Early detection of treatment response by diffusion-weighted 1H-NMR spectroscopy in a murine tumour in vivo

Associations of Multiparametric Breast MRI Features, Tumor-Infiltrating Lymphocytes, and Immune Gene Signature Scores Following a Single Dose of Trastuzumab in HER2-Positive Early-Stage Breast Cancer

Dynamic biomarkers that permit the real-time monitoring of the tumor microenvironment response to therapy are an unmet need in breast cancer. Breast magnetic resonance imaging (MRI) has demonstrated value as a predictor of pathologic complete response and may reflect immune cell changes in the tumor microenvironment. The purpose of this pilot study was to investigate the value of breast MRI features as early markers of treatment-induced immune response. Fourteen patients with early HER2+ breast cancer were enrolled in a window-of-opportunity study where a single dose of trastuzumab was administered and both tissue and MRIs were obtained at the pre- and post-treatment stages. Functional diffusion-weighted and dynamic contrast-enhanced MRI tumor measures were compared with tumor-infiltrating lymphocytes (TILs) and RNA immune signature scores. Both the pre-treatment apparent diffusion coefficient (ADC) and the change in peak percent enhancement (DPE) were associated with increased tumor-infiltrating lymphocytes with trastuzumab therapy (r = -0.67 and -0.69, p < 0.01 and p < 0.01, respectively). Low pre-treatment ADC and a greater decrease in PE in response to treatment were also associated with immune-activated tumor microenvironments as defined by RNA immune signatures. Breast MRI features hold promise as biomarkers of early immune response to treatment in HER2+ breast cancer.

Amide Proton Transfer Could Provide More Accurate Lesion Characterization in the Transition Zone of the Prostate

BACKGROUND

There is an overlap comparing transition zone prostate cancer (TZ PCa) and benign prostatic hyperplasia (BPH) on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI), creating additional challenges for assessment of TZ tumors on MRI.

PURPOSE

To evaluate whether amide proton transfer-weighted (APTw) imaging provides new diagnostic ideas for TZ PCa.

STUDY TYPE

Prospective.

POPULATION

A total of 51 TZ PCa patients (age, 49-89), 44 stromal BPH (age, 57-92), and 45 glandular BPH patients (age, 56-92).

FIELD STRENGTH/SEQUENCE

A 3 T; T2WI turbo spin echo (TSE), quantitative T2*-weighted imaging, DWI echo planar imaging, 3D APTw TSE.

ASSESSMENT

Differences in APTw, apparent diffusion coefficient (ADC), and T2* among three lesions were compared by one-way analysis of variance (ANOVA). Regions of interest were drawn by two radiologists (X.Q.Z. and X.Y.Q., with 21 and 15 years of experience, respectively).

STATISTICAL TESTS

Multivariable logistic regression analyses; ANOVA with post hoc testing; receiver operator characteristic curve analysis; Delong test. Significance level: P < 0.05.

RESULTS

APTw among TZ PCa, stromal BPH, and glandular BPH (3.48% ± 0.83% vs. 2.76% ± 0.49% vs. 2.72% ± 0.45%, respectively) were significantly different except between stromal BPH and glandular BPH (P > 0.99). Significant differences were found in ADC (TZ PCa 0.76 ± 0.16 × 10^-3  mm² /sec vs. stromal BPH 0.91 ± 0.14 × 10^-3  mm² /sec vs. glandular BPH 1.08 ± 0.18 × 10^-3  mm² /sec) among three lesions. APTw (OR = 12.18, 11.80, respectively) and 1/ADC (OR = 703.87, 181.11, respectively) were independent predictors of TZ PCa from BPH and stromal BPH. The combination of APTw and ADC had better diagnostic performance in the identification of TZ PCa from BPH and stromal BPH.

DATA CONCLUSION

APTw imaging has the potential to be of added value to ADC in differentiating TZ PCa from BPH and stromal BPH.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 2.

Diffusion-weighted MRI to detect early response to chemoradiation in cervical cancer: A systematic review and meta-analysis

Objective

Diffusion-weighted magnetic resonance imaging (DWI) has shown promise in predicting response to therapy in several malignancies. This systematic review and meta-analysis aimed to evaluate DWI in the prediction of response to treatment in patients with cervical cancer.

Methods

A systematic search was conducted on PubMed, Web of Science, Cochrane and Google Scholar databases Studies that evaluated DWI and apparent diffusion coefficient (ADC) for response evaluation before, during and after treatment with a correlation to conventional response criteria were included. The primary endpoint was the mean ADC values of cervical cancer at these timepoints. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) was used to assess the quality of the studies.

Results

Nine studies, comprising 270 patients, were included. Pre-treatment ADC values showed no correlation with eventual response. However, in our meta-analysis, there was a significant correlation with early treatment ADC values obtained within the first 3 weeks of therapy and response, as well as a significant correlation with the percentage change in ADC (ΔADC) and response. In addition, the pooled mean ΔADC percentage was also significantly higher in responders than in non-responders (49.7% vs 19.7%, respectively, p = 0.016).

Conclusion

DWI shows potential as a biomarker of early treatment response in patients with cervical carcinoma. Use of the change in ADC particularly within the first 3 weeks of therapy seems to be predictive of response and may serve as a suitable marker in the determination of early response.

Parametric Response Mapping of FLAIR MRI Provides an Early Indication of Progression Risk in Glioblastoma

RATIONALE AND OBJECTIVES

Glioblastoma image evaluation utilizes Magnetic Resonance Imaging contrast-enhanced, T1-weighted, and noncontrast T2-weighted fluid-attenuated inversion recovery (FLAIR) acquisitions. Disease progression assessment relies on changes in tumor diameter, which correlate poorly with survival. To improve treatment monitoring in glioblastoma, we investigated serial voxel-wise comparison of anatomically-aligned FLAIR signal as an early predictor of GBM progression.

MATERIALS AND METHODS

We analyzed longitudinal normalized FLAIR images (rFLAIR) from 52 subjects using voxel-wise Parametric Response Mapping (PRM) to monitor volume fractions of increased (PRM_rFLAIR+), decreased (PRM_rFLAIR-), or unchanged (PRM_rFLAIR0) rFLAIR intensity. We determined response by rFLAIR between pretreatment and 10 weeks posttreatment. Risk of disease progression in a subset of subjects (N = 26) with stable disease or partial response as defined by Response Assessment in Neuro-Oncology (RANO) criteria was assessed by PRM_rFLAIR between weeks 10 and 20 and continuously until the PRM_rFLAIR+ exceeded a defined threshold. RANO defined criteria were compared with PRM-derived outcomes for tumor progression detection.

RESULTS

Patient stratification for progression-free survival (PFS) and overall survival (OS) was achieved at week 10 using RANO criteria (PFS: p <0.0001; OS: p <0.0001), relative change in FLAIR-hyperintense volume (PFS: p = 0.0011; OS: p <0.0001), and PRM_rFLAIR+ (PFS: p <0.01; OS: p <0.001). PRM_rFLAIR+ also stratified responding patients' progression between weeks 10 and 20 (PFS: p <0.05; OS: p = 0.01) while changes in FLAIR-volume measurements were not predictive. As a continuous evaluation, PRM_rFLAIR+ exceeding 10% stratified patients for PFA after 5.6 months (p<0.0001), while RANO criteria did not stratify patients until 15.4 months (p <0.0001).

CONCLUSION

PRM_rFLAIR may provide an early biomarker of disease progression in glioblastoma.

Advanced gastric cancer: CT radiomics prediction and early detection of downstaging with neoadjuvant chemotherapy

OBJECTIVES

To develop and evaluate machine learning models using baseline and restaging computed tomography (CT) for predicting and early detecting pathological downstaging (pDS) with neoadjuvant chemotherapy in advanced gastric cancer (AGC).

METHODS

We collected 292 AGC patients who received neoadjuvant chemotherapy. They were classified into (a) primary cohort (206 patients with 3-4 cycles chemotherapy) for model development and internal validation, (b) testing cohort I (46 patients with 3-4 cycles chemotherapy) for evaluating models' predictive ability before and after the complete course, and (c) testing cohort II (n = 40) for model evaluation on its performance at early treatment. We extracted 1,231 radiomics features from venous phase CT at baseline and restaging. We selected radiomics models based on 28 cross-combination models and measured the areas under the curve (AUC). Our prediction radiomics (PR) model is designed to predict pDS outcomes using baseline CT. Detection radiomics (DR) model is applied to restaging CT for early pDS detection.

RESULTS

PR model achieved promising outcomes in two testing cohorts (AUC 0.750, p = .009 and AUC 0.889, p = .000). DR model also showed a good predictive ability (AUC 0.922, p = .000 and AUC 0.850, p = .000), outperforming the commonly used RECIST method (NRI 39.5% and NRI 35.4%). Furthermore, the improved DR model with averaging outcome scores of PR and DR models showed boosted results in two testing cohorts (AUC 0.961, p = .000 and AUC 0.921, p = .000).

CONCLUSIONS

CT-based radiomics models perform well on prediction and early detection tasks of pDS and can potentially assist surgical decision-making in AGC patients.

KEY POINTS

• Baseline contrast-enhanced computed tomography (CECT)-based radiomics features were predictive of pathological downstaging, allowing accurate identification of non-responders before therapy. • Restaging CECT-based radiomics features were predictive to achieve pDS after and even at an early stage of neoadjuvant chemotherapy. • Combination of baseline and restaging CECT-based radiomics features was promising for early detection and preoperative evaluation of pathological downstaging of AGC.

Magnetic resonance imaging of mean cell size in human breast tumors

PURPOSE

Cell size is a fundamental characteristic of all tissues, and changes in cell size in cancer reflect tumor status and response to treatments, such as apoptosis and cell-cycle arrest. Unfortunately, cell size can currently be obtained only by pathological evaluation of tumor tissue samples obtained invasively. Previous imaging approaches are limited to preclinical MRI scanners or require relatively long acquisition times that are impractical for clinical imaging. There is a need to develop cell-size imaging for clinical applications.

METHODS

We propose a clinically feasible IMPULSED (imaging microstructural parameters using limited spectrally edited diffusion) approach that can characterize mean cell sizes in solid tumors. We report the use of a combination of pulse sequences, using different gradient waveforms implemented on clinical MRI scanners and analytical equations based on these waveforms to analyze diffusion-weighted MRI signals and derive specific microstructural parameters such as cell size. We also describe comprehensive validations of this approach using computer simulations, cell experiments in vitro, and animal experiments in vivo and demonstrate applications in preoperative breast cancer patients.

RESULTS

With fast acquisitions (~7 minutes), IMPULSED can provide high-resolution (1.3 mm in-plane) mapping of mean cell size of human tumors in vivo on clinical 3T MRI scanners. All validations suggest that IMPULSED provides accurate and reliable measurements of mean cell size.

CONCLUSION

The proposed IMPULSED method can assess cell-size variations in tumors of breast cancer patients, which may have the potential to assess early response to neoadjuvant therapy.

Magnetic Resonance Imaging for Translational Research in Oncology

The translation of results from the preclinical to the clinical setting is often anything other than straightforward. Indeed, ideas and even very intriguing results obtained at all levels of preclinical research, i.e., in vitro, on animal models, or even in clinical trials, often require much effort to validate, and sometimes, even useful data are lost or are demonstrated to be inapplicable in the clinic. In vivo, small-animal, preclinical imaging uses almost the same technologies in terms of hardware and software settings as for human patients, and hence, might result in a more rapid translation. In this perspective, magnetic resonance imaging might be the most translatable technique, since only in rare cases does it require the use of contrast agents, and when not, sequences developed in the lab can be readily applied to patients, thanks to their non-invasiveness. The wide range of sequences can give much useful information on the anatomy and pathophysiology of oncologic lesions in different body districts. This review aims to underline the versatility of this imaging technique and its various approaches, reporting the latest preclinical studies on thyroid, breast, and prostate cancers, both on small laboratory animals and on human patients, according to our previous and ongoing research lines.

Translating preclinical MRI methods to clinical oncology

The complexity of modern in vivo magnetic resonance imaging (MRI) methods in oncology has dramatically changed in the last 10 years. The field has long since moved passed its (unparalleled) ability to form images with exquisite soft-tissue contrast and morphology, allowing for the enhanced identification of primary tumors and metastatic disease. Currently, it is not uncommon to acquire images related to blood flow, cellularity, and macromolecular content in the clinical setting. The acquisition of images related to metabolism, hypoxia, pH, and tissue stiffness are also becoming common. All of these techniques have had some component of their invention, development, refinement, validation, and initial applications in the preclinical setting using in vivo animal models of cancer. In this review, we discuss the genesis of quantitative MRI methods that have been successfully translated from preclinical research and developed into clinical applications. These include methods that interrogate perfusion, diffusion, pH, hypoxia, macromolecular content, and tissue mechanical properties for improving detection, staging, and response monitoring of cancer. For each of these techniques, we summarize the 1) underlying biological mechanism(s); 2) preclinical applications; 3) available repeatability and reproducibility data; 4) clinical applications; and 5) limitations of the technique. We conclude with a discussion of lessons learned from translating MRI methods from the preclinical to clinical setting, and a presentation of four fundamental problems in cancer imaging that, if solved, would result in a profound improvement in the lives of oncology patients. Level of Evidence: 5 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1377-1392.

Multimodal Molecular Imaging: Current Status and Future Directions

Molecular imaging has emerged at the end of the last century as an interdisciplinary method involving in vivo imaging and molecular biology aiming at identifying living biological processes at a cellular and molecular level in a noninvasive manner. It has a profound role in determining disease changes and facilitating drug research and development, thus creating new medical modalities to monitor human health. At present, a variety of different molecular imaging techniques have their advantages, disadvantages, and limitations. In order to overcome these shortcomings, researchers combine two or more detection techniques to create a new imaging mode, such as multimodal molecular imaging, to obtain a better result and more information regarding monitoring, diagnosis, and treatment. In this review, we first describe the classic molecular imaging technology and its key advantages, and then, we offer some of the latest multimodal molecular imaging modes. Finally, we summarize the great challenges, the future development, and the great potential in this field.

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.

Predictive value of pre-treatment apparent diffusion coefficient (ADC) in radio-chemiotherapy treated head and neck squamous cell carcinoma

OBJECTIVE

This study aimed at evaluating the role of "baseline" apparent diffusion coefficent (ADC), in patients affected by head and neck cancer treated with radio-chemotherapy, as a potential marker of response to therapy.

METHODS

Fifty-seven patients underwent pretreatment ADC maps. Minimum, maximum, and medium ADC were computed. Age, dose, treatment time, and ADC values were compared between the two groups (Group 1: local control; Group 2: relapse/persistence of disease) using the Student t test two-tailed unpaired. Two-tailed Fischer exact test was used to compare T stage, N stage, grading and type of treatment between two groups. We have analyzed the receiver operating characteristic (ROC) of statistically significant variables.

RESULTS

In patients with local control, values of pre-treatment medium and minimum ADC were lower than ADC values of patients with persistent or recurrent disease, with values, respectively, of 0.83 ± 0.02 × 10^-3 mm²/s and 0.59 ± 0.02 × 10^-3 mm²/s (vs 0.94 ± 0.05 × 10^-3 mm²/s and 0.70 ± 0.05 × 10^-3 mm²/s). ROC curve analysis displayed statistical significance as regarding the medium ADC value, showing a sensitivity of 50% and a specificity of 84.8%. ROC analysis of the values minimum ADC showed a sensitivity of 42.9% and specificity of 87.9%.

CONCLUSION

The value of the ADC pre-treatment of patients with local control of the disease is lower than that of patients with persistent disease or recurrence.

A comparative assessment of preclinical chemotherapeutic response of tumors using quantitative non-Gaussian diffusion MRI

BACKGROUND

Diffusion-weighted MRI (DWI) signal attenuation is often not mono-exponential (i.e. non-Gaussian diffusion) with stronger diffusion weighting. Several non-Gaussian diffusion models have been developed and may provide new information or higher sensitivity compared with the conventional apparent diffusion coefficient (ADC) method. However the relative merits of these models to detect tumor therapeutic response is not fully clear.

METHODS

Conventional ADC, and three widely-used non-Gaussian models, (bi-exponential, stretched exponential, and statistical model), were implemented and compared for assessing SW620 human colon cancer xenografts responding to barasertib, an agent known to induce apoptosis via polyploidy. Bayesian Information Criterion (BIC) was used for model selection among all three non-Gaussian models.

RESULTS

All of tumor volume, histology, conventional ADC, and three non-Gaussian DWI models could show significant differences between control and treatment groups after four days of treatment. However, only the non-Gaussian models detected significant changes after two days of treatment. For any treatment or control group, over 65.7% of tumor voxels indicate the bi-exponential model is strongly or very strongly preferred.

CONCLUSION

Non-Gaussian DWI model-derived biomarkers are capable of detecting tumor earlier chemotherapeutic response of tumors compared with conventional ADC and tumor volume. The bi-exponential model provides better fitting compared with statistical and stretched exponential models for the tumor and treatment models used in the current work.

Diffusion-weighted imaging in musculoskeletal radiology-clinical applications and future directions

Diffusion-weighted imaging (DWI) is an established diagnostic tool with regards to the central nervous system (CNS) and research into its application in the musculoskeletal system has been growing. It has been shown that DWI has utility in differentiating vertebral compression fractures from malignant ones, assessing partial and complete tears of the anterior cruciate ligament (ACL), monitoring tumor response to therapy, and characterization of soft-tissue and bone tumors. DWI is however less useful in differentiating malignant vs. infectious processes. As of yet, no definitive qualitative or quantitative properties have been established due to reasons ranging from variability in acquisition protocols to overlapping imaging characteristics. Even with these limitations, DWI can still provide clinically useful information, increasing diagnostic accuracy and improving patient management when magnetic resonance imaging (MRI) findings are inconclusive. The purpose of this article is to summarize recent research into DWI applications in the musculoskeletal system.

Structural information revealed by the dispersion of ADC with frequency

Diffusion MRI provides a non-invasive means to characterize tissue microstructure at varying length scales. Temporal diffusion spectra reveal how the apparent diffusion coefficient (ADC) varies with frequency. When measured using oscillating gradient spin echo sequences, the manner in which ADC disperses with gradient frequency (which is related to the reciprocal of diffusion time) provides information on the characteristic dimensions of restricting structures within the medium. For example, the dispersion of ADC with oscillating gradient frequency (ΔfADC) has been shown to correlate with axon sizes in white matter and provide novel tissue contrast in images of mouse hippocampus and cerebellum. However, despite increasing interest in applying frequency-dependent ADC to derive novel information on tissue, the interpretations of ADC spectra are not always clear. In this study, the relation between ADC spectra and restricting dimensions are further elucidated and used to derive novel image contrast related to the sizes of intrinsic microstructures.

Time-Dependent Influence of Cell Membrane Permeability on MR Diffusion Measurements

PURPOSE

To investigate the influence of cell membrane permeability on diffusion measurements over a broad range of diffusion times.

METHODS

Human myelogenous leukemia K562 cells were cultured and treated with saponin to selectively alter cell membrane permeability, resulting in a broad physiologically relevant range of 0.011-0.044 μm/ms. Apparent diffusion coefficient (ADC) values were acquired with the effective diffusion time (Δeff ) ranging from 0.42 to 3000 ms. Cosine-modulated oscillating gradient spin echo (OGSE) measurements were performed to achieve short Δeff from 0.42 to 5 ms, while stimulated echo acquisitions were used to achieve long Δeff from 11 to 2999 ms. Computer simulations were also performed to support the experimental results.

RESULTS

Both computer simulations and experiments in vitro showed that the influence of membrane permeability on diffusion MR measurements is highly dependent on the choice of diffusion time, and it is negligible only when the diffusion time is at least one order of magnitude smaller than the intracellular exchange lifetime.

CONCLUSION

The influence of cell membrane permeability on the measured ADCs is negligible in OGSE measurements at moderately high frequencies. By contrast, cell membrane permeability has a significant influence on ADC and quantitative diffusion measurements at low frequencies such as those sampled using conventional pulsed gradient methods.

Early changes in apparent diffusion coefficient as an indicator of response to sorafenib in hepatocellular carcinoma

OBJECTIVE

The relationship between apparent diffusion coefficient (ADC) and chemotherapy has been established. However, whether ADC could be considered as a measure for monitoring response to sorafenib in hepatocellular carcinoma (HCC) has not been demonstrated. This study was to investigate the ADC changes of advanced HCC under sorafenib treatment.

METHODS

Athymic mice with HepG2 xenografts were allocated to two groups: control and sorafenib (40 mg/kg, bid). T2 and diffusion images were acquired at each time point (0, 10, 14, and 18 d post-therapy). Tumor volume and changes in ADC were calculated.

RESULTS

Tumor volumes on Days 10, 14, and 18 after treatment showed significant decreases in the sorafenib-treated group compared with the control. Pretreatment ADC values were not significantly different between the control and treated groups. A slow increase in ADC in the peripheral zone of tumors appeared in the treated group, which was significantly higher compared with the control group on Days 10, 14, and 18. In the central part of tumors on Day 10 after treatment, an increase in ADC appeared in the treated and control groups, the ADC of the control group being significantly lower compared with the treated tumors. From Day 10 to Day 14, the ADC map showed a progressive decrease in the central region of tumors in the treated and control groups. However, this change is more significant in the treated groups.

CONCLUSIONS

Early changes in mean ADC correlated with sorafenib treatment in HCC, which are promising indicators for predicting sorafenib response in this carcinoma.

Diffusion-weighted MRI in early assessment of tumour response to radiotherapy in high-risk prostate cancer

OBJECTIVE

The objective of this study was to assess the efficacy of diffusion-weighted MRI (DWI) in monitoring response to radiotherapy in high-risk prostate cancer (PC).

METHODS

This retrospective study included 78 patients with high-risk PC undergoing 3.0-T MRI (supplemented by DWI) before and after intensity-modulated radiotherapy (IMRT). Based on follow-up clinical examinations, patients were divided into two groups: the recurrence group (patients who suffered biochemical/clinical recurrence within 3 years, n = 13) and the non-recurrence group (patients who were recurrence free for over 3 years, n = 65). The apparent diffusion coefficient (ADC) values before and after IMRT were compared between these two groups. The receiver-operating characteristics (ROC) analysis was carried out to investigate the discriminatory capability for pre- and post-IMRT ADC values.

RESULTS

The overall ADC values were 1.04 ± 0.18 × 10(-3) mm(2) s(-1) for PCs before IMRT and 1.45 ± 0.15 × 10(-3) mm(2) s(-1) after IMRT (p < 0.001). A statistically significant difference in post-IMRT ADC values was noted between patients with and without recurrence (1.27 ± 0.14 × 10(-3) mm(2) s(-1) vs 1.49 ± 0.12 × 10(-3)mm(2) s(-1); p < 0.001), although there was no statistical difference between them in pre-IMRT ADC values (1.00 ± 0.17 × 10(-3) mm(2) s(-1) vs 1.05 ± 0.18 × 10(-3) mm(2) s(-1); p = 0.31). The ROC curve analysis revealed that the post-IMRT ADC values could help identify patients suffering recurrences (area under the curve, 0.88; p < 0.001).

CONCLUSION

Marked increase in ADC values was observed in PC after radiotherapy, especially in good responders. DWI is a valuable tool for monitoring the response to radiotherapy.

ADVANCES IN KNOWLEDGE

This study examined the relationship between ADC changes and tumour response to treatment of PC.

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.

DW-MRI as a Predictive Biomarker of Radiosensitization of GBM through Targeted Inhibition of Checkpoint Kinases

PURPOSE

The inherent treatment resistance of glioblastoma (GBM) can involve multiple mechanisms including checkpoint kinase (Chk1/2)-mediated increased DNA repair capability, which can attenuate the effects of genotoxic chemotherapies and radiation. The goal of this study was to evaluate diffusion-weighted magnetic resonance imaging (DW-MRI) as a biomarker for Chk1/2 inhibitors in combination with radiation for enhancement of treatment efficacy in GBM.

EXPERIMENTAL DESIGN

We evaluated a specific small molecule inhibitor of Chk1/2, AZD7762, in combination with radiation using in vitro human cell lines and in vivo using a genetically engineered GBM mouse model. DW-MRI and T1-contrast MRI were used to follow treatment effects on intracranial tumor cellularity and growth rates, respectively.

RESULTS

AZD7762 inhibited clonal proliferation in a panel of GBM cell lines and increased radiosensitivity in p53-mutated GBM cell lines to a greater extent compared to p53 wild-type cells. In vivo efficacy of AZD7762 demonstrated a dose-dependent inhibitory effect on GBM tumor growth rate and a reduction in tumor cellularity based on DW-MRI scans along with enhancement of radiation efficacy.

CONCLUSION

DW-MRI was found to be a useful imaging biomarker for the detection of radiosensitization through inhibition of checkpoint kinases. Chk1/2 inhibition resulted in antiproliferative activity, prevention of DNA damage-induced repair, and radiosensitization in preclinical GBM tumor models, both in vitro and in vivo. The effects were found to be maximal in p53-mutated GBM cells. These results provide the rationale for integration of DW-MRI in clinical translation of Chk1/2 inhibition with radiation for the treatment of GBM.

Musculoskeletal tumors: how to use anatomic, functional, and metabolic MR techniques

Although the function of magnetic resonance (MR) imaging in the evaluation of musculoskeletal tumors has traditionally been to help identify the extent of disease prior to treatment, its role continues to evolve as new techniques emerge. Conventional pulse sequences remain heavily used and useful, but with the advent of chemical shift imaging, diffusion-weighted imaging, perfusion imaging and MR spectroscopy, additional quantitative metrics have become available that may help expand the role of MR imaging to include detection, characterization, and reliable assessment of treatment response. This review discusses a multiparametric approach to the evaluation of musculoskeletal tumors, with a focus on the utility and potential added value of various pulse sequences in helping establish a diagnosis, assess pretreatment extent, and evaluate a tumor in the posttreatment setting for recurrence and treatment response.

Neuroendocrine liver metastasis treated by using intraarterial therapy: volumetric functional imaging biomarkers of early tumor response and survival

PURPOSE

To determine if volumetric changes of diffusion-weighted and contrast material-enhanced magnetic resonance (MR) imaging can help assess early tumor response to intraarterial therapy (IAT) in neuroendocrine liver metastasis (NELM).

MATERIALS AND METHODS

This retrospective single-center comprehensive imaging analysis was performed in compliance with HIPAA and was institutional review board approved. Informed patient consent was waived. Seventy-one patients (39 men; mean age, 62.3 years) with NELM treated with IAT were analyzed retrospectively. MR studies were performed before and 3-4 weeks after therapy. The index lesion was segmented to provide volumetric functional analysis of apparent diffusion coefficient (ADC) and contrast-enhanced MR imaging in the hepatic arterial phase (HAP) and portal venous phase (PVP). Tumor response was defined as increase in volumetric ADC of 15% or greater and decrease in volumetric enhancement of 25% or greater during the HAP or of 50% or greater during the PVP. Patient overall survival was the primary end point after therapy initiation. Univariate analysis included Kaplan-Meier survival curves. The Cox proportional hazards regression model was used to detect interactions between volumetric ADC and contrast-enhanced MR imaging and to calculate the hazard ratio.

RESULTS

There was significant increase in mean volumetric ADC (27%, P < .0001) and significant decrease in mean volumetric enhancement during the HAP (-25.3%, P < .0001) and the PVP (-22.4%, P < .0001) in all patients. Patients who had 15% or greater volumetric ADC increase (n = 49) after therapy had better prognosis than those who had less than 15% increase in volumetric ADC (n = 22) (log-rank test, P < .002). Patients who had 25% or greater decrease in volumetric arterial enhancement (n = 40) or 50% or greater decrease in venous enhancement (n = 18) had better prognosis than those who had less than 25% decrease in volumetric arterial enhancement (n = 31) or less than 50% decrease in venous enhancement (n = 53) (log-rank test, P < .02).

CONCLUSION

Volumetric functional MR imaging criteria may act as biomarkers of early response, indicating that these criteria may be important to incorporate in future NELM clinical trials.

Characterizing tumor response to chemotherapy at various length scales using temporal diffusion spectroscopy

Measurements of apparent diffusion coefficient (ADC) using magnetic resonance imaging (MRI) have been suggested as potential imaging biomarkers for monitoring tumor response to treatment. However, conventional pulsed-gradient spin echo (PGSE) methods incorporate relatively long diffusion times, and are usually sensitive to changes in cell density and necrosis. Diffusion temporal spectroscopy using the oscillating gradient spin echo (OGSE) sequence is capable of probing short length scales, and may detect significant intracellular microstructural changes independent of gross cell density changes following anti-cancer treatment. To test this hypothesis, SW620 xenografts were treated by barasertib (AZD1152), a selective inhibitor of Aurora B kinase which causes SW620 cancer cells to develop polyploidy and increase in size following treatment, ultimately leading to cell death through apoptosis. Following treatment, the ADC values obtained by both the PGSE and low frequency OGSE methods increased. However, the ADC values at high gradient frequency (i.e. short diffusion times) were significantly lower in treated tumors, consistent with increased intracellular restrictions/hindrances. This suggests that ADC values at long diffusion times are dominated by tumor microstructure at long length scales, and may not convey unambiguous information of subcellular space. While the diffusion temporal spectroscopy provides more comprehensive means to probe tumor microstructure at various length scales. This work is the first study to probe intracellular microstructural variations due to polyploidy following treatment using diffusion MRI in vivo. It is also the first observation of post-treatment ADC changes occurring in opposite directions at short and long diffusion times. The current study suggests that temporal diffusion spectroscopy potentially provides pharmacodynamic biomarkers of tumor early response which distinguish microstructural variations following treatment at both the subcellular and supracellular length scales.

Using magnetic resonance imaging and spectroscopy in cancer diagnostics and monitoring: preclinical and clinical approaches

Nuclear Magnetic Resonance (MR) based imaging has become an integrated domain in today's oncology research and clinical management of cancer patients. MR is a unique imaging modality among numerous other imaging modalities by providing access to anatomical, physiological, biochemical and molecular details of tumour with excellent spatial and temporal resolutions. In this review we will cover established and investigational MR imaging (MRI) and MR spectroscopy (MRS) techniques used for cancer imaging and demonstrate wealth of information on tumour biology and clinical applications MR techniques offer for oncology research both in preclinical and clinical settings. Emphasis is given not only to the variety of information which may be obtained but also the complementary nature of the techniques. This ability to determine tumour type, grade, invasiveness, degree of hypoxia, microvacular characteristics, and metabolite phenotype, has already profoundly transformed oncology research and patient management. It is evident from the data reviewed that MR techniques will play a key role in uncovering molecular fingerprints of cancer, developing targeted treatment strategies and assessing responsiveness to treatment for personalized patient management, thereby allowing rapid translation of imaging research conclusions into the benefit of clinical oncology.

The HIF-pathway inhibitor NSC-134754 induces metabolic changes and anti-tumour activity while maintaining vascular function

Background:

Hypoxia-inducible factor-1 (HIF-1) mediates the transcriptional response to hypoxic stress, promoting tumour progression and survival. This study investigated the acute effects of the small-molecule HIF-pathway inhibitor NSC-134754.

Methods:

Human PC-3LN5 prostate cancer cells were treated with NSC-134754 for 24 h in hypoxia. Orthotopic prostate tumour-bearing mice were treated with a single dose of NSC-134754 for 6, 24 or 48 h. Treatment response was measured using magnetic resonance spectroscopy and imaging. Ex-vivo histological validation of imaging findings was also sought.

Results:

In vitro, NSC-134754 significantly reduced lactate production and glucose uptake (P<0.05), while significantly increasing intracellular glucose (P<0.01) and glutamine uptake/metabolism (P<0.05). Increased glutamine metabolism was independent of c-Myc, a factor also downregulated by NSC-134754. In vivo, a significantly higher tumour apparent diffusion coefficient was determined 24 h post-treatment (P<0.05), with significantly higher tumour necrosis after 48 h (P<0.05). NSC-134754-treated tumours revealed lower expression of HIF-1α and glucose transporter-1, at 6 and 24 h respectively, while a transient increase in tumour hypoxia was observed after 24 h. Vessel perfusion/flow and vascular endothelial growth factor levels were unchanged with treatment.

Conclusion:

NSC-134754 induces metabolic alterations in vitro and early anti-tumour activity in vivo, independent of changes in vascular function. Our data support the further evaluation of NSC-134754 as an anti-cancer agent.

Incorporation of diffusion-weighted magnetic resonance imaging data into a simple mathematical model of tumor growth

We build on previous work to show how serial diffusion-weighted MRI (DW-MRI) data can be used to estimate proliferation rates in a rat model of brain cancer. Thirteen rats were inoculated intracranially with 9L tumor cells; eight rats were treated with the chemotherapeutic drug 1,3-bis(2-chloroethyl)-1-nitrosourea and five rats were untreated controls. All animals underwent DW-MRI immediately before, one day and three days after treatment. Values of the apparent diffusion coefficient (ADC) were calculated from the DW-MRI data and then used to estimate the number of cells in each voxel and also for whole tumor regions of interest. The data from the first two imaging time points were then used to estimate the proliferation rate of each tumor. The proliferation rates were used to predict the number of tumor cells at day three, and this was correlated with the corresponding experimental data. The voxel-by-voxel analysis yielded Pearson’s correlation coefficients ranging from −0.06 to 0.65, whereas the region of interest analysis provided Pearson’s and concordance correlation coefficients of 0.88 and 0.80, respectively. Additionally, the ratio of positive to negative proliferation values was used to separate the treated and control animals (p <0.05) at an earlier point than the mean ADC values. These results further illustrate how quantitative measurements of tumor state obtained non-invasively by imaging can be incorporated into mathematical models that predict tumor growth.

Effect of implantation site and growth of hepatocellular carcinoma on apparent diffusion coefficient of water and sodium MRI

Hepatocellular carcinoma (HCC) and liver metastases are an increasing problem worldwide. Non-invasive methods for the early detection of HCC and understanding of the tumor growth mechanisms are highly desirable.  Both the diffusion-weighted (1)H (DWI) and (23)Na MRI reflect alterations in tissue compartment volumes in tumors, as well as physiological and metabolic transformation in cells. Effects of untreated growth on apparent diffusion coefficient of water (ADC), single quantum (SQ) and triple quantum-filtered (TQF) (23)Na MRI were compared in intrahepatically and subcutaneously implanted HCCs in rats. Animals were examined weekly for 4 weeks after injection of N1S1 cells. ADC of intrahepatic HCC was 1.5-times higher compared to the nearby liver tissue, and with growth, the ADC did not increase. ADC of subcutaneous HCC was lower compared to intrahepatic HCC and it increased with growth. Untreated growth of both intrahepatic and subcutaneous HCCs was associated with an increase in SQ and TQF (23)Na signal intensity suggesting an increase in tissue Na(+) and intracellular Na(+) (Na(+)(i)), respectively, most likely due to an increase in relative extracellular space and Na(+)(i) concentration as a result of changes in tissue structure and cellular metabolism. Thus, SQ and TQF (23)Na MRI may be complementary to diffusion imaging in areas susceptible to motion for characterizing hepatic tumors and for other applications, such as, predicting and monitoring therapy response.

Predicting treatment efficacy via quantitative magnetic resonance imaging: a Bayesian joint model

The prognosis for patients with high grade gliomas is poor, with a median survival of 1 year. Treatment efficacy assessment is typically unavailable until 5-6 months post diagnosis. Investigators hypothesize that quantitative magnetic resonance imaging can assess treatment efficacy 3 weeks after therapy starts, thereby allowing salvage treatments to begin earlier. The purpose of this work is to build a predictive model of treatment efficacy by using quantitative magnetic resonance imaging data and to assess its performance. The outcome is 1-year survival status. We propose a joint, two-stage Bayesian model. In stage I, we smooth the image data with a multivariate spatiotemporal pairwise difference prior. We propose four summary statistics that are functionals of posterior parameters from the first-stage model. In stage II, these statistics enter a generalized non-linear model as predictors of survival status. We use the probit link and a multivariate adaptive regression spline basis. Gibbs sampling and reversible jump Markov chain Monte Carlo methods are applied iteratively between the two stages to estimate the posterior distribution. Through both simulation studies and model performance comparisons we find that we can achieve higher overall correct classification rates by accounting for the spatiotemporal correlation in the images and by allowing for a more complex and flexible decision boundary provided by the generalized non-linear model.

Diffusion weighted imaging in predicting progression free survival in patients with squamous cell carcinomas of the head and neck treated with induction chemotherapy

RATIONALE AND OBJECTIVES

The aim of this study was to assess the role of diffusion-weighted imaging in predicting progression-free survival in patients with head and neck squamous cell carcinoma (HNSCC) treated with induction chemotherapy.

MATERIALS AND METHODS

Eighteen patients with HNSCC underwent diffusion-weighted imaging studies prior to treatment and within 3 weeks after completion of induction chemotherapy. Median apparent diffusion coefficient (ADC) values were computed from the largest cervical metastatic lymph node. Percentage changes in ADC values from pretreatment to posttreatment time points were compared between alive and dead patients using the Mann-Whitney U test. P values < .05 were considered statistically significant.

RESULTS

A 22% increase in ADC was observed after induction chemotherapy in alive patients (n = 15), while patients who died from HNSCC (n = 3) demonstrated a 33% decrease in ADC. The difference in percentage change in ADC between alive and dead patients was significant (P = .039).

CONCLUSIONS

ADC may be a useful marker in predicting progression-free survival in patients with HNSCC undergoing induction chemotherapy.

Early evaluation of cancer response by a new functional biomarker: apparent diffusion coefficient

OBJECTIVE

The purpose of this article is to investigate whether apparent diffusion coefficient (ADC) might be used as a universal biomarker for response evaluation in different tumors.

SUBJECTS AND METHODS

Twenty-one patients with lung cancer, 12 patients with esophageal cancer, 19 patients with liver metastases, 24 patients with gastric cancer, and 26 patients with rectal cancer were recruited to the study. Percentage changes in the ADC and changes in the size of responding and nonresponding lesions of different tumors after treatment were analyzed using repeated measures analysis of variance.

RESULTS

There was no significant difference among the percentage ADC changes of different tumors (F = 1.57; p = 0.192). Clear differences were seen in the percentage ADC changes between responding and nonresponding tumors (F = 21.62; p < 0.001), which were significant at every time point after the start of treatment (early time point, F = 19.75 and p < 0.001; middle time point, F = 11.23 and p = 0.001; and later time point, F = 15.98 and p < 0.001). The percentage size changes after treatment between responding and nonresponding tumors were significantly different (F = 19.38; p < 0.001). However, at the early time point after treatment, the difference was not statistically significant (F = 0.02; p = 0.894).

CONCLUSION

The ADC changes correlated with treatment response in five types of body tumor but were independent of the tumor's location. Early increases in ADC during treatment indicate good response to treatment. ADC change is a promising biomarker for detecting therapeutic responses at an early stage that could be widely used.

Applications of the repeatability of quantitative imaging biomarkers: a review of statistical analysis of repeat data sets

Repeat imaging data sets performed on patients with cancer are becoming publicly available. The potential utility of these data sets for addressing important questions in imaging biomarker development is vast. In particular, these data sets may be useful to help characterize the variability of quantitative parameters derived from imaging. This article reviews statistical analysis that may be performed to use results of repeat imaging to 1) calculate the level of change in parameter value that may be seen in individual patients to confidently characterize that patient as showing true parameter change, 2) calculate the level of change in parameters value that may be seen in individual patients to confidently categorize that patient as showing true lack of parameter change, 3) determine if different imaging devices are interchangeable from the standpoint of repeatability, and 4) estimate the numbers of patients needed to precisely calculate repeatability. In addition, we recommend a set of statistical parameters that should be reported when the repeatability of continuous parameters is studied.

Functional Imaging for Assessing Tumor Response in Cancer of the Cervix

Treatment options for carcinoma of the cervix are guided by tumor stage, and include radical surgery, in cases where the tumor is confined to the cervix, or concurrent chemotherapy and radiotherapy. In those cases treated with chemoradiation, the ability to monitor the response to treatment in order to adapt the management plan during its course may be beneficial. This approach has the potential to offer an individualized treatment plan, allowing for differences in behavior between tumors to be addressed early, rather than a ‘one size fits all’ treatment approach. This article aims to review the use of evolving functional imaging techniques including diffusion-weighted MRI, dynamic contrast-enhanced MRI, and PET as tools for the evaluation of response to treatment of uterine cervical carcinoma.

Magnetic resonance in the era of molecular imaging of cancer

Magnetic resonance imaging (MRI) has played an important role in the diagnosis and management of cancer since it was first developed, but other modalities also continue to advance and provide complementary information on the status of tumors. In the future, there will be a major continuing role for noninvasive imaging in order to obtain information on the location and extent of cancer, as well as assessments of tissue characteristics that can monitor and predict treatment response and guide patient management. Developments are currently being undertaken that aim to provide improved imaging methods for the detection and evaluation of tumors, for identifying important characteristics of tumors such as the expression levels of cell surface receptors that may dictate what types of therapy will be effective and for evaluating their response to treatments. Molecular imaging techniques based mainly on radionuclide imaging can depict numerous, specific, cellular and molecular markers of disease and have unique potential to address important clinical and research challenges. In this review, we consider what continuing and evolving roles will be played by MRI in this era of molecular imaging. We discuss some of the challenges for MRI of detecting imaging agents that report on molecular events, but highlight also the ability of MRI to assess other features such as cell density, blood flow and metabolism which are not specific hallmarks of cancer but which reflect molecular changes. We discuss the future role of MRI in cancer and describe the use of selected quantitative imaging techniques for characterizing tumors that can be translated to clinical applications, particularly in the context of evaluating novel treatments.

Earlier detection of tumor treatment response using magnetic resonance diffusion imaging with oscillating gradients

An improved method for detecting early changes in tumors in response to treatment, based on a modification of diffusion-weighted magnetic resonance imaging, has been demonstrated in an animal model. Early detection of therapeutic response in tumors is important both clinically and in pre-clinical assessments of novel treatments. Noninvasive imaging methods that can detect and assess tumor response early in the course of treatment, and before frank changes in tumor morphology are evident, are of considerable interest as potential biomarkers of treatment efficacy. Diffusion-weighted magnetic resonance imaging is sensitive to changes in water diffusion rates in tissues that result from structural variations in the local cellular environment, but conventional methods mainly reflect changes in tissue cellularity and do not convey information specific to microstructural variations at sub-cellular scales. We implemented a modified imaging technique using oscillating gradients of the magnetic field for evaluating water diffusion rates over very short spatial scales that are more specific for detecting changes in intracellular structure that may precede changes in cellularity. Results from a study of orthotopic 9L gliomas in rat brains indicate that this method can detect changes as early as 24 h following treatment with 1,3-bis(2-chloroethyl)-1-nitrosourea, when conventional approaches do not find significant effects. These studies suggest that diffusion imaging using oscillating gradients may be used to obtain an earlier indication of treatment efficacy than previous magnetic resonance imaging methods.

Dependence of temporal diffusion spectra on microstructural properties of biological tissues

The apparent diffusion coefficient (ADC) measured using magnetic resonance imaging methods provides information on microstructural properties of biological tissues, and thus has found applications as a useful biomarker for assessing changes such as those that occur in ischemic stroke and cancer. Conventional pulsed gradient spin echo methods are in widespread use and provide information on, for example, variations in cell density. The oscillating gradient spin echo (OGSE) method has the additional ability to probe diffusion behaviors more readily at short diffusion times, and the temporal diffusion spectrum obtained by the OGSE method provides a unique tool for characterizing tissues over different length scales, including structural features of intracellular spaces. It has previously been reported that several tissue properties can affect ADC measurements significantly, and the precise biophysical mechanisms that account for ADC changes in different situations are still unclear. Those factors may vary in importance depending on the time and length scale over which measurements are made. In the present work, a comprehensive numerical simulation is used to investigate the dependence of the temporal diffusion spectra measured by OGSE methods on different microstructural properties of biological tissues, including cell size, cell membrane permeability, intracellular volume fraction, intranucleus and intracytoplasm diffusion coefficients, nuclear size and T(2) relaxation times. Some unique characteristics of the OGSE method at relatively high frequencies are revealed. The results presented in the paper offer a framework for better understanding possible causes of diffusion changes and may be useful to assist the interpretation of diffusion data from OGSE measurements.

Predicting and monitoring cancer treatment response with diffusion-weighted MRI

An imaging biomarker that would provide for an early quantitative metric of clinical treatment response in cancer patients would provide for a paradigm shift in cancer care. Currently, nonimage based clinical outcome metrics include morphology, clinical, and laboratory parameters, however, these are obtained relatively late following treatment. Diffusion-weighted MRI (DW-MRI) holds promise for use as a cancer treatment response biomarker as it is sensitive to macromolecular and microstructural changes which can occur at the cellular level earlier than anatomical changes during therapy. Studies have shown that successful treatment of many tumor types can be detected using DW-MRI as an early increase in the apparent diffusion coefficient (ADC) values. Additionally, low pretreatment ADC values of various tumors are often predictive of better outcome. These capabilities, once validated, could provide for an important opportunity to individualize therapy thereby minimizing unnecessary systemic toxicity associated with ineffective therapies with the additional advantage of improving overall patient health care and associated costs. In this report, we provide a brief technical overview of DW-MRI acquisition protocols, quantitative image analysis approaches and review studies which have implemented DW-MRI for the purpose of early prediction of cancer treatment response.

ADC response to radiation therapy correlates with induced changes in radiosensitivity

PURPOSE

Magnetic resonance imaging was used to compare the responses of human glioma tumor xenografts to a single fraction of radiation, where a change in radiosensitivity was induced by use of a suture-based ligature.

METHODS

Ischemia was induced by use of a suture-based ligature. Six mice were treated with 800 cGy of 200 kVp x rays while the ligature was applied. An additional six mice had the ligature applied for the same length of time but were not irradiated. Quantitative maps of each tumor were produced of water apparent diffusion coefficient (ADC) and transverse relaxation time (T2). Mice were imaged before and at multiple points after treatment. Volumetric, ADC, and T2 responses of the ligated groups were compared to previously measured responses of the same tumor model to the same radiation treatment, as well as those from an untreated control group.

RESULTS

Application of the ligature without irradiation did not affect tumor ADC values, but did produce a temporary decrease in tumor T2 values. Average tumor T2 was reduced by 6.2% 24 h after the ligature was applied. Average tumor ADC increased by 9.6% 7 days after irradiation with a ligature applied. This response was significantly less than that observed in the same tumor model when no ligature is present (21.8% at 7 days after irradiation).

CONCLUSIONS

These observations indicate that the response of ADC to radiation therapy is not determined entirely by physical dose deposition, but at least in part by radiosensitivity and resultant biological response.

Assessment of multiexponential diffusion features as MRI cancer therapy response metrics

The aim of this study was to empirically test the effect of chemotherapy-induced tissue changes in a glioma model as measured by several diffusion indices calculated from nonmonoexponential formalisms over a wide range of b-values. We also compared these results to the conventional two-point apparent diffusion coefficient calculation using nominal b-values. Diffusion-weighted imaging was performed over an extended range of b-values (120-4000 sec/mm(2) ) on intracerebral rat 9L gliomas before and after a single dose of 1,3-bis(2-chloroethyl)-1-nitrosourea. Diffusion indices from three formalisms of diffusion-weighted signal decay [(a) two-point analytical calculation using either low or high b-values, (b) a stretched exponential formalism, and (c) a biexponential fit] were tested for responsiveness to therapy-induced differences between control and treated groups. Diffusion indices sensitive to "fast diffusion" produced the largest response to treatment, which resulted in significant differences between groups. These trends were not observed for "slow diffusion" indices. Although the highest rate of response was observed from the biexponential formalism, this was not found to be significantly different from the conventional monoexponential apparent diffusion coefficient method. In conclusion, parameters from the more complicated nonmonoexponential formalisms did not provide additional sensitivity to treatment response in this glioma model beyond that observed from the two-point conventional monoexponential apparent diffusion coefficient method.

Comparison of apparent diffusion coefficients and distributed diffusion coefficients in high-grade gliomas

PURPOSE

To compare apparent diffusion coefficients (ADCs) with distributed diffusion coefficients (DDCs) in high-grade gliomas.

MATERIALS AND METHODS

Twenty patients with high-grade gliomas prospectively underwent diffusion-weighted MRI. Traditional ADC maps were created using b-values of 0 and 1000 s/mm(2). In addition, DDC maps were created by applying the stretched-exponential model using b-values of 0, 1000, 2000, and 4000 s/mm(2). Whole-tumor ADCs and DDCs (in 10(-3) mm(2)/s) were measured and analyzed with a paired t-test, Pearson's correlation coefficient, and the Bland-Altman method.

RESULTS

Tumor ADCs (1.14 +/- 0.26) were significantly lower (P = 0.0001) than DDCs (1.64 +/- 0.71). Tumor ADCs and DDCs were strongly correlated (R = 0.9716; P < 0.0001), but mean bias +/- limits of agreement between tumor ADCs and DDCs was -0.50 +/- 0.90. There was a clear trend toward greater discordance between ADC and DDC at high ADC values.

CONCLUSION

Under the assumption that the stretched-exponential model provides a more accurate estimate of the average diffusion rate than the mono-exponential model, our results suggest that for a little diffusion attenuation the mono-exponential fit works rather well for quantifying diffusion in high-grade gliomas, whereas it works less well for a greater degree of diffusion attenuation.

Histo-proteomic profiling of formalin-fixed, paraffin-embedded tissue

In the functional proteome era, the proteomic profiling of clinicopathologic-annotated tissues is an essential step for mining and evaluating candidate biomarkers for disease. For many diseases, but especially cancer, the development of predictive biomarkers requires performing assays directly on the diseased tissue. The last decade has seen the explosion of both prognostic and predictive biomarkers in the research setting but few of these biomarkers have entered widespread clinical use. Previously, application of routine proteomic methodologies to clinical formalin-fixed and paraffin-embedded tissue specimens has provided unsatisfactory results. In this paper, we will discuss recent advancements in proteomic profiling technology for clinical applications. These approaches focus on the retention of histomorphologic information as an element of the proteomic analysis.

Squamous cell carcinoma of the head and neck: diffusion-weighted MR imaging for prediction and monitoring of treatment response

OBJECTIVE

To investigate the role of diffusion-weighted imaging (DWI) in predicting and monitoring chemoradiotherapy response in head and neck squamous cell carcinoma (HNSCC).

METHODS

Diffusion-weighted imaging was performed pre-treatment (n = 50), intra-treatment (n = 41) and post-treatment (n = 20). Apparent diffusion coefficient (ADC) values were correlated with locoregional failure (LF).

RESULTS

Locoregional failure occurred in 20/50 (40%) patients. A significant correlation was found between LF and post-treatment ADC (p = 0.02) but not pre- or intra-treatment ADC. Serial change in ADC was even more significant (p = 0.00001), using a fall in ADC early (pre- to intra-treatment) or late (intra- to post-treatment) to indicate LF, achieved 100% specificity, 80% sensitivity and 90% accuracy.

CONCLUSIONS

Single ADC measurements pre- or intra-treatment did not predict response, but ADC post-treatment was a marker for LF. Serial change in ADC was an even stronger marker, when using an early or late treatment fall in ADC to identify LF.