Treatment planning for prostate implants using magnetic-resonance spectroscopy imaging1
To view the full text, please login as a subscribed user or purchase a subscription. Click here to view the full text on ScienceDirect.
Fig. 1
Baseline 1H MRSI data from a patient with Gleason Grade 7, PSA level 8 ng/mL. Single slice extracted from three-dimensional CSI data set. The spectral voxels corresponding to the grid overlaid on the image are shown to the right. Image acquisition parameters: T2-weighted fast spin-echo sequence: TR 5000, effective TE 102 ms, 256 × 192 matrix, 14-cm FOV, slice thickness 3 mm, gap 0, 3 averages. CSI acquisition parameters: PRESS voxel selection with dual-BASING water and lipid suppression, TR = 1 s, TE = 130 ms, FOV = 50 mm, 8 × 8 × 8 matrix (6.25 mm in plane resolution, zero-filled to 3.1 mm in z (slice) direction), 2 averages, 17-min scan time. [H = healthy peripheral zone, SC = suspicious for cancer, VC very suspicious for cancer, ND = nondiagnostic, ∗voxel comprised only partially of peripheral zone.]
Fig. 2
Two MR images of a patient who underwent prostate brachytherapy implantation: In (a) a whole body coil was used and the prostate is not deformed. In (b) a 100-cc rectal coil was used, and thus the prostate assumes a flattened, wider shape. The two studies were coregistered using bony pelvic anatomy. Contours of the prostate and rectum as well as marks of two seeds were drawn on each image set and simultaneously registered on the other.
Fig. 3
Schematic representation of the procedure used for mapping prostate voxels between MR and US volumes (see text).
Fig. 4
MRS-guided isodose curves for two nonconsecutive prostate slices of a patient. The square box in each slice indicates a tumor pocket as identified by the MRS image. The first slice shows a clear dose escalation around the tumor, while the second shows that while dose is escalated around the tumor pocket, the dose around the urethra is maintained at an acceptably low level (U = urethra, S = seed implanted).
Fig. 5
Dose–volume histograms for the three tumor pocket volumes discussed in the text: large (V = 3.71 cm3), medium (V = 2.35 cm3), and small (1.36 cm3). For each of the three volumes, the two sets of DVH curves represent, respectively, plans with (MRS-guided) or without (standard) dose escalation in the tumor pockets.
Fig. 6
Prob(n = 0|D), the probability that none of the n cells exposed to dose D survive the treatment, as a function of prescription dose (144 Gy). Each curve corresponds to a different initial number (n) of tumor cells. Prob(n = 0|D) has been calculated using Eq. 6 with: α = 0.155 Gy−1, β = 0.052 Gy−2, Tpot = 2 d, t0 = 1 h.
Fig. 7
Average TCP reduction factor (the expected value of Prob(n = 0|D) of Fig. 7 over the dose distribution in a typical prostate treatment) as a function of the number of undetected tumor cells. For further details, see text.
Abstract
Purpose: Recent studies have demonstrated that magnetic-resonance spectroscopic imaging (MRSI) of the prostate may effectively distinguish between regions of cancer and normal prostatic epithelium. This diagnostic imaging tool takes advantage of the increased choline plus creatine versus citrate ratio found in malignant compared to normal prostate tissue. The purpose of this study is to describe a novel brachytherapy treatment-planning optimization module using an integer programming technique that will utilize biologic-based optimization. A method is described that registers MRSI to intraoperative-obtained ultrasound images and incorporates this information into a treatment-planning system to achieve dose escalation to intraprostatic tumor deposits.
Methods: MRSI was obtained for a patient with Gleason 7 clinically localized prostate cancer. The ratios of choline plus creatine to citrate for the prostate were analyzed, and regions of high risk for malignant cells were identified. The ratios representing peaks on the MR spectrum were calculated on a spatial grid covering the prostate tissue. A procedure for mapping points of interest from the MRSI to the ultrasound images is described. An integer-programming technique is described as an optimization module to determine optimal seed distribution for permanent interstitial implantation. MRSI data are incorporated into the treatment-planning system to test the feasibility of dose escalation to positive voxels with relative sparing of surrounding normal tissues. The resultant tumor control probability (TCP) is estimated and compared to TCP for standard brachytherapy-planned implantation.
Results: The proposed brachytherapy treatment-planning system is able to achieve a minimum dose of 120% of the 144 Gy prescription to the MRS positive voxels using 125I seeds. The preset dose bounds of 100–150% to the prostate and 100–120% to the urethra were maintained. When compared to a standard plan without MRS-guided optimization, the estimated TCP for the MRS-optimized plan is superior. The enhanced TCP was more pronounced for smaller volumes of intraprostatic tumor deposits compared to estimated TCP values for larger lesions.
Conclusions: Using this brachytherapy-optimization system, we could demonstrate the feasibility of MRS-optimized dose distributions for 125I permanent prostate implants. Based on probability estimates of anticipated improved TCP, this approach may have an impact on the ability to safely escalate dose and potentially improve outcome for patients with organ-confined but aggressive prostatic cancers. The magnitude of the TCP enhancement, and therefore the risks of ignoring the MR data, appear to be more substantial when the tumor is well localized; however, the gain achievable in TCP may depend quite considerably on the MRS tumor-detection efficiency.
Keywords:
Magnetic resonance spectroscopy, Prostate cancer, Brachytherapy, Optimization, Tumor control probabilityTo access this article, please choose from the options below
Society Member Login
Society Members, full access to the journal is a member benefit. Use your society credentials to access all journal content and features
Non-Member Login
Register
Create a new accountPurchase access to this article
Claim Access
If you are a current subscriber with Society Membership or an Account Number, claim your access now.
Subscribe to this title
Purchase a subscription to gain access to this and all other articles in this journal.
Institutional Access
Visit ScienceDirect to see if you have access via your institution.
1The authors acknowledge generous support from the National Institutes of Health (grant 1R21CA78626-01) and funds from Jan Calloway and her late husband Wayne Calloway.
☆This work was supported in part by NIH R21 CA 84258.
