Automated image analysis: rescue for diffusion-MRI of threat to radiologists?
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This document discusses the role of automated image analysis and imaging biomarkers in personalized medicine, highlighting the importance of genetics and biobanks in understanding disease. It contrasts radiomics and radiogenomics, emphasizing the potential for improved diagnostic accuracy through big data and deep learning. Additionally, it addresses the challenges and advantages of diffusion-weighted MRI (dwi-mri) as a cancer imaging biomarker and the future integration of machine learning in radiology.
Automated image analysis: rescue for diffusion-MRI of threat to radiologists?
- 1. Automated image analysis: rescue for diffusion-MRI of threat to radiologists? Dr. Erik Ranschaert erik.ranschaert@gmail.com
- 2. 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
- 3. 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
- 4. Genetics Clinical manifestation TreatmentEtiology LINK Shift to Personalised Medicine Predisposition to disease development and responsiveness to treatment depends on information enclosed in genoma.
- 5. Biobanks are crucial Long-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.
- 6. Radiomics vs. Radiogenomics RADIOMICS 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.
- 7. “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
- 8. Imaging Biobanks QIBA 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 EIBALL Founded by ESR in March 2015 Coordination 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
- 10. DWI as biomarker of cancer Toronto 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 processPadhani 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.
- 11. Advantages of DWI-MRI Improved tissue characterisation (malignant vs. benign) Monitoring of treatment after chemotherapy or radiation DD of post-therapeutic changes from residual active tumor Detection of recurrent cancer Prediction of treatment outcome Tumour staging Detection of lymph node involvement
- 12. Remaining challenges for DWI to assess cancer Divergence among and between vendors on data measurements/analysis No accepted standards for measurements and analysis Multiple data acquisition protocols depending on body part and usage of data Qualitative to quantitative assessments Lack of understanding of DW-MRI at a microscopic level Incomplete validation and documentation of reproducibility Divergent nomenclature and symbols Lack of multicenter working methodologies, accepted quality assurance (QA) standards, and physiologically realistic phantoms
- 13. Biomarkers - ratio metrics SI ratio’s
- 14. Hybrid imaging: PET/MRI vs. PET/CT Current Status of Hybrid PET/MRI in Oncologic Imaging Andrew B. Rosenkrantz et al., American Journal of Roentgenology 2016 206:1, 162-172
- 15. 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.
- 16. 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
- 17. 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.”
- 18. 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
- 19. 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
- 20. 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