CT Perfusion Characteristics Identify Metastatic Sites in Liver

Abstract

Tissue perfusion plays a critical role in oncology because growth and migration of cancerous cells require proliferation of new blood vessels through the process of tumor angiogenesis. Computed tomography (CT) perfusion is an emerging functional imaging modality that measures tissue perfusion through dynamic CT scanning following intravenous administration of contrast medium. This noninvasive technique provides a quantitative basis for assessing tumor angiogenesis. CT perfusion has been utilized on a variety of organs including lung, prostate, liver, and brain, with promising results in cancer diagnosis, disease prognostication, prediction, and treatment monitoring. In this paper, we focus on assessing the extent to which CT perfusion characteristics can be used to discriminate liver metastases from neuroendocrine tumors from normal liver tissues. The neuroendocrine liver metastases were analyzed by distributed parameter modeling to yield tissue blood flow (BF), blood volume (BV), mean transit time (MTT), permeability (PS), and hepatic arterial fraction (HAF), for tumor and normal liver. The result reveals the potential of CT perfusion as a tool for constructing biomarkers from features of the hepatic vasculature for guiding cancer detection, prognostication, and treatment selection.

A physiologically based pharmacokinetic model for [68Ga]Ga-(HA-)DOTATATE to predict whole-body distribution and tumor sink effects in GEP-NET patients

“…Besides, this was comparable to median tumor volumes that were used (or fitted) in previous PBPK models [21,27,38,39]. Vascular sub-compartments within tumors were estimated based on literature values for primary tumors and liver metastases, resulting in fraction vascular of 0.21 and 0.17, respectively [40][41][42][43][44][45].…”

“…In absence of relevant data for distant metastatic sites, the same value was used for these sites. For liver metastases, a higher blood flow of 203 mL/min/100 g was used as an input parameter, due to their nature of hyperperfusion [42][43][44]. SSTRs were added to the tumor compartments with an expression relative to spleen.…”

“…Minimum and maximum ranges for administered peptide amounts per group and SSTR2 organ expression (derived from the previously published PBPK model) were imputed to derive prediction intervals for organ and tumor radionuclide distributions [26]. In addition, to take into account a high variability in tumor uptake, prediction intervals in all three tumor compartments were based on observed (inter)quartile ranges in blood flow and blood volumes of the specific compartment [42][43][44][45]. For other metastases, blood flow was assumed to be comparable to primary tumors and fraction vascular was assumed to vary from 50 to 150% relative to the median.…”

Imaging-based characterization of convective tissue properties

“…In the assessment of tumor biology and angiogenesis, compartmental analysis of CT perfusion imaging provides a noninvasive volumetric assessment of tumor vasculature [11,[43][44][45][46]. Correlation between parametric maps obtained from compartmental analysis and microvessel structure has been used as a predictor of disease severity and treatment outcomes [47][48][49][50].…”

A Bayesian Nonparametric Approach for Functional Data Classification with Application to Hepatic Tissue Characterization

“…In particular, blood volume (BV) and blood flow (BF) are higher in malignant tumors due to the proliferation of new blood vessels through the process of tumor angiogenesis. 12,13 Therefore, it has been suggested that perfusion imaging can yield biomarkers of angiogenesis and tumor growth and thereby greatly enhance the clinical development of antiangiogenic therapies. This is because changes in tumor perfusion appear to occur soon after therapy initiation with antiangiogenic drugs.…”