Magnetic Resonance Imaging for IMRT

Magnetic Resonance Imaging for IMRT

Lynn J. Verhey, Cynthia Chuang, Andrea Pirzkall

3

Contents

3.1 Introduction . . . 177

3.2 Use of MR Data in IMRT and 3DCRT Treatment Planning . . . 177

3.3 Technical Aspects of MRI and MRS . . . 178

3.4 Clinical Applications of MRI/MRS . . . 179

3.4.1 Recent Advances in MR Imaging . . . 179

3.4.2 Applications in Head and Neck Cancer . . . 180

3.4.3 Applications in Breast Cancer . . . 180

3.4.4 Applications in Lung and Elsewhere . . . 180

3.4.5 Potential Applications of MRSI in Treatment Planning for Radiotherapy . . . 181

3.4.6 MRSI for Brain Gliomas . . . 181

3.4.7 MRI Combined with MRSI for Prostate Cancer 182 3.4.8 Other Potential Applications of MRSI for Cancer182 3.4.9 Potential of MRSI for Targeting IMRT . . . 183

3.5 Future Directions . . . 184

References . . . 185

3.1 Introduction

The planning of radiotherapy has evolved rapidly in the past 10 to 15 years, from two-dimensional treat- ment planning based on projection images, to three- dimensional planning based on thin-section computer- ized tomography (CT) and, more recently, to computer- optimized planning using CT anatomical images com- bined with other imaging information from modalities such as MR, PET and SPECT. These other modalities can add information about tissue identification, tissue boundaries and tissue function that can be extremely important in both the diagnosis and treatment of cancer.

Methods of radiation treatment delivery have also evolved rapidly in recent years. The linear accelerator control systems are now primarily digital and are ca- pable of delivering and controlling large numbers of patient-specific beams in a short period of time. The introduction and nearly universal adoption of the mul- tileaf collimators for two-dimensional beam shaping, combined with computer-controlled beam delivery, has

virtually eliminated the need for radiation therapists to enter the room during a patient treatment, thereby mak- ing the daily treatments much more efficient. Finally, the introduction of intensity modulated radiotherapy (IMRT) in the mid-1990s makes possible the delivery of higher doses to defined tumors while keeping constant, or reducing, the dose to surrounding sensitive tissues.

These new capabilities for sophisticated treatment planning and treatment delivery can be effectively used only if the target volumes and critical normal tissues can be accurately defined on the treatment planning CT study. In particular, magnetic resonance imaging (MRI) is capable of providing excellent soft tissue definition, unrestricted multiplanar and volumetric imaging data as well as functional information with the addition of spectroscopy (MRSI).

This chapter will concentrate on the use of MR data in the planning of precision radiotherapy, especially IMRT and conformal radiotherapy (3DCRT), technical aspects of MRI and MRSI including potential and limitations, current clinical applications of MRI and MRSI and future directions for research and development. The role of PET and SPECT in IMRT is discussed elsewhere in this volume [1].

3.2 Use of MR Data in IMRT

and 3DCRT Treatment Planning

Clearly, MRI provides soft tissue contrast that can be critically important for the definition of target and sensitive organs for precision radiotherapy. There are technical issues, however, that need to be considered before these images can be used. First, the spatial accu- racy of the MRI data needs to be assured. This accuracy is a function of the linearity of the magnetic field gra- dients in the MR magnet as well as eddy currents [2].

These system distortions tend to be larger at the edges of the magnet than in the center, so are a larger prob- lem when imaging the pelvis than the head and neck.

With good quality assurance, the distortions should be

no larger than the basic uncertainty of the MR pixel

location, which is typically less than 3 mm [3]. System distortions can be measured and corrected through the use of phantoms of known geometry. Such a system can reduce the uncorrected distortions to less than the imag- ing uncertainty of 1–2 mm. Second, the MR data must be registered relative to the CT data that are needed for dose calculations. This process is called image fu- sion [4, 5]. Three-dimensional image fusion is relatively easy in areas such as brain [6] and head and neck, where there are numerous anatomical landmarks and where structures can be considered fixed in position. It be- comes much more difficult in areas such as the pelvis and thorax, where organs move due to variable fill- ing of neighboring organs or breathing. In some cases, such as the pelvis, only qualitative image fusion might be possible [7]. Powerful imaging tools make it possi- ble to verify the accuracy of the image fusion prior to the radiotherapy planning process [8]. In some cases, such as stereotactic irradiation of intracranial targets, MRI images can be used as primary planning data, be- cause the approximation of uniform water-equivalent tissue density along each beam path is quite good in the brain.

The use of MRI to augment CT in treatment planning for head and neck tumors has become rather routine [9].

This is due to the critical importance of accurate delin- eation of sensitive normal organs within the head and neck region. Often the gross tumor volume (GTV) can be observed either with contrast-enhanced CT or with gadolinium-enhanced MRI. The clinical target volume (CTV), however, is often a large volume containing nodal volumes and many tissues not explicitly defined as nor- mal tissues. MRI is capable of defining sensitive normal tissues within the image that can be critical in the defi- nition of a treatment plan. Typically, treatment plans are created by selecting beam directions that avoid most of the sensitive normal tissues defined with the assistance of MR, although for IMRT, inverse planning methods are capable of creating excellent dose plans simply by defining the desired doses to defined targets and normal tissues.

3.3 Technical Aspects of MRI and MRS

Magnetic resonance imaging (MRI) is a non-invasive imaging technique that makes use of the fact that certain atomic nuclei, such as

H,

P,

F, and

C, have inher- ent spin properties that allow them to acquire discrete amounts of energy in the presence of a static magnetic field. The application of electromagnetic fields (non- ionizing radiofrequency radiation) at right angles to a static magnetic field causes these nuclei to jump to higher energy levels. After removal of the electromag- netic fields, the nuclei subsequently drop back to their original spin states by emitting electromagnetic radi-

ation at a rate that can be characterized by their T1 (spin-lattice) and T2 (spin-spin) relaxation times. A re- ceiver coil detects the emitted radiation and records the time domain of the MR signal that, once processed using a Fourier transform, reveals the spectrum of in- tensities and frequencies of the nuclei from different chemical species within the excited volume. The lo- cation of peaks in the spectrum defines the chemicals within the sample; the peak intensity reflects their con- centration. Conventional MRI uses the properties of the protons from water to obtain information about their spatial distribution in different tissues. Specialized ra- diofrequency pulses and magnetic field gradients are used to label the water signal as a function of space and, after appropriate post-processing, provide an anatomic image of the changes in proton density and relaxation properties.

MR spectroscopic data are typically acquired by sup- pressing the large signal from water and allowing the properties of other compounds to be recorded and an- alyzed. Water suppressed 1H spectroscopy techniques are commercially available for obtaining spectra from selected regions within the brain and prostate and can be combined with additional localization techniques to produce either a single spectrum from a region of interest (single-voxel MRS) or a multidimensional array of spectra from the region of interest (3D multi- voxel MRS, MRSI, chemical shift imaging (CSI)). The peaks in individual spectra reflect the relative con- centrations of cellular chemicals within that spatial location. The peak heights and | or areas under the curve relate to the concentration of the respective metabolites; differences in these concentrations can be used to distinguish “normal | healthy” tissue from neo- plastic or necrotic tissue. As an efficient method for obtaining arrays of spatially localized spectra at spa- tial resolutions of 0.2 to 1 cc, 3D multivoxel MRSI is of greater potential value than single-voxel MRS in target delineation and monitoring response to ther- apy and allows the generation of maps of the spatial distribution of cellular metabolites. This is an ideal representation for integrating the information into RT treatment planning. Fig. 1 shows 3D-MRSI superim- posed on an axial T1 post-contrast MRI for a patient with GBM.

A significant advantage of

H-MRSI over other metabolic imaging techniques is that the data can be obtained as part of a conventional MRI and the data can be directly overlaid upon each other. This enhances the display of metabolic data and allows it to be correlated with the anatomy as revealed by MRI, thereby allowing areas of anatomic abnormality to be directly correlated with the corresponding areas of metabolic abnormality.

As will be described below, the combination of conven-

tional MRI imaging with MRSI metabolic data and with

anatomic CT, promises to vastly improve the definition

of both tumor volumes and normal tissues as required

Fig. 1a–d. Patient with left temporo-occipital GBM. Axial T1 post contrast MRI shows typical contrast enhancement (CE) with cen- tral necrosis. 3D-MRSI performed within a defined area using short-echo point-resolved spectroscopy (referred to as PRESS box), represented by gridlines, reveals metabolic signature of ex- amined brain tissue. Four examples of spectral patterns are given detecting the following metabolites: 1) choline (Cho), 2) creatine (Cr), 3) N-acetylaspartate (NAA), 4) lipid (Lip), and 5) lactate (Lac).

(a)Normal brain tissue marked by high peak of NAA and low peak of Cho. The resulting Cho-to-NAA ratio is therefore low (about 1 : 2). Cho and Cr are exhibiting similar peak heights.(b)Tumor spectrum characterized by an increase of Cho and a decrease in

NAA as compared to the normal tissue voxel(a). The Cho-to-NAA ratio is high (about 1 : 0.5). (Note that the spectrum is derived from a single voxel that contains only partially CE.) (c),(d)Mix of tu- mor and necrosis revealed by lactate edited sequences which are postprocessed to separate Lip and Lac that overlap due to resonat- ing at the same frequency. (c)Summed spectrum shows peaks of high Cho and extremely diminished NAA (Cho-to NAA ratio is about 1 : 0.3), and in addition, the presence of Lip. (Note that the spectrum is derived from a single voxel that contains partially CE and macroscopic necrosis.)(d)Difference spectrum allows quan- tification of Lac as a marker of hypoxia. The Cho-to-NAA-Index (CNI) values for the above voxels are:(a)−0. 6,(b)3.6,(c)+(d)3.4

for full exploitation of highly conformal radiotherapy delivery methods such as IMRT.

3.4 Clinical Applications of MRI/MRS

Accurate delivery of the prescribed dose to target vol- umes is essential for successful local control of diseases.

Technological advancement has led to the widespread use of three-dimensional conformal radiation therapy and intensity modulated radiation therapy (IMRT) in recent years. The goal of IMRT is to tailor radiation dose to be highly conformal to the three- dimensional shape of the tumor target, and to minimize radiation damage to the surrounding sensitive tissues. This high confor- mality of IMRT plans often allows dose escalation of the tumor target while keeping the critical normal tissue dose within tolerance.

3.4.1 Recent Advances in MR Imaging

To be truly able to realize the potential offered by IMRT, accurate target delineation is essential. Recent advances in magnetic resonance imaging and magnetic resonance spectroscopy imaging have the potential to offer better target delineation in multiple tumor sites and for differ- ent tumor types, thus facilitating the use of IMRT and other highly conformal radiotherapy methods for those tumors.

One of the recent advances in MR is Dynamic Con- trast Enhanced MRI (DCE-MRI), which has made the successful transition from methodological development to clinical validation, and is now rapidly becoming a mainstream clinical tool [10]. DCE-MRI was devel- oped in the mid-1990s, in which fast spoiled gradient echo sequences are performed with rapid sampling, ap- proximately 5–10 s per image after the administration of a bolus of intravenous contrast medium. It allows the study of the microcirculation of tumors and normal tis- sues. Enhancement of a specific body tissue depends on a wide variety of factors, including vascularity, cap- illary permeability, renal clearance and volume, and composition of extracellular fluid [11].

After the intravenous administration of param- agnetic, low-molecular-weight contrast medium, the contrast will pass through the capillary bed and be con- fined transiently within the vascular space. The contrast then passes rapidly into the extravascular-extracellular space at a rate determined by the permeability of the microvasculature, its surface area and blood flow [10].

Therefore, tumor will be visualized with high contrast, due to greater microvascular permeability and diameter, increased blood flow and volume. The contrast enhance- ment will eventually appear in the normal tissue. Both T1 and T2

weighted MR sequences can be used to detect the initial vascular phase, thus enabling tissue perfusion and blood volume estimation.

The tracer kinetic principle-based two-compartment

pharmacokinetic model has been used to study blood

volume, permeability or extraction flow effects, provid-

ing estimates of relative blood volume (rBV), relative blood flow (rBF), and mean transit time (MTT) [10].

Methods using time-signal intensity curves (TIC) and parameters, such as Time of peak enhancement (Tpeak), initial and mean gradient of the rise of enhancement curves, rate of enhancement, maximum signal intensity and washout ratio (WR) have also been used to semi- quantitatively [11] study the dynamic contrast enhanced effects as a means of differentiating between tumor and benign lesions.

3.4.2 Applications in Head and Neck Cancer

DCE-MRI has been used recently for differentiating between malignant and benign lesions for salivary gland tumors [12] and solitary pulmonary nodules [13], diagnosis and screening of breast lesions [14–19], as- sessment of metastatic cervical lymph nodes [20], staging of urinary bladder cancer [21], and possible identification of malignant lymphoma of the head and neck [22].

Yabuuchi et al. used DCE-MRI to examine 33 salivary gland tumors in 29 patients. Time of peak enhance- ment (Tpeak) and washout ratio (WR) were correlated with microvessel count and cellularity-stromal grade obtained from histopathological evaluation. It was de- termined that a Tpeak of 120 s and a WR of 30% had high sensitivity and specificity for differentiation between malignant and benign salivary gland tumors, demon- strating that DCE-MRI could be very effective for these tumors [12].

Asaumi et al. conducted a small study of DCI-MRI of lymphoma of head and neck in which they studied 18 lymphoma lesions in 8 patients. It was found that the contrast intensity curves showed a relatively rapid increase, reaching a maximum at 45–120 s, and a rela- tively rapid decrease in most lesions. These patterns may suggest characteristic features useful for distinguishing malignant lymphomas from other lesions [22].

3.4.3 Applications in Breast Cancer

Many studies have utilized DCE-MRI for breast cancer diagnosis and screening. In the study done by Heinisch et al., in which 40 lesions in 36 patients were studied, MRI was more sensitive than FDG-PET in disclosing ma- lignant breast tumors. DCE-MRI was also more accurate than FDG-PET in the assessment of multifocal disease.

Although the authors did speculate that the lower sensi- tivity of FDG-PET compared to MRI seems to be due to difficulties in reliably imaging lobular carcinomas [17]

and carcinomas smaller than 10 mm.

The most significant and largest study is from a col- laborative study by the Magnetic Imaging Screening Study Group. 1909 eligible women, including 358 car-

riers of germ-line mutations, were screened. During the study, 51 tumors (44 invasive cancers, 6 ductal carcinomas in situ, and 1 lymphoma) and 1 lobular car- cinoma in situ were detected within a median follow-up period of 2.9 years. The sensitivity of clinical breast examination, mammography, and MRI for detecting invasive breast cancer was 17.9, 33.3, and 79.5%, re- spectively, and the specificity was 98.1, 95.0, and 89.8%, respectively. The overall discriminating capacity of MRI was significantly better than that of mammography (P < 0. 05) [16].

3.4.4 Applications in Lung and Elsewhere

Schaefer et al. studied 51 solitary 5–40 mm pulmonary nodules, out of which 27 were malignant. It was found that stronger enhancement, higher maximum peak and faster slope characterized malignancy for solitary pul- monary nodules. Malignant nodules also exhibited more significant washout [13].

Fischbein et al. used time to peak enhancement, peak enhancement, maximum slope and washout slope for their study of DCE-MRI of cervical lymph nodes of 21 patients with newly diagnosed squamous cell car- cinomas. It was found that Tpeak was longer, the peak enhancement and the maximum slope of wash-in were lower, and that washout was slower in tumor-involved lymph nodes [20].

Barentsz et al. stated that DCE-MRI results in im- proved local and nodal staging, aided in improved separation of transurethral granulation tissue and edema from tumor, and also helped in monitoring and evaluating the effects of chemotherapy [21].

There are other technical advances in the acquisi- tion pulse sequencing that are enabling better detection and characterization of other types of tumors. Ohno et al. have used short inversion time inversion-recovery (STIR) turbo spin-echo (TSE) MR imaging in 110 pa- tients with non-small cell lung cancer for the detection and differentiation of metastases in Mediastinal and Hi- lar lymph nodes. By using lymph node to saline ratios (LSR), it was found that metastases have higher LSR.

Quantitative analysis of LSR showed that sensitivity was 93%, and specificity was 87% [23]. Plathow et al. have used dynamic MRI to examine intrathoracic tumor mo- bility during breathing cycle in 20 patients. They used three images per second and measured positions of the diaphragm, upper, middle, and lower lung regions, and the tumor in three dimensions for both the deep inspi- ratory and expiratory breathing positions. It was found that lower lung regions move more significantly than the upper regions, and that tumor motion shows a high variability during quiet respiration [24].

Although the above-described applications of MR are

very useful for screening and diagnosis of cancer, they

are also of great potential value for the quantitative defi-

nition of the gross tumor volume required for precision radiotherapy.

3.4.5 Potential Applications of MRSI in Treatment Planning for Radiotherapy

Two major disease sites will be discussed with respect to the potential and actual incorporation of MRS imaging

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a b

c

d

e

Fig. 2a–e. Patient with recurrent, initially low-grade, glioma; sta- tus post resection and fractionated RT with 59.4 Gy. A subsequent boost with Gamma Knife (GK) radiosurgery was planned based upon MRI|^MRSI:(a)T1 weighted axial MRI with superimposed MRSI PRESS box;(b)enlarged spectra and actual Cho-to- NAA Index (CNI) for a subset of voxels in immediate vicinity of the resection cavity. Shaded voxels highlight those with a CNI of≥ 2;

(c)gray scale CNI image; the brighter the voxels the higher the respective CNI;(d)high resolution CNI image resulting from sam- pling the low resolution CNI image to match the resolution of the MR image. Superimposed are CNI contours of 2 (bright line), 3 (dark middle contour) and 4 (dark inner contour) as a result of in- terpolation;(e)CNI contours of 2, 3 and 4 superimposed onto the respective MRI slice in preparation for treatment planning

into the treatment planning process for RT: prostate cancer and brain gliomas. Imaging protocols for both disease sites are described in detail elsewhere [25–27].

3.4.6 MRSI for Brain Gliomas

High-grade gliomas (HGG) comprise up to 86% of newly

diagnosed primary CNS tumors in the adult population

age 35 to 64 years, with an increasing percentage in advanced ages [28]. Despite multimodality treatment approaches including surgery, radiation therapy and chemotherapy, the prognosis for patients with HGG re- mains dismal. Median survival averages 9–12 months for patients with grade IV (glioblastoma multiforme, GBM) and 20–36 months for patients with grade III (anaplastic astrocytoma, AA) gliomas [29]. Dose esca- lation appears to be needed because the local failure rate remains very high after treatment using conven- tional doses of 60 Gy with conformal radiation therapy (CRT) [30]. One possible reason for this continued fail- ure could be the use of inappropriate target volumes for high dose delivery.

The current target definition for RT of brain gliomas encompasses the extent of abnormality on MRI (contrast enhancement on T1 weighted images and hyperinten- sity seen on the T2 weighted images) enlarged by several centimeters [31]. Using this definition, a rather large volume of uninvolved brain tissue may be exposed un- necessarily and the dose that can be safely delivered may be limited by the risk of complications. This sug- gests that there would be value in restricting the dose prescription to the tumor extent only and possibly direct higher doses to smaller subregions of more aggressive tumor, a targeting and dose prescription process that is ideally realized through the use of IMRT.

Several studies have been performed to quantify the difference of spatial extent derived from MRI vs MRSI, respectively, in patients with high-grade and low-grade gliomas. A measure for metabolic abnormality based on ratios of metabolite levels (CNI) was used to com- pare the spatial extent and heterogeneity of metabolic (MRSI) and anatomic (MRI) information in patients with newly diagnosed [32] and surgically resected [33]

gliomas in order to explore the value that MRSI might have for defining the target for radiation therapy in brain gliomas. Significant differences have been found be- tween anatomic and metabolic determinants of volume and spatial extent of the neoplastic lesion for patients with newly diagnosed HGG [32]. These findings sug- gest that MRSI-derived volumes are likely to be more reliable in defining the location and volume of micro- scopic and actively growing disease when compared to conventional MRI.

Preliminary evaluation of MRSI follow-up exams that were performed post-RT has shown a predictive value for MRSI with respect to focal recurrence [33]. For ten patients without contrast enhancing residual disease fol- lowing surgical resection we have been able to establish a spatial correspondence between areas of new CE, de- veloped during follow-up, and areas of CNI abnormality, as assessed after surgery but prior to RT. We found a very strong inverse correlation between the volume of the CNI abnormality and the time to onset of new contrast enhancement; the greater the volume of CNI, the shorter the time to recurrence. Fig. 2 shows a pa-

tient with low grade glioma post-resection with areas of high CNI adjacent to the surgical cavity. This infor- mation can be used to define a boost target volume for radiotherapy. MRSI has also proved to be of value in predicting overall survival in patients with GBM; the larger the volume of the CNI abnormality the shorter the survival [33].

Additional metabolic indices have been evaluated by Li et al. [34]. These studies suggest that tumor burden, as measured with either the volume of the metabolic abnor- malities or the maximum magnitude of the metabolic indices, correlates with the degree of malignancy. The spatial heterogeneity within the tumor, and the finding that metabolic disease activity appears to extend beyond MRI changes, may be responsible for the continuing failure of current treatment approaches.

3.4.7 MRI Combined with MRSI for Prostate Cancer

Conventional MRI of the prostate relies on signal in- tensities that are due to morphological changes within the gland that can help define the presence and ex- tent of cancer [35]. The optimal current technique uses a combination of an endorectal coil and a pelvic external coil array to produce high resolution T2-weighted im- ages that can be used to differentiate prostatic zonal anatomy, prostate cancer and surrounding soft tis- sues [27]. Unfortunately, these images are still lacking metabolic information that can accurately define the presence and spatial extent of active tumor. By combin- ing metabolic information from MRSI with the excellent morphological information of MRI, it becomes possi- ble to obtain a clear picture of the location of active foci of tumor cells within the prostate, with a high de- gree of confidence [27]. The quantity of the metabolites choline, citrate, creatine, which can be independently determined by MRSI, is considered an indicator of cellular activity that can be used to demonstrate the location and extent of active tumor with a high de- gree of specificity [36]. In particular, the ratio of the metabolites (choline + creatine) | citrate has proven to be a reliable marker of active disease. Figure 3 shows a T2-weighted axial MRI of the prostate gland with a su- perimposed proton spectral array identifying a focus of tumor within the left midgland. Such displays of infor- mation are now becoming routinely available at some institutions [27].

3.4.8 Other Potential Applications of MRSI for Cancer

As shown above, Magnetic Resonance Spectroscopy

Imaging offers a more precise, biochemical-based tu-

mor definition for GBM and for the prostate. Recent

advances in identifying biochemical markers in other

types of tumor have also emerged, and could possibly

Fig. 3. (a) A representative reception-profile corrected T2 weighted Fast Spin Echo (FSE) axial image demonstrating a tumor in the left midgland to apex.(b)Superimposed PRESS selected volume encompassing the prostate with the corresponding axial 0.3 cm³proton spectral array.(c)Corresponding individual voxels

with spectral pattern and their overall spectroscopic grading along the peripheral zone. Marked voxels suggest “definitely healthy” (1) and “probably healthy” (2) prostate metabolism on the right side but “definitely cancer” (5) on the left side in spatial agreement with the anatomic abnormality

aid in better delineation of tumor extent, most notably in breast cancer, in which spectroscopic studies of the breast have confirmed that high levels of choline- con- taining compounds at 3.2 ppm accumulate mostly in malignant lesions [18, 37–39].

Huang et al. studied 50 breast cancer patients using DCE-MRI and MRSI. It was determined that although DCE-MRI has great sensitivity (100%), a combined T1-weighted DCE-MRI with 1H MR spectroscopy of choline-containing compounds could increase the speci- ficity of breast cancer detection from 62.5 to 87.5%.

Further addition of perfusion MR imaging could in- crease the specificity up to 100% [18].

Yeung et al. used 1H MR spectroscopy to character- ize different breast histopathologic subtypes and also studied the feasibility of using 1H MRS to assess axil- lary lymph node involvements. They found that for most cases of DCIS, the choline-to-creatine ratio was less than 1.7, which is similar to the ratio in normal breast tissue and benign lesions. However, for invasive breast cancers, choline level is consistently elevated, unless there is an extensive in situ component. The study also found that choline-containing compounds can be reliably detected in metastatic nodes in patient with breast cancer, there- fore, in vivo 1H MRS of axillary lymph nodes appears to be feasible [38].

In addition to using choline for breast cancer detec- tion, there is a report of using 1H MRS to characterize bone and soft-tissue tumors. Pui et al. performed MRS

imaging in 36 patients with bone and soft-tissue tu- mors larger than 1.5 cm in diameter. It was found that choline was detected in 18 out of 19 patients with malignant tumors, and not detected in 14 out of 17 patients with benign tumors. The sensitivity is 95%

and specificity is 82%, with accuracy of 89% [40].

This initial result is encouraging for the use of MRS imaging to accurately characterize musculoskeletal tu- mors.

3.4.9 Potential of MRSI for Targeting IMRT

The use of MRI | MRSI imaging data in radiotherapy treatment planning for prostate cancer has been demon- strated [7]. In particular, these investigators developed a simple IMRT treatment plan that irradiated the MRSI- positive regions within the prostate to a high dose of 90 Gy or above while simultaneously irradiating the en- tire prostate to a conventional dose of 72–75 Gy using conventional irradiation. Figure 4 shows a dose distribu- tion designed with IMRT to satisfy this goal [7]. Such an application of MR methods to IMRT planning demon- strates the power of this technology, although the clinical benefit of this targeted dose escalation has not yet been proven.

The difference in spatial extent of gliomas as seen

on MRSI vs MRI and the spatial heterogeneity within

gliomas as assessed on MRSI in patients with a newly

Fig. 4. Intensity modulated radi- ation therapy prescribing 92 Gy (green) to the DIL (Dominant In- traprostatic Lesion) and 73.8 Gy (blue) to the entire prostate while sparing surrounding normal structures (red: 60 Gy, turquoise:

25 Gy)

diagnosed brain glioma, are forcing a reassessment of our targeting and dosing concepts for the delivery of RT to malignant gliomas. IMRT offers the potential to si- multaneously deliver differential doses to user-defined regions. It is critical that the regions identified for dif- ferential dose distributions be defined accurately; areas suitable for high dose must be identified separately from areas that are appropriate for lower dose. Therefore, what is required is a means of determining which region requires which dose.

The MRSI-derived CNI index, since it has been shown to correlate with active disease and to patterns of fail- ure, appears to have potential as a guide for defining high-dose appropriate regions. However, it is not yet clear how the CNI should be used to delineate these re- gions. On first pass it might be assumed that regions with a high metabolic activity, indicating active disease, should be targeted. However, it also could be argued that it is the regions with a lower metabolic activity that will require a higher dose of radiation; these re- gions may have suffered from poor oxygenation, thus requiring a higher dose of radiation in order for the ra- diation to be effective in controlling the cell population.

An in-depth analysis of other MRSI metabolites, such as creatine and lactate, may help differentiate regions of aerobic from regions of anaerobic metabolism, thus de- tecting hypoxic areas. In addition, MR-based perfusion and diffusion measuring techniques such as cerebral blood volume (CBV) and apparent diffusion coefficient (ADC) may allow an indirect determination of oxygen

rich (or oxygen starved) areas. By combining the values of these indices that look at different metabolites, it may be possible to enhance interpretation of each individual component.

Applying metabolic and physiologic MR-based imag- ing for target guidance and utilizing the powerful capability of IMRT to increase dose selectively to appropriate areas while simultaneously prescribing a conventional dose to areas at lower risk seems an ap- propriate goal. The feasibility of incorporating MRSI data into the IMRT treatment planning process has been tested and methods have been established for necessary image data analysis and transfer [41, 42].

Recent studies have suggested, however, that the use of CNI abnormalities to enlarge the definition of the GTV may not be the optimal approach. These showed that the addition of CNI abnormality to the volume of contrast enhancement would increase its average volume by 60% (CNI ≥ 3) and 50% (CNI ≥ 4), rela- tive to contrast enhancement alone [32]. Treatment of such large volumes to very high doses might not be feasible.

3.5 Future Directions

More recently, other forms of MR-based physiologic

imaging have been developed, such as perfusion (PWI)

and diffusion weighted imaging (DWI). Combining the

information from these imaging techniques with MRSI is likely to be of value for defining differential dose re- quirements for treating high-grade gliomas, and GBM in particular. MRSI seems to be more sensitive to the detection of microscopic tumor infiltration and resid- ual disease after surgical resection as compared to MRI, PWI contributes superior information on tumor vas- cularity and DWI on the water content and cell density within a neoplastic lesion. We anticipate that incorporat- ing all of these data into the treatment planning process will provide a more reliable description of the biological properties of the tumor that will be important for im- proving the target definition and possibly the efficacy of RT.

New, higher strength magnetic fields (7 T) are now becoming available for MR and are expected to lead to improved spatial resolution of spectroscopy data as well as improved ability to identify specific metabo- lites. These promising new developments in imaging promise to revolutionize our ability to define tumor cell distributions in the patient which are required for full exploitation of IMRT.

Efforts are now underway to define metabolic | ^phy-

siologic imaging parameters that are indicating areas at higher risk for tumor recurrence and subsequently to consider those for higher dose prescription. Although the clinical application of MRSI for precision radiother- apy is most developed for gliomas and prostate cancer, there is every reason to believe that MRS will provide critical information on the location and tumor cell den- sity within the defined target volumes in many other areas of the body. The superposition of metabolic infor- mation on the morphological MRI data and in turn, on the CT data needed for treatment planning, promises to provide the capability of using IMRT to “paint” 3D dose distributions that are appropriate for the local tu- mor cell density. Ideally, this will lead to improved local tumor control.

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