davies multicentre mrs study · – of prognosis to improve treatment stratification, ... • data...
TRANSCRIPT
Experiences of an ongoing multicentre study
investigating the use of 1H MRS to aid the clinical
management of childhood brain tumours
Davies NP, Arvanitis TN, Auer D, French A, Grazier R, Grundy R, Hargraves D, Howe FA, Jaspan T, Lateef S, Leach MO, MacPherson L, Natarajan K, Orphanidou E, Payne
G, Saunders D, Sun Y, Peet AC
Background
• Cancer: commonest cause of death from
disease in children.
• MRI techniques probing tumour biology provide
potential non-invasive biomarkers and surrogate
markers of response to targeted agents.
• Small number of children treated in one centre
⇒ Robust evaluation of imaging biomarkers can only take place in a multi-centre setting.
Participating Centres
• University of Birmingham / Birmingham Children’s Hospital
• Institute of Cancer Research / Royal Marsden Hospital
• University of Nottingham / Queen’s Medical Centre
• University College London / Great Ormond Street Hospital
• St George’s University London
• Alderhey Children’s Hospital, Liverpool
Programme Development
• Functional Imaging Group of Childrens Cancer and Leukemia Group (CCLG)
• MRS methods for characterisation of childhood brain tumours (5+ years collaboration)
• CRUK / EPSRC 5 year programme combining MRS with diffusion / perfusion MRI applied to specific scientific hypotheses and management questions in childhood cancer
Aims and Objectives
• To develop functional imaging techniques in parallel with
novel molecularly targeted agents in collaboration with the
CCLG Therapeutics Steering Group and Biological Studies
Group.
• To develop and evaluate imaging biomarkers:
– for improved non-invasive classification
– of prognosis to improve treatment stratification,
– for treatment planning
– predictive of drug response to tailor treatment to the individual,
– for early monitoring of drug response to optimise treatment.
Database Development
• Robust database design
• Clinical, surgical, histological and MRI/MRS data
• Auto archiving, complete log of any changes
• Easy to use remote data entry systems
– PHP based web application
– Index tables to tie interface to schema
• Allow for future data storage requirements
• Platform independent software
CCLG Database Structure
Patient
Trial Study
Histology / Diagnosis
Presentation
MRI
MRS
Surgery
Web application GUI GUI to DB Index Table Database
Hospitals
Oncologists
WHO diagnosis
Other lists
(Yes/No/Unknown/etc)
Web to Database link:
Users
Remote Data Entry
QA: Localisation Phantom
Inner Cube: (pH 7.6)
0.1 M Li lactate
0.1 M Cr
2cm
2cm
15cm
0.3cm
2cm
21cm
Outer Volume: (pH 8.3)
0.15 M Na acetate
QA Results: Localisation
Siemens 1.5T Siemens 1.5T
GE 1.5TPhilips 1.5T
Accrual
• Over 320 cases from 6 centres
– Approx 200 from BCH + 120 from other centres
– Follow-up MRS available in many cases
• Data collection issues
– Non-standard data formats for MRS raw data complicates
data capture and transfer
– Incomplete datasets (lack of clinical info, MRS raw data)
– Adherance to protocol not 100%
– Growing prevalence of 3 T scanners
– Compatibility of MRS?
Study 1: Brain Stem Tumours
• Important clinical group due to difficulties of surgery and often very poor prognosis
– Diagnosis often by radiological / clinical criteria
• Accrual: 32 cases from 4 centres
– Central radiological review
– Diagnostic categories:
• Diffuse pontine glioma (DPG) / diffuse glioma (DG)
• Focal low grade glioma (LGG)
• Focal high grade glioma (HGG)
• Uncertain
Study 1: BST Results
• DPG+DG had higher Cr / Cho (P<0.01) and mI / Cho
(P<0.01) ratios vs focal tumours
• LGG had higher mI / Cho vs HGG (P<0.05)
• DGs had higher lipid levels vs DPGs (P<0.05).
• Lipid levels in DGs were comparable to focal HGG
(higher than LGG)
Study 2: Low Grade Gliomas
• Background
– LGG: 40-50% of CNS tumours
– Diverse group, many follow more indolent course but
some have poorer prognosis
– Many decision points for treatment
– Need for non-invasive aids to clinical decision-making
• Aims
– Develop non-invasive biomarkers for characterisation and
monitoring of LGG in childhood using 1H MRS
Study 2: LGG Accrual
• Challenge of collating and validating more detailed
clinical information = 2 centres so far
• Total of 110 patients with pre-treatment MRS
– 34 patients failed QC, 12 patients had missing data.
– 64 patients for further analysis (57 BCH, 7 QMC)
• Many diagnostic categories and
sub-categories
LGG Mean Spectra
Pilocytic
Astrocytoma
Unbiopsied optic
pathway glioma
Diffuse
astrocytoma
Tectal plate
glioma
DNET Ganglioglioma
Pilocytic Astrocytomas
Linear Discriminant Analysis of 3 PA sub-groups
Study 2: Summary
• Significant metabolic differences found between LGG based on location of tumour, NF1 status and metastatic status
– Diagnostic aid
• Promising prognostic indicators found for sub-groups of LGG
– Potential guidance for treatment decisions and monitoring
Study 3: Prospective Classification
• Purpose
– To evaluate MRS as a diagnostic tool for paediatric
brain tumours
• Method
– Use multi-centre MRS data as an independent,
“pseudo-prospective” test of classifiers trained
with single-centre MRS data
Training dataset:
* acquired Mar 2003 – Apr 2008 with biopsy
MRS acquisition:
• Following standard pre- & post-contrast MRI
• PRESS (TR 1500ms / TE 30ms), Bandwidth 1 Hz/point
• Single-Voxel placed inside solid/enhancing portion
• Voxel vol. 3.4 ml (NSA 256) – 8 ml (NSA 128)
Methods: Classifier Training
Centre No. of test cases Scanners
BCH 83 * Siemens & GE 1.5 T
Methods: Classifier Training
Classifier Methodology: (Davies et al NMR Biomed 2008)
• MRS processing and metabolite profile estimation: LCModel™(Provencher, Magn Reson Med 1993)
• QC filter: SNR > 4; Linewidth(H2O) < 10 Hz; artefacts
• Principle Components Analysis (PCA)
• Linear Discriminant Analysis (LDA)
• Cross-validation (Leave-one-out and Bootstrap)
• Two binary classifiers: (Davies et al, ESMRMB 2008)
1) PNET vs glial
2) Low Grade (WHO I&II) vs High Grade (WHO III & IV)
Test dataset:
* May08–Dec09 / * QC + diagnosis / * QC + biopsy
* 1.5 or 3T, TE (23-40ms), TR (1500-2000ms)
• QC filter: same as for training
Methods: Classifier Testing
Centre No. of test cases Scanners
BCH 49* / 39* / 33* Siemens & GE 1.5 T
QMC 35 * Philips 1.5 T & 3 T
GOS 8 Siemens 1.5 T
RMH / SGH 10 Philips 1.5 T
Combined 92*
Glial vs PNET Classifier
Results: Prospective Classifier
Test cohort
Classifier Accuracy
PNET vs Glial
Low Grade vs High
Grade
BCH 90% (N=31) 87% (N=39)
External Centres 79% (N=42) 81% (N=37)
Combined 84% (N=73) 85% (N=76)
Conclusions
• Multicentre studies involving 1H MRS are possible and
can be clinically useful, but challenges remain
• Multivariate analysis of MRS is a promising tool for
enhanced clinical management of childhood brain
tumours
• Further development of multicentre MRS data
collection and analysis is justified
– Combination with perfusion and diffusion MRI
Acknowledgements
• All members of the CCLG Functional Imaging
Group
• Radiographers and data managers in
participating centres
• Funding bodies:
– CRUK, EPSRC, MRC, NIHR, eTUMOUR