Characterization of lung metastasis based on radiomics ... Colloquia 2018/Sli… · Authors: F....

Authors: F. Ambrogi2, P. E. Colombo1-2, C. De Mattia1-2, D. Lizio1-2, M. Pecorilla1-2, R. Ronza1, A. Sartore Bianchi1-2

1 ASST GOM Niguarda2 Università degli Studi di Milano

Supervisor:S. Siena1-2 , A. Torresin 1-2, A. Vanzulli 1-2

Characterization of lung metastasisbased on radiomics features: issues related to acquisitionparameters and to lesion segmentation

RADIOMICS PROJECT TEAMA multidisciplinar equipe

MedicalPhysics

Oncology

Radiology

ICT

Statistics

RADIOMICS

‘’ The high-throughput extraction of large amounts of image features from radiographic images ,,

Lambin, European Journal of Cancer 48 (2012)

‘’ The underlying hypothesis of Radiomics is that advanced image analysis on conventionaland novel medical imaging could capture additional information not currently used, andmore specifically, that genomic and proteomics patterns can be expressed in terms ofmacroscopic image-based features. If proven, we can infer phenotypes or gene–proteinsignatures, possibly containing prognostic information, from the quantitative analysis ofmedical image data. ,,

RADIOMICS

R.N. Sutton, E.L. Hall, Texture meausures for automatic classification of pulmonarydisease, IEEE Trans. On Computers, C-21: 667-676, July 1972

Y.P. Chien, K.S.Fu, Recognition of X-Ray picture patterns, IEEE Trans. on Syst. Man. AndCyber., SMC-4: 145-156, March 1974.

E.L. Hall et al., Computer classification of pneumo-coniosis from radiographs of coalworkers, IEEE Trans. On Biomed. Engg., BME 22: 518-527, Nov. 1975

RADIOMICS

R.N. Sutton, E.L. Hall, Texture meausures for automatic classification of pulmonarydisease, IEEE Trans. On Computers, C-21: 667-676, July 1972

Y.P. Chien, K.S.Fu, Recognition of X-Ray picture patterns, IEEE Trans. on Syst. Man. AndCyber., SMC-4: 145-156, March 1974

E.L. Hall et al., Computer classification of pneumo-coniosis from radiographs of coalworkers, IEEE Trans. On Biomed. Engg., BME 22: 518-527, Nov. 1975

The workflow of radiomicsLambin, Clinical Oncology 14 (2017)

Acquisition ROIFeauturesExtraction

AnalysisPredictive

Model

The workflow of radiomicsLambin, Clinical Oncology 14 (2017)


AnalysisPredictive

Model

OUR workflow of radiomics

Multi-ModalityTumor Tracking

DICOM node:CTRT-Struct

IBEX RFs SET + clinical data


AnalysisPredictive

Model





Process time consuming!

A challenge: we need a large amount of studies, but each case requires a lot of time!


AnalysisPredictive

Model





The optimization is a continuos process!

A FIRST APPLICATION:A RETROSPECTIVE STUDY

Lung Lesion

Lepidic Growth

BronchioalveolarCarcinoma

Pancreas Metastasis

Solid nodule

Pancreas Metastasis

Colon Metastasis 97 pz

80 pz

25%

75%

92 pz


SCOPE

IDENTIFICATION OF RADIOMICSFEAUTURES THAT CAN CHARACTERIZELUNG METASTASIS OF PANCREATIC ANDCOLON ORIGIN IN ORDER TODESCRIMINATE THE PRIMITIVE TUMOR


PANCREAS COLON

N° patients selected by Oncologist 92 97

N° patients excluded by Radiologist 9 4

Gender 39F - 44M 32F - 61M

Age 72 [61-74] 63 [54-68]

NAIVE 47 24

After CT 34 47

ND 2 22



AnalysisPredictive

Model

I PHASE: ACQUISITION


• SCANNER• VOLTAGE• CTDI• RECONSTRUCTION FILTER• SLICE THICKNESS• COLLIMATION• TIMING (WASH-IN/WASH-OUT)

CT ACQUISITION

SCAN REGION


MODALITY: CT

ANATOMIC DISTRICT: THORAX

STUDY COMMON NAMES: - CT CHEST WO- CT CHEST W/WO- CT NECH CHST ABD PELVIS MULTIPH WO & W IVCON- CT CHEST ABD PELVIS WO & W IVCON

To comparison the results, we have considered the same CT procedure for all the patients:For each patient, we have chosen the first CT exam after the metastasis diagnosis, that was a totalbodyarterioso protocol.

We have a spread of the time between the diagnosis and the CT exam under analysis!

TOTAL BODY ARTERIOSO PROTOCOL

I PHASE: ACQUISITION PROTOCOL : TOTAL BODY ARTERIOSO

SMDC PHASE ARTERIOSA PHASE VENOSA PHASE TARDIVA PHASE

20-25 s

70-80 s

about 3 min


Protocollo Tomografo Studi Fasi kVCTDIvol DLP Collimazione

PitchStrato

mGy mGy·cm mm mm

Totalbodyarterioso

Siemens

Sensation 6436%

SMDC 3.0 B30f

120 6,8 252 28,8

1,2 3

Arteriosa 3.0 B30f

1208,3 417

19.2

99%Venosa 3.0 B30f

1207,5 323

99%Tardiva

3.0 B30f120

7,5 32599%

Siemens

SomatomDefinition

19,5%

Addome SMDC

3.0 B30f

120 88% 9,9 32319,2

0,9 3

140 22% 15,1 621,5

Arteriosa 3.0 B30f

100 74% 8,5 39819,2

120 22% 15,2 744

Venosa 3.0 B30f

100 67% 8,8 343,528,8-19,2

120 26% 11,2 483,5

Tardiva 3.0 B30f

120 88% 9,2 37728,8

140 12% 13,8 660,5

Philips

Brilliance 6410%

Addome

SMDC120 6 221

40 0,58 3Arteriosa 1

80 70% 3,7 201

100 29% 4,8 254

Venosa100

99,8%4,5 203

Tardiva100

99,8%4,4 186

CT CHEST ABD PELVIS WO & W• 4606 exams• 3731 executed with the

totalbody arterioso protocol

2016


Protocollo Tomografo Studi Fasi kVCTDIvol DLP Collimazione

PitchStrato

mGy mGy·cm mm mm

Totalbodyarterioso

Siemens

Sensation 6433%

Addome SMDC

3.0 B30f

1206,7 233 28,8

1,2 3

99%

Arteriosa 3.0 B30f

1207,9 460

19.2

99%

Venosa 3.0 B30f

1207,5 315

99%

Tardiva 3.0 B30f

1207,5 304

99%

Siemens

Somatom Definition

15%

Addome SMDC

3.0 B30f120 90% 9,7 302 19,2

0,9 3

Arteriosa 3.0 B30f

100 76% 8,2 46819,2

120 21% 14,6 828

Venosa 3.0 B30f

100 67% 8,6 35828,8-19,2

120 26% 12 505,5

Tardiva 3.0 B30f

120 89% 9,2 367,528,8

140 11% 14,3 664

CT NECK CHST ABD PELVIS MULTIPH WO & W IVCON• 2596 exams• 1558 executed with the

totalbody arterioso protocol

2016

I PHASE: ACQUISITION RECONSTRUCTION FILTER

B 30 f

B 70 f

Arteriosa 1

Parenchima

SIEMENS PHILIPS

I PHASE: ACQUISITION PATIENTS SELECTION BASED ON CT PARAMETER

PROTOCOL TOTALBODY ARTERIOSO

PHASE ARTERIOSA

FILTER HIGH RESOLUTION

SLICE THICHNESS 3 mm


PANCREAS COLON

N° patients selected by Oncologist 92 97

N° patients excluded by Radiologist 9 4

N° patients excluded by Physicist 16 9

N° patients analyzed 67 84

Gender 39F - 44M 32F - 61M

Age 72 [61-74] 63 [54-68]

NAIVE 47 24

After CT 34 47

ND 2 22


PANCREAS COLON

PHILIPS BRILLIANCE 64 19 18

SIEMENS SENSATION 64 25 41

SOMATOM DEFINITION 120 kV 11 7

SOMATOM DEFINITION 100 kV 10 15

SOMATOM DEFINITION 140 kV - 1


PARTIAL VOLUME EFFECT

SLICE THICKNESS

I PHASE: ACQUISITION SLICE THICKNESS

PARTIAL VOLUME EFFECT

Also the slice thickness isimportant!We have considered only thoseexams with reconstructedthickness of 3mm!

I PHASE: ACQUISITION TWO (TOO) ISSUES!

ARTIFACT CAUSED BY PATIENT MOTION

I PHASE: ACQUISITION TWO (TOO) ISSUES!

HELICAL RECONSTRUCTION &

POISSON NOISE



AnalysisPredictive

Model

II PHASE: SEGMENTATION MULTI-MODALITY TUMORTRACKING APPLICATION

Semi-automatic segmentation tool:Region growing starting from a seed point selected by the radiologist on the image

II PHASE: SEGMENTATION MULTI-MODALITY TUMORTRACKING APPLICATION

Semi-automatic segmentation tool:Region growing starting from a seed point selected by the radiologist on the image

RAPID SEGMENTATION

SMART ROI

II PHASE: SEGMENTATION

For each point the local metric can beadpated to the image around the point.

Partition of the image in twohomogeneus

regions.

SEGMENTATION

N CONTROL POINTS + NON-EUCLIDEAN KERNEL


UNIFORMITY 2.5

ADAPTABILITY 200


UNIFORMITY 2.5

ADAPTABILITY 200

WL -600

WW 1600


ADAPTABILITY 0 UNIFORMITY 5



ADAPTABILITY 200 UNIFORMITY 3.5


II PHASE: SEGMENTATION NODULES MORPHOLOGY

SOLID NODULE WITH SMOOTH MARGINS


SOLID NODULE WITH SPICULATED MARGINS


SOLID NODULE EXCAVATED


AIR SPACE PATTERN NODULE:nodule with lepidic growth,that seems to correlate moreto a pancreatic origin, insteadof colon one.


The distinction is notalways so clear!

IS THIS A AIR SPACEPATTERN NODULE?


…or a partial volume effect?

II PHASE: SEGMENTATION NODULES MORPHOLOGYThe distinction is not always so clear!

CARCINOMATOSIS LYMPHANGITIS CASE:patient excluded!

II PHASE: SEGMENTATION NODULES NEAR TO OTHER STRUCTURES:contouring not so easy!

IMPORTANT MANUAL CORRECTION BY THE RADIOLOGIST

II PHASE: SEGMENTATION NODULES NEAR TO OTHER STRUCTURES:segmentation parameters

Changing the windowing:

WL -150 WL -600 WW 590 WW 1600


ADAPTABILITY 50UNIFORMITY 2.5


ADAPTABILITY 200UNIFORMITY 5



DIFFERENT PARAMETERS DIFFERENT CONTOURING DIFFERENT RFs VALUES

WE HAVE TRIED TO KEEP THE SAME CONTOURING CRITERIA FOR ALL NODULES:NOT ALWAYS POSSIBLE!

357 NODULES: 171 PANCREAS - 186 COLON


AnalysisPredictive

Model


UNIVARIATE

III PHASE: RADIOMICS FEAUTURES EXTRACTION

IMAGING BIOMARKER EXPLORER

OPEN INFRASTRUCTURE SOFTWARE PLATFORM

Written: Matlab 2011 ac/c++

Alpha version: Hunter et al. (Med. Phys. 40, 2013)

1.0 beta version: stand-alone without the requirement of MATLAB license

Reference:Zhang et al., IBEX: an open infrastructure software platform to faciliate collaborative work in radiomics, Med. Phys. 42 (3), March 2015

III PHASE: RADIOMICS FEAUTURES EXTRACTION

IBEX CT NUMBER: Hounsfield Unit +1000

IBEX: RFs categories 10 CATEGORY

SHAPE

INTENSITY HISTOGRAM

INTENSITY DIRECT

GRADIENT ORIENT HISTOGRAM

INTENSITY HISTOGRAM GAUSS FIT

GRAY LEVEL COOCCURENCE MATRIX 25

GRAY LEVEL COOCCURENCE MATRIX 3

GRAY LEVEL RUN LENGHT MATRIX

NEIGHBOUR INTENSITY DIFFERENCE 25

NEIGHBOUR INTENSITY DIFFERENCE 3

5 RFs FAMILIES

IBEX: RFs categoriesINTENSITY HISTOGRAM INTENSITY DIRECT

CATEGORY: GRAY LEVEL COOCCURENCE MATRIX

2.5D3D

IBEX: RFs categories CATEGORY: GRAY LEVEL COOCCURENCE MATRIX 2.5D3D

CATEGORY: GRAY LEVEL RUN LENGHT MATRIX 25IBEX: RFs categories

RFs: acquisition CT parameter dependenceIntensity

HistogramIntensity

DirectGLCM 2,5D GLRLM

Skew

ne

ss

Loca

l Std

Min

Au

to

Co

rre

lati

on

Clu

ste

r Sh

ade

Sum

Ave

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Sum

V

aria

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Hig

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un

Emp

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Low

GL

Ru

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ph

asis

Lon

g R

un

H

igh

GL

Emp

has

is

Lon

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un

Lo

w G

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ph

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Sho

rt R

un

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GL

Emp

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Co

lon

Br64Mean -0,646 36 1924 -3451 86 6825 1829 0,001 2645 0,001 1674 0,001

RSD 84% 63% 19% 97% 11% 21% 21% 57% 26% 61% 22% 56%

Sens64Mean -0,49 64,3 1726 -3184 80,7 6076 1672 0,0018 2001 0,002 1599 0,0018

RSD 96% 44% 23% 130% 13% 25% 24% 96% 34% 91% 23% 97%

Def120kV

Mean -0,53 69,6 1740 -3239 81,3 6139 1705 0,0028 2027 0,003 1632 0,0028

RSD 82% 46% 23% 82% 14% 24% 22% 213% 26% 207% 22% 215%

Def 100kV

Mean -0,35 74,3 1634 -2425 78,5 5740 1587 0,0019 1878 0,0021 1520 0,0019

RSD 124% 30% 25% 135% 14% 26% 26% 182% 27% 183% 26% 183%

Pan

cre

as

Br64Mean 0,05 82,3 1295 -637 68,6 4505 1233 0,0039 1493 0,0108 1177 0,003

RSD 898% 52% 36% 556% 20% 38% 37% 246% 37% 452% 37% 184%

Sens64Mean -0,2 86,6 1685 -1240 78,8 5984 1601 0,0018 2145 0,0019 1529 0,0017

RSD 265% 57% 48% 293% 23% 52% 43% 148% 109% 138% 41% 151%

Def 120kV

Mean 0,06 101,8 1237 -71,9 67,6 4298 1210 0,0049 1364 0,0053 1173 0,0048

RSD 560% 25% 31% 4364% 17% 33% 31% 136% 32% 136% 31% 136%

Def 100kV

Mean -0,33 65,3 1691 -3759 79,5 5935 1621 0,0014 1967 0,0015 1542 0,0013

RSD 113% 42% 28% 107% 0,2 29% 29% 82% 31% 82% 29% 82%

ANOVA 0,04 1,E-13 0,002 0,65 0,004 0,002 0,02 0,05 7,E-11 0,09 0,27 0,04



AnalysisPredictive

Model

MULTIVARIATA

PRINCIPAL COMPONENT ANALYSIS:preliminary results

Purpose: FEATURE REDUCTION

Linear transformation of the original variables in order to obtain a new cartesiansystem where the component that explain the main variance is projected on thefirst axes, the second component on the second axes, etc.

DATA:• Only a nodule for each patient• V> 50 voxels• No RF of Shape and Gauss Fit Histogram• Missing vriables→ 5 patients excluded: 135 patients (PANCREAS 60 – COLON 75)• 865 variables for each patient

Prof. F. AmbrogiCampus Cascina Rosa


11PC: THE HEATMAP


11PC: THE HEATMAP

Columns: pricipal componentsRow: patients


11PC: THE HEATMAP


The algorithms shifts the rows inorder to create a local uniformity.The order of PC explains theclustering criteria.


11PC: THE HEATMAP


The algorithms shifts the rows inorder to create a local uniformity.The order of PC explains theclustering criteria.

We have obtained 9 clustering, butwe can note two main groups, at thetop of the dendogramma.


11PC: THE HEATMAP

These two gruops follow the changein colour gradiation of PC1 and PC2.


THE CLUSTERING: what about the clusters obtained? Is it a good partition?

CLUSTER 1-4 1-5 5-9 6-9

PANCREAS 27% 50% 73% 50%

AIR SPACE PATTERN 13% 27% 87% 73%

COLON 51% 72% 49% 28%

None of the clusters seems to match with the partitionbased on the CT acquisition parameters!

RFs that allow to descriminate pancreas and colon metastasis:

26 nodules (135)19 pancreas (60)7 colon (75)10 air space pattern (15): 1 colon - 9 pancreas

25 nodules (135)19 pancreas (60)6 colon (75)11 air space pattern (15): 1 colon - 10 pancreas

AUTOCORRELATION < 1200

SKEWNESS > 0.04

0 nodules: CLUSTER 3-41 nodule: CLUSTER 5

24 nodules: CLUSTER 6-9

2 nodules: CLUSTER 3-41 nodule: CLUSTER 523 nodules: CLUSTER 6-9

WORK IN PROGRESS

• CLUSTERING OPTIMIZATION

• ANALYSIS OF THE LUNG PRIMITIVE TUMOR TO COMPARISON WITH METASTASIS ORIGINATED BY PANCREAS AND COLON

• OPTIMIZATION OF RADIOMICS WORKFLOW

• OPTIMIZATION OF PATIENTS RESEARCH

• PREDICTIVE MODEL DEFINITION

THANKS FOR YOUR ATTENTION

THANKS FOR YOUR ATTENTION

THANKS TO NIGUARDA TEAM


Can radiomics answer to this question?NO AIR SPACE PATTERN NODULE

Characterization of lung metastasis based on radiomics ... Colloquia 2018/Sli… · Authors: F....

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