Development and Validation of an Automated Radiomic CT Signature for Detecting COVID-19
Julien Guiot, Akshayaa Vaidyanathan, Louis Deprez, Fadila Zerka, Denis Danthine, Anne-Noëlle Frix, Marie Thys, Monique Henket, Gregory Canivet, Stephane Mathieu, Evanthia Eftaxia, Philippe Lambin, Nathan Tsoutzidis, Benjamin Miraglio, Sean Walsh, Michel Moutschen, Renaud Louis, Paul Meunier, Wim Vos, Ralph T H Leijenaar, Pierre Lovinfosse, Julien Guiot, Akshayaa Vaidyanathan, Louis Deprez, Fadila Zerka, Denis Danthine, Anne-Noëlle Frix, Marie Thys, Monique Henket, Gregory Canivet, Stephane Mathieu, Evanthia Eftaxia, Philippe Lambin, Nathan Tsoutzidis, Benjamin Miraglio, Sean Walsh, Michel Moutschen, Renaud Louis, Paul Meunier, Wim Vos, Ralph T H Leijenaar, Pierre Lovinfosse
Abstract
The coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status. Drastic measures of social distancing are enforced in society and healthcare systems are being pushed to and beyond their limits. To help in the fight against this threat on human health, a fully automated AI framework was developed to extract radiomics features from volumetric chest computed tomography (CT) exams. The detection model was developed on a dataset of 1381 patients (181 COVID-19 patients plus 1200 non COVID control patients). A second, independent dataset of 197 RT-PCR confirmed COVID-19 patients and 500 control patients was used to assess the performance of the model. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC). The model had an AUC of 0.882 (95% CI: 0.851-0.913) in the independent test dataset (641 patients). The optimal decision threshold, considering the cost of false negatives twice as high as the cost of false positives, resulted in an accuracy of 85.18%, a sensitivity of 69.52%, a specificity of 91.63%, a negative predictive value (NPV) of 94.46% and a positive predictive value (PPV) of 59.44%. Benchmarked against RT-PCR confirmed cases of COVID-19, our AI framework can accurately differentiate COVID-19 from routine clinical conditions in a fully automated fashion. Thus, providing rapid accurate diagnosis in patients suspected of COVID-19 infection, facilitating the timely implementation of isolation procedures and early intervention.
Keywords: COVID-19; artificial intelligence; computed tomography; machine learning; radiomics.
Conflict of interest statement
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: A.V., F.Z., B.M., N.T. are salaried employees ofOncoradiomicsSA. P.L. (Philippe Lambin) reports, within and outside the submitted work, grants/sponsored research agreements from Varian medical, Oncoradiomics, ptTheragnostic/DNAmito, Health Innovation Ventures. He received an advisor/presenter fee and/or reimbursement of travel costs/external grant writing fee and/orin kindmanpower contribution fromOncoradiomics, BHV, Merck, Varian, Elekta, ptTheragnosticand Convert pharmaceuticals. P.L. (Philippe Lambin) has shares in the companyOncoradiomicsSA, Convert pharmaceuticals SA and The Medical Cloud Company SPRL and is co-inventor of two issued patents with royalties on radiomics (PCT/NL2014/050248, PCT/NL2014/050728) licensed toOncoradiomicsand one issue patent onmtDNA(PCT/EP2014/059089) licensed toptTheragnostic/DNAmito, three non-patented invention (softwares) licensed toptTheragnostic/DNAmito, Oncoradiomicsand Health Innovation Ventures and three non-issues, non licensedpatents on Deep Learning-Radiomics and LSRT (N2024482, N2024889, N2024889. R.T.H.L. has shares in the companyOncoradiomicsand is co-inventor of an issued patent with royalties on radiomics (PCT/NL2014/050728) licensed toOncoradiomics. S.W. and W.V. have shares in the companyOncoradiomics. The rest of the co-authors have no known competing financial interests or personal relationships to declare.
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References
- WHO Landing Page. [(accessed on 20 December 2020)]; Available online:
- Johns Hopkins University & Medicine Coronavirus Resource Center. [(accessed on 20 December 2020)]; Available online: .
- Wu J.T., Leung K., Leung G.M. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: A modelling study. Lancet. 2020;395:689–697. doi: 10.1016/S0140-6736(20)30260-9.
- Yang S., Shi Y., Lu H., Xu J., Li F., Qian Z., Hua X., Ding X., Song F., Shen J., et al. Clinical and CT features of early-stage patients with COVID-19: A retrospective analysis of imported cases in Shanghai, China. Eur. Respir. J. 2020:2000407. doi: 10.1183/13993003.00407-2020.
- Ai T., Yang Z., Hou H., Zhan C., Chen C., Lv W., Tao Q., Sun Z., Xia L. Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases. Radiology. 2020;296:E32–E40. doi: 10.1148/radiol.2020200642.
- Fang Y., Zhang H., Xie J., Lin M., Ying L., Pang P., Ji W. Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR. Radiology. 2020:200432. doi: 10.1148/radiol.2020200432.
- Huang C., Wang Y., Li X., Ren L., Zhao J., Hu Y., Zhang L., Fan G., Xu J., Gu X., et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395:497–506. doi: 10.1016/S0140-6736(20)30183-5.
- Li R., Liu G., Zhang X., Li H. Letter to the Editor: Chest CT and RT-PCR: Radiologists’ Experience in the Diagnosis of COVID-19 in China. [(accessed on 20 December 2020)]; Available online:
- Hope M. A role for CT in COVID-19? What data really tell us so far. Lancet. 2020;395:1189–1190. doi: 10.1016/S0140-6736(20)30728-5.
- Huang Y., Cheng W., Zhao N., Qu H., Tian J. Correspondence CT screening for early diagnosis of SARS-CoV-2. Lancet Infect. Dis. 2020;51:30241. doi: 10.1016/S1473-3099(20)30241-3.
- Aerts H.J.W.L., Velazquez E.R., Leijenaar R.T.H., Parmar C., Grossmann P., Carvalho S., Bussink J., Monshouwer R., Haibe-Kains B., Rietveld D., et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 2014;5:4006. doi: 10.1038/ncomms5006.
- Walsh S.L.F., Calandriello L., Silva M., Sverzellati N. Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: A case-cohort study. Lancet Respir. Med. 2018;6:837–845. doi: 10.1016/S2213-2600(18)30286-8.
- McKinney S.M., Sieniek M., Godbole V., Godwin J., Antropova N., Ashrafian H., Back T., Chesus M., Corrado G.C., Darzi A., et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577:89–94. doi: 10.1038/s41586-019-1799-6.
- Ardila D., Kiraly A.P., Bharadwaj S., Choi B., Reicher J.J., Peng L., Tse D., Etemadi M., Ye W., Corrado G., et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat. Med. 2019;25:954–961. doi: 10.1038/s41591-019-0447-x.
- Deist T.M., Dankers F.J.W.M., Valdes G., Wijsman R., Hsu I.-C., Oberije C., Lustberg T., Van Soest J., Hoebers F., Jochems A., et al. Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers. Med. Phys. 2018;45:3449–3459. doi: 10.1002/mp.12967.
- Lambin P., Rios-Velazquez E., Leijenaar R., Carvalho S., Van Stiphout R.G.P.M., Granton P., Zegers C.M.L., Gillies R., Boellard R., Dekker A., et al. Radiomics: Extracting more information from medical images using advanced feature analysis. Eur. J. Cancer. 2012;48:441–446. doi: 10.1016/j.ejca.2011.11.036.
- Lambin P., Leijenaar R.T.H., Deist T.M., Peerlings J., De Jong E.E.C., Van Timmeren J., Sanduleanu S., Larue R.T.H.M., Even A.J.G., Jochems A., et al. Radiomics: The bridge between medical imaging and personalized medicine. Nat. Rev. Clin. Oncol. 2017;14:749–762. doi: 10.1038/nrclinonc.2017.141.
- Parmar C., Rios Velazquez E., Leijenaar R., Jermoumi M., Carvalho S., Mak R.H., Mitra S., Shankar B.U., Kikinis R., Haibe-Kains B., et al. Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation. PLoS ONE. 2014;9:e102107. doi: 10.1371/journal.pone.0102107.
- Parmar C., Grossmann P., Bussink J., Lambin P., Aerts H.J.W.L. Machine Learning methods for Quantitative Radiomic Biomarkers. Sci. Rep. 2015;5:13087. doi: 10.1038/srep13087.
- Selvaraju R.R., Cogswell M., Das A., Vedantam R., Parikh D., Batra D. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. Int. J. Comput. Vis. 2020;128:336–359. doi: 10.1007/s11263-019-01228-7.
- Xie X., Zhong Z., Zhao W., Zheng C., Wang F., Liu J. Chest CT for Typical 2019-nCoV Pneumonia: Relationship to Negative RT-PCR Testing. Radiology. 2020:200343. doi: 10.1148/radiol.2020200343.
- Sheridan C. Coronavirus and the race to distribute reliable diagnostics. Nat. Biotechnol. 2020 doi: 10.1038/d41587-020-00002-2.
- Revel M.P., Parkar A.P., Prosch H., Silva M., Sverzellati N., Gleeson F., Brady A. COVID-19 patients and the radiology department–Advice from the European Society of Radiology (ESR) and the European Society of Thoracic Imaging (ESTI) Eur. Radiol. 2020 doi: 10.1007/s00330-020-06865-y.
- Liu J., Yu H., Zhang S. The indispensable role of chest CT in the detection of coronavirus disease 2019 (COVID-19) Eur. J. Nucl. Med. Mol. Imaging. 2020;47:1638–1639. doi: 10.1007/s00259-020-04795-x.
- Song Y., Zheng S., Li L., Zhang X., Zhang X., Huang Z., Chen J., Zhao H., Jie Y., Wang R., et al. Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images. MedRxiv. 2020 doi: 10.1101/2020.02.23.20026930.
- Wang S., Kang B., Ma J., Zeng X., Xiao M., Guo J., Cai M., Yang J., Li Y., Meng X., et al. A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19) MedRxiv. 2020 doi: 10.1101/2020.02.14.20023028.
- Chen J., Wu L., Zhang J., Zhang L., Gong D., Zhao Y., Hu S., Wang Y., Hu X., Zheng B., et al. Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: A prospective study. MedRxiv. 2020 doi: 10.1101/2020.02.25.20021568.
- Leijenaar R.T.H., Bogowicz M., Jochems A., Hoebers F.J.P., Wesseling F.W.R., Huang S.H., Chan B., Waldron J.N., O’Sullivan B., Rietveld D., et al. Development and validation of a radiomic signature to predict HPV (p16) status from standard CT imaging: A multicenter study. Br. J. Radiol. 2018;91:20170498. doi: 10.1259/bjr.20170498.
- Wu J., Liu J., Li S., Peng Z., Xiao Z., Wang X., Yan R., Luo J. Detection and analysis of nucleic acid in various biological samples of COVID-19 patients. Travel Med. Infect. Dis. 2020:101673. doi: 10.1016/j.tmaid.2020.101673.
- Oxford Centre for Evidence-Based Medicine Levels of Evidence (March 2009)–CEBM. [(accessed on 1 December 2020)]; Available online: .
- Shi F., Wang J., Shi J., Wu Z., Wang Q., Tang Z., He K., Shi Y., Shen D. Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19. IEEE Rev. Biomed. Eng. 2020;1 doi: 10.1109/RBME.2020.2987975.
- Kundu S., Elhalawani H., Gichoya J.W., Kahn C.E. How Might AI and Chest Imaging Help Unravel COVID-19’s Mysteries? Radiol. Artif. Intell. 2020;2:e200053. doi: 10.1148/ryai.2020200053.
- Mei X., Lee H.C., Diao K., Huang M., Lin B., Liu C., Xie Z., Ma Y., Robson P.M., Chung M., et al. Artificial intelligence–enabled rapid diagnosis of patients with COVID-19. Nat. Med. 2020;26:1224–1228. doi: 10.1038/s41591-020-0931-3.
- Harmon S.A., Sanford T.H., Xu S., Turkbey E.B., Roth H., Xu Z., Yang D., Myronenko A., Anderson V., Amalou A., et al. Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets. Nat. Commun. 2020;11:1–7. doi: 10.1038/s41467-020-17971-2.
- Ozsahin I., Sekeroglu B., Musa M.S., Mustapha M.T., Uzun Ozsahin D. Review on Diagnosis of COVID-19 from Chest CT Images Using Artificial Intelligence. Comput. Math. Methods Med. 2020;2020:1–10. doi: 10.1155/2020/9756518.
- Jalaber C., Lapotre T., Morcet-Delattre T., Ribet F., Jouneau S., Lederlin M. Chest CT in COVID-19 pneumonia: A review of current knowledge. Diagn. Interv. Imaging. 2020;101:431–437. doi: 10.1016/j.diii.2020.06.001.
- Hope M.D., Raptis C.A., Henry T.S. Chest Computed Tomography for Detection of Coronavirus Disease 2019 (COVID-19): Don’t Rush the Science. Ann. Intern. Med. 2020;173:147–148. doi: 10.7326/M20-1382.
- Bai H.X., Wang R., Xiong Z., Hsieh B., Chang K., Halsey K., Tran T.M.L., Choi J.W., Wang D.-C., Shi L.-B., et al. Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT. Radiology. 2020;296:E156–E165. doi: 10.1148/radiol.2020201491.
- Li L., Qin L., Xu Z., Yin Y., Wang X., Kong B., Bai J., Lu Y., Fang Z., Song Q., et al. Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy. Radiology. 2020;296:E65–E71. doi: 10.1148/radiol.2020200905.
- Xie C., Ng M.-Y., Ding J., Leung S.T., Lo C.S.Y., Wong H.Y.F., Vardhanabhuti V. Discrimination of pulmonary ground-glass opacity changes in COVID-19 and non-COVID-19 patients using CT radiomics analysis. Eur. J. Radiol. Open. 2020;7 doi: 10.1016/j.ejro.2020.100271.
- Fang M., He B., Li L., Dong D., Yang X., Li C., Meng L., Zhong L., Li H., Li H., et al. CT radiomics can help screen the Coronavirus disease 2019 (COVID-19): A preliminary study. Sci. China Inf. Sci. 2020;63:172103. doi: 10.1007/s11432-020-2849-3.
- Fu L., Li Y., Cheng A., Pang P., Shu Z. A Novel Machine Learning-derived Radiomic Signature of the Whole Lung Differentiates Stable from Progressive COVID-19 Infection: A Retrospective Cohort Study. J. Thorac. Imaging. 2020;35:361. doi: 10.1097/RTI.0000000000000544.
- Van Timmeren J.E., Leijenaar R.T.H., Van Elmpt W., Wang J., Zhang Z., Dekker A., Lambin P. Test-Retest Data for Radiomics Feature Stability Analysis: Generalizable or Study-Specific? Tomography. 2016;2:361–365. doi: 10.18383/j.tom.2016.00208.
Source: PubMed