Abstract
We present the LUng CAncer Screening (LUCAS) Dataset for evaluating lung cancer diagnosis with both imaging and clinical biomarkers in a realistic screening setting. We extract key information from anonymized clinical records and radiology reports, and we use it as a natural complement to low-dose chest CT scans of patients. We formulate the task as a detection problem and we develop a deep learning baseline to serve as a future reference of algorithmic performance. Our results provide solid empirical evidence for the difficulty of the task in the LUCAS Dataset and for the interest of including multimodal biomarkers in the analysis. All the resources of the LUCAS Dataset are publicly available.
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References
Abbosh, C., et al.: Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution. Nature 545(7655), 446–451 (2017)
Alilou, M., Kovalev, V., Snezhko, E., Taimouri, V.: A comprehensive framework for automatic detection of pulmonary nodules in lung CT images. Image Anal. Stereol. 33, 13 (2014). https://doi.org/10.5566/ias.v33.p13-27
Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2010)
Ardila, D., et al.: Endto-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat. Med. 25(6), 954–961 (2019). https://doi.org/10.1038/s41591-019-0447-x
Armato, S.G., et al.: LUNGx challenge for computerized lung nodule classification. J. Med. Imaging 3(4), 044506 (2016). https://doi.org/10.1117/1.jmi.3.4.044506
Armato III, S.G., et al.: The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med. Phys. 38(2), 915–931 (2011)
Astaraki, M., Toma-Dasu, I., Smedby, Ö., Wang, C.: Normal appearance autoencoder for lung cancer detection and segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 249–256. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32226-7_28
AstraZeneca, I.: Summary of product characteristics (2007)
Balagurunathan, Y., et al.: Isbi 2018 - lung nodule malignancy prediction challenge (2018). http://isbichallenges.cloudapp.net/competitions/15
Beckles, M.A., Spiro, S.G., Colice, G.L., Rudd, R.M.: Initial evaluation of the patient with lung cancer: symptoms, signs, laboratory tests, and paraneoplastic syndromes. Chest 123(1), 97S–104S (2003)
Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., Jemal, A.: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J. Clin. 68(6), 394–424 (2018). https://doi.org/10.3322/caac.21492
del Ciello, A., Franchi, P., Contegiacomo, A., Cicchetti, G., Bonomo, L., Larici, A.R.: Missed lung cancer: when, where, and why? Diagn. Interv. Radiol. 23(2), 118–126 (2017). https://doi.org/10.5152/dir.2016.16187
Flach, P., Kull, M.: Precision-recall-gain curves: PR analysis done right. In: Advances in Neural Information Processing Systems, pp. 838–846 (2015)
Hansell, D.M., Bankier, A.A., MacMahon, H., McLoud, T.C., Müller, N.L., Remy, J.: Fleischner society: glossary of terms for thoracic imaging. Radiology 246(3), 697–722 (2008). https://doi.org/10.1148/radiol.2462070712
Hariharan, B., Arbeláez, P., Bourdev, L., Maji, S., Malik, J.: Semantic contours from inverse detectors. In: 2011 International Conference on Computer Vision, pp. 991–998. IEEE (2011)
Jacobs, C., Setio, A.A.A., Traverso, A., van Ginneken, B.: Luna16 (2016). https://luna16.grand-challenge.org
Kaggle: Data science bowl 2017 (2017). https://www.kaggle.com/c/data-science-bowl-2017
Memon, W., et al.: Can computer assisted diagnosis (CAD) be used as a screening tool in the detection of pulmonary nodules when using 64-slice multidetector computed tomography? Int. J. Gen. Med. 4, 815 (2011). https://doi.org/10.2147/ijgm.s26127
Pedrosa, J., Ferreira, C., Aresta, G.: Grand challenge on automatic lung cancer patient manager (2020). https://lndb.grand-challenge.org/
Pérez, G., Arbeláez, P.: Lung cancer prediction (2018). https://biomedicalcomputervision.uniandes.edu.co/index.php/research?id=33
Simpson, A.L., et al.: A large annotated medical image dataset for the development and evaluation of segmentation algorithms. CoRR abs/1902.09063 http://arxiv.org/abs/1902.09063 (2019)
Way, T., et al.: Computer-aided diagnosis of lung nodules on CT scans. Acad. Radiol. 17(3), 323–332 (2010). https://doi.org/10.1016/j.acra.2009.10.016
Wiesenfarth, M., Reinke, A., Landman, A.L., Cardoso, M., Maier-Hein, L., Kopp-Schneider, A.: Methods and open-source toolkit for analyzing and visualizing challenge results. arXiv preprint arXiv:1910.05121 (2019)
Wong, M.C., Lao, X.Q., Ho, K.F., Goggins, W.B., Shelly, L.: Incidence and mortality of lung cancer: global trends and association with socioeconomic status. Sci. Rep. 7(1), 1–9 (2017)
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This project was partially funded by the Google Latin America Research Awards (LARA) 2019.
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Daza, L., Castillo, A., Escobar, M., Valencia, S., Pinzón, B., Arbeláez, P. (2020). LUCAS: LUng CAncer Screening with Multimodal Biomarkers. In: Syeda-Mahmood, T., et al. Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures. CLIP ML-CDS 2020 2020. Lecture Notes in Computer Science(), vol 12445. Springer, Cham. https://doi.org/10.1007/978-3-030-60946-7_12
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