Difusion-Weighted MRI: from Brownian Motion to Head&Neck Tumor Characterization

This paper describes basic physics as well as clinical applications of diffusion-weighted magnetic resonance imaging. This is a technique that provides complementary information to conventional imaging sequences and it is applied in the field of oncologic imaging. This paper focuses on its specific application in head and neck, mainly in cancer patients, for characterization of primary tumors, and also for monitoring and predicting treatment response after chemotherapy or radiation therapy. Last, although diffusion-weighted imaging is shown to add value in several areas by being part of the multi-parametric magnetic resonance imaging approach, there are some unsolved challenges, which are proposed as future work

Development and validation of novel and quantitative MRI methods for cancer evaluation

Quantitative imaging biomarkers (QIB) offer the opportunity to further the evaluation of cancer at presentation as well as predict response to anti-cancer therapies before and early during treatment with the ultimate goal of truly personalised medical care and the mitigation of futile, often detrimental, therapy. Few QIBs are successfully translated into clinical practice and there is increasing recognition that rigorous methodologies and standardisation of research pipelines and techniques are required to move a theoretically useful biomarker into the clinic. To this end, I have aimed to give an overview of what I believe to be some of key elements within the research field beginning with the concept of imaging biomarkers, introducing concepts in development and validation, before providing a summary of the current and future utility of a range of quantitative MR imaging biomarkers techniques within the oncological imaging field. The original, prospective, research moves from the technical and analytical validation of a novel QIB use (T1 mapping in cancer), first in vivo qualification of this biomarker in cancer patient response assessment and prediction (sarcoma and breast cancer as well as prostate cancer separately), and then moving on to application of more established QIBs in cancer evaluation (R2*/BOLD imaging in head and neck cancer) as well as how existing MR data can be post-processed to improved cancer evaluation (further metrics derived from diffusion weighted imaging in head and neck cancer and textural analysis of existing clinical MR images utility in prostate cancer detection)

Functional magnetic resonance imaging: diffusion weighted and chemical shift imaging in head and neck.

Fong, Kwan Ying.Thesis (M.Phil.)--Chinese University of Hong Kong, 2010.Includes bibliographical references (leaves 90-103).Abstracts in English and Chinese.Chapter Chapter 1: --- "Introduction, problems and objectives" --- p.1Chapter 1.1 --- Introduction --- p.1Chapter 1.2 --- Problems --- p.3Chapter 1.3 --- Objectives --- p.3Chapter Chapter 2: --- Background --- p.4Chapter 2.1. --- Head and Neck Cancer --- p.4Chapter 2.2 --- Diagnostic Imaging of Head and Neck Cancer --- p.5Chapter 2.3. --- Magnetic Resonance Imaging- Physics --- p.8Chapter 2.3.1 --- Nuclear Magnetic Resonance Principle --- p.8Chapter 2.3.2 --- Proton Magnetic Resonance Imaging --- p.8Chapter 2.3.3 --- Relaxation --- p.12Chapter 2.3.4 --- Tl- and T2-weighted Imaging --- p.12Chapter 2.3.5 --- Diffusion Weighted Imaging (DWI) --- p.13Chapter 2.3.6 --- Magnetic Resonance Spectroscopy- Single Voxel Spectroscopy and Chemical Shift Imaging --- p.15Chapter Chapter 3: --- Diffusion-weighted imaging in the evaluation head of and neck cancer --- p.21Chapter 3.1 --- Introduction - Diffusion-Weighted Imaging in Tumors --- p.21Chapter 3.2 --- DWI of Nasopharyngeal Carcinoma --- p.22Chapter 3.2.1 --- Introduction and Objectives --- p.22Chapter 3.2.2. --- Methods --- p.23Chapter 3.2.3. --- Results --- p.27Chapter 3.2.4 --- Discussion --- p.31Chapter 3.3 --- DWI of Primary Tumors: Comparison of NPC with Squamous Cell Carcinoma and Extra-nodal Non-Hodgkin Lymphoma --- p.33Chapter 3.3.1 --- Introduction and Objectives --- p.33Chapter 3.3.2. --- Methods --- p.34Chapter 3.3.3. --- Results --- p.35Chapter 3.3.4 --- Discussion --- p.42Chapter 3.3.5 --- Summary of DWI in Head and Neck Cancer --- p.44Chapter Chapter 4: --- Chemical shift imaging of head and neck tumors --- p.45Chapter 4.1 --- Introduction - Single Voxel Spectroscopy and Chemical Shift Imaging --- p.45Chapter 4.2 --- CSI - Methods Used to Reduce Magnetic Field Inhomogeneity --- p.48Chapter 4.3 --- Phantom studies - CSI Experiments Using Phantoms --- p.51Chapter 4.3.1 --- Introduction and Objectives --- p.51Chapter 4.3.2. --- Methods --- p.51Chapter 4.3.3 --- Experiment and MR Protocol --- p.54Chapter 4.3.4 --- Data Analysis --- p.58Chapter 4.3.5 --- Phantom Experimental Results --- p.59Chapter 4.3.6 --- Discussion and Conclusion on Phantom Experiments --- p.69Chapter 4.4 --- In vivo CSI Study of Human Head and Neck Tumors --- p.72Chapter 4.4.1 --- Introduction and Objectives --- p.72Chapter 4.4.2 --- Patient Selection --- p.73Chapter 4.4.3 --- MRI and CSI Protocol --- p.73Chapter 4.4.4 --- Data Analysis --- p.74Chapter 4.4.5 --- Results from CSI on Patients --- p.74Chapter 4.4.6 --- Discussion and Conclusion of CSI on Patients --- p.81Chapter Chapter 5: --- "Summary, conclusion and future studies" --- p.87Chapter 5.1 --- Summary --- p.87Chapter 5.2 --- Conclusion --- p.89Chapter 5.3 --- Future Studies --- p.89References --- p.90Publications --- p.10

Head and Neck Critical Illness

There are various malignant tumors in the head and neck area, including oral cavity, pharynx, sinonasal cavity, and salivary glands. Squamous cell carcinoma is the most common cancer among head and neck cancers. In salivary glands, there are many types of malignancies that can develop, such as malignant lymphoma, adenoid cystic carcinoma, adenocarcinoma, and mesenchymal tumors. In a clinical setting, imaging, such as computed tomography (CT) and magnetic resonance imaging (MRI), is very important in terms of the prediction of the histological type and the evaluation of the extent of invasion of adjacent structures. In basic research, there are few animal models in head and neck malignancies. In this Special Issue, we broadly discuss the basic and clinical research in head and neck malignancies

To study the role of Diffusion weighted MRI imaging in predicting the response to concurrent chemoradiotherapy in patients with nasopharyngeal malignancies

OBJECTIVE: To prospectively evaluate the feasibility of diffusion-weighted magnetic resonance imaging (DW-MRI) in predicting the response to concurrent chemoradiotherapy in patients with nasopharyngeal malignancies. MATERIALS AND METHODS: Thirteen patients with Non-metastatic carcinoma nasopharynx underwent DWI-MRI before the start of chemoradiation (Pre-RT), one week after start of chemoradiation and at 4-6 weeks follow up. Mean and minimum ADC measurements were recorded for a given region by drawing regions of interest (ROIs) on the ADC map. Statistical analysis was done using SPSS. The difference between complete response and partial responders was assessed by Mann Whitney U test. ROC analysis was done to calculate predictive value of ADC as a biomarker. RESULTS: An increase in ADC was observed at each stage of therapy in the primary site and nodes. The difference in ADC values from Pre-RT to follow up was higher in complete responders than partial responders (p=.003). The baseline ADC were higher in complete responder than partial responder. We conclude that serial DWI-MRI is a potential tool for response prediction in carcinoma nasopharynx treated with chemoradiotherapy. This study needs to be continued and a larger sample size and long term follow up may help in establishing its role in clinical practice

Pathologically-Validated Tumor Prediction Maps in MRI

Glioblastoma (GBM) is an aggressive cancer with an average 5-year survival rate of about 5%. Following treatment with surgery, radiation, and chemotherapy, diagnosing tumor recurrence requires serial magnetic resonance imaging (MRI) scans. Infiltrative tumor cells beyond gadolinium enhancement on T1-weighted MRI are difficult to detect. This study therefore aims to improve tumor detection beyond traditional tumor margins. To accomplish this, a neural network model was trained to classify tissue samples as ‘tumor’ or ‘not tumor’. This model was then used to classify thousands of tiles from histology samples acquired at autopsy with known MRI locations on the patient’s final clinical MRI scan. This combined radiological-pathological (rad-path) dataset was then treated as a ground truth to train a second model for predicting tumor presence from MRI alone. Predictive maps were created for seven patients left out of the training steps, and tissue samples were tested to determine the model’s accuracy. The final model produced a receiver operator characteristic (ROC) area under the curve (AUC) of 0.70. This study demonstrates a new method for detecting infiltrative tumor beyond conventional radiologist defined margins based on neural networks applied to rad-path datasets in glioblastoma

Imaging of Tumour Microenvironment for the Planning of Oncological Therapies Using Positron Emission Tomography

Tumour cells differ from normal tissue cells in several important ways. These differences, like for example changed energy metabolism, result in altered microenvironment of malignant tumours. Non-invasive imaging of tumour microenvironment has been at the centre of intense research recently due to the important role that this changed environement plays in the development of malignant tumours and due to the role it plays in the treatment of these tumours. In this respect, perhaps the most important characteristics of the tumour microenvironment from this point of view are the lack of oxygen or hypoxia and changes in blood flow (BF). The purpose of this thesis was to investigate the processes of energy metabolism, BF and oxygenation in head and neck cancer and pancreatic tumours and to explore the possibilities of improving the methods for their quantification using positron emission tomography (PET). To this end [18F]EF5, a new PET tracer for detection of tumour hypoxia was investigated. Favourable uptake properties of the tracer were observed. In addition, it was established that the uptake of this tracer does not correlate with the uptake of existing tracers for the imaging of energy metabolism and BF, so the information about the presence of tissue hypoxia cannot therefore be obtained using tracers such as [18F]FDG or [15O]H2O. These results were complemented by the results of the follow-up study in which it was shown that the uptake of [18F]EF5 in head and neck tumours prior to treatment is also associated with the overall survival of the patients, indicating that tumour hypoxia is a negative prognostic factor and might be associated with therapeutic resistance. The influences of energy metabolism and BF on the survival of patients with pancreatic cancer were investigated in the second study. The results indicate that the best predictor of survival of patients with pancreatic cancer is the relationship between energy metabolism and BF. These results suggest that the cells with high metabolic activity in a hypoperfused tissue have the most aggressive phenotype.Siirretty Doriast

The impact of arterial input function determination variations on prostate dynamic contrast-enhanced magnetic resonance imaging pharmacokinetic modeling: a multicenter data analysis challenge, part II

This multicenter study evaluated the effect of variations in arterial input function (AIF) determination on pharmacokinetic (PK) analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using the shutter-speed model (SSM). Data acquired from eleven prostate cancer patients were shared among nine centers. Each center used a site-specific method to measure the individual AIF from each data set and submitted the results to the managing center. These AIFs, their reference tissue-adjusted variants, and a literature population-averaged AIF, were used by the managing center to perform SSM PK analysis to estimate Ktrans (volume transfer rate constant), ve (extravascular, extracellular volume fraction), kep (efflux rate constant), and τi (mean intracellular water lifetime). All other variables, including the definition of the tumor region of interest and precontrast T1 values, were kept the same to evaluate parameter variations caused by variations in only the AIF. Considerable PK parameter variations were observed with within-subject coefficient of variation (wCV) values of 0.58, 0.27, 0.42, and 0.24 for Ktrans, ve, kep, and τi, respectively, using the unadjusted AIFs. Use of the reference tissue-adjusted AIFs reduced variations in Ktrans and ve (wCV = 0.50 and 0.10, respectively), but had smaller effects on kep and τi (wCV = 0.39 and 0.22, respectively). kep is less sensitive to AIF variation than Ktrans, suggesting it may be a more robust imaging biomarker of prostate microvasculature. With low sensitivity to AIF uncertainty, the SSM-unique τi parameter may have advantages over the conventional PK parameters in a longitudinal study