As cancer treatment moves towards more targeted therapy, there is an increasing need for tools to guide therapy selection
and to evaluate response. Biochemical and molecular imaging can complement existing in vitro assay methods and is likely to
play a key role in early drug testing and development, as well as future clinical practice. Imaging is ideally suited to assessing
the spatial and temporal heterogeneity of cancer and to measure in vivo drug effects. This chapter highlights imaging approaches
to guide cancer therapy, focusing on positron emission tomography and on those approaches that have undergone preliminary
testing in patients. Examples showing how positron emission tomography imaging can be used to (1) assess the therapeutic target,
(2) identify resistance factors, and (3) measure early response are described.
"Positron emission tomography (PET) imaging has gained wide acceptance primarily for its use in cancer staging, therapy response monitoring, and drug discovery (Mankoff and Krohn 2006). PET scanners however are relatively expensive imaging systems ranging between 1–3 million dollars and hence are less accessible to patients and clinicians in regional and community centers (Saif et al 2010). "
[Show abstract][Hide abstract] ABSTRACT: Compressed sensing (CS) aims to recover images from fewer measurements than that governed by the Nyquist sampling theorem. Most CS methods use analytical predefined sparsifying domains such as total variation, wavelets, curvelets, and finite transforms to perform this task. In this study, we evaluated the use of dictionary learning (DL) as a sparsifying domain to reconstruct PET images from partially sampled data, and compared the results to the partially and fully sampled image (baseline).
A CS model based on learning an adaptive dictionary over image patches was developed to recover missing observations in PET data acquisition. The recovery was done iteratively in two steps: a dictionary learning step and an image reconstruction step. Two experiments were performed to evaluate the proposed CS recovery algorithm: an IEC phantom study and five patient studies. In each case, 11% of the detectors of a GE PET/CT system were removed and the acquired sinogram data were recovered using the proposed DL algorithm. The recovered images (DL) as well as the partially sampled images (with detector gaps) for both experiments were then compared to the baseline. Comparisons were done by calculating RMSE, contrast recovery and SNR in ROIs drawn in the background, and spheres of the phantom as well as patient lesions.
For the phantom experiment, the RMSE for the DL recovered images were 5.8% when compared with the baseline images while it was 17.5% for the partially sampled images. In the patients' studies, RMSE for the DL recovered images were 3.8%, while it was 11.3% for the partially sampled images. Our proposed CS with DL is a good approach to recover partially sampled PET data. This approach has implications toward reducing scanner cost while maintaining accurate PET image quantification.
Physics in Medicine and Biology 08/2015; 60(15):5853-5871. DOI:10.1088/0031-9155/60/15/5853 · 2.76 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Biochemical and molecular imaging of cancer using positron emission tomography (PET) plays an increasing role in the care of cancer patients. Most clinical work to date uses the glucose analogue [(18)F]fluorodeoxyglucose (FDG) to detect accelerated and aberrant glycolysis present in most tumors. Although clinical FDG PET has been used largely to detect and localize cancer, more detailed studies have yielded biological insights and showed the utility of FDG as a prognostic marker and as a tool for therapeutic response evaluation. As cancer therapy becomes more targeted and individualized, it is likely that PET radiopharmaceuticals other than FDG, aimed at more specific aspects of cancer biology, will also play a role in guiding cancer therapy. Clinical trials designed to test and validate new PET agents will need to incorporate rigorous quantitative image analysis and adapt to the evolving use of imaging as a biomarker and will need to incorporate cancer outcomes, such as survival into study design.
Clinical Cancer Research 07/2007; 13(12):3460-9. DOI:10.1158/1078-0432.CCR-07-0074 · 8.72 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Drug resistance is the main cause of the failure of chemotherapy of malignant tumors, resistance being either preexisting (intrinsic resistance) or induced by the drugs (acquired resistance). At present, resistance is usually diagnosed during treatment after a long period of drug administration.
In the present paper, methods for a rapid assessment of drug resistance are described. Three main classes of test procedures can be found in the literature, i.e. fresh tumor cell culture tests, cancer biomarker tests and positron emission tomography (PET) tests. The methods are based on the evaluation of molecular processes, i.e. metabolic activities of cancer cells. Drug resistance can be diagnosed before treatment in-vitro with fresh tumor cell culture tests, and after a short time of treatment in-vivo with PET tests. Cancer biomarker tests, for which great potential has been predicted, are largely still in the development stage. Individual resistance surveillance with tests delivering rapid results signifies progress in cancer therapy management, by providing the possibility to avoid drug therapies that are ineffective and only harmful.
International journal of medical sciences 03/2011; 8(3):245-53. · 2.00 Impact Factor
Note: This list is based on the publications in our database and might not be exhaustive.
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.