PRedictive In-silico Radiomica•Description & Aims: The European Biobanking and BioMolecular resources Research Infrastructure (BBMRI-ERIC) and the European Society of Radiology (ESR)

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

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.

Imaging biomarker vs Pathologic mechanism

2018

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

Typical industrial product development (mean 12 years!!!)

Development of imaging biomarkers:

Translational gaps

“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

Flowchart showing the process of radiomics.

Bin Zhang et al. Clin Cancer Res 2017;23:4259-4269

©2017 by American Association for Cancer Research

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

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

1

2


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


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


-0,212 0,479 0,659


D2D Value


0,193 -0,933 -0,639

p-value (two-tailed) 0,594 <0,0001 0,047

Volume Max


-0,492 0,607


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)


0

100

200

300

400

500

600

700

Year of publication

#of articles

2012 2013 2014 2015 2016 2017 2018

Radiomics

Liquid biopsy

Radiomics vs Liquid biopsy in literature

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

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

• 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

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

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

Biomarkers

Phenotype

Genotype

Radiogenomic

Radiomics workflow

Final report and Decision support

Final report and Decision Support

Genomic report

Pathology report

Radiology report

Diagnostic

report

Liquid biopsy report

Decision Support

Diagnostic

report

Artificial Intelligence

Diagnosis Treatment

Biobanks (Digital twins / Patients models)

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

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.

Documents

PRedictive In-silico Radiomica•Description & Aims: The European Biobanking and BioMolecular resources Research Infrastructure (BBMRI-ERIC) and the European Society of Radiology (ESR)