The biological correlates of macroscopic breast tumour structure measured using fractal analysis in patients undergoing neoadjuvant chemotherapy.

Breast cancers are evolving, multi-scale systems that are characterized by varied complex spatial structures. In this study, we measured the structural characteristics of 33 breast tumours in patients who were to receive neoadjuvant chemotherapy using dynamic contrast enhanced MRI and fractal geometry. The results showed a significant association between fractal measurements and tumour characteristics. The fractal dimension was associated with receptor status (ER and PR) and the fractal fit was associated with response to chemotherapy, measured using a validated pathological response scale, tumour grade and size. This study describes structure measures that may be a consequence of known prognostic factors during the initial and/or maturation phase of tumour growth. These results suggest that measuring tumour structure in this way can predict an individual's response to neoadjuvant therapy and may identify those who will benefit least from neoadjuvant chemotherapy, allowing alternative treatment options to be selected in those patients.

Use of a capillary input function with cardiac output for the estimation of lesion pharmacokinetic parameters: preliminary results on a breast cancer patient.

The objective of this work was to propose and demonstrate a novel technique for the assessment of tumour pharmacokinetic parameters together with a regionally estimated vascular input function. A breast cancer patient T2*-weighted dynamic contrast enhanced MRI (DCE-MRI) dataset acquired at high temporal resolution during the first-pass bolus perfusion was used for testing the technique. Extraction of the lesion volume transfer constant K(trans) together with the intravascular plasma volume fraction v(p) was achieved by optimizing a capillary input function with a measure of cardiac output using the principle of intravascular indicator dilution theory. For a region of interest drawn within the breast lesion a v(p) of 0.16 and a K(trans) of 0.70 min(-1) were estimated. Despite the value of v(p) being higher than expected, estimated K(trans) was in accordance with the literature values. In conclusion, the technique proposed here, has the main advantage of allowing the estimation of breast tumour pharmacokinetic parameters from first-pass perfusion T2*-weighted DCE-MRI data without the need of measuring an arterial input function. The technique may also have applicability to T1-weighted DCE-MRI data.

The accuracy of pharmacokinetic parameter measurement in DCE-MRI of the breast at 3 T.

The purpose of this work is to quantify the accuracy of pharmacokinetic parameter measurement in DCE-MRI of breast cancer at 3 T in relation to three sources of error. Individually, T1 measurement error, temporal resolution and transmitted RF field inhomogeneity are considered. Dynamic contrast enhancement curves were simulated using standard acquisition parameters of a DCE-MRI protocol. Errors on pre-contrast T1 due to incorrect RF spoiling were considered. Flip angle errors were measured and introduced into the fitting routine, and temporal resolution was also varied. The error in fitted pharmacokinetic parameters, K(trans) and v(e), was calculated. Flip angles were found to be reduced by up to 55% of the expected value. The resultant errors in our range of K(trans) and v(e) were found to be up to 66% and 74%, respectively. Incorrect T1 estimation results in K(trans) and v(e) errors up to 531% and 233%, respectively. When the temporal resolution is reduced from 10 to 70 s K(trans) drops by up to 48%, while v(e) shows negligible variation. In combination, uncertainties in tissue T1 map and applied flip angle were shown to contribute to errors of up to 88% in K(trans) and 73% in v(e). These results demonstrate the importance of high temporal resolution, accurate T1 measurement and good B1 homogeneity.

Baseline MRI delivery characteristics predict change in invasive ductal breast carcinoma PET metabolism as a result of primary chemotherapy administration.

BACKGROUND: The aim of the study was to investigate whether pre-therapy vascular delivery assessment [using dynamic contrast enhanced magnetic resonance imaging (DCE-MRI)] can predict reduction in breast cancer metabolism [detected using 2-[(18)F] fluoro-2-deoxy-D-glucose positron emission tomography ((18)F(-)FDG-PET)] after a single cycle of chemotherapy. Reduction in (18)F-FDG PET metabolism has previously been shown to correlate with histological response to primary chemotherapy. PATIENTS AND METHODS: Seventeen patients with large or locally advanced invasive ductal carcinomas of the breast were imaged using DCE-MRI and (18)F-FDG-PET prior to therapy and 20 days after the first cycle of chemotherapy. MRI data were analysed using a multi-compartment model. PET data were analysed using standardised uptake value (SUV) analysis. RESULTS: A significant association (P <0.05) was observed between pre-therapy DCE-MRI vascular parameters and the reduction in PET metabolism resulting from administration of one cycle of chemotherapy. CONCLUSIONS: A relationship was demonstrated between pre-therapy DCE-MRI vascular parameters and the reduction in PET metabolism after a single cycle of chemotherapy. This suggests that reduction in PET metabolism as a result of chemotherapy may be dependent, at least in part, on pre-therapy vascular delivery. These pre-therapy vascular characteristics may be suitable for use as a surrogate measure for initial chemotherapy delivery, a key factor in chemotherapeutic efficacy.

The use of the Levenberg-Marquardt curve-fitting algorithm in pharmacokinetic modelling of DCE-MRI data.

The use of curve-fitting and compartmental modelling for calculating physiological parameters from measured data has increased in popularity in recent years. Finding the 'best fit' of a model to data involves the minimization of a merit function. An example of a merit function is the sum of the squares of the differences between the data points and the model estimated points. This is facilitated by curve-fitting algorithms. Two curve-fitting methods, Levenberg-Marquardt and MINPACK-1, are investigated with respect to the search start points that they require and the accuracy of the returned fits. We have simulated one million dynamic contrast enhanced MRI curves using a range of parameters and investigated the use of single and multiple search starting points. We found that both algorithms, when used with a single starting point, return unreliable fits. When multiple start points are used, we found that both algorithms returned reliable parameters. However the MINPACK-1 method generally outperformed the Levenberg-Marquardt method. We conclude that the use of a single starting point when fitting compartmental modelling data such as this produces unsafe results and we recommend the use of multiple start points in order to find the global minima.

The effects of renal variation upon measurements of perfusion and leakage volume in breast tumours.

Dynamic contrast enhanced MRI (DCE-MRI) and pharmacokinetic models have been used to measure tumour permeability (K(trans)) and leakage volume (ve) in numerous studies. The construction of pharmacokinetic models describing such tissue properties relies on defining the blood plasma concentration of contrast agent with respect to time (Cp(t)). When direct measurement is not possible a bi-exponential decay has been applied using data from healthy volunteers. This work investigates, by simulation, the magnitude of errors resulting from this definition with respect to normal variation in renal function and for cases with renal impairment. Errors up to 23% in ve and 28% in K(trans) were found for the normal simulations, and 67% in ve and 61% in K(trans) for the impaired simulations. If this bi-exponential curve is used as an input function to the generalized kinetic model and used in oncology, estimates of tissue permeability and leakage volume will possess large errors due to variation in Cp(t) curves between subjects.