Automated image

analysis: rescue for

diffusion-MRI of threat to

radiologists?Dr. Erik Ranschaerterik.ranschaert@gmail.com

Automated Image Analysis: what is it about?

Automatic reading of images by A.I.

Big Data & Deep Learning

Radiological & non-radiological images

Imaging Biomarkers

Radiomics

Radiogenomics

Imaging Biobanks

Structured Reporting

Personalised Disease Evolution

• What do we want to know for each patient?– Is a tumor present?– Is it aggressive?– Is it focally treatable?– Is it radiosensitive?– Will it metastasize?

• Evidence based medicine

– has succeeded in defining effective therapeutics for large populations

– is lacking when applied to small subpopulations (“precision medicine”) – is insufficient when applied to the individual level (“personalized medicine”).Krishnaraj A et al. The future of imaging biomarkers in radiologic practice.

J Am Coll Radiol 2014;11:20-23

Genetics Clinical manifestation TreatmentEtiology

LINK

Shift to Personalised Medicine

Predisposition to disease development and responsiveness to treatment depends on information enclosed in genoma.

Biobanks are crucialLong-term storage and retrieval of tissue samples is needed in biobanks.

Human biobanks include biological material of healthy subjects and patients with specific pathologies, most cancer-related.

Data from radiological imaging are not included in the human biobanks.

Association between phenotype (imaging) and genotype will become possible by means of imaging biomarkers.

Quantitative medical imaging with identification of imaging biomarkers represents a crucial part of personalised medicine.

Several imaging biobank projects have been started.

Registration, collaboration, connection and coordination between ALL biobanks is essential.

Radiomics vs. RadiogenomicsRADIOMICS

Automated extraction, storage and analysis of a large amount of imaging features (morphology and imaging biomarkers)

Cloud-based deep-learning techniques

Conversion of images to mineable data, in order to create accessible databases (biobanks)

...and to reveal quantitative predictive or prognostic associations between images and medical outcomes.

RADIOGENOMICS

Imaging findings can be considered as the phenotypic expression of a patient, which can be correlated to the genotype.

Radiogenomics is the extension of radiomics, aiming to identify a link between genotype and phenotype imaging.

“To explore the full potential of radiomics, we have to enter the era of big data, team science and, most of all, the new age of imaging bioinformatics” Dr. Hricak

Imaging BiobanksQIBA

Founded in 2007 by RSNA

Mission: to improve the value and practicality of quantitative imaging biomarkers by reducing variability across devices, patients, and time.

Support from volunteer committee members from academia, medical device, pharmaceutical and other business sectors, and government.

4 Modality-based committees: Q-CT, Q-MR, Q-NM, Q-US

10 Biomarker committees

EIBALLFounded by ESR in March 2015Coordination of ESR activities concerning

imaging biomarkers

Merging of activities of

ESR Subcommittee on Imaging Biomarkers

ESR Working Group on Personalised Medicine

ESR-EORTC Working Group

QIBA10 Biomarker committees

DWI as biomarker of cancerToronto 2008: Consensus and Recommendations on use of

DW-MRI as cancer imaging biomarker

DWI-MRI should be tested as imaging biomarker in clinical trials

DWI-MRI measurements should be compared with histologic indices

Standards for measurement, analysis and display are needed

Annotated data should be made available

MRI vendors should be engaged in process

Task force of experts should established

Padhani AR, Liu G, Mu-Koh D, et al. Diffusion-Weighted Magnetic Resonance Imaging as a Cancer Biomarker: Consensus and Recommendations. Neoplasia (New York, NY). 2009;11(2):102-125.

Advantages of DWI-MRIImproved tissue characterisation (malignant vs. benign)Monitoring of treatment after chemotherapy or radiationDD of post-therapeutic changes from residual active tumorDetection of recurrent cancerPrediction of treatment outcome Tumour stagingDetection of lymph node involvement

Remaining challenges for DWI to assess cancerDivergence among and between vendors on data measurements/analysis No accepted standards for measurements and analysisMultiple data acquisition protocols depending on body part and usage of dataQualitative to quantitative assessmentsLack of understanding of DW-MRI at a microscopic levelIncomplete validation and documentation of reproducibilityDivergent nomenclature and symbolsLack of multicenter working methodologies, accepted quality assurance (QA)

standards, and physiologically realistic phantoms

Biomarkers - ratio metrics

SI ratio’s

Hybrid imaging: PET/MRI vs. PET/CT

Current Status of Hybrid PET/MRI in Oncologic ImagingAndrew B. Rosenkrantz et al., American Journal of Roentgenology 2016 206:1, 162-172

85-year-old man with prostate cancer who underwent initial staging workup that showed metastases to bone.Standard bone scan shows one metastatic lesion in left acetabulum (solid arrow) and small subtle lesion in upper thoracic spine (dashed arrow) which was attributed to degenerative spine disease. ANT = anterior view, POS = posterior view.

NaF PET/MR image obtained 3 weeks later reveals nine metastatic lesions (circles), showing higher sensitivity of PET/MRI.

Lesion in T2 spinous process on PET/MRI (dashed arrows) corresponds to small subtle lesion on bone scan

Radiologists of the future

Medisch Contact, 5 dec 2016

Healthcare in Europe, 28 nov 2016

“Machine learning can discover whether certain image data point towards certain diseases; it can discover correlations as yet unknown, or confirm suspected correlations respectively by analysing the large amounts of data.”

Are biomarkers and A.I. threatening radiology?“Automatic reading of images by A.I. is not developed enough to replace the trained and experienced observer with his/her ability to interpret and judge during image reading sessions”

“Nevertheless, subjective, and therefore, qualitative interpretations are observer dependent and highly variable, and variability inevitably degrades outcomes in healthcare in general”

“Extracting objective, quantitative results from medical images is one way to reduce the variability...and thus will improve patient outcomes”.

Siegfried Trattnig, Chair of EIBALL

12 Opinion Leaders’ ideas

Paul M. Parizel

Geraldine McGinty

Lluis Donoso Bach

Luis Marti-Bonmati

Nicola Strickland

Koenraad Mortele

Wiro Niessen

Charles Kahn

Marion Smits

Peter Mildenberger

Mario Maas

Vasileios Katsaros

Redefinition of radiology

Copyright Dr. E. R. Ranschaert

• Multidisciplinary integration, expert consultancy, therapy guidance• Disease-focused approach, gatekeeping, lean approach

Workflow management

• Deal with errors, reduce failure and mistake• Measure outcomes & improve performanceQuality management

• Embrace power of digital networks, cloud services, big data, deep learning & Artificial IntelligenceImaging Informatics

• Structured reporting (+ coding), multimedia, actionable reports• Patient-oriented approach, lay-language, open notesCommunication

Precision medicine• Functional imaging & imaging biomarkers, radiomics & radiogenomics,

integrated diagnostics• Image-guided interventions