Chapter 14 MOLECULAR IMAGING GUIDED RADIOTHERAPY

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MOLECULAR IMAGING GUIDED RADIOTHERAPY

Imaging Resistance Factors and Response to Treatment

Kenneth A. Krohn

and Lothar Spies

1

University of Washington, Seattle, WA, USA;

Philips Research, Hamburg, Germany

Abstract: Assessment of tumor markers, including ones which determine the tumor's radiosensitivity and its response to radiation, are playing a pivotal role in future definition of radiotherapies. Two key characteristics, hypoxia and cell proliferation, as examples for dynamic imaging using two PET agents, FMISO and FLT, are addressed. Developmental pathways toward possibilities that exist to extract more quantitative information from these images are sketched.

Furthermore, we propose how his information may be incorporated in radiotherapy planning.

Keywords: Molecular imaging, radiotherapy, tumor hypoxia, cell proliferation, FLT, FMISO, radiotherapy planning

1. INTRODUCTION

Currently, several tumor characteristics are used to stage and grade a cancer and to predict its overall response to treatment, including its sensitivity to ionizing radiation. Some of these include size, site of growth, histology and certain biomarkers assayed in tissue or plasma samples. In addition, the overall health, age and performance parameters of the patient are also important factors to help define the appropriate treatment and to predict the response to a given therapy. However, even after the most aggressive radiotherapies, inter-patient response can be heterogeneous, local-

217-233.

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regional failures are too high, and the five-year survival rates are variable.

The underlying mechanisms of this heterogeneous response of patients with similar tumors to the same therapy are not adequately understood.

G. Spekowius and T. Wendler (Eds.), Advances in Healthcare Technology,

Over the past years molecular imaging has made great advances to provide functional characterization at the sub-organ level using in-vivo assays. To some extent, this advance was triggered by novel molecules being labeled with radionuclides or other markers, which can probe and quantify aspects of molecular pathways characteristic of certain diseases. The most prominent imaging modalities being used for molecular imaging in the more than two decades to visualize glucose metabolism using PET. FDG- PET is of great significance in the diagnostic work-up of oncologic patients due to its sensitivity for a wide spectrum of neoplasms

. Whole-body scans are used for screening for metastatic disease. Tumor staging is significantly facilitated by FDG-PET as documented in several tumor entities

and it is being used increasingly to monitor response to treatment

.

Recent advances in radiotherapy concern the development of Intensity Modulated Radiotherapy (IMRT) deploying inverse planning technologies in combination with advanced delivery equipment such as computer-controlled multi-leaf collimators

. This novel methodology has become widely available and is becoming part of routine clinical practice. IMRT provides unprecedented precision in 3D dose delivery, enabling dose escalation and organ sparring in the millimeter regime. Radiotherapy planning is based on CT information representing the anatomy of the region to be treated. The CT information is key since it correlates with the electron density of the tissue, which is needed to calculate the dose to be imparted by the megavoltage x- ray beam. MR images complement CT in cases where CT does not provide enough anatomic contrast, such as in the prostate, the female breast or in the brain. As opposed to its role in diagnosis and tumor characterization, the role of molecular imaging for treatment definition remains embryonic.

In 2000, Ling and coworkers

proposed the concept of biological target volume (BTV). The authors hypothesized that this BTV could be derived from biological images that represent metabolic, functional, physiologic, genotypic, and phenotypic data, to improve target delineation and dose delivery. Furthermore, they realized the potential of IMRT for intra-tumoral dose modulation using dose boosts for selected regions, for example areas that showed a higher radioresistance. This could be accomplished while sparring dose in radiosensitive regions, thus keeping the integral dose to the volume constant. They furthermore hypothesized that ‘noninvasive biological imaging may provide the pertinent information to guide the painting or sculpting of the optimal dose distribution.’ Since this landmark paper appeared, a few publications have emerged to develop Ling s theme.

They can be divided into two categories: Papers of the first category have investigated how functional information can be used to improve target delineation, for example in cases where anatomical images give inconclusive

’

clinical setting are PET and SPECT. Traditionally FDG has been used for

results

. The second category comprises papers that investigate how molecular imaging can be used to better modulate a certain amount of integral dose to a target to closely adapt to intra-tumoral biology, for example non-uniformity of radiosensitivity. Recent examples demonstrate how functional information provided by SPECT and PET can be utilized to realize this adaptation

. To this end, an accurate quantification of intra- tumor biology is a prerequisite. Furthermore, a quantitative understanding of response measured by PET and SPECT is indispensable if treatment parameters or even treatment regimens have to be changed at an early stage.

This chapter is organized as follows: firstly, imaging assays for tumor hypoxia and cell proliferation as the most important tools for assessing tumor radioresistance and treatment response, respectively, are discussed.

We focus on FMISO and FLT-PET imaging and elaborate what techniques are required to extract quantitative information. The chapter ends with a proposition how to incorporate quantitative tumor information into treatment definition.

2. IMAGING TUMOR MARKERS

The definition of molecular imaging is sometimes restricted to imaging specific molecular pathways, for example, cellular signaling and regulation.

A more general definition would extend imaging to all of the molecular characteristics of a disease process that impact on planning appropriate therapy and following response to that therapy. Thus, we include in molecular imaging attempts to quantify with imaging the extent and spatial variance in numerous factors that affect resistance to therapy, including hypoxia, expression of multidrug resistance transporters, down-regulation of hormonal receptors, and methyl-alkyl transferase activity, as examples. In this chapter, hypoxia as one of the most imaged factors in resistance to therapy is addressed.

For imaging response to treatment, it is most useful to quantify common

characteristics of the tumor phenotype. A benchmark essay by Hanahan and

Weinberg

described six hallmarks of the cancer cell: Three are closely

related as self-sufficiency in growth signals, insensitivity to anti-growth

signals, and limitless replicative potential and the other three are invasion

and metastasis, evading apoptosis and sustained angiogenesis. While

molecular imaging strategies are being developed for each of these

characteristics of the tumor phenotype, the general property of cellular

proliferation is probably the most important way to quantify response to

cancer therapy

. The death of a cancer cell often causes FDG uptake to go

down, but the imaging signal can have a transient increase due to energy-

intensive processes responding to the insult of therapy. In contrast, successful therapy will shut down cellular proliferation; the molecular machinery for DNA synthesis will halt. Thus, imaging proliferation is an unambiguous way to verify response to cytotoxic or cytostatic therapy

. 2.1 Tumor hypoxia

Figure 14-1. Reaction pathways of FMISO in a cell.

Many tracers for hypoxia imaging have been developed and tested to

date

. Very few have made it into clinical trials with a significant number of

patients. The most widely used agent is [

F]-FMISO, fluoromisonidazole. It

is a derivative of one of the earliest radiosensitizers used in clinical radiation

oncology, the azomycin called misonidazole. The drug is bioreductively

activated by electron transport, but the 1-electron reduction reaction to

produce the nitro radical anion is reversible in the presence of oxygen in

tissues. Addition of a second electron generates a bioreductive alkylation

agent, a non-reversible reaction where the reduced FMISO binds quite

The physiologic microenvironment of a tumor influences its response to

therapy. For example, low levels of oxygen, perhaps as a result of

poor vascularization in the tumor, reduce the cytotoxic effectiveness of

ionizing radiation by about three-fold. The radiation oncology community

has been aware of this for fifty years

, but attempts to circumvent the cure-

limiting effects of hypoxia have met with only very limited success

. To

overcome radioresistance, strategies to target tumor hypoxia, an inherently

heterogeneous phenomenon, are key. Hence, identification and quantification

of hypoxic domains in a tumor may aid therapy definition, with the impact

of a better cure that could be quantified as fewer local recurrences. One

promising pathway to this goal is dose escalation on hypoxic tumor

subvolumes, as proposed by Ling et al.

. The underlying hypothesis is that

such a scheme may improve tumor control and reduce unwanted side effects.

indiscriminantly to macromolecules within the cell (Figure 14-1). The result is a positive image of FMISO at low levels of intracellular oxygen. Because there is a continuous gradient in oxygen tension in tissues, the contrast in FMISO images is only modest but the information content in terms of this important factor in resistance to ionizing radiation is large and is just becoming widely appreciated.

2.2 Cell proliferation

Measurement of cellular proliferation in tumors has played a key role in cancer research for many decades. The thymidine labeling index using tritiated thymidine, the only nucleoside that is exclusively incorporated into DNA, has long been the gold standard for quantifying changes in cellular growth in response to cancer therapy

. A decrease in cellular proliferation is one of the earliest events in response to successful cancer therapy. While the assay with tritium-labeled thymidine was applied to biopsy specimens, the advent of

C-labeled thymidine

opened the door for non-invasive imaging of this important measure of response to treatment.

Imaging cellular proliferation has a number of advantages over metabolic imaging with FDG-PET. Increased and uncontrolled cellular proliferation is a unique characteristic of tumors. In contrast, increased energy metabolism as measured by FDG is associated with a variety of other processes, included inflammation, multidrug resistance transporter function and apoptosis.

Cellular proliferation changes earlier and is a more definitive indicator of successful response to treatment

.

Imaging studies with [

C]-thymidine are proving useful for answering important questions in clinical research

, but the procedure is complicated because of the abundance of metabolites and the short nuclear decay half- life. Therefore a series of non-metabolized thymidine analogs has been evaluated

and 3 -deoxy-3 -[

F]-fluorothymidine, FLT, was selected as a useful analogue agent for imaging cellular proliferation. FLT reacts with thymidine kinase 1, the cytosolic enzyme that produces the mono- phosphorylated nucleotide of FLT, but this molecule does not continue all the way to DNA synthesis because the fluorine substitution at the 3’-position terminates the polymerization chain. For FLT the rate-limiting step is thymidine kinase activity; for thymidine the rate is limited by DNA polymerase activity. Because these nucleoside analogs are not natural components of DNA, their uptake may not accurately reflect the synthetic rate of DNA. FLT and related radiopharmaceuticals still require more thorough validation studies. Nevertheless, Shields et al. published the first human image with FLT

, and a dog image of FLT was recognized as ‘Image of the Year’ at the 1997 SNM

. Since that time publications involving FLT

, ,

imaging have proliferated, along with attempts to further validate the interpretation of FLT images

.

3. QUANTIFICATION

The goal of quantitative data analysis in molecular imaging is to move from pictures to statistically defensible rate parameters that have a specific biological interpretation. The advantage of the positron emission signal is that it is quantitative and, with knowledge of the specific activity of the injectate, can be directly converted from imaging counts in a voxel to picomoles per volume. But this signal is a composite of the amount of tracer that was delivered to the imaged volume, the amount that was retained, and the amount that washed out or was otherwise metabolized. Because the relative contribution of these components varies from patient to patient and day to day, dynamic data analysis is a useful tool to resolve a series of dynamic images into a map of flux versus transport, for example

. The test- retest variability is better for modeled images than for simple dpm/voxel or SUV images.

Images of accurate electron densities provided by X-ray CT have formed the basis for accurate dose calculation, which is a prerequisite for treatment planning. Image distortions and artifacts in CT images propagate into sub- optimal treatment parameters, which eventually compromise treatment quality. A similar trend must be anticipated for molecular image information being used to guide dose prescription. Accurate and quantitative molecular parameters have the best potential to improve the quality of care in future applications. New technology that combines the biological information from PET and the anatomic information from CT in the same instrument will be important input for radiation treatment plans in the next decade.

3.1 Tumor hypoxia

The FMISO image potentially contains two complementary pieces of

information: what volume or regions of the tumor are hypoxic and how

hypoxic are these regions. At this time we do not know whether ‘how much

hypoxia’ or ‘how bad’ is the more significant determinant of outcome. For

quantification of the hypoxic volume from FMISO-PET images, a simple

standard has been established by calculating a tissue-to-blood ratio, T:B, for

each voxel. FMISO has a partition coefficient near unity so that the T:B ratio

should be approximately one in well-oxygenated tissues. The numbers are

normally extracted from late time images, commonly acquired between 90 to

120 min after tracer injection and ratioed to activity in a venous blood

sample acquired at the imaging time. In an earlier work the tissue-to-muscle ratio was quantified and a value of 1.4 was considered as a practical level to discriminate between predominantly hypoxic versus normoxic tissue

. More recently a value of 1.2 for the tissue-to-blood ratio has been adopted as a discriminator

. The latter value was found representative because more than 99% of normal tissue does not show an uptake greater than 1.2 (Figure 14-2).

Thus, a simple analysis would describe the volume of pixels with T:B>1.2 as the hypoxic volume and the raw image would show the spatial heterogeneity of hypoxia in the tumor field. The highest pixel ratio, T:B

, would then be a measure of the level of hypoxia and could be used to compare the level of hypoxia in patient images of FMISO before, during and

26

patients with lung cancer, who were being treated with photons, to observe the rate and extent of reoxygenation over the course of treatment.

Figure 14-2. FMISO pixel histogram: Brain to blood (solid line) and tumor to blood (dashed line).

Alternative imaging agents for hypoxia are designed for more rapid plasma clearance via renal excretion. While this change in the radiopharmaceutical results in higher contrast images, it also introduces a component of blood flow into the data analysis. It is no longer valid to expect a constant ratio for all normoxic tissues at late times and so more complex analysis is required to distinguish effects of delivery (blood flow) from those of local retention (hypoxia).

Due to the high fraction of non-specific binding of FMISO in tissues,

falsely indicating hypoxia, early attempts were made to extract kinetic

after radiation therapy. For example, Koh et al. used serial FMISO images in

parameters from time series of PET images. The suspicion was that non- specific binding might obscure the specific binding component, which is solely responsible for tracer accumulation and thus directly correlated with oxygen content. The hypothesis was that a compartmental model might offer advantages for discriminating non-specific from specific binding.

The compartmental model

was derived from a physiological picture, which modeled the various biological pathways of the tracer in tissue, such as uptake, retention and wash out of tracer and the pathways of chemically modified tracer molecules in the tissue (Figure 14-3). A parameter was identified, which quantifies the trapping rate of metabolized FMISO, termed κ

. This parameter could be directly related to the oxygen concentration in the tissue. Having presented the full analysis, the work concluded that simple tissue-to-blood activity ratios correlate well with κ

. They exemplified it for a human patient with a base of tongue squamous cell carcinoma, for whom they reported the following correlation: ^{T : B = 3 . 73 + 1 . 07 ⋅ log}

( ) ^κ

^,

suggesting that the T:B ratio can be used as a surrogate for the oxygen concentration in the tissue. In Figure 14-4, we present results comparing T:B ratios and κ

for recent data from patients with non-small cell lung cancer on a voxel basis

. The Philips Research pharmacokinetic modeling tool VOXULUS was employed for non-linear regression using a Levenberg- Marquardt algorithm with linear weights to estimate the free model parameters of the Casciari model (Figure 14-3).

Figure 14-3. Compartmental model for FMISO transport and reaction rates in tissue. F is the flow rate of tracer from blood, C

, into tissue and interstitial space. F also governs the transport of metabolized FMISO back to the blood. The dashed box indicates the compartments belonging to the cell.

out FMISO metabolites.

⋅

A is the rate of tracer trapped in the tissue.

Implementation of analytical solutions of the differential equations associated with the Casciari-model enabled processing of more than 50

is the branching ratio between trapped and washed-

voxel per second on a standard PC. The scatter plot suggests that the above

relation may not always be an accurate representation of the trapping rate

and consequently of hypoxia. By design, the kinetic model is able to discriminate between specific and non-specific binding, which might be an essential prerequisite for accurate quantification of hypoxia since the tracer’s clearance is relatively slow and unbound tracer equilibrated with the vascular pool may always be present in the tissue even four hours after administration.

3.2 Cell proliferation

There is a substantial history on the modeling of nucleoside kinetics to infer DNA synthesis. Thymidine is incorporated into DNA via the salvage pathway, but not through the de novo pathway, and DNA synthesis from thymidine is substantially up regulated during early S-phase

. The approach to modeling [

C]-thymidine metabolism is based on the pioneering work of Cleaver

. Thymidine nucleotides from both endogenous and exogenous sources freely mix within the intracellular DNA precursor pool and so relative utilization can be predicted based on the concentration of extracellular thymidine

. Unless there is a shortage of precursor, the rate of DNA synthesis depends on the ‘proliferative state’ of the tissue and not on the concentration of precursors; the flux of nucleotides through the precursor pool and into DNA is the rate of synthesis.

Thymidine in the body is rapidly degraded by thymidine phosphorylase

into the pyrimidine nucleic acid, thymine, and deoxyribose. Thus, while a

simple standard uptake value (SUV) or uptake ratio is easy to calculate for

images derived from [

C]-thymidine, it leads to a significant bias in

estimating flux into DNA because it fails to account for the substantial level

of metabolites that are in plasma and tissues. Using time-activity curves

obtained from blood samples, kinetic parameters can be estimated by

optimizing the fit of a model to the time course of tissue uptake measured

by dynamic PET imaging and blood samples assayed for radioactive

metabolites

. Since PET images integrate the entire radioactivity in a

region, labeled metabolites affect quantitative interpretation of images from

labeled thymidine, which is rapidly catabolized in vivo. In tumor imaging the

approach of subtracting the metabolite background estimated from a

reference tissue does not work because there is no tissue with sufficiently

similar properties. To overcome this, a detailed model accounting for both

thymidine and metabolites using data obtained from blood analysis was

developed

. Animal studies were used to validate this model’s ability to

predict the time course of [

C]-thymidine incorporation into DNA, and

simulations and animal studies showed that this analysis provided reliable

values for the pseudo-rate constant for DNA flux

. Preliminary results using

a separate [

C]-CO

injection to independently measure kinetics of this

diffusible metabolite showed that compartmental analysis corrected for the

metabolite background in the brain and separated the effects of transport and retention by DNA synthesis in the tumor

.

PET images of cellular proliferation contain unique and clinically useful information. For example, Eary

compared PET imaging with [2-

C]- thymidine, FDG, and MRI and found that thymidine showed different uptake in about half of the patients, indicating that different information was being obtained. Brain tumor images of thymidine showed promising results and an ability to distinguish active tumor from barrier effects that was superior to FDG. Thymidine has also been used to image high-grade sarcomas and small cell lung cancer and to monitor response to chemo- therapy

. After successful therapy, the fractional decline in thymidine was greater than with FDG, suggesting that thymidine may be of particular value for measuring early response.

[2-

C]-thymidine has also been used to evaluate response to new anti- cancer agents and to verify their mechanisms of action. For example, imaging was used in a small study of patients receiving experimental inhibitors of thymidylate synthase, TS, the enzyme that supplies thymidine nucleotides via the de novo pathway. These drugs, typified by 5- fluorouracil, block the de novo pathway and should increase demand for utilization by the salvage pathway. Images showed increased thymidine retention in tumors after the administration of experimental inhibitors as compared to a pre-drug study

. Thymidine may be particularly helpful in evaluating response to experimental immunotherapy, where the high demand for energy during an immune response may result in confusing FDG images.

The literature on quantitative analysis of FLT images is much less advanced. The early human studies have focused on SUV data from FLT- PET imaging and correlations with the Ki-67 index

. However, in view of the advantages of full compartmental modeling that have been documented for [

C]-thymidine, a more sophisticated analysis of FLT biodistribution kinetics is clearly warranted. Muzi and colleagues

have developed and evaluated a method based on the analogy between the biochemistry of FLT and thymidine. The model measures the retention of FLT-monophosphate generated by the phosphorylation of FLT by thymidine kinase 1, the initial enzyme in the salvage pathway. This model assumes a steady-state biosynthesis and incorporation of nucleotides into DNA because, unlike authentic thymidine, FLT nucleotides do not get incorporated into the DNA polymer. This analysis also assumes equilibrium between nucleoside levels in tissue and plasma and that the relative rates of FLT and thymidine phosphorylation can be approximated by direct analysis of in vitro samples.

The resulting 2-compartment model fits the dynamic imaging data collected

over 120 minutes and the metabolite-corrected blood curve. This model was

able to distinguish FLT flux from FLT transport. A companion paper

described the performance of the model for human data in patients with

lung cancer

. In this report the flux constants for FLT phosphorylation in tumor, bone marrow (high flux) and muscle (low flux) were determined in a series of 17 patients, 18 tumors, and compared with an in vitro assay of proliferation, the Ki-67 index. Compartmental modeling results were also compared with simple model-independent measures of FLT uptake. This analysis lead to robust estimates of the flux constant for FLT that correlated with in vitro measure of proliferation for tumor, marrow and muscle. The correlation with SUV was considerably weaker, substantiating the concept that more detailed biochemical parameters from imaging are more useful.

Further direct comparisons will be required to determine the fidelity of FLT flux as a proxy for thymidine flux, the gold standard for imaging cellular proliferation. This work will require sequential imaging studies with [

C]- thymidine and [

F]-FLT and should involve tumors with a range of histologies and it should also evaluate tumors that have been treated.

4. TREATMENT DEFINITION

Once tumor markers relevant for treatment definition are quantified, a translation into an optimal dose prescription is needed. The translation is associated with two major problems. Firstly, parametric images are relatively noisy. This is because they are estimated from a series of PET images, which individually feature a high noise level. It is not surprising that the corresponding parametric maps have noise levels of 10 to 20%. A direct translation of these parameters into dose would result in a similar or greater level of noise in the dose prescription. This additional noise may not only prolong the inverse planning procedure, but may produce sub-optimal treatment parameters. Furthermore, physiological or molecular parameters are not directly proportional to the therapy dose, which maximizes cell kill.

Very recently, Yang and Xing

proposed a theoretical framework, which relates radiobiological parameters such as clonogen density, radiosensitivity, and cell proliferation rate to dose. They defined a tumor control probability (TCP) utilizing the linear-quadratic model

to represent the tumor clonogen survival. The dose is then derived from a maximization of the TCP under the restriction that the integral dose to the tumor volume is a constant. We refer here to their publication for further details.

An appropriate means to reduce noise is clustering, e.g. using a k-means

classifier

. Normally, dose confidence levels are provided by modeling in

addition to the dose levels. The total number of classes ideally adapts to the

noise content and the dose range in the target to be prescribed. Two dose

levels or classes can generally be discriminated in a noisy environment if the

difference of the class-representatives ∆ S is at minimum five times greater

than the noise, represented by the standard deviation σ

. This argumentation

is based on the model of image detection, which states similar criteria for a reliable detection of a uniform object in a noisy background, namely that the ratio of signal difference and noise shall be greater than five, or

. 5

/ ≥

∆ S σ

Consequently, the number of dose classes can be determined in accordance with:

D

D clusters D

σ δ ⋅

=

−

# , [1]

where D

are the maximum and minimum dose, respectively, of the target volume; σ

is the confidence level or standard deviation of the dose and δ is a constant with a value of five or greater according to the aforementioned imaging criterion.

Figure 14-4. Flowchart of the algorithm. Numbers label the operation steps, which are described in the text.

To evaluate this strategy, the algorithm, illustrated in Figure 14-5, has

been implemented as a research plug-in (‘BioGuide’) into a Pinnacle

treatment planning platform (Philips Medical Systems, Milpitas, CA, USA).

It comprises the following main steps:

1. Co-registration of anatomical and functional image data cubes.

2. Kinetic modeling of disease or molecular parameters based on the provided functional information to create 3D parametric maps.

3. Translation of disease or molecular parameters into a voxel-based dose distribution for the target region and tolerance dose for the organs at risk.

4. Clustering of dose distributions into ‘reasonable’ sub-target and sub- tumor regions. Here operator interaction is indispensable to review and amend the automatically generated dose plateaus in the target and organ at risk.

Once the sub-tumor and sub-organ volumes have been generated and assigned to an appropriate dose level, inverse planning (step 5) can be executed to generate treatment parameters. A feasibility study for a prostate tumor featuring a non-uniform radioresistance was conducted to test the implementation. The algorithm yielded two distinct regions: for the more radioresistant region a 20% higher dose was prescribed than in the remaining tumor region. The study compares a standard treatment plan and one with adaptation to sub-tumor radioresistance. The result is shown in Figure 14-6.

5. SUMMARY AND OUTLOOK

Tumor hypoxia and cell proliferation are important parameters, which may allow characterization of tumor radio-resistance and response to radiotherapy. Accurate quantification of these parameters is a prerequisite if this novel information shall be optimally used for treatment definition.

Kinetic modeling has the potential to provide the level of accuracy necessary. It has been discussed how a surrogate for the oxygen content in hypoxic tumors and the cell proliferation can be extracted from time series of PET images using the agents FMISO and FLT, respectively. Furthermore, a pathway how this information can be used to improve treatment definition and to enable sub-tumor dose sculpting has been presented.

Imaging and quantification of molecular markers will play an essential

role in future radiotherapy planning. Technologies are emerging, which will

allow processing and handling of quantitative molecular images in the

radiotherapy planning suite, thereby facilitating clinical usage of these

technologies. Certainly, much more clinical experience is needed before

molecular imaging guided radiotherapy can be considered as a clinical

reality.

Figure 14-5. (A) T:B ratio at 240 min after tracer administration. (B) Parametric map of the rate constant κ

for the same region. The parameter α was kept fixed at 0.36 as suggested in the model

. (C) Scatter plot, T:B versus κ

, for lung cancer data (black dots) and the correlation hypothesized by Casciari

(red dashed line). (Data courtesy Dr. S.M. Eschmann, University of Tübingen).

Figure 14-6. Standard prostate plan without (A) and with dose boosting (B) of a radioresistant

sub-tumor region. The target volume is delineated in red color. The radioresistant sub-tumor

region is delineated in green (contours are drawn in A and B).

ACKNOWLEDGEMENT

The PET program in cancer imaging at the University of Washington is generously supported by P01 CA42045 from the National Cancer Institute and S10 RR17229.

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