Topics to be Discussed

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Innovative Computerized Treatment Planning System for Permanent Prostate Implants Eva K. Lee, Radiation Oncology, Emory University; Industrial & Systems Engineering, Georgia Institute of Technology. Topics to be Discussed. - PowerPoint PPT Presentation

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Innovative Computerized Innovative Computerized Treatment Planning System for Treatment Planning System for Permanent Prostate ImplantsPermanent Prostate Implants

Eva K. Lee, Radiation Oncology, Emory University; Eva K. Lee, Radiation Oncology, Emory University; Industrial & Systems Engineering, Georgia Institute of Technology.Industrial & Systems Engineering, Georgia Institute of Technology.

Topics to be Discussed

• Automated Treatment Planning System for Automated Treatment Planning System for Brachytherapy in Permanent Prostate Brachytherapy in Permanent Prostate ImplantsImplants

• MRS-image guided Dose-Escalation MRS-image guided Dose-Escalation Planning Planning

• Extended-time Dose Control and Planning Extended-time Dose Control and Planning Taking Edema Shrinkage and Seed Taking Edema Shrinkage and Seed Displacement into AccountDisplacement into Account

Brachytherapy for Prostate Carcinoma

• Radiation therapy that involves the placement of radioactive sources permanently inside the prostate.

Transperineal Implantation of Radionuclides using Transrectal

Ultrasound (TRUS) Device

Part I:Part I:Automated Treatment Planning Automated Treatment Planning

System for BrachytherapySystem for Brachytherapy

Computerized Optimization Approach

• Include strict dose bounds for different anatomies• Impose clinically desired properties • Superior plans / Time savings

– Can generate a plan within 5 minutes

• Allow intra-operative planning for clinicians– overcome current pre-planning problems– allow real-time alteration of plans due to unforeseen

implantation problems

• First-of-its-kind• Research tool to push frontier of understanding

Manual Plan shows poor post-implant coverage & conformity (white curve represents contour of prostate

slice, green curve represents the 100% isodose curve)

Optimized Plan from Automated System showssuperior coverage & conformity (white curve represents contour of prostate slice, green curve

represents the 100% isodose curve)

Part II:Part II:MRS-image guided Dose-MRS-image guided Dose-

Escalation PlanningEscalation Planning

MRS-Image Guided Planning

• Explore feasibility of designing treatment plans with localized escalated dose in identifiable tumor regions of the prostate and gauge the biological significance of doing so.

• Escalate dose in tumor regions within prostate identified by MRS-images

• Case study of a patient shows drastic improvement of tumor control probability from 65% to 95% in dose escalated plans

Example of dose escalation around the tumor regionExample of dose escalation around the tumor region::

Here the tumor spot is in the vicinity of the urethra: the Here the tumor spot is in the vicinity of the urethra: the dose received by the urethra is kept within strict pre-set dose received by the urethra is kept within strict pre-set levels and reasonable escalation is observed in the tumor levels and reasonable escalation is observed in the tumor area.area.

Estimated Tumor Control Probability (TCP) Estimated Tumor Control Probability (TCP) values for 3 different tumor volumes: MRS-values for 3 different tumor volumes: MRS-

guided and standard plansguided and standard plans

MRS-guided plan appears consistently superior to MRS-guided plan appears consistently superior to the non-dose-escalated (standard) plan.the non-dose-escalated (standard) plan.

Table 3: Estimated TCP values (n = 1.36 109 cells, Prostate volume = 38.1 cm3)

Tumor volume

(cm3)

Standard plan

(Plan A)

MRS-guided plan

(Plan B)

Ratio of Plan B to

Plan A

1.36 0.649 0.943 1.45

2.35 0.650 0.965 1.48

3.71 0.761 0.948 1.25

Part III:Part III:Extended-time Dose Control and Extended-time Dose Control and

Planning Taking Edema Planning Taking Edema Shrinkage and Seed Shrinkage and Seed

Displacement into AccountDisplacement into Account

Automated Planning with Extended Dose Control

• Patient case studies reveal excessive irradiation to prostate exterior, urethra and rectum when no extended dosimetric constraints, seed displacement or gland shrinkage information are included in the planning process.

• Dosimetric control of irradiation to the prostate, urethra and rectum; seed displacement; and gland shrinkage information are incorporated into planning over the entire 30 day period.

Automated Planning with Extended Dose Control: Findings

• Multi-period planning provides conformal dosimetry to the gland over a period of 30 days, and a reduction of over 21% of external normal tissue receiving excessive irradiation.

• Multi-period planning demonstrates the potential for urethra and rectum morbidity reduction without compromising local tumor control.

The figure below shows a plot of coverage and conformity scores over the 30-day horizon for several multi-period plans. For comparison, the single-period EPV[0] plan's 30-day coverage and conformity plot is also shown. Note that while initial coverage is somewhat better for EPV[0] than for the multi-period plans, overall conformity for EPV[0] is much worse.The lines with values above (below) 1.0 on the vertical axis correspond to the conformity (coverage) indices for the six plans.

Statistics over 30 day horizon for a signle-period model with urethra and rectum dose imposed at multiple times Urethra Dose Statistics Day (t) 0 6 12 18 24 30 % < 1.2D(t) 100 100 100 100 100 100 Max dose / (1.2 D(t))

0.94 0.99 0.99 1.0 0.99 1.0

Rectum Dose Statistics Day (t) 0 6 12 18 24 30 % < 0.8 D(t) 100 100 100 100 100 100 Max dose / (0.8 D(t))

0.94 0.94 0.95 0.96 0.97 1.0

.

Statistics over 30 day horizon for a single-period model with urethra and rectum dose imposed at time 0 Urethra Dose Statistics Day (t) 0 6 12 18 24 30 % < 1.2D(t) 100 33.33 22.22 22.22 22.22 11.11 Max dose / (1.2 D(t))

0.98 1.03 1.07 1.11 1.15 1.18

Rectum Dose Statistics Day (t) 0 6 12 18 24 30 % < 0.8 D(t) 100 70 60 60 60 60 Max dose / (0.8 D(t))

0.99 1.04 1.08 1.12 1.15 1.16

References• E.K. Lee, M. Zaider, Intra-Operative Iterative Treatment-Plan Optimization

for Prostate Permanent Implants. 2nd International Innovative Solutions for Prostate Cancer Care meeting, 32-33, 2001.

• M. Zaider, E.K. Lee, MRS-guided Dose-Escalation Treatment Planning Optimization for Permanent Prostate Implants. 2nd International Innovative Solutions for Prostate Cancer Care meeting, 36, 2001.

• E.K. Lee, M. Zaider, Determining an Effective Planning Volume for Permanent Prostate Implants. International Journal of Radiation Oncology, Biology and Physics, 49(5) (2001), in print.

• M. Zaider, M. Zelefsky, E.K. Lee, K. Zakian, H.A. Amols, J. Dyke, J. Koutcher. Treatment Planning for Prostate Implants Using MR Spectroscopy Imaging. International Journal of Radiation Oncology, Biology and Physics, 47(4): 1085-96 (2000)

• E.K. Lee, R. Gallagher, M. Zaider, Planning implants of radionuclides for the treatment of prostate cancer: an application of mixed integer programming. Optima (Mathematical Programming Society Newsletter), feature article, 1999; 61: 1 – 10.

References• C.S. Wuu, R.D. Ennis, P.B. Schiff, E.K. Lee, M. Zaider, Dosimetric and

Volumetric Criteria for Selecting a Source Type I-125 or Pd-103 and Source Activity in the Presence of Irregular Seed Placement in Permanent Prostate Implants. International Journal of Radiation Oncology, Biology and Physics, 47: 815-820 (2000).

• E.K. Lee, R. Gallagher, D. Silvern, C.S. Wuu, and M. Zaider, Treatment Planning for Brachytherapy: An Integer Programming Model, Two Computational Approaches and Experiments with Permanent Prostate Implant Planning. Physics in Medicine and Biology Vol. 44 (1), pp.~145-165, 1999.

• D. Silvern, E.K. Lee, R. Gallagher, L.G. Stabile, R.D. Ennis, C.R. Moorthy, and M. Zaider, Treatment Planning for Permanent Prostate Implants: Genetic Algorithm versus Integer Programming. Medical & Biological Engineering & Computing, vol.~35, Supplement Part 2, 1997.

• R. Gallagher, E.K. Lee, Mixed Integer Programming Optimization Models for Brachytherapy Treatment Planning. In: Daniel R. Masys, Ed. Proceedings of the 1997 American Medical Informatics Association Annual Fall Symposium, 278-282, 1997.