Assessment of induced rat mammary tumour response to chemotherapy using the apparent diffusion coefficient of tissue water as determined by diffusion-weighted 1H-NMR spectroscopy in vivo

The distribution of the apparent diffusion coefficient as an indicator of the response to chemotherapeutics in ovarian tumour xenografts

Diffusion-weighted magnetic resonance imaging (DW-MRI) was used to evaluate the effects of single-agent and combination treatment regimens in a spheroid-based animal model of ovarian cancer. Ovarian tumour xenografts grown in non-obese diabetic/severe-combined-immunodeficiency (NOD/SCID) mice were treated with carboplatin or paclitaxel, or combination carboplatin/paclitaxel chemotherapy regimens. After 4 weeks of treatment, tumours were extracted and underwent DW-MRI, mechanical testing, immunohistochemical and gene expression analyses. The distribution of the apparent diffusion coefficient (ADC) exhibited an upward shift as a result of each treatment regimen. The 99-th percentile of the ADC distribution (“maximum ADC”) exhibited a strong correlation with the tumour size (r² = 0.90) and with the inverse of the elastic modulus (r² = 0.96). Single-agent paclitaxel (n = 5) and combination carboplatin/paclitaxel (n = 2) treatment regimens were more effective in inducing changes in regions of higher cell density than single-agent carboplatin (n = 3) or the no-treatment control (n = 5). The maximum ADC was a good indicator of treatment-induced cell death and changes in the extracellular matrix (ECM). Comparative analysis of the tumours’ ADC distribution, mechanical properties and ECM constituents provides insights into the molecular and cellular response of the ovarian tumour xenografts to chemotherapy. Increased sample sizes are recommended for future studies. We propose experimental approaches to evaluation of the timeline of the tumour’s response to treatment.

Pre-treatment MRI minimum apparent diffusion coefficient value is a potential prognostic imaging biomarker in cervical cancer patients treated with definitive chemoradiation

Background

Diffusion Weighted (DW) Magnetic Resonance Imaging (MRI) has been studed in several cancers including cervical cancer. This study was designed to investigate the association of DW-MRI parameters with baseline clinical features and clinical outcomes (local regional control (LRC), disease free survival (DFS) and disease specific survival (DSS)) in cervical cancer patients treated with definitive chemoradiation.

Methods

This was a retrospective study approved by an institutional review board that included 66 women with cervical cancer treated with definitive chemoradiation who underwent pre-treatment MRI at our institution between 2012 and 2013. A region of interest (ROI) was manually drawn by one of three radiologists with experience in pelvic imaging on a single axial CT slice encompassing the widest diameter of the cervical tumor while excluding areas of necrosis. The following apparent diffusion coefficient (ADC) values (×10⁻³ mm²/s) were extracted for each ROI: Minimum - ADC_min, Maximum - ADC_max, Mean - ADC_mean, and Standard Deviation of the ADC - ADC_dev. Receiver operating characteristic (ROC) curves were built to choose the most accurate cut off value for each ADC value. Correlation between imaging metrics and baseline clinical features were evaluated using the Mann Whitney test. Confirmatory multi-variate Cox modeling was used to test associations with LRC (adjusted by gross tumor volume – GTV), DFS and DSS (both adjusted by FIGO stage). Kaplan Meyer curves were built for DFS and DSS. A p-value < 0.05 was considered significant.

Women median age was 52 years (range 23–90). 67 % had FIGO stage I-II disease while 33 % had FIGO stage III-IV disease. Eighty-two percent had squamous cell cancer. Eighty-eight percent received concurrent cisplatin chemotherapy with radiation. Median EQD2 of external beam and brachytherapy was 82.2 Gy (range 74–84).

Results

Women with disease staged III-IV (FIGO) had significantly higher mean ADC_max values compared with those with stage I-II (1.806 (0.4) vs 1.485 (0.4), p = 0.01). Patients with imaging defined positive nodes also had significantly higher mean (±SD) ADC_max values compared with lymph node negative patients (1.995 (0.3) vs 1.551 (0.5), p = 0.03).

With a median follow-up of 32 months (range 5–43) 11 patients (17 %) have developed recurrent disease and 8 (12 %) have died because of cervical cancer. ROC curves based on DSS showed optimal cutoffs for ADC_min (0.488 × 10⁻³), ADC_mean (0.827 × 10⁻³), ADC_max (1.838 × 10⁻³) and ADC_dev (0.148 × 10⁻³). ADC_min higher than the cutoff was significantly associated with worse DFS (HR = 3.632–95 % CI: 1.094–12.054; p = 0.035) and DSS (HR = 4.401–95 % CI: 1.048–18.483; p = 0.043).

Conclusion

Pre-treatment ADC_max measured in the primary tumor may be associated with FIGO stage and lymph node status. Pre-treatment ADC_min may be a prognostic factor associated with disease-free survival and disease-specific survival in cervical cancer patients treated with definitive chemoradiation. Prospective validation of these findings is currently ongoing.

Correlation of ADC value with pathologic indexes in colorectal tumor homografts in Balb/c mouse

OBJECTIVE

Noninvasive diffusion-weighted magnetic resonance imaging (DWI) is a well-studied MR imaging technique for quantifying water diffusion especially in tumor area. The correlation between apparent diffusion coefficient (ADC) value and apoptosis or proliferation is not clear by now. This study aimed to investigate whether DWI-ADC value could be used as an imaging marker related with pathologic indexes of tumors.

METHODS

A total of 30 Balb/c mice with HT29 colorectal carcinoma were subjected to DWI and histologic analysis. The percentage of ADC changes and the apoptotic and proliferating indexes were calculated at predefined time points. Kolmogorov-Smirnov distances were considered to determine whether the percentage of ADC changes, and the apoptotic and proliferating indexes were normally distributed. An independent-samples t-test was used to analyze the difference between apoptotic and proliferating indexes in the two groups.

RESULTS

THERE WAS A STATISTICALLY SIGNIFICANT DIFFERENCE IN PROLIFERATING INDEX BETWEEN THE RADIOTHERAPY AND CONTROL GROUPS (MEAN PROLIFERATING INDEX: 49.27% vs. 83.09%), and there was a statistically significant difference in apoptotic index between the two groups (mean apoptotic index: 37.7% vs. 2.71%). A significant positive correlation was found between the percentage of ADC changes of the viable tissue and apoptotic index. Pearson's correlation coefficient was 0.655 (P=0.015). A significant negative correlation was found between the percentage of ADC changes of the viable tissue and ki-67 proliferation index. Pearson's correlation coefficient was 0.734 (P<0.001).

CONCLUSIONS

Our results suggest that ADC value may be used in measurement of cell apoptotic and proliferating indexes in colorectal carcinoma.

High-field small animal magnetic resonance oncology studies

This review focuses on the applications of high magnetic field magnetic resonance imaging (MRI) and spectroscopy (MRS) to cancer studies in small animals. High-field MRI can provide information about tumor physiology, the microenvironment, metabolism, vascularity and cellularity. Such studies are invaluable for understanding tumor growth and proliferation, response to treatment and drug development. The MR techniques reviewed here include (1)H, (31)P, chemical exchange saturation transfer imaging and hyperpolarized (13)C MRS as well as diffusion-weighted, blood oxygen level dependent contrast imaging and dynamic contrast-enhanced MRI. These methods have been proven effective in animal studies and are highly relevant to human clinical studies.

Diffusion-weighted magnetic resonance application in response prediction before, during, and after neoadjuvant radiochemotherapy in primary rectal cancer carcinoma

Introduction. Our interest was to monitor treatment response using ADC value to predict response of rectal tumour to preoperative radiochemotherapy. Materials and Methods. Twenty-two patients were treated with long course of radiochemotherapy, followed by surgery. Patients were examined by diffusion-weighted imaging MRI at three-time points (prior, during, and after radiochemotherapy) and were classified as responders and nonresponders. Results. A statistical significant correlation was found between preradiochemotherapy ADC values and during treatment ADC values, in responders (F = 21.50, P value <0.05). An increase in ADC value during treatment was predictive of at least a partial response. Discussion. Response of tumour to neoadjuvant therapy cannot be easily evaluated, and such capability might be of great importance in clinical practice, because the number of irradiated and operated patients may be superior to the number of who will really benefit from this multimodal treatment. A reliable prediction of the final clinical TN stage would allow radiotherapist to adapt multidisciplinary approach to a less invasive management, sparing surgical procedure in responder patients or even allowing an early surgery in nonresponders, which would significantly reduce radiochemotherapy related toxicity. Conclusion. Early evaluation of response during neoadjuvant radiochemotherapy treatment shows great promise to predict tumour response.

Prediction of glioblastoma multiform response to bevacizumab treatment using multi-parametric MRI

Glioblastoma multiform (GBM) is a highly malignant brain tumor. Bevacizumab is a recent therapy for stopping tumor growth and even shrinking tumor through inhibition of vascular development (angiogenesis). This paper presents a non-invasive approach based on image analysis of multi-parametric magnetic resonance images (MRI) to predict response of GBM to this treatment. The resulting prediction system has potential to be used by physicians to optimize treatment plans of the GBM patients. The proposed method applies signal decomposition and histogram analysis methods to extract statistical features from Gd-enhanced regions of tumor that quantify its microstructural characteristics. MRI studies of 12 patients at multiple time points before and up to four months after treatment are used in this work. Changes in the Gd-enhancement as well as necrosis and edema after treatment are used to evaluate the response. Leave-one-out cross validation method is applied to evaluate prediction quality of the models. Predictive models developed in this work have large regression coefficients (maximum R² = 0.95) indicating their capability to predict response to therapy.

Diffusion-weighted imaging in head and neck cancers

This article reviews the utility of diffusion-weighted imaging (DWI) in the diagnosis, prognosis and monitoring of treatment response in tumors arising in the head and neck region. The apparent diffusion coefficient (ADC) value, determined from DWI, can help in cancer staging and detection of subcentimeter nodal metastasis. The ADC value also discriminates carcinomas from lymphomas, benign lesions from malignant tumors and tumor necrosis from abscesses. Low pretreatment ADC values typically predict a favorable response to chemoradiation therapy. These promising reports indicate the potential of DWI as a potential biomarker for diagnosis and monitoring of treatment response in head and neck cancers. In view of the overlapping ADC values between different salivary gland tumors, care should be taken when interpreting these results and other imaging parameters should be considered for a better diagnosis. Susceptibility and motion-induced artifacts may sometimes degrade DWI image quality; however, novel techniques are being developed to overcome these drawbacks.

Pretreatment prediction of brain tumors' response to radiation therapy using high b-value diffusion-weighted MRI

Diffusion-weighted magnetic resonance imaging (DWMRI) is sensitive to tissues' biophysical characteristics, including apparent diffusion coefficients (ADCs) and volume fractions of water in different populations. In this work, we evaluate the clinical efficacy of DWMRI and high diffusion-weighted magnetic resonance imaging (HDWMRI), acquired up to b = 4000 sec/mm(2) to amplify sensitivity to water diffusion properties, in pretreatment prediction of brain tumors' response to radiotherapy. Twelve patients with 20 brain lesions were studied. Six ring-enhancing lesions were excluded due to their distinct diffusion characteristics. Conventional and DWMRI were acquired on a 0.5-T MRI. Response to therapy was determined from relative changes in tumor volumes calculated from contrast-enhanced T1-weighted MRI, acquired before and a mean of 46 days after beginning therapy. ADCs and a diffusion index, R(D), reflecting tissue viability based on water diffusion were calculated from DWMRIs. Pretreatment values of ADC and R(D) were found to correlate significantly with later tumor response/nonresponse (r = 0.76, P <.002 and r = 0.77, P <.001). This correlation implies that tumors with low pretreatment diffusion values, indicating high viability, will respond better to radiotherapy than tumors with high diffusion values, indicating necrosis. These results demonstrate the feasibility of using DWMRI for pretreatment prediction of response to therapy in patients with brain tumors undergoing radiotherapy.

Changes in water mobility measured by diffusion MRI predict response of metastatic breast cancer to chemotherapy

The goal of oncology is the individualization of patient care to optimize therapeutic responses and minimize toxicities. Achieving this will require noninvasive, quantifiable, and early markers of tumor response. Preclinical data from xenografted tumors using a variety of antitumor therapies have shown that magnetic resonance imaging (MRI)-measured mobility of tissue water (apparent diffusion coefficient of water, or ADCw) is a biomarker presaging cell death in the tumor. This communication tests the hypothesis that changes in water mobility will quantitatively presage tumor responses in patients with metastatic liver lesions from breast cancer. A total of 13 patients with metastatic breast cancer and 60 measurable liver lesions were monitored by diffusion MRI after initiation of new courses of chemotherapy. MR images were obtained prior to, and at 4, 11, and 39 days following the initiation of therapy for determination of volumes and ADCw values. The data indicate that diffusion MRI can predict response by 4 or 11 days after commencement of therapy, depending on the analytic method. The highest concordance was observed in tumor lesions that were less than 8 cm3 in volume at presentation. These results suggest that diffusion MRI can be useful to predict the response of liver metastases to effective chemotherapy.

Molecular imaging in oncology

Cancer is a genetic disease that manifests in loss of normal cellular homeostatic mechanisms. The biology and therapeutic modulation of neoplasia occurs at the molecular level. An understanding of these molecular processes is therefore required to develop novel prognostic and early biomarkers of response. In addition to clinical applications, increased impetus for the development of such technologies has been catalysed by pharmaceutical companies investing in the development of molecular therapies. The discipline of molecular imaging therefore aims to image these important molecular processes in vivo. Molecular processes, however, operate at short length scales and concentrations typically beyond the resolution of clinical imaging. Solving these issues will be a challenge to imaging research. The successful implementations of molecular imaging in man will only be realised by the close co-operation amongst molecular biologists, chemists and the imaging scientists.

Applications of magnetic resonance in model systems: cancer therapeutics

The lack of information regarding the metabolism and pathophysiology of individual tumors limits, in part, both the development of new anti-cancer therapies and the optimal implementation of currently available treatments. Magnetic resonance [MR, including magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS), and electron paramagnetic resonance (EPR)] provides a powerful tool to assess many aspects of tumor metabolism and pathophysiology. Moreover, since this information can be obtained nondestructively, pre-clinical results from cellular or animal models are often easily translated into the clinic. This review presents selected examples of how MR has been used to identify metabolic changes associated with apoptosis, detect therapeutic response prior to a change in tumor volume, optimize the combination of metabolic inhibitors with chemotherapy and/or radiation, characterize and exploit the influence of tumor pH on the effectiveness of chemotherapy, characterize tumor reoxygenation and the effects of modifiers of tumor oxygenation in individual tumors, image transgene expression and assess the efficacy of gene therapy. These examples provide an overview of several of the areas in which cellular and animal model studies using MR have contributed to our understanding of the effects of treatment on tumor metabolism and pathophysiology and the importance of tumor metabolism and pathophysiology as determinants of therapeutic response.