automated image analysis: rescue for diffusion-mri of threat to radiologists?
TRANSCRIPT
Automated image
analysis: rescue for
diffusion-MRI of threat to
radiologists?Dr. Erik [email protected]
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