Abstract
BackgroundIn modern cancer medicine, morphological magnetic resonance imaging (MRI) is routinely used in diagnostics, treatment planning and assessment of therapeutic efficacy. During the past decade, functional imaging techniques like diffusion-weighted (DW) MRI and dynamic contrast-enhanced (DCE) MRI have increasingly been included into imaging protocols, allowing extraction of intratumoral information of underlying vascular, molecular and physiological mechanisms, not available in morphological images. Separately, pre-treatment and early changes in functional parameters obtained from DWMRI and DCEMRI have shown potential in predicting therapy response. We hypothesized that the combination of several functional parameters increased the predictive power.MethodsWe challenged this hypothesis by using an artificial neural network (ANN) approach, exploiting nonlinear relationships between individual variables, which is particularly suitable in treatment response prediction involving complex cancer data. A clinical scenario was elicited by using 32 mice with human prostate carcinoma xenografts receiving combinations of androgen-deprivation therapy and/or radiotherapy. Pre-radiation and on days 1 and 9 following radiation three repeated DWMRI and DCEMRI acquisitions enabled derivation of the apparent diffusion coefficient (ADC) and the vascular biomarker Ktrans, which together with tumor volumes and the established biomarker prostate-specific antigen (PSA), were used as inputs to a back propagation neural network, independently and combined, in order to explore their feasibility of predicting individual treatment response measured as 30 days post-RT tumor volumes.ResultsADC, volumes and PSA as inputs to the model revealed a correlation coefficient of 0.54 (p < 0.001) between predicted and measured treatment response, while Ktrans, volumes and PSA gave a correlation coefficient of 0.66 (p < 0.001). The combination of all parameters (ADC, Ktrans, volumes, PSA) successfully predicted treatment response with a correlation coefficient of 0.85 (p < 0.001).ConclusionsWe have in a preclinical investigation showed that the combination of early changes in several functional MRI parameters provides additional information about therapy response. If such an approach could be clinically validated, it may become a tool to help identifying non-responding patients early in treatment, allowing these patients to be considered for alternative treatment strategies, and, thus, providing a contribution to the development of individualized cancer therapy.
Highlights
In modern cancer medicine, morphological magnetic resonance imaging (MRI) is routinely used in diagnostics, treatment planning and assessment of therapeutic efficacy
Alterations in functional imaging parameters have been shown to precede tumor volume reductions, enabling identification of good and poor responders at an early time-point, and facilitation of individualized treatment schedules [6,7,8,9,10,11,12,13]. These functional MRI techniques are increasingly becoming in routine use in many radiological departments, the approach presented in the current study suggests a further use of these data, by exploiting them in prediction modeling together with standard clinical parameters
Ultimate treatment response was measured as tumor volumes at day 30 (V30)
Summary
Morphological magnetic resonance imaging (MRI) is routinely used in diagnostics, treatment planning and assessment of therapeutic efficacy. Alterations in functional imaging parameters have been shown to precede tumor volume reductions, enabling identification of good and poor responders at an early time-point, and facilitation of individualized treatment schedules [6,7,8,9,10,11,12,13]. These functional MRI techniques are increasingly becoming in routine use in many radiological departments, the approach presented in the current study suggests a further use of these data, by exploiting them in prediction modeling together with standard clinical parameters
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