Semiquantitative and Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging Measurements Predict Radiation Response in Cervix Cancer

Zahra, M.; Tan, Li Tee; Priest, Andrew N.; Graves, Martin J.; Arends, Mark; Crawford, Robin; Brenton, James D.; Lomas, David J.; Sala, Evis

doi:10.1016/j.ijrobp.2008.08.023

Cited by 144 publications

(105 citation statements)

References 34 publications

(33 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both approaches have been widely used in previous studies. In our previous work on pelvic tumors (8), reliable AIF measurement proved not to be possible from the acquired data, so a modeled AIF was used. The same approach has been used in this study as initial investigations using the data suggested that extracting a reliable AIF from every patient would prove challenging due to the concerns mentioned above.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic contrast-enhanced MRI in ovarian cancer: Initial experience at 3 tesla in primary and metastatic disease

et al. 2010

Self Cite

View full text Add to dashboard Cite

The aim of this study was to develop and demonstrate a methodology for dynamic contrast-enhanced MRI at 3 T in patients with advanced ovarian cancer and to report the results from pharmacokinetic modeling of the data. Nineteen patients with suspected advanced ovarian carcinoma (FIGO stage 3 or higher) were enrolled in this prospective study. Up to three marker lesions were identified: primary ovarian mass, omental ''cake'', and peritoneal deposits. Dynamic contrastenhanced MRI was performed using a three-dimensional T 1 -weighted gradient-echo acquisition with a temporal resolution of 1.6 sec, following intravenous administration of 0.1 mmol/ kg gadobutrol. Precontrast T 1 mapping, using an inversion-recovery fast gradient-echo sequence, was also performed. Imaging was completed in 18/19 patients, although two were subsequently excluded based on pathology results. Pharmacokinetic modeling of the data was performed according to the extended Kety model, using an arterial input function formed by concatenation of the Fritz-Hansen and Weinmann curves. No statistically significant differences were found between the results for the three marker lesions. In the future, this work will allow kinetic modeling results from ovarian dynamic contrast-enhanced MRI to be correlated with response to treatment. The high temporal resolution allows good characterization of the rapid contrast agent uptake in these vascular tumors.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Additionally, the local tumor environment is known to influence treatment response as tumors with reduced perfusion are more likely to be hypoxic and to have compromised delivery of chemotherapy (2). DCE-MRI may therefore be useful in predicting response to treatment in some tumors (5)(6)(7)(8).…”

mentioning

confidence: 99%

Dynamic contrast-enhanced MRI in ovarian cancer: Initial experience at 3 tesla in primary and metastatic disease

et al. 2010

Self Cite

View full text Add to dashboard Cite

show abstract

“…As the tumor evolves, the periphery and the core of the tumor may have different enhancing rates during the CA uptake with very different K trans distribution. 50,51 To account for the problem, some studies evaluated the treatment response in the separated tumor subregions, 52,53 while some studies utilized the K trans histogram information involving kurtosis and skewness. 54 In this work, the K trans kurtosis of both the treatment and the control group increased along with the experiment, which indicated that K trans distribution evolved toward leptokurtosis (high "peakedness").…”

Section: Discussionmentioning

confidence: 99%

Dynamic fractal signature dissimilarity analysis for therapeutic response assessment using dynamic contrast‐enhanced MRI

et al. 2016

View full text Add to dashboard Cite

Purpose: To develop a dynamic fractal signature dissimilarity (FSD) method as a novel image texture analysis technique for the quantification of tumor heterogeneity information for better therapeutic response assessment with dynamic contrast-enhanced (DCE)-MRI. Methods: A small animal antiangiogenesis drug treatment experiment was used to demonstrate the proposed method. Sixteen LS-174T implanted mice were randomly assigned into treatment and control groups (n = 8/group). All mice received bevacizumab (treatment) or saline (control) three times in two weeks, and one pretreatment and two post-treatment DCE-MRI scans were performed. In the proposed dynamic FSD method, a dynamic FSD curve was generated to characterize the heterogeneity evolution during the contrast agent uptake, and the area under FSD curve (AUC FSD ) and the maximum enhancement (ME FSD ) were selected as representative parameters. As for comparison, the pharmacokinetic parameter K trans map and area under MR intensity enhancement curve AUC MR map were calculated. Besides the tumor's mean value and coefficient of variation, the kurtosis, skewness, and classic Rényi dimensions d 1 and d 2 of K trans and AUC MR maps were evaluated for heterogeneity assessment for comparison. For post-treatment scans, the Mann-Whitney U-test was used to assess the differences of the investigated parameters between treatment/control groups. The support vector machine (SVM) was applied to classify treatment/control groups using the investigated parameters at each post-treatment scan day. Results: The tumor mean K trans and its heterogeneity measurements d 1 and d 2 values showed significant differences between treatment/control groups in the second post-treatment scan. In contrast, the relative values (in reference to the pretreatment value) of AUC FSD and ME FSD in both post-treatment scans showed significant differences between treatment/control groups. When using AUC FSD and ME FSD as SVM input for treatment/control classification, the achieved accuracies were 93.8% and 93.8% at first and second post-treatment scan days, respectively. In comparison, the classification accuracies using d 1 and d 2 of K trans map were 87.5% and 100% at first and second post-treatment scan days, respectively. Conclusions: As quantitative metrics of tumor contrast agent uptake heterogeneity, the selected parameters from the dynamic FSD method accurately captured the therapeutic response in the experiment. The potential application of the proposed method is promising, and its addition to the existing DCE-MRI techniques could improve DCE-MRI performance in early assessment of treatment response. C

show abstract

“…As in CTP, these data can be used to assess tumour perfusion and permeability using quantitative and semiquantitative measures. Zahra et al [48] found that cervical cancers that had relatively increased perfusion exhibited Figure 2 68 Ga-DOTATATE PET/CT in the same patient as Fig. 1 with metastatic carcinoid disease.…”

Section: Hypoperfusionmentioning

confidence: 92%

Predicting tumour response

Kyle¹,

Law²,

Miles³

2013

Cancer Imaging

View full text Add to dashboard Cite

Response prediction is an important emerging concept in oncologic imaging, with tailored, individualized treatment regimens increasingly becoming the standard of care. This review aims to define tumour response and illustrate the ways in which imaging techniques can demonstrate tumour biological characteristics that provide information on the likely benefit to be received by treatment. Two imaging approaches are described: identification of therapeutic targets and depiction of the treatment-resistant phenotype. The former approach is exemplified by the use of radionuclide imaging to confirm target expression before radionuclide therapy but with angiogenesis imaging and imaging correlates for genetic response predictors also demonstrating potential utility. Techniques to assess the treatment-resistant phenotype include demonstration of hypoperfusion with dynamic contrast-enhanced computed tomography and magnetic resonance imaging (MRI), depiction of necrosis with diffusion-weighted MRI, imaging of hypoxia and tumour adaption to hypoxia, and 99m Tc-MIBI imaging of P-glycoprotein mediated drug resistance. To date, introduction of these techniques into clinical practice has often been constrained by inadequate cross-validation of predictive criteria and lack of verification against appropriate response end points such as survival. With further refinement, imaging predictors of response could play an important role in oncology, contributing to individualization of therapy based on the specific tumour phenotype. This ability to predict tumour response will have implications for improving efficacy of treatment, cost-effectiveness and omission of futile therapy.

show abstract

Semiquantitative and Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging Measurements Predict Radiation Response in Cervix Cancer

Cited by 144 publications

References 34 publications

Dynamic contrast-enhanced MRI in ovarian cancer: Initial experience at 3 tesla in primary and metastatic disease

Dynamic contrast-enhanced MRI in ovarian cancer: Initial experience at 3 tesla in primary and metastatic disease

Dynamic fractal signature dissimilarity analysis for therapeutic response assessment using dynamic contrast‐enhanced MRI

Predicting tumour response

Contact Info

Product

Resources

About