Multi-Organ Models for Personalized and Evidence Based Medicine - - PowerPoint PPT Presentation

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Multi-Organ Models for Personalized and Evidence Based Medicine - - PowerPoint PPT Presentation

Jan 25, 2023 9 likes •382 views

Multi-Organ Models for Personalized and Evidence Based Medicine Evidence-Based Medicine Kristy K Brock, Ph.D. Physicist, Radiation Medicine Program, Princess Margaret Hospital Assistant Professor, Department of Radiation Oncology, University of

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SLIDE 1

Multi-Organ Models for Personalized and Evidence Based Medicine Evidence-Based Medicine

Kristy K Brock, Ph.D.

Physicist, Radiation Medicine Program, Princess Margaret Hospital Assistant Professor, Department of Radiation Oncology, University of Toronto Manager, Core IV, STTARR

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SLIDE 2

Acknowledgements

MORFEUS Team Adil Al M h Clinicians

  • Laura Dawson

Collaboration

  • David Jaffray
  • Adil Al-Mayah
  • Deidre McGrath
  • Joanne Moseley
  • Charles Catton
  • Cynthia Ménard
  • Michael Milosevic
  • Anthony Fyles
  • David Jaffray
  • Michael Sharpe
  • Doug Moseley
  • Jeff Siewerdsen
  • Andrea McNiven
  • Carolyn Niu
  • Thao-Nguyen Nguyen
  • Anthony Fyles
  • Andrea Bezjak
  • Rebecca Wong
  • Masoom Haider
  • Jeff Siewerdsen
  • Robert Weersink
  • Martin Yaffe

A F t Thao Nguyen Nguyen

  • Michael Velec
  • Andrew Zasowski
  • Steve Gallinger
  • Maha Guindi
  • Aaron Fenster

Funding Disclosures

  • NIH R01, NCI Canada – Terry Fox Foundation
  • Ontario Institute for Cancer Research, Cancer Care Ontario Research Chair
  • Elekta Oncology Systems, Philips Medical Systems, RaySearch Laboratories
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SLIDE 3

Image-Guided Treatment

New Tumor Position! New Tumor Position!

Sh

Begin

hift

Initial g Intervention Initial Set-up

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SLIDE 4

Why integration is essential…

DCE DWI

  • Diagnostic imaging

provides the optimal soft tissue contrast and tumor detail Ti

  • +++Time
  • Diagnostic imaging at

h F i t l each Fx is not always necessary

MRSI T2

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SLIDE 5

What if more than just geometry i h i ? is changing?

  • In room imaging

allows identification of response during Tx

  • Adapt to these

h changes

  • Offline diagnostic

i i + d imaging + dose accumulation + integration integration

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SLIDE 6

…greater than the sum of the parts

  • Unique information from each

CT MR

  • Unique information from each

image

  • Geometric discrepancies

prevent accurate correlation of prevent accurate correlation of local information

  • Resolving these can lead to

improved understanding of improved understanding of disease and therapeutic response

– Multi-Modality Image Integration Multi Modality Image Integration and Validation – Physiological Modeling – Image Guided Therapeutics age Gu ded e apeut cs – Response Assessment

Histology CT/PET

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SLIDE 7

Why Deformable?

Shrinking Filling Breathing Shrinking Filling Breathing

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SLIDE 8

Registration:Completing the Loop

Accurate Target Definition: Multi-modality Image Registration Pl d Accurate Response Assessment: Follow-up Image Accurate Motion Assessment: Planned Dose Dose Predicted p g Registration and Correlation with Tx Motion Assessment: Multi-instance Image Registration Dose Effect Predicted Dose Accurate T G id Delivered Dose Tumor Guidance: Online Image Registration

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SLIDE 9

Planned = Delivered

?

Planned Delivered

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SLIDE 10

Planned = Delivered

?

Pl d

Target Definition

Planned Delivered

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SLIDE 11

Accurate Target Definition

coronal

Prior to Deformable Registration

coronal sagittal Before After Deformable Registration GTV Volume CT = 13.9 cc MR = 6.7 cc ΔVol = 7.2 cc (52%)

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SLIDE 12

Removing Confounding Geometry

CT-exhale CTGRV MR-exhale

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SLIDE 13

In Vivo Image Validation

Triphasic CT Images Multiple Sequence MR Images FDG-18 PET Images S i l E i i f Li L b Surgical Excision of Liver Lobe Fresh Specimen MR Imaging Specimen Fixation Fixed Specimen MR imaging Fixed Specimen MR imaging Specimen dissection Histological Analysis of Tumor

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SLIDE 14

Pathology @ Fresh w/ in Pathology w/ Fixed S i Aligned Fresh w/ in Vivo Liver Specimen Aligned Aligned g Tumor Comparison Aligned Pathology @ Fixed w/ Fresh Specimen

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SLIDE 15

Planned = Delivered

?

Planned

Target Definition Internal Motion

Delivered

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SLIDE 16

Physiological Modeling

  • Complex differences

Complex differences between structures and patients

  • Understand

physiologic processes

– Interplay with image analysis Influence on delivering – Influence on delivering therapeutic intent

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SLIDE 17

Multi-Organ Model

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SLIDE 18

Sliding Interface

  • A. Al-Mayah, K. Brock
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SLIDE 19

Planned = Delivered

?

Targeting

Planned

Target Definition Internal Motion Targeting

Delivered

Target Definition

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SLIDE 20

Image Guided Therapeutics

  • Speed and Accuracy

is essential

  • Tissue loss/therapy

response i compromises algorithms I t i t ti

  • Intra-intervention

images lack detail

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SLIDE 21

Precision Image-Guidance

Resolve Geometric discrepancies

New Tumor Position! Planning CT [w contrast] CBCT [w/o contrast]

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SLIDE 22

Accurate Tumor Guidance

12 i i 6 h 12 Liver Patients: 6 Fx Each Rigid Reg → Deformable Reg g g g

dLR dAP dSI abs(dLR) abs(dAP) abs(dSI) AVG

  • 0.03
  • 0.01
  • 0.02

0.07 0.10 0.08 SD 0 10 0 16 0 14 0 07 0 12 0 12

Δ Tumor

SD 0.10 0.16 0.14 0.07 0.12 0.12 Max 0.28 0.65 0.52 0.36 0.65 0.57 Min

  • 0.36
  • 0.64
  • 0.57

0.00 0.00 0.00 Median

  • 0 03

0 01 0 00 0 05 0 06 0 04

  • 33% (4/12) Patients had at least 1 Fx with a

Median 0.03 0.01 0.00 0.05 0.06 0.04

( ) ΔCOM of > 3 mm in one direction

  • 10% of Fx had a ΔCOM of > 3 mm in 1 dir.
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SLIDE 23

Planned = Delivered

?

Targeting

Planned

Target Definition Internal Motion Targeting

Geometric

Delivered

Target Definition

Dosimetric

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SLIDE 24

Planned = Delivered

?

Targeting

Planned

Target Definition Internal Motion

Geometric

Delivered

Target Definition

Dosimetric

Magnitude?

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SLIDE 25

Change in Delivered Dose

Planned Dose Predicted Dose

EXH INH

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SLIDE 26

Data

14 SBRT Li P ti t A R d f 40 G i 6 F 14 SBRT Liver Patients: Avg Rx dose of 40 Gy in 6 Fx

  • Liver, GTV, critical

normal tissues, external surface external surface, and spleen were contoured on the exhale scan.

  • External surface,

li d l liver, and spleen were also contoured

  • n the inhale CT
  • e

a e C scan.

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SLIDE 27

Change in Predicted Dose

  • Normal Tissue

Complication Probability:

13 t 9%

7 Gy Reduction in mean tumor dose

– -13 to 9%

  • Change in Max dose to

critical structures

10 Gy Reduction in Max Dose

critical structures

– Up to 10 Gy

  • Change in Min dose to

g tumor

– -8 to 2 Gy

This is PREDICTED – not DELIVERED!

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SLIDE 28

Change in Delivered Dose

Planned Dose Predicted Dose Delivered Dose Tx

EXH EXH INH INH

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SLIDE 29

Changes in Dose: Accumulated through

2000 1500 1000

Accumulated through 6 Fx

1000 500 500

  • 500
  • 1000
  • 1500 cGy

Static (exhale) Difference Accumulated

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SLIDE 30

Results [Gy]

Dstat vs Dpred Dpred vs Dacc GTV

  • 1 5 to 3 7
  • 4 2 to 2 3

GTV MIN dose 1.5 to 3.7 4.2 to 2.3 Stomach

  • 4.0 to 0.4
  • 2.9 to 5.1

MAX dose Esophagus MAX dose

  • 0.4 to 5.2
  • 3.4 to 0.1

MAX dose Duodenum MAX dose

  • 7.6 to 0.0
  • 0.3 to 5.7

Liver MEAN dose

  • 1.0 to 1.2
  • 1.4 to 0.9

Kidneys MEAN dose

  • 3.8 to 0.0
  • 0.9 to 3.9
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SLIDE 31

Planned ≠ Delivered

Targeting

Planned

Target Definition Internal Motion

Geometric

Delivered

Target Definition

Dosimetric

Magnitude

Clinical Results

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SLIDE 32

Response Assessment

  • Longitudinal studies

Pl i CT F ll U CT

  • Longitudinal studies

enable understanding of disease response

Planning CT Follow-Up CT

  • Gross volume/structure

changes confound correlation between correlation between information time points

  • Inhibit correlation with

locally delivered therapies

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SLIDE 33

Accurate Response Assessment

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SLIDE 34

Accurate Response Assessment

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SLIDE 35

Volume Change vs. Delivered Dose

15 20

%]

5 10

Change [

A B

5

n Volume

C D E F

  • 10
  • 5

tive Mean

G H I

  • 20
  • 15

Relat

  • 20

1000 2000 3000 4000 5000 6000 7000

Dose [cGy]

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SLIDE 36

Summary

  • Advances in multi-modality, multi-temporal,

and online imaging is generating large amounts of data amounts of data

  • To fully understand this information,

integration and correlation is essential integration and correlation is essential

  • Geometry (physiological, therapeutic

response, intervention) confounds integration, therefore limiting knowledge

  • Deformable modeling can remove these

confounding factors confounding factors

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SLIDE 37

Summary

  • Multi-modality integration can improve

d t di f di t t understanding of disease extent

– Correlation with pathology can validate this information and aid in development of new imaging p g g techniques, contrast agents, and probes

  • Physiological modeling can improve our

understanding of these confounding factors understanding of these confounding factors

– Aid in reducing them during therapeutic intervention

  • Improvements in image guidance through

Improvements in image guidance through accurate incorporation with pre-Tx information

  • Longitudinal studies with response assessment

g p can improve understanding of therapeutic response and normal tissue toxicities