PRedictive In-silico Radiomica•Description & Aims: The European Biobanking and BioMolecular...
Transcript of PRedictive In-silico Radiomica•Description & Aims: The European Biobanking and BioMolecular...
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Radiomica
Emanuele Neri
Department of Translational Research
University of Pisa
Italian Society of Medical and Interventional Radiology (SIRM)
SIRM Foundation
H2020 EU PROJECT | Topic SC1-DTH-07-2018 | GA: 826494
PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers
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Biomarker (definition)
Ideally must be a measurement!
A characteristic that is objectively measured and
evaluated as an indicator of normal biologic processes,
pathogenic processes (abnormal biologic processes), or
biological responses to a therapeutic intervention
Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: preferred definitions and
conceptual framework. Clin Pharmacol Ther. 2001 Mar;69(3):89-95.
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Imaging biomarker vs Pathologic mechanism
2018
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Quantitative vs qualitative imaging biomarkers
Qualitative or categorical imaging biomarker
A biomarker that cannot be expressed as a quantity value. All ordinal biomarkers are examples.
Pathological grading systems BI-RADS LI-RADS PI-RADS C-RADS Clinical TNM Stage
A biomarker whose magnitude is expressed as a quantity value.
Volume, diameter, density, intensity, perfusion, diffusion, radiomics features, dose parameters, etc
Quantitative imaging biomarker
NATURE REVIEWS | CLINICAL ONCOLOGY
VOLUME 14 | MARCH 2017 | 175
H2020 EU PROJECT | Topic SC1-DTH-07-2018 | GA: 826494
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Typical industrial product development (mean 12 years!!!)
Development of imaging biomarkers:
Translational gaps
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“Radiomics” refers to the extraction
and analysis of large amounts of
advanced quantitative imaging
features with high throughput from
medical images.
Radiomics: a new biomarker
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Flowchart showing the process of radiomics.
Bin Zhang et al. Clin Cancer Res 2017;23:4259-4269
©2017 by American Association for Cancer Research
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PR; 40%
SD; 40%
PD; 20%
RECIST CRITERIA
Materials and methods: feasibility study In progress analysis of more 45 cases:
Exosome mRNA
#6 #3
Population Disease Treatment
NSCLC III-IV STAGE
Pembrolizumab Nivolumab
Liquid Biopsy
CTCs circulating tumour-derived nucleic acids
INF-ƴ PD-L1 TNF-α
E. Neri, M. Del Re, Danesi R. University of Pisa
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QUIBIM report for tumour texture
analysis.
Every CT slices has a similar report
Materials and methods
Acquisition of CT images and ROI segmentation with Quibim software
Texture analysis and features extraction
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E. Neri, M. Del Re, Danesi R. University of Pisa
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Results: RECIST vs Radiomics
T test results: 4 of 27 Features are significant (p<0,05)
Features Clinical event N Mean St Dev p-value
Cluster Prominence Value PR 4 943.974,5 59.723,8
0,012 SD+PD 6 578.074,5 240.339,3
Cluster Shade Value PR 4 -12.204,8 1.354,3
0,034 SD+PD 6 -7.738,8 3.250,6
Information Measure Of Correlation2 Value PR 4 0,9 0,0
0,077 SD+PD 6 0,8 0,1
Volume Value PR 4 1,5 0,7
0,068 SD+PD 6 3,7 2,3
E. Neri, M. Del Re, Danesi R. University of Pisa
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Pearson's Correlation Analysis: Features vs Liquid Biopsy
Parameters
Features Statistics PD-L1 INF-ƴ TNF-α
Information Measure Of Correlation1 Value
Pearson's correlation coefficient
-0,159 -0,593 -0,631
p-value (two-tailed) 0,661 0,071 0,05
Information Measure Of Correlation2 Value
Pearson's correlation coefficient
-0,212 0,479 0,659
p-value (two-tailed) 0,557 0,161 0,038
D2D Value
Pearson's correlation coefficient
0,193 -0,933 -0,639
p-value (two-tailed) 0,594 <0,0001 0,047
Volume Max
Pearson's correlation coefficient
-0,492 0,607
p-value (two-tailed) 0,148 0,063 0,442
Results: Radiomics vs Liquid biopsy
Inverse proportionality
Direct proportionality
Inverse proportionality
Direct proportionality 0,275
• Programmed death-ligand 1 (PD-L1)
• Interferon gamma (IFNγ)
• Tumor necrosis factor alpha (TNFα)
E. Neri, M. Del Re, Danesi R. University of Pisa
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Year of publication
#of articles
2012 2013 2014 2015 2016 2017 2018
Radiomics
Liquid biopsy
Radiomics vs Liquid biopsy in literature
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Radiomics: workflow in oncology
Precision Medicine
Personalized Medicine
Radiomic
Biomedical Images
Artificial Intelligence
Quantitative features
extraction Multi-omics data processing
Biobanks
• Risk prediction • Treatment Response
Assessment • Treatment Simulation • Prognosis
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Fields of Applications of Radiomics
• Oncologic Imaging (90%)
• Neurodegenerative disease
• Other (polycystic kidney disease, etc)
• Imaging Modalities • Ultrasound
• Computed Tomography • Magnetic Resonance • PET/CT
• PET/MR • X-ray Mammography and Thomosynthesis
• Potentialities • Prediction of response to treatment • Risk assessment • Aggressiveness vs tumor biology
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• 120 patients with pathologically-confirmed Head and Neck squamous cell carcinoma of which 101 HPV/P16 positive
• HPV with P16 expression is associated with an increased overall survival
• median follow-up time was of 49.3 months • Oncoradiomics research software using Matlab R2012b
• 544 radiomics features • Grouped into (I) tumor intensity, (II) shape, (III) texture and (IV)
wavelet feature
• The radiomics signature showed prognosis capacity for predicting 5-year survival in the whole population with an AUC of 0.67 (95% CI, 0.58–0.76)
chemoradiotherapy (CRT) bioradiotherapy (BRT)
Radiomic signature as predictive biomarker
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Adenocarcinoma, lymphoma, and GIST • Retrospective study • Arterial and venous phases were
evaluated
• Arterial phase • of 47 patients with adenocarcinoma
(AC), 15 GIST and 5 lymphoma
• Venous phase • 48 patients with adenocarcinoma
(AC), 17 GIST and 8 lymphoma
• MaZda 4.6 texture analysis software • developed within the COST
(European Cooperation In The Field Of Scientific And Technical Research) projects B11 and B21
Misclassification rates: lower in the arterial phase Best differential diagnosis • GIST vs lymphoma • Gastric cancer vs lymphoma
Texture analysis as diagnostic biomarker
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Haralick’s texture analysis is a statistical technique, known as spatial gray-level dependence matrix method: second-order statistics of pixels at different spacings and direction of adjacent or nearest-neighbor pixels
5 features were able to differentiate between complete responder and non-responder patients affected by rectal cancer (all p < 0.05):
Texture analysis as predictive biomarker
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Biomarkers
Phenotype
Genotype
Radiogenomic
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Radiomics workflow
Final report and Decision support
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Final report and Decision Support
Genomic report
Pathology report
Radiology report
Diagnostic
report
Liquid biopsy report
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Decision Support
Diagnostic
report
Artificial Intelligence
Diagnosis Treatment
Biobanks (Digital twins / Patients models)
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Biobanche delle immagini
• Le biobanche di imaging possono essere definite come "banche dati organizzate di immagini mediche associate ai biomarcatori di imaging (radiologia e non solo), condivisi tra più ricercatori e collegati ad alter biobanche".
• Le grandi biobanche possono divenire una raccolta di pazienti digitali (Avatar o Gemelli digitali) utilizzabili dall'intelligenza artificiale per simulazioni di progressione di malattia, stima della prognosi e della risposta ai trattamenti
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ESR – BBMRI Memorandum Of Understanding
• Description & Aims: The European Biobanking and BioMolecular resources Research Infrastructure (BBMRI-ERIC) and the European Society of Radiology (ESR) established an official collaboration in November 2015, signing a Memorandum of Understanding, which will facilitate development in the integration of imaging data with biobank databases.
• The main goals of the collaboration are to promote the importance and visibility of biomarkers, to coordinate efforts to establish a European imaging biobank infrastructure, and to ensure its linking to existing biobanks.
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