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