towards a decision-aid tool in the case of chemotherapy

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Towards a decision-aid tool in the case of chemotherapytreatment for low-grade glioma

Sophie Wantz Mézières, Meriem Ben Abdallah, Marie Blonski, Jean-MarieMoureaux, Yann Gaudeau, Luc Taillandier

To cite this version:Sophie Wantz Mézières, Meriem Ben Abdallah, Marie Blonski, Jean-Marie Moureaux, Yann Gaudeau,et al.. Towards a decision-aid tool in the case of chemotherapy treatment for low-grade glioma. 17thAnnual Conference of the European Network for Business and Industrial Statistics, ENBIS’17, Sep2017, Naples, Italy. �hal-02055217�

Towards a decision-aid tool in the case ofchemotherapy treatment for low-grade glioma

Interdisciplinary project:

CRANJean-Marie Moureaux

Yann GaudeauMeriem Ben Abdallah (PhD 12/2016)

new PhD from 10/17 ?

IECL - Inria-team BIGSSophie Mezieres

C.H.R.U. NancyLuc Taillandier (neuro-oncologist)

Marie Blonski (hospital practitioner,PhD)

11/09/17 ENBIS-2017 Napoli

Medical context: Diffuse Low-Grade Glioma

asymptomatic ↪→few symptoms ↪→functional prognosis↪→bad vital prognosis ↪→

Grade(ANOCEF)

Low-GradeIII

High-GradeIIIIV

←↩ slow growth: 4 mm/year ED

←↩ quick growth : > 8 mm/year

I Cerebral tumor

I median age : 35 years, survival median : 15 years, (Duffau & al 04,Cancer)

I 750 new cases by year in France (Blonski 11, these de medecine)

I diffuse aspect (ANOCEF 12)

I slow evolution with 4 mm/year spontaneous linear growth ofequivalent diameter (Mandonnet 03, Ann Neurol)

I no treatment ⇒ anaplasic transformation (evolution to high-grade)(Bracard, Taillandier & al 06, Jour Radio)

Therapeutic strategy

surgeryand/or chemotherapy TMZ, PCV

and/or radiotherapy

⇓Aim: control of the tumorevolution in order to extendpatient’s lifetimetumor diameter = good predictorof the evolution of DLGGs(Mandonnet 08 Neurosurgical rw)

⇓

longitudinal supervision of tumor’sdiameter evolution

⇓volume estimation from MRIdatasets: security ?

Our aimCrucial questions posed by the oncologists to be answered:

I identifying subgroups of patients for chemotherapyI determining the best time to initiate or stop chemotherapyI evaluating the optimal time to perform surgery, or otherwise

radiotherapy

personnalised treatment ⇒ tool for the current medical practice

proposed procedure to determinethe best time to stop chemotherapy

First phase: tumor volume estimation

still in progressReliability of the methods used to estimate the volume :

I 3-diameter method

I software-based reconstruction from manual segmentation

I semi-automatic method

Questions: reliability and reproducibilityImpact on the segmentation of the individual practitioner, and ofthe factors: years of experience, medical speciality ?with respect to the others methods ?

3-diameter Method

I 2 largest diameters in axialplane and the largest one insagittal or coronal plane

I ellipsoidal approximation ofthe tumor volume:V ' D1×D2×D3

2

Software-based reconstructionfrom manual segmentation

I digitized MRI, calculus oftumor volume from manualsegmentations by Osirixsoftware

Subjective test of reproducibility

Materials:

I one expert neuroradiologist selected 12 longitudinal MRI inthe axial plane from 9 patients: 11 FLAIR-weighted (3Cube-FLAIR), 1 T2-weighted.

I Osirix software

I Living Lab PROMETEE, TELECOM Nancy (standardizedenvironment, ITU-BT.500-13 recommendations)

I panel of 13 participants

I test procedure: visual test,training dataset,delineation of the 12 exams(5 mn. break in half-time)

Results:

I variability between participants: one-way analysis of variance ⇒ nosignificant impact on the tumor volume variable

I variability by specialty and years of experience: Fisher’s exact teston the standard deviation of the standard volume ⇒ no significantimpact on the tumor volume variable

I Objective metrics: Values of COV, AI, pixellic IV confirm thestatistical results

COVj =σjx j

;AI(i,i ′),j = 1− 2 | xi,j − xi ′,j |xi,j + xi ′,j

; IV = 1−AMi ∩ AM′

i

AMi ∪ AM′i

T2 Cube Flair Cube Flair Cube Flair

0

25

50

75

100

125

Examen 1 Examen 2 Examen 3 Examen 4 Examen 5 Examen 6 Examen 7 Examen 8 Examen 9 Examen 10 Examen 11 Examen 12

Examens

Vo

lum

es (

cm

3)

Participants

moyenne

participant1

participant 2

participant 3

participant 4

participant 5

participant 6

participant 7

participant 8

participant 9

participant 10

participant 11

participant 12

participant 13

Comparaison with the 3-diameter methodMaterials:

I preceeding 12 exams in the axial plane + sagittal(11) or coronal(1)plane associated

I 1 participant from the test, close to the average of all thesegmented tumor volumes (ground truth)

I Wilcoxon signed-rank test on all tumor volumes : with a significancelevel of 5 %, we do not reject the equality hypothesis between3-diameter and manual segmentation methods

Advantages Disadvantages

3D quick, easy for overestimate the volume,pre-surgery tumor difficult in post-treatment

Osirix reproducible time-consumingaccurate, easy

First works on semi-automatic segmentation

I Partnership with LIO Laboratory (C.H.R.U. Montreal, Pr J.De Guise, post-doc MBA)

I semi-automatic segmentation algorithm from Zhou & al(EMBC 16)

I Procedure: initialization by manual segmentation on 6 MRI inorder to generate a 3D mesh, splitting of the mesh in axialslices then 2D delineation in each interest slide, Level-setalgorithm applied on the 2D delineations

I MRI from test, ground truth = mean of the segmentedvolumes

I Computation time: 15-185 s

Results:

I Wilcoxon signed-rank test rejects equality hypothesis of the twomethods

I tendency to under-segmentation

I good result for Cube-Flair 4 and 9

I segmentation algorithm applied to knees, considered improvementson this application domain

Advantages Disadvantages Recommendations

3D quick, easy for overestimate the volume, no digitisedpre-surgery tumor difficult in post-treatment MRI, pre-surgery

Osirix reproducible time-consuming all MRI except Cube-Flairaccurate, easy

S-AUTO S quick, good results post-treatment Cube-Flairon Cube-Flair under-segmentation

I comparison between methods to estimate the tumor volume

I medical recommendations to assure a more secure follow-upof the tumor evolution

I Trials with ITK SNAP: method strongly dependant oninitialisation parameters (based on a priori good knowledge ofthe tumor: localization, shape)

Second phase: modelling of DLGG under chemotherapy

I tumor diameter: good predictor of the evolution of DLGGs(Mandonnet 03, Ann Neurol)

I state-of-the-art models: microscopic (Ribba & al 2012, Rojas &al 2016) or macroscopic (Swanson& al 2000, Mandonnet & al2003) approach

I Our approach: data-driven, very simple to implement

I Database of 55 patients from both Nancy and Montpellier, FranceUniversity Hospitals (CHRU)

I inclusion criteria: established histologic diagnosis of DLGG, first-lineTMZ chemotherapy, assessment of the tumor volume with Osirixand manual segmentation method

I 2 models based on classical regression technics, with AICc criteriafor model selection

I 38 following a linear model, 4 an exponential model, 13 noidentifiable model (hieratic behaviour)

2 examples

-

4.4

4.8

5.2

Months

Dia

mete

r (c

m)

0 11 19 25

6 initial points: learning datasetReal clinical dataPrediction intervalRegression line

r2 = 0.8750266

6 initial points: learning datasetReal clinical dataPrediction intervalRegression line

r2 = 0.8750266

linear model

D = bO + b1T + ε

Warning message: alarmingregrowth of the tumor, the

patient is not reponding anymore to treatment

0 5 10 15 20 25

5.0

5.5

6.0

6.5

7.0

7.5

Months

Dia

me

ter

(cm

)

6 initial points: learning datasetReal clinical dataPrediction interval

exponential model

D = aO − a1e−a2T + ε

Message: expected decrease ofthe tumor, improved response to

treatment

I encouraging but multifactorial problem

I IDH mutation, 1p19q codeletion are now considered asprognostic and/or predictive factors (Louis & al 2016, 2016

OMS classification)

I identifiable biological, anatomopathological factors ?

I in the database of 55: only 19 with known mutation andcodeletion statuses

⇓

Necessity of a bigger database

I more patients, more molecular’s factors, more MRIs

I difficulty in data collection

Conclusion and Future work

I First two simple models that operates on 42 patients,encouraging, need to be enhanced

I Perspective 1: Bigger database30 variables selected by neuro-oncologists: 10 patient

variables, 13 treatment variables, 7 tumor variables

partnership with Montpellier, NENO base

variable selection, unsupervised and/or supervised

classification...

I Perspective 2: complete phase 1 with semi-automatic method(future, partnership with Montreal LIO) ; second subjectivetest to assess intra-participant variability

I Perspective 3: practical tool with additionnal imageprocessing features

References

Meriem Ben Abdallah, Marie Blonski, Sophie Wantz-Mezieres, YannGaudeau, Luc Taillandier et Jean-Marie MoureauxPredictive models for diffuse low-grade glioma patients underchemotherapyStatistical evaluation of manual segmentation of a diffuselow-grade glioma MRI dataset38th Annual International Conference of the IEEE Engineering inMedicine and Biology Society, EMBC-16, Aug 2016, Orlando, Florida,United States

On the relevance of two manual tumor volume estimation methodsfor diffuse low-grade gliomas, in revision IET Healthcare TechnologyLetters