Markerless Pancreatic Tumor Target Localization Enabled By Deep Learning
Open accessAbstract
Purpose Deep learning is an emerging technique that allows us to capture imaging information beyond the visually recognizable level of a human being. Because of the anatomic characteristics and location, on-board target verification for radiation delivery to pancreatic tumors is a challenging task. Our goal was to use a deep neural network to localize the pancreatic tumor target on kV x-ray images acquired using an on-board imager for image guided radiation therapy.Methods and materials The network is set up in such a way that the input is either a digitally reconstructed radiograph image or a monoscopic x-ray projection image acquired by the on-board imager from a given direction, and the output is the location of the planning target volume in the projection image. To produce a sufficient number of training x-ray images reflecting the vast number of possible clinical scenarios of anatomy distribution, a series of changes were introduced to the planning computed tomography images, including deformation, rotation, and translation, to simulate inter- and intrafractional variations. After model training, the accuracy of the model was evaluated by retrospectively studying patients who underwent pancreatic cancer radiation therapy. Statistical analysis using mean absolute differences (MADs) and Lin's concordance correlation coefficient were used to assess the accuracy of the predicted target positions.Results MADs between the model-predicted and the actual positions were found to be less than 2.60 mm in anteroposterior, lateral, and oblique directions for both axes in the detector plane. For comparison studies with and without fiducials, MADs are less than 2.49 mm. For all cases, Lin's concordance correlation coefficients between the predicted and actual positions were found to be better than 93%, demonstrating the success of the proposed deep learning for image guided radiation therapy.Conclusions We demonstrated that markerless pancreatic tumor target localization is achievable with high accuracy by using a deep learning technique approach.
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Zhao et al. introduced a deep learning-based approach to localize pancreatic tumor in projection X-ray images for image-guided radiation therapy without the need for fiducials.
“Zhao et al. [269] introduced a new deep learning-based approach to localize pancreatic tumor in projection X-ray images for image-guided radiation therapy without the need for fiducials.”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [269]
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Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works
Siegel, R. L., Miller, K. D., & Jemal, A. (2017) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21387 · Cited by 14,712 · see citing works
A concordance correlation coefficient to evaluate reproducibility.
Lin, L. I.-K. (1989) · Biometrics · doi:10.2307/2532051 · Cited by 8,785 · see citing works
Spencer, P. M., Cameron, J. L., & Lin, L. (2000) · Cited by 7,202 · see citing works
Vincent, A., Herman, J. M., Schulick, R., Hruban, R. H., & Goggins, M. (2011) · The Lancet · doi:10.1016/s0140-6736(10)62307-0 · Cited by 2,613 · see citing works
Proceedings of IEEE International Conference on Computer Vision
(1995) · doi:10.1109/iccv.1995 · Cited by 2,339 · see citing works
The management of respiratory motion in radiation oncology report of AAPM Task Group 76a)
Keall, P., Mageras, G. S., Balter, J. M., et al. (2006) · Medical Physics · doi:10.1118/1.2349696 · Cited by 2,326 · see citing works
Flat-panel cone-beam computed tomography for image-guided radiation therapy
Jaffray, D. A., Siewerdsen, J. H., Wong, J. W., & Martinez, A. A. (2002) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/s0360-3016(02)02884-5 · Cited by 1,331 · see citing works
Physical aspects of a real-time tumor-tracking system for gated radiotherapy
Shirato, H., Shimizu, S., Kunieda, T., et al. (2000) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/s0360-3016(00)00748-3 · Cited by 662 · see citing works
Image-guided radiotherapy: from current concept to future perspectives
Jaffray, D. A. (2012) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2012.194 · Cited by 498 · see citing works
Overview of image-guided radiation therapy
Xing, L., Thorndyke, B., Schreibmann, E., Yang, Y., Li, T., Kim, G.-Y., Luxton, G., & Koong, A. C. (2006) · Medical dosimetry · doi:10.1016/j.meddos.2005.12.004 · Cited by 469 · see citing works
Self-citationInnovations in image-guided radiotherapy
Verellen, D., Ridder, M. D., Linthout, N., Tournel, K., Soete, G., & Storme, G. A. (2007) · Nature reviews. Cancer · doi:10.1038/nrc2288 · Cited by 386 · see citing works
Image-guided radiotherapy: rationale, benefits, and limitations
Dawson, L. A., & Sharpe, M. B. (2006) · The Lancet Oncology · doi:10.1016/s1470-2045(06)70904-4 · Cited by 341 · see citing works
Veldeman, L., Madani, I., Hulstaert, F., Meerleer, G. D., Mareel, M., & Neve, W. D. (2008) · The Lancet Oncology · doi:10.1016/s1470-2045(08)70098-6 · Cited by 332 · see citing works
Online Magnetic Resonance Image Guided Adaptive Radiation Therapy: First Clinical Applications
Acharya, S., Fischer-Valuck, B. W., Kashani, R., et al. (2015) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2015.10.015 · Cited by 302 · see citing works
Real-time tumour-tracking radiotherapy
Shirato, H., Shimizu, S., Shimizu, T., et al. (1999) · The Lancet · doi:10.1016/s0140-6736(99)00700-x · Cited by 252 · see citing works
A Review of the Clinical Evidence for Intensity-modulated Radiotherapy
Staffurth, J. N. (2010) · Clinical Oncology · doi:10.1016/j.clon.2010.06.013 · Cited by 235 · see citing works
Bladder Cancer Treatment Response Assessment in CT using Radiomics with Deep-Learning
Kenny, H., Hadjiiski, L. M., Chan, H., Weizer, A. Z., Alva, A. S., Cohan, R. H., Caoili, E. M., Paramagul, C., & Samala, R. K. (2017) · Scientific Reports · doi:10.1038/s41598-017-09315-w · Cited by 208 · see citing works
Ibragimov, B., Toesca, D. A. S., Chang, D. T., Yuan, Y., Koong, A. C., & Xing, L. (2018) · Medical Physics · doi:10.1002/mp.13122 · Cited by 125 · see citing works
Self-citationCombined kV and MV imaging for real‐time tracking of implanted fiducial markersa)
Wiersma, R. D., Mao, W., & Xing, L. (2008) · Medical Physics · doi:10.1118/1.2842072 · Cited by 120 · see citing works
Self-citationKhashab, M. A., Kim, K. J., Tryggestad, E. J., Wild, A. T., Roland, T., Singh, V. K., Lennon, A. M., Shin, E. J., Ziegler, M. A., Sharaiha, R. Z., et al. (2012) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2012.07.006 · Cited by 93 · see citing works
Monitoring tumor motion by real time 2D/3D registration during radiotherapy
Gendrin, C., Furtado, H., Weber, C., Bloch, C., Figl, M., Pawiro, S. A., Bergmann, H., Stock, M., Gabor Fichtinger, A., Georg, D., et al. (2011) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2011.07.031 · Cited by 87 · see citing works
van der Horst, A., Wognum, S., Fajardo, R. D., et al. (2013) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2013.05.001 · Cited by 78 · see citing works
Interfractional Uncertainty in the Treatment of Pancreatic Cancer With Radiation
Jayachandran, P., Minn, A. Y., Dam, J. V., et al. (2009) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2009.06.029 · Cited by 62 · see citing works
Real-time 3D internal marker tracking during arc radiotherapy by the use of combined MV–kV imaging
Liu, W., Wiersma, R., Mao, W., et al. (2008) · Physics in Medicine and Biology · doi:10.1088/0031-9155/53/24/013 · Cited by 56 · see citing works
Luo, Y., McShan, D. L., Ray, D., Matuszak, M. M., Jolly, S., Lawrence, T. S., Kong, F., Haken, R. K. T., & Naqa, I. E. (2018) · IEEE Transactions on Radiation and Plasma Medical Sciences · doi:10.1109/trpms.2018.2832609 · Cited by 51 · see citing works
Quantifying motion for pancreatic radiotherapy margin calculation
Whitfield, G. A., Jain, P., Green, M., et al. (2012) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2012.02.012 · Cited by 50 · see citing works
Gurney‐Champion, O. J., Lens, E., van der Horst, A., et al. (2015) · Medical Physics · doi:10.1118/1.4918753 · Cited by 45 · see citing works
Endoscopic Ultrasound–Guided Pancreatic Fiducial Placement
Majumder, S., Berzin, T. M., Mahadevan, A., et al. (2013) · Pancreas · doi:10.1097/mpa.0b013e3182864559 · Cited by 42 · see citing works
The role of radiotherapy in management of pancreatic cancer.
Fen, W., & Kumar, P. (2011) · PubMed · doi:10.3978/j.issn.2078-6891.2011.032 · Cited by 34 · see citing works
Automated target tracking in kilovoltage images using dynamic templates of fiducial marker clusters
Campbell, W. G., Miften, M. M., & Jones, B. L. (2016) · Medical Physics · doi:10.1002/mp.12073 · Cited by 24 · see citing works
Tracking tumor boundary in MV‐EPID images without implanted markers: A feasibility study
Zhang, X., Homma, N., Ichiji, K., et al. (2015) · Medical Physics · doi:10.1118/1.4918578 · Cited by 22 · see citing works
Omari, E. A., Erickson, B. A., Ehlers, C., et al. (2016) · Medical Physics · doi:10.1118/1.4960004 · Cited by 17 · see citing works
Shepard, A. J., Matrosic, C. K., Radtke, J. L., et al. (2018) · Medical Physics · doi:10.1002/mp.13191 · Cited by 13 · see citing works
Hamilton, D. G., McKenzie, D. P., & Perkins, A. E. (2017) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.12119 · Cited by 8 · see citing works
Zhao, W., Han, B., Yang, Y., Buyyounouski, M. K., Hancock, S., Bagshaw, H. P., & Xing, L. (2018) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2018.06.319 · Cited by 8 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00444-8 · Cited by 7,906 · see citing works
Artificial intelligence in pancreatic cancer
Huang, B., Huang, H., Zhang, S., Zhang, D., Shi, Q., Liu, J., & Guo, J. (2022) · Theranostics · doi:10.7150/thno.77949 · Cited by 142 · see citing works
“In addition to the research mentioned above, AI has also been applied to many aspects of PC, including predicting gene mutation [250,251], nucleus segmentation [252], tumor target localization [253], and predicting the risk of ICU admission for PC patients [254], etc.”
Method
- Authors
- Zhao, W., Shen, L., Han, B., Yang, Y., Cheng, K., Toesca, D. A. S., Koong, A. C., Chang, D. T., & Xing, L.
- Affiliations
- Stanford Medicine (US)
- The University of Texas MD Anderson Cancer Center (US)
- Corresponding author
- Xing, Lei
- ORCID
- Venue
- International Journal of Radiation Oncology*Biology*Physics
- Publisher
- Elsevier BV
- Published
- 2019-10
- Volume
- 105
- Issue
- 2
- Pages
- 432-439
- ISSN
- 0360-3016 (ISSN-L)
- PMC
- PMC6732032
- OpenAlex
- W2951743624
- Cited by
- 88
- References
- 37
- Citations by year
- 2026: 7 · 2025: 16 · 2024: 10 · 2023: 11 · 2022: 14 · 2021: 15 · 2020: 11 · 2019: 4
- FWCI
- 9.64
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Journal of Radiation Oncology*Biology*Physics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (1R01 CA176553, R01CA227713)
- Topics
- Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- pancreatic cancer, image-guided radiotherapy, deep neural network, planning target volume, digitally reconstructed radiographs, fiducial markers
- MeSH terms
- Organ Motion, Deep Learning, Humans, Image Processing, Computer-Assisted, Pancreas, Pancreatic Neoplasms, Radiography, Neural Networks, Computer, Fiducial Markers, Radiotherapy, Image-Guided, Datasets as Topic
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