Identification of Breast Cancer Using Integrated Information from MRI and Mammography
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OBJECTIVES: Integration of information from corresponding regions between the breast MRI and an X-ray mammogram could benefit the detection of breast cancer in clinical diagnosis. We aimed to provide a framework of registration from breast MRI to mammography and to evaluate the diagnosis using the combined information. MATERIALS AND METHODS: 43 patients with 46 lesions underwent both MRI and mammography scans, and the interval between the two examinations was around one month. The distribution of malignant to benign lesions was 31/46 based on histological results. Maximum intensity projection and thin-plate spline methods were applied for image registration for MRI to mammography. The diagnosis using integrated information was evaluated using results of histology as the reference. The assessment of annotations and statistical analysis were performed by the two radiologists. RESULTS: For the cranio-caudal view, the mean post-registration error between MRI and mammography was 2.2±1.9 mm. For the medio-lateral oblique view, the proposed approach performed even better with a mean error of 3.0±2.4 mm. In the diagnosis using MRI assessment with information of mammography, the sensitivity was 91.9±2.3% (29/31, 28/31), specificity 70.0±4.7% (11/15, 10/15), accuracy 84.8±3.1% (40/46, 38/46), positive predictive value 86.4±2.1% (29/33, 28/33) and negative predictive value 80.8±5.4% (11/13, 10/13). CONCLUSION: MRI with the aid of mammography shows potential improvements of sensitivity, specificity, accuracy, PPV and NPV in clinical breast cancer diagnosis compared to the use of MRI alone.
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Combination of data types, especially the sum of low diagnostics data, provides a multi-level overview, better understanding of complex multifactorial conditions, and leads to faster diagnostics [78,79].
“The combination of data types, especially the sum of low diagnostics data, provides a multi-level overview and better understanding of complex multifactorial conditions, and also leads to a faster diagnostics [78, 79].”
From Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare (2018) · cites this paper as [79]
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Kuhl, C. K., Mielcarek, P., Klaschik, S., Leutner, C., Wardelmann, E., Gieseke, J., & Schild, H. H. (1999) · Radiology · doi:10.1148/radiology.211.1.r99ap38101 · Cited by 1,279 · see citing works
Houssami, N., Ciatto, S., Macaskill, P., Lord, S. J., Warren, R. M. L., Dixon, J. M., & Irwig, L. (2008) · Journal of Clinical Oncology · doi:10.1200/jco.2007.15.2108 · Cited by 827 · see citing works
Kuhl, C. K. (2007) · Radiology · doi:10.1148/radiol.2442051620 · Cited by 772 · see citing works
Systematic Review: Using Magnetic Resonance Imaging to Screen Women at High Risk for Breast Cancer
Warner, E., Messersmith, H., Causer, P., Eisen, A., Shumak, R., & Plewes, D. (2008) · Annals of Internal Medicine · doi:10.7326/0003-4819-148-9-200805060-00007 · Cited by 564 · see citing works
Current Status of Breast MR Imaging Part 2. Clinical Applications
Kuhl, C. K. (2007) · Radiology · doi:10.1148/radiol.2443051661 · Cited by 375 · see citing works
Background Parenchymal Enhancement at Breast MR Imaging and Breast Cancer Risk
King, V., Brooks, J. D., Bernstein, J. L., Reiner, A. S., Pike, M. C., & Morris, E. A. (2011) · Radiology · doi:10.1148/radiol.11102156 · Cited by 368 · see citing works
Lord, S. J., Lei, W., Craft, P., Cawson, J. N., Morris, I. T., Walleser, S., Griffiths, A. D., Parker, S., & Houssami, N. (2007) · European Journal of Cancer · doi:10.1016/j.ejca.2007.06.007 · Cited by 280 · see citing works
Diffusion-weighted Imaging Improves the Diagnostic Accuracy of Conventional 3.0-T Breast MR Imaging
Khouli, R. H. E., Jacobs, M. A., Mezban, S. D., Huang, P., Kamel, I. R., Macura, K. J., & Bluemke, D. A. (2010) · Radiology · doi:10.1148/radiol.10091367 · Cited by 267 · see citing works
False-Positive Findings at Contrast-Enhanced Breast MRI: A BI-RADS Descriptor Study
Baltzer, P., Benndorf, M., Dietzel, M., Gajda, M. R., Runnebaum, I. B., & Kaiser, W. A. (2010) · American Journal of Roentgenology · doi:10.2214/ajr.09.3486 · Cited by 197 · see citing works
Dynamic Contrast-Enhanced MRI of the Breast: Quantitative Method for Kinetic Curve Type Assessment
Khouli, R. H. E., Macura, K. J., Jacobs, M. A., Khalil, T. H., Kamel, I. R., Dwyer, A. A., & Bluemke, D. A. (2009) · American Journal of Roentgenology · doi:10.2214/ajr.09.2483 · Cited by 144 · see citing works
Yabuuchi, H., Matsuo, Y., Sunami, S., Kamitani, T., Kawanami, S., Setoguchi, T., Sakai, S., Hatakenaka, M., Kubo, M., Tokunaga, E., et al. (2010) · European Radiology · doi:10.1007/s00330-010-1890-8 · Cited by 130 · see citing works
A Review of Current Evidence-Based Clinical Applications for Breast Magnetic Resonance Imaging
DeMartini, W. B., & Lehman, C. D. (2008) · Topics in Magnetic Resonance Imaging · doi:10.1097/rmr.0b013e31818a40a5 · Cited by 100 · see citing works
Doğan, B. E., Whitman, G. J., Kushwaha, A. C., Phelps, M. J., & Dempsey, P. J. (2006) · American Journal of Roentgenology · doi:10.2214/ajr.05.0572 · Cited by 88 · see citing works
Pediconi, F., Catalano, C., Roselli, A., Padula, S., Altomari, F., Moriconi, E., Pronio, A., Kirchin, M. A., & Passariello, R. (2007) · Radiology · doi:10.1148/radiol.2433060838 · Cited by 81 · see citing works
Hopp, T., Dietzel, M., Baltzer, P., Kreisel, P., Kaiser, W. A., Gemmeke, H., & Ruiter, N. V. (2012) · Medical Image Analysis · doi:10.1016/j.media.2012.10.003 · Cited by 77 · see citing works
Digital mammography: An update
Schulz-Wendtland, R., Fuchsjäger, M., Wacker, T., & Hermann, K.-P. (2009) · European Journal of Radiology · doi:10.1016/j.ejrad.2009.05.052 · Cited by 74 · see citing works
Thomassin‐Naggara, I., Trop, I., Chopier, J., David, J., Lalonde, L., Daraï, É., Rouzier, R., & Uzan, S. (2011) · Radiology · doi:10.1148/radiol.11110190 · Cited by 72 · see citing works
Schmitz, A. C., Peters, N. H. G. M., Veldhuis, W. B., Gallardo, A. M. F., van Diest, P. J., Stapper, G., van Hillegersberg, R., Mali, W. P. T. M., & van den Bosch, M. A. A. J. (2007) · European Radiology · doi:10.1007/s00330-007-0766-z · Cited by 65 · see citing works
Cai, H., Peng, Y., Ou, C., Chen, M., & Li, L. (2014) · PLoS ONE · doi:10.1371/journal.pone.0087387 · Cited by 54 · see citing works
Hopp, T., Baltzer, P., Dietzel, M., Kaiser, W. A., & Ruiter, N. V. (2011) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-011-0623-z · Cited by 43 · see citing works
Mertzanidou, T., Hipwell, J. H., Johnsen, S., Han, L., Eiben, B., Taylor, Z. A., Sebastien Ourselin, A., Huisman, H. J., Mann, R. M., Bick, U., et al. (2014) · Medical Image Analysis · doi:10.1016/j.media.2014.03.003 · Cited by 39 · see citing works
Three-dimensional mr imaging of mammographically detected suspicious microcalcifications
Nakahara, H., Namba, K., Fukami, A., Watanabe, R., Maeda, Y., Furusawa, H., Matsu, T., Akiyama, F., Nakagawa, H., Ifuku, H., et al. (2001) · Breast Cancer · doi:10.1007/bf02967490 · Cited by 28 · see citing works
MRI to X-ray mammography registration using a volume-preserving affine transformation
Mertzanidou, T., Hipwell, J., Cardoso, M. J., Zhang, X., Tanner, C., Ourselin, S., Bick, U., Huisman, H., Karssemeijer, N., & Hawkes, D. (2012) · Medical Image Analysis · doi:10.1016/j.media.2012.03.001 · Cited by 24 · see citing works
Jiang, Y., Lou, J., Wang, S., Zhao, Y., Wang, C., & Wang, D. (2014) · PLoS ONE · doi:10.1371/journal.pone.0099669 · Cited by 19 · see citing works
Gweon, H. M., Cho, N., Seo, M., Chu, A. J., & Moon, W. K. (2014) · European Radiology · doi:10.1007/s00330-014-3166-1 · Cited by 11 · see citing works
Mammographic screening and reporting: a need for standardisation. A review
Akhigbe, A., & Igbinedion, B. O.-E. (2013) · Nigerian Postgraduate Medical Journal · doi:10.4103/1117-1936.164643 · Cited by 6 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [24]Tsai YC, Lin HD, Hu YC, Yu CL, Lin KP. Thin-plate spline technique for medical image deformation. Journal of Medical and Biomedical Engineering 2000; 20: 203– 210.
Cited by
Sorted by citations
Mamoshina, P., Ojomoko, L., Yanovich, Y., Ostrovski, A., Botezatu, A., Prikhodko, P., Izumchenko, E., Aliper, A., Romantsov, K., Zhebrak, A., et al. (2018) · Oncotarget · doi:10.18632/oncotarget.22345 · Cited by 471 · see citing works
Primary and secondary prevention of breast cancer
Kolak, A., Kamińska, M., Sygit, K., Budny, A., Surdyka, D., Kukiełka-Budny, B., & Burdan, F. (2017) · Annals of Agricultural and Environmental Medicine · doi:10.26444/aaem/75943 · Cited by 356 · see citing works
Zhang, Y., Satapathy, S. C., Guttery, D. S., Górriz, J. M., & Wang, S. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102439 · Cited by 353 · see citing works
Zhang, Y., Pan, C., Chen, X., & Wang, F. (2018) · Journal of Computational Science · doi:10.1016/j.jocs.2018.05.005 · Cited by 163 · see citing works
Utilization of DenseNet201 for diagnosis of breast abnormality
Yu, X., Zeng, N., Liu, S., & Zhang, Y. (2019) · Machine Vision and Applications · doi:10.1007/s00138-019-01042-8 · Cited by 104 · see citing works
Review of breast screening: Toward clinical realization of microwave imaging
Modiri, A., Goudreau, S., Rahimi, A. S., & Kıasaleh, K. (2017) · Medical Physics · doi:10.1002/mp.12611 · Cited by 87 · see citing works
Wavelet energy entropy and linear regression classifier for detecting abnormal breasts
Chen, Y., Zhang⋆, Y., Lu, H., Chen, X., Li, J., & Wang, S. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-4161-0 · Cited by 43 · see citing works
Transfer learning privileged information fuels CAD diagnosis of breast cancer
Shaikh, T. A., Ali, R., & Beg, M. M. S. (2020) · Machine Vision and Applications · doi:10.1007/s00138-020-01058-5 · Cited by 28 · see citing works
Khan, T. M., Xu, S., Khan, Z. G., & chishti, M. U. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/5577636 · Cited by 27 · see citing works
Over time prevalence of illicit substance use in Saudi Arabia
Alfaifi, A., & El‐Setouhy, M. (2022) · World Family Medicine Journal/Middle East Journal of Family Medicine · doi:10.5742/mewfm.2022.9525150 · Cited by 19 · see citing works
Zhao, Y., Chen, Z., Zhang, Y., Zhou, J., Chen, J., Lee, K. E., Combs, F. J., Parajuli, R., Mehta, R. S., Wang, M., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.774248 · Cited by 14 · see citing works
Ashoor, M., & Khorshidi, A. (2023) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-023-02994-w · Cited by 8 · see citing works
Otani, H., Udagawa, J., & Naito, K. (2016) · The Journal of Biochemistry · doi:10.1093/jb/mvw020 · Cited by 8 · see citing works
Liu, F., Nakamura, K., & Payne, R. (2017) · International Conference on Mechatronics Engineering and Information Technology (ICMEIT) · doi:10.2991/icmeit-17.2017.69 · Cited by 5 · see citing works
Chen, Y., Yang, L., Wang, C., & Wang, C. (2023) · Oncology Letters · doi:10.3892/ol.2023.14089 · Cited by 5 · see citing works
Bakhtavar, K., Saran, M., Behzadifar, M., & Farsi, M. (2017) · Electronic physician · doi:10.19082/5083 · Cited by 5 · see citing works
Alqifari, S. F., Aldawish, R., Binsaqr, M. A., Khojah, A. A., Alshammari, M. R., Esmail, A. K., & AL‐Rasheedi, M. (2022) · World Family Medicine Journal/Middle East Journal of Family Medicine · doi:10.5742/mewfm.2022.9525130 · Cited by 4 · see citing works
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Salmanoğlu, E., Klinger, K., Bhimani, C., Sevrukov, A., & Thakur, M. L. (2019) · Clinical and Translational Imaging · doi:10.1007/s40336-019-00346-z · Cited by 2 · see citing works
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Mu, J., Nazar, A., Ali, M. A. M., & Hussain, A. (2025) · Gene Reports · doi:10.1016/j.genrep.2025.102325 · Cited by 2 · see citing works
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Karrar, H. R., Nouh, M. I., Alanazi, B. M., Hakami, A. O. A., Alzahrani, W. J. S., Aldoweirah, W. K., Alanazi, W. S., Alshammari, N. E., Alanazi, B. M., Alharbi, R. A., et al. (2022) · World Family Medicine Journal/Middle East Journal of Family Medicine · doi:10.5742/mewfm.2022.9525148 · Cited by 2 · see citing works
Alzahrani, K. M., Faydh, J. A., Althuwaybi, S. E., Dakhil, S. S. F., Althobaiti, F. D. A., & Althagafi, B. (2022) · World Family Medicine Journal/Middle East Journal of Family Medicine · doi:10.5742/mewfm.2022.9525132 · Cited by 2 · see citing works
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Bhatt, D. M., Oza, P., Sharma, P., & Patel, S. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-31723-1_1 · Cited by 1 · see citing works
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Ali, A. A., Aledili, F. J., Albahrani, F., Albisher, H., Bomouzah, F. A., Aleliwi, F., Alghasham, G., & Alalawi, L. Y. (2022) · World Family Medicine Journal/Middle East Journal of Family Medicine · doi:10.5742/mewfm.2022.9525138 · Cited by 1 · see citing works
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- Authors
- Yang, S.-N., Li, F.-J., Liao, Y.-H., Chen, Y.-S., Shen, W.-C., & Huang, T.-C.
- Affiliations
- China Medical University (TW)
- China Medical University Hospital (TW)
- Asia University (TW)
- Corresponding author
- Huang, Tzung-Chi
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-06-09
- Volume
- 10
- Issue
- 6
- Pages
- e0128404
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4461246
- OpenAlex
- W1810199329
- Cited by
- 36
- References
- 26
- Citations by year
- 2025: 2 · 2023: 2 · 2022: 17 · 2021: 2 · 2020: 3 · 2019: 2 · 2018: 1 · 2017: 5 · 2016: 2
- FWCI
- 2.16
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- China Medical University
- China Medical University, Taiwan
- Division of Materials Research
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › MRI in cancer diagnosis
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Digital Radiography and Breast Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- breast cancer diagnosis, breast MRI, mammography, multimodal image registration, maximum intensity projection, breast lesions, diagnostic accuracy
- MeSH terms
- Adult, Aged, Breast Neoplasms, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Mammography, Middle Aged, Cohort Studies
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