected to contain cells with similar genotypes and phenotypes, including those that confer therapy resistance.…”

“…151,181,182 Preclinical studies are valuable because these habitats can be identified by coregistration with histology. 151,181,182 Preclinical studies are valuable because these habitats can be identified by coregistration with histology.…”

“…Necrotic regions with a long T2 value and high ADC value were confirmed as acellular, whereas the short T2 value in the other necrotic regions was ascribed to the presence of deoxyhemoglobin due to either hemorrhage or leaky vasculature. 151 In another study, DCE-MRI parameters were evaluated over the viable and necrotic subpopulations previously classified by the multispectral analysis of DWI-MRI, T2, and proton density. In a similar analysis, viable and necrotic areas were identified in primary tumors and metastases of a 4T1 syngeneic mouse model of breast cancer.…”

High-field small animal magnetic resonance oncology studies

“…Henning et al . (Henning et al , 2007) applied k-means clustering to the ADC and T 2 data of RIF-1 mouse tumors treated with single-dose radiotherapy (1000 cGy). Four clusters were identified: two viable clusters (oxygenated and hypoxic) and two necrotic clusters (cellular and acellular).…”