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Multi-parametric multi-modality imaging for prognostic and
Multi-parametric multi-modality imaging for prognostic and

Multi-parametric multi-modality imaging for prognostic and - PowerPoint Presentation

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Multi-parametric multi-modality imaging for prognostic and - Description

Dimitris Visvikis Director of Research National Institute of Health and Medical Research INSERM LaTIM UMR 1101 Brest France Cancer Oncology Gold standard for diagnosis ID: 540620 Download Presentation

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Presentation on theme: "Multi-parametric multi-modality imaging for prognostic and"— Presentation transcript

Slide1

Multi-parametric multi-modality imaging for prognostic and predictive modeling in oncology

Dimitris Visvikis

Director

of

Research

National Institute of

Health

and

Medical

Research

(INSERM),

LaTIM

, UMR 1101

Brest, FranceSlide2

Cancer

Oncology

Gold standard for diagnosis

Other applications of interest:

Radiotherapy planning

Prognosis, therapy assessment

PET/CT multimodality imaging

Quantification

uptake measurement

functional tumor volume

uptake distribution analysis

IntroductionSlide3

Activity concentration indices

Semi-quantitative index of concentration of activity in a given ROI:

ROI choise

Robustness

Reproducibility

Repeatibility

No ROI

Robustness

Reproducibility

Repeatibility

ROI fixed

Robustness

Reproducibility

RepeatibilitySlide4

Dynamic acquisitions:

Analysis of dynamic sequence of images

Arterial sampling

Mathematical modelling

Kinetic behaviour of FDG in a particular ROI

Input function

Parameters adjustment

Acquisition protocols Slide5

Other popular PET image derived

indices

Tumour / background Metabolically active tumor volume (MATV, cm3)Total lesion glycolysis (TLG = MATV ×

SUVmean)

SUV

max

SUV

peak

MATV

SUV

mean

TLGSlide6

Image noise

(differences in acquisition protocols)

Partial Volume effects

(spatial resolution)

Voxel size

(4-5 mm)

Tumour heterogeneity

Functional

volume segmentationSlide7

Nestle U et al., J Nucl Med, 2005

T

hresholding

approachesSlide8

Probability of observation

P(Y|X)

Spatial correlation probability

P(X)

1

2

3

4

5

6

7

8

9

[0 , 1]

(

μ

,

σ

)

tumor

(

μ

,

σ

)

Physiological background

(

μ

,

σ

)

Fuzzy transitions

Final map

Hatt M et al. IEEE TMI 2009; Int J Rad Onc Biol Phys 2010

FLAB:

Fuzzy

Localy

Adaptive

BayesianSlide9

Predictive

and prognostic value of functional volume for different cancer models Initial FDG PET scan

Better patient stratification and management (

survival, therapy response) Example: 45 esophageal cancer patient

Therapy response

Survival

Hatt et al EJNM 2010

Accurate

MATV:

prediction

,

prognosisSlide10

Lungs

Heart

Spinal Cord

PTV

FLAB

PTV

THRES

Accurate

MATV:

radiotherapy

Le Maitre A et al., Phys Med Biol, 2012Slide11

Tumor characterisation: geometric forms

Hypotheses: associated with tumor aggresivity, metastasis potential…

morphological, functional and/or morpho-functional:Form descriptors(a)sphericity, solidity, convexity, rectangularity, excentricity…Intra-tumor PET activity distribution form : independent prognostic value demonstrated onH&N cancer1Lung cancer2Sarcoma3

CT

PET - FDG

Fusion PET/CT

Apostolova I,

et al

.

BMC Cancer

. 2014

Hofheinz F,

et al

.

Eur J Nucl Med Mol Imaging

. 2015

Eary J,

et al.

J Nucl Med 2008Slide12

18

FLT PET during chemo-radiotherapy

1

Hoeben

BA,

et al

. J Nucl Med. 2013Majdoub M, et al. (submitted) 2016

High sphericity

and low SUV

Low sphericity

et high SUV

Time (months)

12

24

36

4860

72Survival probability (%)

Predictive value of sphericity

2

Hazard ratio = 6,7 (p<0,0001)

(with SUV only HR = 4,1, p=0,02)

(with sphericity only HR = 4,2, p=0,01)

Tumor

characterisation

: geometric formsSlide13

Activity conc, volume, … Global mesures

Intra-tumor activity distribution heterogeneity characterisation

Tumor

heterogeneity

characterisationSlide14

Textural features can quantify different types of voxels intensity variability in the tumor volume, at different scales

M.

Tixier F et al, J Nucl Med, 2011

Tumor

heterogeneity

characterisationSlide15

Jensen. Introductory Image Processing 3rd ed. Upper Saddle River, NJ: Prentice-Hall 2005

Histogram analysis

No spatial information

Co-occurrence

Matrices

Local spatial information

Regional spatial information

Complexity

Versatility and potential

Tumor

heterogeneity

characterisationSlide16

PET/CT image derived features

Parameters for tumor heterogeneity characterisation

Textural features can quantify different types of voxels intensity variability in the tumor volume, at different scales.

Chicklore, et al.

Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis. Eur J Nucl Med Mol Imaging 2013

First-order parameters:

a = b = c = d

Second-order features:

a

# (

b

=

c

= d).Third-order features:a # b #

c #

dSlide17

Heterogeneity

quantification

1. Tumor delineation [1-3]

[1] Hatt, et al. IEEE Trans Med Imaging. 2009;28:881-893

[2] Hatt, et al.

Int J Radiat Oncol Biol Phys. 2010;77(1):301-8[3] Hatt, et al. Eur J Nucl Mol Imaging. 2011;38:663-672

Co-occurrence matrix

Size-zone matrix

2. Heterogeneity quantification using textural features [4-6]

[4] Tixier, et al. JNM. 2011;52:369-378

[5] Tixier, et al. JNM. 2012;53:693-700

[6] Hatt, et al. EJNM. 2013;40:1662-1671

2. Quantization Slide18

M.

Tixier F et al, J Nucl Med, 2011

Tumor

heterogeneity

characterisationSlide19

Example

: NSCLC

NSCLC without metastasis (n=100)Stade I (n=18), II (n=29), III n=(53)Males (n=78), Females (n=22)Age 64±9Treatment :Surgery (n=47)Chemotherapy (n=82)Radiotherapy (n=51)

18

19

9

12

41

1

0

Surgery

Chemo

Radio

Tumor

heterogeneity

characterisationSlide20

12

24

36

48

60

3 year survival ~ 30%

Médian 18 months

Global survival

Time (months)

Survival probabiity (%)

Tumor

heterogeneity

characterisationSlide21

12

24

36

48

60

Stade I : médiane -

Stade III : médian 18 months

p = 0.008

Stade II : médian 21 months

Time (months)

Survival probability (%)

Tumor

heterogeneity

characterisationSlide22

Multimodality tumor characterisation

Complimentarity

: functional volume – PET heterogeneity

M. Hatt,

et al. J Nuc Med 2015

N=34, 17 deaths (50%)

Median 45 months

N=38, 25 deaths (66%)

Median 21 months, HR=2.3

N=29, 27 deaths (93%)

Median 9 mo, HR=3.8

Probabilité de survie (%)

Time (months)

Volume PET (cm

3

) logarithmic scale

Entropy

1

2

3

4Slide23

PET/CT image derived features

Heterogeneity characterization

What about CT or other modalities (MRI)?Several studies have investigated the use of textural features or other metrics to characterize CT or MRI tumor volumes1

Colon cancer

Contrast CT1. Davnall, et al. Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging 2013Slide24

PET/CT image derived features

Heterogeneity characterization

What about CT or other modalities (MRI)?

Rectal cancer

T2-weighted MRI1. Davnall, et al. Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging 2013Slide25

PET/CT image derived features

Heterogeneity characterization

What about CT or other modalities (MRI)?

Pre-treatment (baseline)

Post-treatment (neoadjuvant chemo)

1. Davnall, et al.

Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging 2013

Esophageal cancer - CTSlide26

Multi-parametric & Multi-modality

Prognostic

model PET/CT (116 NSCLC patients)Stage, PET volume and heterogeneity, CT heterogeneity

Desseroit

et al Eur J Nucl

Med 2016Slide27

Perspectives

Future disease characterization with imaging

Multi tracers

MRI T2, diffusion, elastography…

Metabolism, hypoxia, proliferation

Multi modalities

PET / CT

PET / MRI

Temporal data

Therapy follow-up, adaptive

Tracer kinetics

Full tumor « signature » in the multi modal spectrumSlide28

Perspectives

Actual

ParadigmProposed Paradigm

Ex.

Astronomy

images

(3 observations)

FusionSlide29

Perspectives

Future disease characterization

Combination and correlation of multimodal tumor signatures with other informationInformation fusion → Building predictive modelsSlide30

Conclusions

PET can provide

useful information for early response assessmentextract more information from PET requiresstandardization, image enhancement accurate, robust, reproducible automated delineationnew features such as texture or shapeIn the future:Multi-center trials and large patient cohortscombination of omics data with multimodal anatomo-functional characterization (tumor “signature”)→ efficient predictive models for improved patient managementSlide31

Thank you for your attentionSlide32
Slide33

PET tumor heterogeneity: reproducibilitySlide34

34

M. Hatt,

et al

.

J Nuc Med 2015

Corrélation = 0.98

Corrélation = 0.93

Corrélation = 0,56

Local entropy (co-occurrence matrix)

Volume (log, cm

3

)

Quantization

= 128

13 matrices

+ mean

Quantization

= 64

13 matrices

+ mean

Quantization

= 64

1 matrice

PET tumor heterogeneity: minimum volumeSlide35

PET tumor heterogeneity

Method

Textural features analysis Validation Reproducibility, robustness vs. reconstruction, partial volume effects, tumor delineation [6-8] ApplicationPrediction of therapy response/patient’s outcome from baseline scan[1-5] What does PET heterogeneity represent?Hypothesis: associated with tumor physiology (glucose metabolism, hypoxia, angiogenesis, …)

[1]

Tixier F et al. J Nucl Med. 2011; 52: 369-378[2] George J et al. ISBI 2012[3] Cook GJR et al. J Nucl Med. 2013; 54: 1-8[4] Willaime JMY et al. Phys. Med. Biol. 2013; 58: 187-203[5] Huang B et al. AJR Am J Roentgenol. 2012; 199: 169-174[6] Galavis PE et al. Acta Oncologica. 2010; 49: 1012-1016

[7]

Tixier F et al. J Nucl Med. 2012; 53: 693-700[8] Hatt M et al. Eur J Nucl Med and Mol Imaging. 2013 ahead of print