Positron emission tomography (PET) tumor segmentation and quantification: Development of new algorithms

Tumor functional volume (FV) and its mean activity concentration (mAC) are the quantities derived from positron emission tomography (PET). These quantities are used for estimating radiation dose for a therapy, evaluating the progression of a disease and also use it as a prognostic indicator for predicting outcome. PET images have low resolution, high noise and affected by partial volume effect (PVE). Manually segmenting each tumor is very cumbersome and very hard to reproduce. To solve the above problem I developed an algorithm, called iterative deconvolution thresholding segmentation (IDTS) algorithm; the algorithm segment the tumor, measures the FV, correct for the PVE and calculates mAC. The algorithm corrects for the PVE without the need to estimate camera's point spread function (PSF); also does not require optimizing for a specific camera. My algorithm was tested in physical phantom studies, where hollow spheres (0.5-16 ml) were used to represent tumors with a homogeneous activity distribution. It was also tested on irregular shaped tumors with a heterogeneous activity profile which were acquired using physical and simulated phantom. The physical phantom studies were performed with different signal to background ratios (SBR) and with different acquisition times (1-5 min). The algorithm was applied on ten clinical data where the results were compared with manual segmentation and fixed percentage thresholding method called T50 and T60 in which 50% and 60% of the maximum intensity respectively is used as threshold. The average error in FV and mAC calculation was 30% and -35% for 0.5 ml tumor. The average error FV and mAC calculation were ~5% for 16 ml tumor. The overall FV error was ∼10% for heterogeneous tumors in physical and simulated phantom data. The FV and mAC error for clinical image compared to manual segmentation was around -17% and 15% respectively. In summary my algorithm has potential to be applied on data acquired from different cameras as its not dependent on knowing the camera's PSF. The algorithm can also improve dose estimation and treatment planning.^

Design and Synthesis of 4-N-Alkanoyl and 4-N-Alkyl Gemcitabine Analogues Suitable for Positron Emission Tomography

Gemcitabine is a highly potent chemotherapeutic nucleoside agent used in the treatment of several cancers and solid tumors. However, it is therapeutically limitated because of toxicity to normal cells and its rapid intracellular deamination by cytidine deaminase into the inactive uracil derivative. Modification at the 4-(N) position of gemcitabine's exocyclic amine to an -amide functionality is a well reported prodrug strategy which has been that confers a resistance to intracellular deamination while also altering pharmacokinetics of the parent drug. Coupling of gemcitabine to carboxylic acids with varying terminal moieties afforded the 4-N-alkanoylgemcitabines whereas reaction of 4-N-tosylgemcitabine with the corresponding alkyl amines gave the 4-N-alkylgemcitabines. The 4-N-alkanoyl and 4-N-alkyl gemcitabine analogues with a terminal hydroxyl group on the 4-N-alkanoyl or 4-N-alkyl chain were efficiently fluorinated either with diethylaminosulfur trifluoride or under conditions that are compatible with the synthetic protocols for 18F labeling, such as displacement of the corresponding mesylate with KF/Kryptofix 2.2.2. The 4-N-alkanoylgemcitabine analogues displayed potent cytostatic activities against murine and human tumor cell lines with 50% inhibitory concentration (IC50) values in the range of low nM, whereas cytotoxicity of the 4-N-alkylgemcitabine derivatives were in the low to modest µM range. The cytostatic activity of the 4-N-alkanoylgemcitabines was reduced by several orders of magnitude in the 2'-deoxycytidine kinase (dCK)-deficient CEM/dCK- cell line while the 4-N-alkylgemcitabines were only lowered by 2-5 times. None of the 4-N-modified gemcitabines were found to be substrates for cytosolic dCK, however all were found to inhibit DNA synthesis. As such, the 4-N-alkanoyl gemcitabine derivatives likely need to be converted to gemcitabine prior to achieving their significant cytostatic potential, whereas the 4-N-alkylgemcitabines reach their modest activity without "measurable" conversion to gemcitabine. Thus, the 4-N-alkylgemcitabines provide valuable insight on the metabolism of 4-N-modified gemcitabine prodrugs.

Positron Emission Tomography (PET) Tumor Segmentation and Quantification: Development of New Algorithms

Tumor functional volume (FV) and its mean activity concentration (mAC) are the quantities derived from positron emission tomography (PET). These quantities are used for estimating radiation dose for a therapy, evaluating the progression of a disease and also use it as a prognostic indicator for predicting outcome. PET images have low resolution, high noise and affected by partial volume effect (PVE). Manually segmenting each tumor is very cumbersome and very hard to reproduce. To solve the above problem I developed an algorithm, called iterative deconvolution thresholding segmentation (IDTS) algorithm; the algorithm segment the tumor, measures the FV, correct for the PVE and calculates mAC. The algorithm corrects for the PVE without the need to estimate camera’s point spread function (PSF); also does not require optimizing for a specific camera. My algorithm was tested in physical phantom studies, where hollow spheres (0.5-16 ml) were used to represent tumors with a homogeneous activity distribution. It was also tested on irregular shaped tumors with a heterogeneous activity profile which were acquired using physical and simulated phantom. The physical phantom studies were performed with different signal to background ratios (SBR) and with different acquisition times (1-5 min). The algorithm was applied on ten clinical data where the results were compared with manual segmentation and fixed percentage thresholding method called T50 and T60 in which 50% and 60% of the maximum intensity respectively is used as threshold. The average error in FV and mAC calculation was 30% and -35% for 0.5 ml tumor. The average error FV and mAC calculation were ~5% for 16 ml tumor. The overall FV error was ~10% for heterogeneous tumors in physical and simulated phantom data. The FV and mAC error for clinical image compared to manual segmentation was around -17% and 15% respectively. In summary my algorithm has potential to be applied on data acquired from different cameras as its not dependent on knowing the camera’s PSF. The algorithm can also improve dose estimation and treatment planning.