Abstract
Esophageal cancer is the second most deadly cancer. Early detection of resectable/curable esophageal cancers has a great potential to reduce mortality, but no guideline-recommended screening test is available. Although some screening methods have been developed, they are expensive, might be difficult to apply to the general population, and often fail to achieve satisfactory sensitivity for identifying early-stage cancers. In this work, we investigate the feasibility of esophageal tumor detection and classification (cancer or benign) on the noncontrast CT scan, which could potentially be used for opportunistic cancer screening. To capture the global context, a novel position-sensitive self-attention is proposed to augment nnUNet with non-local interactions. Our model achieves a sensitivity of 93.0% and specificity of 97.5% for the detection of esophageal tumors on a holdout testing set with 180 patients. In comparison, the mean sensitivity and specificity of four doctors are 75.0% and 83.8%, respectively. For the classification task, our model outperforms the mean doctors by absolute margins of 17%, 31%, and 14% for cancer, benign tumor, and normal, respectively. Compared with established state-of-the-art esophageal cancer screening methods, e.g., blood testing and endoscopy AI system, our method has comparable performance and is even more sensitive for early-stage cancer and benign tumor. Our proposed method is a novel, non-invasive, low-cost, and highly accurate tool for opportunistic screening of esophageal cancer.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
U.S. Preventive Services Task Force (USPSTF), “Recommendations,”. https://www.uspreventiveservicestaskforce.org/uspstf/topic_search_results?topic_status=P
Arnal, M.J.D., Arenas, Á.F., Arbeloa, Á.L.: Esophageal cancer: risk factors, screening and endoscopic treatment in western and eastern countries. World J. Gastroenterol. 21(26), 7933 (2015)
Cheng, N.M., et al.: Deep learning for fully automated prediction of overall survival in patients with oropharyngeal cancer using FDG-PET imaging. Clin. Cancer Res. 27(14), 3948–3959 (2021)
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
Doki, Y., et al.: Nivolumab combination therapy in advanced esophageal squamous-cell carcinoma. N. Engl. J. Med. 386(5), 449–462 (2022)
Gehrung, M., Crispin-Ortuzar, M., Berman, A.G., O’Donovan, M., Fitzgerald, R.C., Markowetz, F.: Triage-driven diagnosis of Barrett’s esophagus for early detection of esophageal adenocarcinoma using deep learning. Nat. Med. 27(5), 833–841 (2021)
Heinrich, M.P., Jenkinson, M., Brady, M., Schnabel, J.A.: MRF-based deformable registration and ventilation estimation of lung CT. IEEE Trans. Med. Imaging 32(7), 1239–1248 (2013)
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)
Jang, S., Graffy, P.M., Ziemlewicz, T.J., Lee, S.J., Summers, R.M., Pickhardt, P.J.: Opportunistic osteoporosis screening at routine abdominal and thoracic CT: normative L1 trabecular attenuation values in more than 20 000 adults. Radiology 291(2), 360–367 (2019)
Jin, D., et al.: Deeptarget: gross tumor and clinical target volume segmentation in esophageal cancer radiotherapy. Med. Image Anal. 68, 101909 (2021)
Klein, E., et al.: Clinical validation of a targeted methylation-based multi-cancer early detection test using an independent validation set. Ann. Oncol. 32(9), 1167–1177 (2021)
Lambert, Z., Petitjean, C., Dubray, B., Kuan, S.: Segthor: segmentation of thoracic organs at risk in CT images. In: 2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA), pp. 1–6. IEEE (2020)
Luo, H., et al.: Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study. Lancet Oncol. 20(12), 1645–1654 (2019)
Pickhardt, P.J., et al.: Automated CT biomarkers for opportunistic prediction of future cardiovascular events and mortality in an asymptomatic screening population: a retrospective cohort study. Lancet Dig. Health 2(4), e192–e200 (2020)
Qin, Y., Wu, C.W., Taylor, W.R., Sawas, T., Burger, K.N., Mahoney, D.W., Sun, Z., Yab, T.C., Lidgard, G.P., Allawi, H.T., et al.: Discovery, validation, and application of novel methylated DNA markers for detection of esophageal cancer in plasma. Clin. Cancer Res. 25(24), 7396–7404 (2019)
Rice, T., Ishwaran, H., Hofstetter, W., Kelsen, D., Apperson-Hansen, C., Blackstone, E.: Recommendations for pathologic staging (PTNM) of cancer of the esophagus and esophagogastric junction for the 8th edition AJCC/UICC staging manuals. Dis. Esophagus 29(8), 897–905 (2016)
Thompson, W.M.: Esophageal carcinoma. Abdom. Imaging 22(2), 138–142 (1997). https://doi.org/10.1007/s002619900158
Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2021. CA: A Cancer Journal for Clinicians 71(1), 7–333 (2021)
Valanarasu, J.M.J., Oza, P., Hacihaliloglu, I., Patel, V.M.: Medical transformer: gated axial-attention for medical image segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 36–46. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87193-2_4
Wang, H., Zhu, Y., Green, B., Adam, H., Yuille, A., Chen, L.-C.: Axial-DeepLab: stand-alone axial-attention for panoptic segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12349, pp. 108–126. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58548-8_7
Wei, W.Q., Chen, Z.F., He, Y.T., Feng, H., Hou, J., Lin, D.M., Li, X.Q., Guo, C.L., Li, S.S., Wang, G.Q., et al.: Long-term follow-up of a community assignment, one-time endoscopic screening study of esophageal cancer in china. J. Clin. Oncol. 33(17), 1951 (2015)
Xia, Y., et al.: Effective pancreatic cancer screening on non-contrast CT scans via anatomy-aware transformers. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12905, pp. 259–269. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87240-3_25
Yan, K., et al.: Learning from multiple datasets with heterogeneous and partial labels for universal lesion detection in CT. IEEE Trans. Med. Imaging 40, 2759–2770 (2021)
Yao, J.: Deepprognosis: preoperative prediction of pancreatic cancer survival and surgical margin via comprehensive understanding of dynamic contrast-enhanced CT imaging and tumor-vascular contact parsing. Med. Image Anal. 73, 102150 (2021)
Ye, X., et al.: Multi-institutional validation of two-streamed deep learning method for automated delineation of esophageal gross tumor volume using planning-ct and fdg-petct. arXiv preprint arXiv:2110.05280 (2021)
Yousefi, S., et al.: Esophageal gross tumor volume segmentation using a 3D convolutional neural network. In: Frangi, A.F., et al. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 343–351. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_40
Zhang, L., Gopalakrishnan, V., Lu, L., Summers, R.M., Moss, J., Yao, J.: Self-learning to detect and segment cysts in lung CT images without manual annotation. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 1100–1103. IEEE (2018)
Zhou, D., et al.: Eso-net: a novel 2.5 d segmentation network with the multi-structure response filter for the cancerous esophagus. IEEE Access 8, 155548–155562 (2020)
Zhu, Z., et al.: Multi-scale coarse-to-fine segmentation for screening pancreatic ductal adenocarcinoma. In: Shen, D. (ed.) MICCAI 2019. LNCS, vol. 11769, pp. 3–12. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32226-7_1
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yao, J. et al. (2022). Effective Opportunistic Esophageal Cancer Screening Using Noncontrast CT Imaging. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13433. Springer, Cham. https://doi.org/10.1007/978-3-031-16437-8_33
Download citation
DOI: https://doi.org/10.1007/978-3-031-16437-8_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16436-1
Online ISBN: 978-3-031-16437-8
eBook Packages: Computer ScienceComputer Science (R0)