Abstract
Pancreatic cancer is a relatively uncommon but most deadly cancer. Screening the general asymptomatic population is not recommended due to the risk that a significant number of false positive individuals may undergo unnecessary imaging tests (e.g., multi-phase contrast-enhanced CT scans) and follow-ups, adding health care costs greatly and no clear patient benefits. In this work, we investigate the feasibility of using a single-phase non-contrast CT scan, a cheaper, simpler, and safer substituent, to detect resectable pancreatic mass and classify the detection as pancreatic ductal adenocarcinoma (PDAC) or other abnormalities (nonPDAC) or normal pancreas. This task is usually poorly performed by general radiologists or even pancreatic specialists. With pathology-confirmed mass types and knowledge transfer from contrast-enhanced CT to non-contrast CT scans as supervision, we propose a novel deep classification model with an anatomy-guided transformer. After training on a large-scale dataset including 1321 patients: 450 PDACs, 394 nonPDACs, and 477 normal, our model achieves a sensitivity of 95.2% and a specificity of 95.8% for the detection of abnormalities on the holdout testing set with 306 patients. The mean sensitivity and specificity of 11 radiologists are 79.7% and 87.6%. For the 3-class classification task, our model outperforms the mean radiologists by absolute margins of 25%, 22%, and 8% for PDAC, nonPDAC, and normal, respectively. Our work sheds light on a potential new tool for large-scale (opportunistic or designed) pancreatic cancer screening, with significantly improved accuracy, lower test risk, and cost savings.
Y. Xia—Work done during an internship at PAII Inc.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Cai, J., Lu, L., Zhang, Z., Xing, F., Yang, L., Yin, Q.: Pancreas segmentation in MRI using graph-based decision fusion on convolutional neural networks. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 442–450. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_51
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213–229. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_13
Chu, L.C., et al.: Utility of CT radiomics features in differentiation of pancreatic ductal adenocarcinoma from normal pancreatic tissue. Am. J. Roentgenol. 213(2), 349–357 (2019)
Chu, L.C., et al.: Application of deep learning to pancreatic cancer detection: lessons learned from our initial experience. J. Am. Coll. Radiol. 16(9), 1338–1342 (2019)
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
Dosovitskiy, A., et al.: An image is worth 16 \(\times \) 16 words: transformers for image recognition at scale. ICLR (2021)
Gibson, E., et al.: Automatic multi-organ segmentation on abdominal CT with dense V-networks. IEEE Trans. Med. Imaging 37(8), 1822–1834 (2018)
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)
Man, Y., Huang, Y., Feng, J., Li, X., Wu, F.: Deep Q learning driven CT pancreas segmentation with geometry-aware U-Net. IEEE Trans. Med. Imaging 38(8), 1971–1980 (2019)
Mizrahi, J.D., Surana, R., Valle, J.W., Shroff, R.T.: Pancreatic cancer. Lancet 395(10242), 2008–2020 (2020)
Oudkerk, M., Liu, S., Heuvelmans, M.A., Walter, J.E., Field, J.K.: Lung cancer LDCT screening and mortality reduction–evidence, pitfalls and future perspectives. Nat. Rev. Clin. Oncol. 18, 1–17 (2020)
Roth, H.R., et al.: DeepOrgan: multi-level deep convolutional networks for automated pancreas segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 556–564. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24553-9_68
Roth, H.R., Lu, L., Farag, A., Sohn, A., Summers, R.M.: Spatial aggregation of holistically-nested networks for automated pancreas segmentation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 451–459. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_52
Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2020. CA: Cancer J. Clin. 70(1), 7–30 (2020). https://doi.org/10.3322/caac.21590
Simpson, A.L., et al.: A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv preprint arXiv:1902.09063 (2019)
Singhi, A.D., Koay, E.J., Chari, S.T., Maitra, A.: Early detection of pancreatic cancer: opportunities and challenges. Gastroenterology 156(7), 2024–2040 (2019)
Springer, S., et al.: A multimodality test to guide the management of patients with a pancreatic cyst. Sci. Transl. Med. 11(501) (2019)
Vaswani, A., et al.: Attention is all you need. arXiv preprint arXiv:1706.03762 (2017)
Xia, Y., Xie, L., Liu, F., Zhu, Z., Fishman, E.K., Yuille, A.L.: Bridging the gap between 2D and 3D organ segmentation with volumetric fusion net. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 445–453. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_51
Xia, Y., Yu, Q., Shen, W., Zhou, Y., Fishman, E.K., Yuille, A.L.: Detecting pancreatic ductal adenocarcinoma in multi-phase CT scans via alignment ensemble. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12263, pp. 285–295. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59716-0_28
Xie, Q., Luong, M.T., Hovy, E., Le, Q.V.: Self-training with noisy student improves ImageNet classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10687–10698 (2020)
Yao, J., Shi, Yu., Lu, L., Xiao, J., Zhang, L.: DeepPrognosis: preoperative prediction of pancreatic cancer survival and surgical margin via contrast-enhanced CT imaging. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12262, pp. 272–282. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59713-9_27
Yu, Q., Xie, L., Wang, Y., Zhou, Y., Fishman, E.K., Yuille, A.L.: Recurrent saliency transformation network: incorporating multi-stage visual cues for small organ segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8280–8289 (2018)
Zhang, L., et al.: Robust pancreatic ductal adenocarcinoma segmentation with multi-institutional multi-phase partially-annotated CT scans. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 491–500. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59719-1_48
Zhao, T., et al.: 3D graph anatomy geometry-integrated network for pancreatic mass segmentation, diagnosis, and quantitative patient management. arXiv preprint arXiv:2012.04701 (2020)
Zheng, S., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. arXiv preprint arXiv:2012.15840 (2020)
Zhou, Y., Xie, L., Shen, W., Wang, Y., Fishman, E.K., Yuille, A.L.: A fixed-point model for pancreas segmentation in abdominal CT scans. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 693–701. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66182-7_79
Zhu, Z., Xia, Y., Shen, W., Fishman, E., Yuille, A.: A 3D coarse-to-fine framework for volumetric medical image segmentation. In: 2018 International Conference on 3D Vision (3DV), pp. 682–690. IEEE (2018)
Zhu, Z., Xia, Y., Xie, L., Fishman, E.K., Yuille, A.L.: Multi-scale coarse-to-fine segmentation for screening pancreatic ductal adenocarcinoma. In: Shen, D., et al. (eds.) 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
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Xia, Y. et al. (2021). Effective Pancreatic Cancer Screening on Non-contrast CT Scans via Anatomy-Aware Transformers. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12905. Springer, Cham. https://doi.org/10.1007/978-3-030-87240-3_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-87240-3_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87239-7
Online ISBN: 978-3-030-87240-3
eBook Packages: Computer ScienceComputer Science (R0)
