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Planning. Clinical Aspects. Radiobiological Aspects. Delivery. Arthur Boyer Stanford University School of Medicine Stanford, California. Verification. Conventional planning. Time. IMRT planning. Complexity. Establish the Correspondence Between Output and Input. Output. Input.

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slide1
Planning
  • Clinical Aspects
  • Radiobiological Aspects

Delivery

Arthur Boyer

Stanford University School of Medicine

Stanford, California

  • Verification
slide3
Conventional planning

Time

IMRT planning

Complexity

slide4
Establish the Correspondence Between Output and Input

Output

Input

Specify quantities that describe a patient’s

quality of life (e.g., Karnofsky status)

Desired

output

Specify TCP and NTCP--biological model

Specify a dose distribution--dose based model

slide5
Procedures of Inverse Planning Computation

Optimal Input

Output

Construct an objective function

F = F (input parameters) = F (w1,w2,w3,….,wJ)

Optimize F and find the optimal beam profiles

Convert beam profiles into MLC leaf sequences

slide6
Dosimetric:

F = [Dc(1)-D0(1)]2 +[Dc(2)-D0(2)]2 + ...

w1

Beam profile

Dc(1)

D0(1)

Dc(2)

D0(2)

slide7
An objective function is a mathematical “measurement” of radiation treatment.
  • ~2000 pencil beam weights, non-linear system.
  • It should, ideally, include all of our knowledge of radiotherapy: dosimetry based and biological model based objective function.
slide8
Optimization of a multi-dimensional
  • objective function
  • Matrix Inversion
  • Iterative methods
  • Computer simulated annealing
  • Genetic optimization
  • Filtered backprojection
slide9
Direct Matrix Inversion

n=4

F = S [Dc(n)-D0(n)]2

Calculated dose

n=1

w3

w4

Prescribed dose

3

4

D3

D4

1.0

0.5

w2

1

2

D1

D2

w1

1.0

1.0

slide10
w4

w3

n=4

F = S [Dc(n)-D0(n)]2

n=1

D3

D4

w2

D1

D2

w1

D1= w1 d11+ w2 d21 + w3 d31 + w4 d41

D2= w1 d12+ w2 d22 + w3 d32 + w4 d42

D3= w1 d13+ w2 d23 + w3 d33 + w4 d43

D4= w1 d14+ w2 d24 + w3 d34 + w4 d44

  • Direct Matrix Inversion
slide11
A Simple Iterative Algorithm

Dose to point n:

1

dn = w1d1n + w2d2n + w3d3n

target

organ

2

3

slide12
Algebraic Iterative Method:

Initial beam profiles

Calculate dose at a voxel n

Compare Dc(n) with D0(n)

n+1

Dc(n) > D0(n) ?

Yes

No

Decrease wi

Increase wi

slide13
Algebraic Iterative Method:

n=6

F = S [Dc(n)-D0(n)]2

Calculated dose

n=1

0.5

0.5

Prescribed dose

1.0

1.0

1.0

1.0

0.5

1.0

0.5

1.0

1.0

0.5

1.0

1.0

1.0

1.0

0.5

slide14
Algebraic Iterative Method:

Iteration step = 5

Iteration step = 1

0.51

0.45

0.50

0.49

1.01

0.95

0.50

0.99

1.00

0.50

0.95

0.99

0.90

0.44

0.97

0.49

1.02

1.01

0.99

1.00

0.50

0.50

slide15
Objective function

Iteration step = 10

i=6

F = S [Dc(i)-D0(i)]2

0.30

0.51

0.42

i=1

0.25

0.20

0.15

0.10

0.51

1.02

0.92

0.05

0.91

0.41

0.82

0

20

40

80

60

1.02

0.92

100

0

0.51

Iteration step

Algebraic Iterative Method:

planning parameters
Planning Parameters
  • Number of beams
  • Beam/Coll orientation
  • Isocenter placement
  • Beamlet size
  • Intensity levels
  • Margins & Targets
  • Tuning structure
beam orientation
Beam Orientation
  • Coplanar vs Non-coplanar
    • Ease of setup
    • Ease of planning
    • Speed of treatment
  • Equi-spaced vs Selected angles
    • Entrance through table/immobilization device
beam orientation1
Beam Orientation

9 equi-spaced beams

beam orientation2
Beam Orientation

9 selected beams

collimator orientation1
Collimator Orientation

180o collimator angle

collimator orientation2
Collimator Orientation

Collimator angle

isocenter placement
Isocenter Placement
  • Issues
    • Can a better plan be achieved by isocenter placement ?
    • Dosimetry and/or QA
    • Patient setup
isocenter placement1
Isocenter Placement

Isocenter in geometric center of targets

isocenter placement2
Isocenter Placement

Isocenter in geometric center of GTV

beamlet size
Beamlet Size

Yi et al. 2000

intensity levels
Intensity Levels

Lehmann et al. 2000

tuning structure
Tuning Structure
  • A structure added to control the dose distribution in IMRT plans
    • Reduce normal tissue dose
    • Reduce/Increase target dose
summary of planning parameters
Summary of Planning Parameters
  • Number of beams
  • Beam/Coll orientation
  • Isocenter placement
  • Beamlet size
  • Intensity levels
  • Margins & Targets
  • Tuning structure
slide34
Planning System Commissioning

a

b

c

d

e

f

specially designed intensity patterns

slide35
Planning System Commissioning

90%

90%

50%

50%

40%

10%

20%

20%

40%

70%

70%

10%

80%

80%

90%

90%

30%

50%

30%

10%

10%

50%

90%

90%

Calculated Measured

specially designed intensity patterns

slide36
Patient Specific Field Verification

Quantitative Comparison of Two Fluence Maps

  • Maximum difference in
  • pixel values---local quantity.
  • Correlation coefficient—
  • global quantity.
quantitative film analysis
FilmQUANTITATIVE FILM ANALYSIS

Courtesy, Tim Solbert

slide38
Quantitative Film Analysis

White = measurement

Red = calculation

Courtesy, Tim Solberg

quantitative film analysis profiles
Calculated

Measured

Quantitative Film Analysis - Profiles

Horizontal and vertical profiles of measured data, calculated data, and  index.

Courtesy, Tim Solberg

slide40
40 cm

1.5 cm

4.5 cm

30 cm

Measurement Tissue Equivalent Phantom

slide41
50%

70%

90%

-1.8%

1mm

-3.5%

2mm

Cylindrical Phantom Dose Verification

Measured in Plane of Isocenter

slide42
BANG gel Dosimetry

Courtesy, Tim Solberg

slide45
Periodic IMRT QA

Test Pattern with Leaf Error

Test Pattern after leaf replacement and MLC calibration

slide46
IMRT MU Checks

QUALITY ASSURANCE OF IMRT TREATMENT PLAN

DEPARTMENT OF RADIATION ONCOLOGY ,STANFORD UNIVERSITY SCHOOL OF MEDICINE

PATIENT NAME: xxx, xxxxxxx

PATIENT ID: xxx-xx-xx

TPS PLAN #: 2512

Treatment Machine: LA7

Beam Energy: 15 MV

Calibration Setup: SSD

Delivery Mode: Step and Shoot

Beamlet Size: 1.0 x 1.0 (cm x cm)

Calibration Factor: 1.000

Isocenter Dose Verification Report

Field MU x1 x2 y1 y2 SSD beam-dose

F 180-000 170 7.80 6.80 4.20 16.20 88.79 50.2

F 180-080 118 4.80 6.80 4.20 17.20 82.03 40.6

F 180-145 108 8.00 1.00 5.00 18.00 90.87 24.0

F 180-145a 101 1.00 8.00 4.00 18.00 90.87 17.9

F 180-215 80 9.00 0.00 3.00 18.00 90.49 1.6

F 180-215a 107 2.00 7.00 5.00 18.00 90.49 48.7

F 180-280 115 6.80 5.80 4.20 17.20 81.68 41.2

slide47
IMRT MU Checks

Calculated Isocenter Dose: 224.2 cGy

TPS Isocenter Dose: 221.3 cGy

Percentage Difference: 1.3 (%).

Leaf Sequence Verification Report

Field ID Gantry Angle Correlation Coefficient Maximum Difference

1 0 1.0000 0.5112 (%)

2 80 1.0000 0.4017 (%)

3 145 1.0000 0.6799 (%)

4 215 1.0000 0.7275 (%)

5 280 1.0000 0.4034 (%)

Physicist: _________________________

DATE: 7/20/2001

slide48
Isocenter Setup Verification with DRRs

Anterior Isocenter Verification

slide49
Isocenter Setup Verification with DRRs

Lateral Isocenter Verification

slide52
1 cm

m

1 cm

Tl,m,k

Beam Modulation Patterns

slide53
Velocity Modulation
  • Step and Shoot
slide54
Velocity Modulation

Fluence Profile Required for IMCRT

slide55
Velocity Modulation

.

(x) =  (x) [tA(x) - tB(x)]

B

A

slide56
Velocity Modulation

.

(x) =  (x) [tA(x) - tB(x)]

 (x)

.

(x) =

(x)

(x) = tA(x) - tB(x) > 0

slide57
Gradient Regions Between Extrema

“Velocity” leaf sequencing

slide58
250

200

Leaf A

(x)

150

100

50

0

Leaf B

0

5

10

15

20

25

30

35

40

Position, X (cm)

Interpretation as Trajectories

“Velocity” leaf sequencing

slide59
250

200

150

(x)

100

50

0

0

5

10

15

20

25

30

35

40

Position, X (cm)

Reflection Operation to Remove Time Reversals

(x) = (x) - 

“Velocity” leaf sequencing

slide60
250

200

150

(x)

100

50

0

0

5

10

15

20

25

30

35

40

Position, X (cm)

Translation Operation to Remove Discontinuities

(x) = (x) + (x)

“Velocity” leaf sequencing

slide61
Reflection Operation to Remove Time Reversal

250

200

150

(x)

100

50

0

0

5

10

15

20

25

30

35

40

Position, X (cm)

“Velocity” leaf sequencing

slide62
250

Leaf A

200

150

(x)

100

Leaf B

50

0

0

5

10

15

20

25

30

35

40

Position, X (cm)

Shear Operation to Remove Infinite Velocity

(x) = (x) + x/vmax

“Velocity” leaf sequencing

slide63
 (x)

.

(x) =

(x)

d(x) =

dx

d (x)/dx

.

(x)

Differentiation of Opening Time

“Velocity” leaf sequencing

slide64
Differentiation of Shear Operation

(x) = (x) + x/vmax

d(x) = d(x) + 1

dx dx  vmax

d(x) = 1

dx v(x)

“Velocity” leaf sequencing

slide65
d (x)/dx

1

1

.

+

=

vmax

v(x)

(x)

vmax

=

v(x)

d (x)/dx

.

vmax

1 

(x)

Substitutions to Obtain Velocity Relation

“Velocity” leaf sequencing

slide66
80

70

Leaf A

60

50

Time (sec)

40

30

20

Leaf B

10

0

0

5

10

15

20

25

30

35

Position (cm)

“Velocity” leaf sequencing

slide67
Step and Shoot

“Step-and-Shoot” leaf sequencing

slide68
5.0

4.0

3.0

Intensity Level

2.0

1.0

12

13

0.0

14

-3

Leaf Pair

15

-2

-1

0

+1

+2

+3

x-Position

slide69
-3 -2 -1 0 +1 +2 +3

-3 -2 -1 0 +1 +2 +3

Leaf Pair 12

Leaf Pair 13

-3 -2 -1 0 +1 +2 +3

-3 -2 -1 0 +1 +2 +3

Leaf Pair 14

Leaf Pair 15

slide70
A

B

5

5

1

4

8

4

4

2

5

7

3

Levels

3

3

6

6

2

2

7

1

1

8

0

-3 -2 -1 0 +1 +2 +3

-

-

-

1

2

3

Position

Instance 1

slide71
A

B

5

5

1

4

8

4

4

2

5

7

3

Levels

3

3

6

6

2

2

7

1

1

8

0

-3 -2 -1 0 +1 +2 +3

-

-

-

1

2

3

Position

Instance 2

slide72
B

A

5

5

1

4

8

4

2

4

7

5

3

3

Levels

3

6

7

6

2

2

7

1

1

8

0

-3 -2 -1 0 +1 +2 +3

-

-

-

1

2

3

3

2

1

Position

Instance 3

slide73
B

A

5

5

1

4

8

4

2

4

7

5

3

3

Levels

3

6

7

6

2

2

7

1

1

8

0

-3 -2 -1 0 +1 +2 +3

-

-

-

1

2

3

3

2

1

Position

Instance 4

slide74
B

A

8

5

7

6

6

7

5

5

1

4

4

2

4

7

3

3

Levels

3

2

2

1

1

8

0

-3 -2 -1 0 +1 +2 +3

-

-

-

1

2

3

3

2

1

Position

Instance 5

slide75
B

A

5

5

1

4

8

4

2

4

7

5

3

3

Levels

3

7

6

6

2

2

7

1

1

8

0

-3 -2 -1 0 +1 +2 +3

-

-

-

1

2

3

3

2

1

Position

Instance 6

slide76
B

A

5

5

1

4

8

4

2

4

7

5

3

3

Levels

3

7

6

6

2

2

7

1

1

8

0

-3 -2 -1 0 +1 +2 +3

-

-

-

1

2

3

3

2

1

Position

Instance 7

slide77
B

A

5

5

1

4

8

4

2

4

7

5

3

3

Levels

3

7

6

6

2

2

7

1

1

8

0

-3 -2 -1 0 +1 +2 +3

-

-

-

1

2

3

3

2

1

Position

Instance 8

slide78
B

A

8

5

7

6

6

7

5

1

4

4

5

2

4

7

3

3

Levels

3

2

2

1

1

8

0

-3 -2 -1 0 +1 +2 +3

-

-

-

1

2

3

3

2

1

Position

Instance 9

slide79
B

A

5

1

4

8

4

5

2

4

7

5

3

3

Levels

3

7

6

6

2

2

7

1

1

8

0

-3 -2 -1 0 +1 +2 +3

-

-

-

1

2

3

3

2

1

Position

Instance 10

slide80
B

A

5

5

1

4

8

4

2

4

7

5

3

3

Levels

3

7

6

6

2

2

7

1

1

8

0

-3 -2 -1 0 +1 +2 +3

-

-

-

1

2

3

3

2

1

Position

Instance 11

slide81
B

A

5

5

1

4

8

4

2

4

7

5

3

3

Levels

3

7

6

6

2

2

7

1

1

8

0

-3 -2 -1 0 +1 +2 +3

-

-

-

1

2

3

3

2

1

Position

Instance 12

slide82
B

A

5

5

1

4

8

4

2

4

7

5

3

3

Levels

3

7

6

6

2

2

7

1

1

8

0

-3 -2 -1 0 +1 +2 +3

-

-

-

1

2

3

Position

Instance 13

slide83
B

A

5

5

1

4

8

4

2

4

7

5

3

3

Levels

3

7

6

6

2

2

7

1

1

8

0

-3 -2 -1 0 +1 +2 +3

-

-

-

1

2

3

Position

Instance 14

slide84
B

A

5

5

1

4

8

4

2

4

7

5

3

3

Levels

3

7

6

6

2

2

7

1

1

8

0

-3 -2 -1 0 +1 +2 +3

-

-

-

1

2

3

3

2

1

Position

Instance 15

slide85
B

A

5

5

1

4

8

4

2

4

7

5

3

3

Levels

3

7

6

6

2

2

7

1

1

8

0

-3 -2 -1 0 +1 +2 +3

-

-

-

1

2

3

3

2

1

Position

Instance 16

slide86
Leaf Pair 12

Leaf Pair 13

Leaf Pair 14

Leaf Pair 15

slide87
15

1&2

14

13

12

slide88
15

3&4

14

13

12

slide89
15

5&6

14

13

12

slide90
15

14

13

7&8

12

slide91
15

14

13

9&10

12

slide92
15

14

13

11&12

12

slide93
15

14

13

12

13&14

slide94
15

14

13

12

15&16

slide95
15

14

13

12

17&18

slide96
15

14

12

13

19&20

slide98
Static File Structure

File Rev = G

Treatment = Static

Last Name = Collimator

First Name = M.L.

Patient ID = 555-1212

Number of Fields = 13

Number of Leaves = 52

Tolerance = 0.3

Field = Left Lung

Index = 0.0

Carriage Group = 1

Operator = DNR

Collimator = 0.0

Leaf 1A = 0.00

Leaf 2A = 1.00

Leaf 3A = 2.00

Leaf 4A = 3.00

Leaf 5A = 4.00

Leaf 6A = 5.00

Leaf ...

For conventional MLC treatments, the STATIC mode is used.

slide99
Dynamic Treatment Files

File Rev = G

Treatment = Dynamic dose

Last Name = Patient

First Name = QA

Patient ID = 555-1212

Number of Fields = 12

Number of Leaves = 120

Tolerance = 0.3

Field = Shape1

Index = 0.000

Carriage Group = 1

Operator = DNR

Collimator = 0.0

Leaf 1A = 0.00

Leaf 2A = 1.00

Leaf 3A = 2.00

Leaf 4A = 3.00

Leaf 5A = 4.00

Leaf 6A = 5.00

Leaf ...

  • Identical file format and syntax as for static treatment
  • Each specified leaf pattern is correlated to value of some Clinac parameter
  • “Treatment = ” and “Index = ” file entries determine behavior

Field = Shape2

Index = 0.050

Carriage Group = 1

Operator = DNR

Collimator = 0.0

Leaf 1A = 0.00

Leaf 2A = 1.00

Leaf 3A = 2.00

Leaf 4A = 3.00

Leaf 5A = 4.00

Leaf 6A = 5.00

Leaf ...

Field = Shape3

Index = 0.072

Carriage Group = 1

Operator = DNR

Collimator = 0.0

Leaf 1A = 0.00

Leaf 2A = 1.00

Leaf 3A = 2.00

Leaf 4A = 3.00

Leaf 5A = 4.00

Leaf 6A = 5.00

Leaf ...

slide100
Treatment Field Index

File Rev = G

Treatment = Dynamic Dose

Last Name = John

First Name = Smith

Patient ID = 1234

Number of Fields = 20

Number of Leaves = 80

Tolerance = 0.1

Field = 1 of 20

Index = 0.0000

Carriage Group = 1

Operator = Physicist

Collimator = 180.0

Leaf 1A = 0.00

Leaf 2A = 0.00

Leaf 3A = 3.00

The file must specify the total number of instances that will be used.

slide101
Treatment Field Index

File Rev = G

Treatment = Dynamic Dose

Last Name = John

First Name = Smith

Patient ID = 1234

Number of Fields = 20

Number of Leaves = 80

Tolerance = 0.1

Field = 1 of 20

Index = 0.0000

Carriage Group = 1

Operator = Physicist

Collimator = 180.0

Leaf 1A = 0.00

Leaf 2A = 0.00

Leaf 3A = 3.00

Varian has 52-leaf, 80-leaf, and 120-leaf MLCs. The file must identify the MLC.

slide102
Treatment Field Index

File Rev = G

Treatment = Dynamic Dose

Last Name = John

First Name = Smith

Patient ID = 1234

Number of Fields = 20

Number of Leaves = 80

Tolerance = 0.1

Field = 1 of 20

Index = 0.0000

Carriage Group = 1

Operator = Physicist

Collimator = 180.0

Leaf 1A = 0.00

Leaf 2A = 0.00

Leaf 3A = 3.00

Tolerance parameter is in units of centimeters.

slide103
Treatment Field Index

File Rev = G

Treatment = Dynamic Dose

Last Name = John

First Name = Smith

Patient ID = 1234

Number of Fields = 20

Number of Leaves = 80

Tolerance = 0.1

Field = 1 of 20

Index = 0.0000

Carriage Group = 1

Operator = Physicist

Collimator = 180.0

Leaf 1A = 0.00

Leaf 2A = 0.00

Leaf 3A = 3.00

Dose (MU) fraction ranging from 0.0 (beginning of treatment) to 1.0 (end of treatment).

slide104
Treatment Field Index

File Rev = G

Treatment = Dynamic Dose

Last Name = John

First Name = Smith

Patient ID = 1234

Number of Fields = 20

Number of Leaves = 80

Tolerance = 0.1

Field = 1 of 20

Index = 0.0000

Carriage Group = 1

Operator = Physicist

Collimator = 180.0

Leaf 1A = 0.00

Leaf 2A = 0.00

Leaf 3A = 3.00

Dose (MU) fraction ranging from 0.0 (beginning of treatment) to 1.0 (end of treatment). Leaf positions (cm) are specified as a function of dose fraction.

slide105
File Footer - CRC

Leaf 51B = 2.25

Leaf 52B = 2.25

Leaf 53B = 1.75

Leaf 54B = -6.20

Leaf 55B = -6.20

Leaf 56B = -6.20

Leaf 57B = -6.20

Leaf 58B = -6.20

Leaf 59B = -6.20

Leaf 60B = -6.20

Note = 0

Shape = 0

Magnification = 0.00

CRC = CF95

â

slide106
File Structure

CRC

  • Ensures file data integrity
    • Against file corruption
    • Against unintentional editing outside of authorized data editing tools
  • Uses industry-standard algorithm
slide107
Step-and-shoot

Dynamic delivery

fMU=0.0

1st MLC

position

fMU=0.0

1st MLC

position

step

fMU=0.14

1st MLC

position

fMU=0.14

2nd MLC

position

shoot

fMU=0.14

2nd MLC

position

fMU=0.25

3rd MLC

position

step

fMU=0.25

2nd MLC

position

fMU=0.33

4th MLC

position

shoot

slide109
Planning

180o

140o

Immobilization

Aquaplast

Position Verification

Ximatron

Network File Management

Varis

Plan Verification

Wellhöfer

Structure Segmentation

AcQsim

Inverse Planning

Corvus

CT/MRI Acquisition

PQ 5000

240o

100o

260o

60o

300o

Delivery

20o

340o

Treatment Delivery

C-Series Clinac Dynamic MLC

IMRT Process

slide111
180o

140o

220o

100o

260o

60o

300o

20o

340o

Example

9-field Head and neck Treatment

slide112
80%

90%

55%

slide113
85%

90%

55%

slide114
55%

80%

90%

slide115
80%

90%

55%

slide116
90%

55%

85%

slide117
80%

80%

90%

55%

imrt clinical aims in prostate cancer
IMRT: Clinical Aims in Prostate Cancer
  • Improve conformality; dose escalation
  • Reduce high dose volumes in rectalwall & bladder
  • Reduced small bowel dose in nodal therapy
slide119
80o

280o

320o

40o

0o

Prostate

Nodes

Irradiate Prostate and Nodal Region in Pelvis

imrt prostate cancer
IMRT: Prostate Cancer

SV

CTV

Rectum

Bladder

slide121
GU or GI Toxicity

80

70

60

IMRT-Prostate and

50

Nodes

40

3D-Prostate and Nodes

30

20

10

0

0

1

2

3

P = 0.002

Maximum RTOG Score

Steven Hancock, 2002

slide122
IMRT: Prostate and Nodes

Intensity Modulated Plan

Field Intensity Maps

slide123
70%

50%

60%

80%

30%

20%

40%

10%

90%

40%

slide124
80%

50%

70%

30%

60%

20%

90%

10%

40%

slide125
80%

50%

70%

60%

90%

40%

30%

20%

10%

3d crt v imrt dose delivery prostate and seminal vesicles
3D-CRT v. IMRT: Dose DeliveryProstate and Seminal Vesicles

Organ 3D CRT IMRT

Mean±SD Mean Max Min

Small field:

Prostate: 74.0 ± 1.5 75.7 82.8 65.3

Seminal Vesicles: 50.0 ± 1.0 63.5 79.1 50.1

Large field:

Prostate: 50.0 ± 1.0 55.1 61.8

+ Boost: 70.0 ± 1.4 77.3 87.7

Nodes: 50.0 ± 1.0 54.2 63.5

Steven Hancock, 2002

slide127
P = 0.05

Steven Hancock, 2002

prostate imrt prescription doses
Prostate IMRT: Prescription Doses

MSKCC: Dose to 98 ± 2% of CTV: 81. Gy

Dose to 95% of PTV: 78. Gy

5% of Bladder > 83. Gy

25-30% Rectum > 75.6 Gy

Dose per fraction 1.8 Gy

2 yr risk of GI bleeding: 2% IMRT v. 10% 3D-CRT

Zelefsky et al. Radiother & Oncol 55:241

slide129
IMRT for Gynecological Cancers
  • CTV in a cervical cancer
  • pt s/p hysterectomy
  • Note the posteriorly and
  • laterally placed lymph
  • nodes regions
  • The central region is
  • where the small bowel
  • is now located

Mundt, 2002

slide130
Intensity Modulated-WPRT

100%

90%

70%

50%

Mundt, 2002

slide131
100

90

80

70

60

50

40

30

20

10

0

Grade 0

Grade 1

Grade 2

Grade 3

Acute GI toxicity IM-WPRT vs. WPRT

IM-WPRT

WPRT

P = 0.002

Mundt et al. Int J Radiat Oncol Biol Phys 52:1330-1337, 2002

slide132
Chronic GI Toxicity IM-WPRT vs. WPRT

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

0

1

2

3

IM-WPRT

WPRT

Multivariate analysis controlling for age, chemo, stage and site,

IMRT remained statistically significant ( p = 0.002)

Mundt et al. ASTRO 2002 (New Orleans)

slide133
SPECT-CT Image Fusion

Based on image fusion, highest intensity BM was contoured and used in planning process

Mundt, 2002

slide134
Isodose Comparison – Mid Pelvis

BM-Sparing Plan

100%

90%

70%

50%

100%

90%

70%

50%

IM-WPRT Plan

Mundt, 2002

slide136
Tumor Motion During Respiraton

Courtesy, David Faffray

slide137
Rt Lung

heart

cord

Lt Lung

conclusions
Conclusions
  • IMRT with MLCs can be implemented in the clinic.
  • “Step-and-Shoot” or sliding window leaf sequence with dynamic MLCs can be used for IMRT.
  • Dose distributions can be computed and delivered that provide treatment options for particularly difficult presentations.
  • Imaging is required for variations in daily set-up.
  • Special procedures are required to compensation for respiration motion
slide140
Phenomenological Lyman Model for NTCP

(Note: The Lyman model does not explicitly take fraction size into consideration.)

slide141
Lyman Model for NTCP

For a biological target uniformly irradiated to dose D, the upper limit of integration is expressed as

where is the dose at which the complication probability is 50%, and m is a slope parameter.

slide143
Lyman Model for NTCP

For a uniformly irradiated partial volume,

define

then the upper limit of integration is

slide144
Lyman Model for NTCP

for n > 0

The ntcp curve moves to the right vs. D for partial volume irradiation.

slide145
Lyman Model : non-uniform irradiation
  • Multiple sub-volumes, at different doses di are each translated to effective sub-volumes at some reference dose, such as the maximum dose in a dvh or TD50(1).
  • The relationship must be reducible, i.e., the NTCP of two equal sub-volumes at a given dose must be the same as that of a single volume of twice the size at the same dose.

[Kutcher–Burman]

slide146
Equivalent Uniform Dose

Equivalent Uniform Dose (EUD) is the uniform dose that gives the same cell kill as a non-uniformly irradiated target.

(Niemierko, Med Phys. 24:103-110; 1997.)

slide147
For the simplest model of exponential cell kill, and uniformly distributed cells,

where really means the surviving fraction at dose Dref , which is often taken as 2 Gy

slide148
climbing the TCP curve

where we should be

Tumor Control Probability

where we are

complication curve

Dose

slide149
the unique biology of CaP

Striking similarities with slowly proliferating normal tissues

 Extremely low proportion of cycling cells (< 2.5%)

 Regression following RT is very slow

• PSA nadir times > 1 year

• regression of post-RT biopsies up to 3 years

 Potential doubling times

• median 40 days (range 15 – 170 days)

 PSA doubling times of untreated CaP

• median 4 years

slide150
Radiobiology 101

Linear-Quadratic equation

s = exp(-ad-bd2)

s

cell survival curve

dose

slide151
Radiobiology 101

Fractionated radiotherapy: n x d = D

S  s…s = s n

S = exp(-ad-bd2) n

S = exp(-aBED)

BED = D(1+ d/(a/b))

Biologic

Equivalent

Dose

slide152
Radiobiology 101

BED = D(1+ d/(a/b))units of Gy

a  intrinsic radiosensitivity

b repair of sub-lethal damage

a/b sensitivity to dose-per-fraction

slide153
Radiobiology 101

BED = D(1+ d/(a/b))

a/btumors > a/bNTLE

tumors a/b ~ 10

normal tissue late effects a/b ~ 3

slide154
tumors vs. NTLE

Tumors & early-

responding tissues

a/b ~ 10

Normal

Tissue late

effects

a/b ~ 3

surviving fraction

dose

slide155
tumor vs. NTLE

BED (Gy) = D(1+ d/(a/b))

D / d / n (Gy) BED a/b=10 BED a/b=3

tumorNTLE

74 / 2 / 37 88.8 123.3

70 / 2.5 / 28 87.5 128.3

69 / 3 / 23 89.7 138

64 / 4 / 16 89.6 149.3

NTLE: Normal Tissue Late Effects

slide156
the a/b ratio for CaP

series method a/b 95% CI

Brenner & Hall (1999) LDR / EBRT data 1.5 [0.8 – 2.2]

King & Fowler (2001) LDR / EBRT model 1.8/2

Fowler et al. (2001) LDR / EBRT data 1.49 [1.25 – 1.76]

Brenner et al. (2002) HDR data 1.2 [0.03 – 4.1]

slide157
36.25 / 7.25 / 5 211.5 123.8 62.5

90 / 2 / 45 210 150 108

what if a/b is that low?

D (Gy) / d / n BEDa/b=1.5BEDa/b=3BEDa/b=10

tumorNTLE acute effects

74 / 2 / 37 172.6 123.3 88.8

NTLE: Normal Tissue Late Effects

slide158
why hypo-fractionate?

Hypo-fractionation for CaP will:

 escalate dose biologically

 reduce acute sequelae

 keep same normal tissue late-effects

 reduce overall treatment course

slide159
potential tumor control

100

90%

62%

SRS

hypo-fractionation

50

Tumor Control Probability

43%

0

50

60

70

80

90

100

Dose (Gy)

slide160
Radiobiology

Sensitivity to dose

fraction size

Other Tumors a/b ~ 10

Normal Tissues  a/b ~ 3

Prostate cancer  a/b ~ 1.5

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