Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework

Scientific Reports

Volumn 7, Issue 1, 2017, Pages

  Oakden Rayner, Luke ^a,b   Carneiro, Gustavo ^b   Bessen, Taryn ^a   Nascimento, Jacinto C ^c   Bradley, Andrew P ^d   Palmer, Lyle J ^b  

^a ROYAL ADELAIDE HOSPITAL   (Australia)

^b UNIVERSITY OF ADELAIDE   (Australia)

^c INSTITUTO SUPERIOR TÉCNICO   (Portugal)

^d UNIVERSITY OF QUEENSLAND   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

AREA UNDER THE CURVE; DIAGNOSTIC IMAGING; HUMAN; IMAGE PROCESSING; KAPLAN MEIER METHOD; LONGEVITY; MORTALITY; PERSONALIZED MEDICINE; PHENOTYPE; PHYSIOLOGY; RADIOLOGY; RECEIVER OPERATING CHARACTERISTIC; REPRODUCIBILITY; RISK FACTOR; TASK PERFORMANCE; X-RAY COMPUTED TOMOGRAPHY;

AREA UNDER CURVE; DEEP LEARNING; DIAGNOSTIC IMAGING; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; KAPLAN-MEIER ESTIMATE; LONGEVITY; MORTALITY; PHENOTYPE; PRECISION MEDICINE; RADIOLOGY; REPRODUCIBILITY OF RESULTS; RISK FACTORS; ROC CURVE; TASK PERFORMANCE AND ANALYSIS; TOMOGRAPHY, X-RAY COMPUTED;

EID: 85019233675     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/s41598-017-01931-w     Document Type: Article

References (64)

1. Collins, F. S. & Varmus, H. A new initiative on precision medicine. New England Journal of Medicine 372, 793-795, doi:10.1056/ NEJMp1500523 (2015).
2. Wild, C. P. Complementing the genome with an "exposome": the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiology Biomarkers & Prevention 14, 1847-1850, doi:10.1158/1055-9965.EPI-05-0456 (2005).
3. Colburn, W. et al. Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework. Biomarkers Definitions Working Group. Clinical Pharmacol & Therapeutics 69, 89-95 (2001).
4. Wallace, K. et al. Report on the NCSS Fact Finding Cardiotoxicity Expert Working Group. http://www.fda.gov/ohrms/dockets/ ac/01/briefing/3798b1-04-holt/sld005.htm (2001).
5. Dancey, J. E., Bedard, P. L., Onetto, N. & Hudson, T. J. The genetic basis for cancer treatment decisions. Cell 148, 409-420, doi:10.1016/j.cell.2012.01.014 (2012).
6. McCarthy, J. J., McLeod, H. L. & Ginsburg, G. S. Genomic medicine: a decade of successes, challenges, and opportunities. Science translational medicine 5, 189sr184-189sr184, doi:10.1126/scitranslmed.3005785 (2013).
7. AIHW, E. (Australia's Health Series, Australian Institute of Health and Welfare (AIHW), Canberra, Australia, 2014).
8. Organization, W. H. Preventing chronic diseases: a vital investment: WHO global report. (2005).
9. Rappaport, S. M. & Smith, M. T. Environment and disease risks. Science 330, 460-461, doi:10.1126/science.1192603 (2010).
10. Rappaport, S. M. Implications of the exposome for exposure science. Journal of Exposure Science and Environmental Epidemiology 21, 5-9, doi:10.1038/jes.2010.50 (2011).
11. Wilkens, L. R. & Lee, J. Nutritional epidemiology. (Wiley Online Library, 1998).
12. Dishman, R., Heath, G. & Lee, I.-M. Physical activity epidemiology. 39-49 (Human Kinetics, 2004).
13. Applegate, W. B., Blass, J. P. & Williams, T. F. Instruments for the functional assessment of older patients. New England Journal of Medicine 322, 1207-1214, doi:10.1056/NEJM199004263221707 (1990).
14. Hripcsak, G. & Albers, D. J. Next-generation phenotyping of electronic health records. Journal of the American Medical Informatics Association 20, 117-121, doi:10.1136/amiajnl-2012-001145 (2013).
15. Gligorijevi, V., MalodDognin, N. & Pržulj, N. Integrative methods for analyzing big data in precision medicine. Proteomics 16, 741-758, doi:10.1002/pmic.v16.5 (2016).
16. Sonnenburg, J. L. & Backhed, F. Diet-microbiota interactions as moderators of human metabolism. Nature 535, 56-64, doi:10.1038/ nature18846 (2016).
17. Aronson, S. J. & Rehm, H. L. Building the foundation for genomics in precision medicine. Nature 526, 336-342, doi:10.1038/ nature15816 (2015).
18. Prasad, V., Fojo, T. & Brada, M. Precision oncology: origins, optimism, and potential. The Lancet Oncology 17, e81-e86, doi:10.1016/ S1470-2045(15)00620-8 (2016).
19. Fox, J. L. Obama catapults patient-empowered Precision Medicine. Nature biotechnology 33, 325-325, doi:10.1038/nbt0415-325 (2015).
20. Butte, A. J. It takes a genome to understand a village: Population scale precision medicine. Proceedings of the National Academy of Sciences, 201615329 (2016).
21. Civelek, M. & Lusis, A. J. Systems genetics approaches to understand complex traits. Nature Reviews Genetics 15, 34-48, doi:10.1038/ nrg3575 (2014).
22. Wei, W.-Q. & Denny, J. C. Extracting research-quality phenotypes from electronic health records to support precision medicine. Genome medicine 7, 1 (2015).
23. Toll, D., Janssen, K., Vergouwe, Y. & Moons, K. Validation, updating and impact of clinical prediction rules: a review. Journal of clinical epidemiology 61, 1085-1094, doi:10.1016/j.jclinepi.2008.04.008 (2008).
24. Yourman, L. C., Lee, S. J., Schonberg, M. A., Widera, E. W. & Smith, A. K. Prognostic indices for older adults: a systematic review. Jama 307, 182-192, doi:10.1001/jama.2011.1966 (2012).
25. Adlassnig, K.-P. Fuzzy set theory in medical diagnosis. IEEE Transactions on Systems, Man, and Cybernetics 16, 260-265, doi:10.1109/TSMC.1986.4308946 (1986).
26. Boyd, S. & Vandenberghe, L. Convex optimization. (Cambridge university press, 2004).
27. Collinson, P. Troponin T or troponin I or CK-MB (or none). European heart journal 19, N16-24 (1998).
28. Drucker, E. & Krapfenbauer, K. Pitfalls and limitations in translation from biomarker discovery to clinical utility in predictive and personalised medicine. EPMA journal 4, 1, doi:10.1186/1878-5085-4-7 (2013).
29. Poste, G. Bring on the biomarkers. Nature 469, 156-157, doi:10.1038/469156a (2011).
30. Bush, W. S., Oetjens, M. T. & Crawford, D. C. Unravelling the human genome-phenome relationship using phenome-wide association studies. Nature Reviews Genetics 17, 129-145, doi:10.1038/nrg.2015.36 (2016).
31. Greenland, P., LaBree, L., Azen, S. P., Doherty, T. M. & Detrano, R. C. Coronary artery calcium score combined with Framingham score for risk prediction in asymptomatic individuals. Jama 291, 210-215, doi:10.1001/jama.291.2.210 (2004).
32. Haruna, A. et al. CT scan findings of emphysema predict mortality in COPD. CHEST Journal 138, 635-640, doi:10.1378/ chest.09-2836 (2010).
33. Johnell, O. et al. Predictive value of BMD for hip and other fractures. Journal of bone and mineral research 20, 1185-1194, doi:10.1359/JBMR.050304 (2005).
34. Aerts, H. J. et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature communications 5 (2014).
35. Kato, H. et al. Computer-aided diagnosis of hepatic fibrosis: preliminary evaluation of MRI texture analysis using the finite difference method and an artificial neural network. American Journal of Roentgenology 189, 117-122, doi:10.2214/AJR.07.2070 (2007).
36. Ito, M. et al. Trabecular texture analysis of CT images in the relationship with spinal fracture. Radiology 194, 55-59, doi:10.1148/ radiology.194.1.7997582 (1995).
37. Gillies, R. J., Kinahan, P. E. & Hricak, H. Radiomics: images are more than pictures, they are data. Radiology 278, 563-577, doi:10.1148/radiol.2015151169 (2015).
38. Kumar, V. et al. Radiomics: the process and the challenges. Magnetic resonance imaging 30, 1234-1248, doi:10.1016/j.mri.2012.06.010 (2012).
39. Forsyth, D. A. & Ponce, J. Computer vision: a modern approach. (Prentice Hall Professional Technical Reference, 2002).
40. Palmer, L. J. & Cardon, L. R. Shaking the tree: mapping complex disease genes with linkage disequilibrium. The Lancet 366, 1223-1234, doi:10.1016/S0140-6736(05)67485-5 (2005).
41. Fu, W., D O'Connor, T. & Akey, J. M. Genetic architecture of quantitative traits and complex diseases. Current opinion in genetics & development 23, 678-683, doi:10.1016/j.gde.2013.10.008 (2013).
42. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436-444, doi:10.1038/nature14539 (2015).
43. Zeiler, M. D. & Fergus, R. In European Conference on Computer Vision. 818-833 (Springer).
44. Gulshan, V. et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316, 2402-2410, doi:10.1001/jama.2016.17216 (2016).
45. Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115-118, doi:10.1038/ nature21056 (2017).
46. Charlson, M. E., Pompei, P., Ales, K. L. & MacKenzie, C. R. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. Journal of chronic diseases 40, 373-383, doi:10.1016/0021-9681(87)90171-8 (1987).
47. Brady, Z., Cain, T. M. & Johnston, P. N. Paediatric CT imaging trends in Australia. Journal of medical imaging and radiation oncology 55, 132-142, doi:10.1111/jmiro.2011.55.issue-2 (2011).
48. de Gonzalez, A. B. et al. Projected cancer risks from computed tomographic scans performed in the United States in 2007. Archives of internal medicine 169, 2071-2077, doi:10.1001/archinternmed.2009.440 (2009).
49. Ganna, A. & Ingelsson, E. 5 year mortality predictors in 498 103 UK Biobank participants: a prospective population-based study. The Lancet 386, 533-540, doi:10.1016/S0140-6736(15)60175-1 (2015).
50. Peng, H., Long, F. & Ding, C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and minredundancy. IEEE Transactions on pattern analysis and machine intelligence 27, 1226-1238, doi:10.1109/TPAMI.2005.159 (2005).
51. Breiman, L. Random forests. Machine learning 45, 5-32, doi:10.1023/A:1010933404324 (2001).
52. D'Agostino, Sr, R. B., Grundy, S., Sullivan, L. M. & Wilson, P. Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. Jama 286, 180-187, doi:10.1001/jama.286.2.180 (2001).
53. Rietbrock, S., Heeley, E., Plumb, J. & van Staa, T. Chronic atrial fibrillation: Incidence, prevalence, and prediction of stroke using the Congestive heart failure, Hypertension, Age 75, Diabetes mellitus, and prior Stroke or transient ischemic attack (CHADS2) risk stratification scheme. American heart journal 156, 57-64, doi:10.1016/j.ahj.2008.03.010 (2008).
54. Antman, E. M. et al. The TIMI risk score for unstable angina/non-ST elevation MI: a method for prognostication and therapeutic decision making. Jama 284, 835-842, doi:10.1001/jama.284.7.835 (2000).
55. Hanley, J. A. & McNeil, B. J. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143, 29-36, doi:10.1148/radiology.143.1.7063747 (1982).
56. Sham, P. C. & Purcell, S. M. Statistical power and significance testing in large-scale genetic studies. Nature Reviews Genetics 15, 335-346, doi:10.1038/nrg3706 (2014).
57. Greenspan, H., van Ginneken, B. & Summers, R. M. Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique. IEEE Transactions on Medical Imaging 35, 1153-1159, doi:10.1109/TMI.2016.2553401 (2016).
58. Pocock, S. J. et al. Predictors of mortality and morbidity in patients with chronic heart failure. European heart journal 27, 65-75, doi:10.1093/eurheartj/ehi555 (2006).
59. Hotelling, H. Analysis of a complex of statistical variables into principal components. Journal of educational psychology 24, 417-441, doi:10.1037/h0071325 (1933).
60. Tibshirani, R. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), 267-288 (1996).
61. Nair, V. & Hinton, G. E. In Proceedings of the 27th International Conference on Machine Learning (ICML-10). 807-814.
62. Srivastava, N., Hinton, G. E., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research 15, 1929-1958 (2014).
63. Dauphin, Y., de Vries, H., Chung, J. & Bengio, Y. RMSProp and equilibrated adaptive learning rates for non-convex optimization (2015). arXiv preprint. arXiv preprint arXiv:1502.04390.
64. Bradley, A. P. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern recognition 30, 1145-1159, doi:10.1016/S0031-3203(96)00142-2 (1997).