Urinary bladder cancer staging in CT urography using machine learning
dc.contributor.author | Garapati, Sankeerth S. | |
dc.contributor.author | Hadjiiski, Lubomir | |
dc.contributor.author | Cha, Kenny H. | |
dc.contributor.author | Chan, Heang‐ping | |
dc.contributor.author | Caoili, Elaine M. | |
dc.contributor.author | Cohan, Richard H. | |
dc.contributor.author | Weizer, Alon | |
dc.contributor.author | Alva, Ajjai | |
dc.contributor.author | Paramagul, Chintana | |
dc.contributor.author | Wei, Jun | |
dc.contributor.author | Zhou, Chuan | |
dc.date.accessioned | 2017-12-15T16:47:44Z | |
dc.date.available | 2019-01-07T18:34:36Z | en |
dc.date.issued | 2017-11 | |
dc.identifier.citation | Garapati, Sankeerth S.; Hadjiiski, Lubomir; Cha, Kenny H.; Chan, Heang‐ping ; Caoili, Elaine M.; Cohan, Richard H.; Weizer, Alon; Alva, Ajjai; Paramagul, Chintana; Wei, Jun; Zhou, Chuan (2017). "Urinary bladder cancer staging in CT urography using machine learning." Medical Physics 44(11): 5814-5823. | |
dc.identifier.issn | 0094-2405 | |
dc.identifier.issn | 2473-4209 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/139956 | |
dc.publisher | American Cancer Society Inc. | |
dc.publisher | Wiley Periodicals, Inc. | |
dc.subject.other | machine learning | |
dc.subject.other | radiomics | |
dc.subject.other | segmentation | |
dc.subject.other | bladder cancer staging | |
dc.subject.other | computerâ aided diagnosis | |
dc.subject.other | CT urography | |
dc.subject.other | feature extraction | |
dc.subject.other | classification | |
dc.title | Urinary bladder cancer staging in CT urography using machine learning | |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | |
dc.subject.hlbsecondlevel | Medicine (General) | |
dc.subject.hlbtoplevel | Health Sciences | |
dc.description.peerreviewed | Peer Reviewed | |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/139956/1/mp12510.pdf | |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/139956/2/mp12510_am.pdf | |
dc.identifier.doi | 10.1002/mp.12510 | |
dc.identifier.source | Medical Physics | |
dc.identifier.citedreference | Rumelhart DE, Hinton GE, Williams RJ. Learning Internal Representation by Error Propagation, Parallel Distributed Processing. Cambridge, MA: MIT Press; 1986. | |
dc.identifier.citedreference | Babjuk M, Bohle A, Burger M, et al. Guidelines on Nonâ muscleâ invasive Bladder Cancer (Ta, T1 and CIS), European Association of Urology; 2016. | |
dc.identifier.citedreference | AJCC Cancer Staging Handbook, 8th ed. Chicago, IL: American Joint Committee on Cancer; 2016. | |
dc.identifier.citedreference | Herr HW, Donat SM. Quality control in transurethral resection of bladder tumours. BJU Int. 2008; 102: 1242 â 1246. | |
dc.identifier.citedreference | Meeks JJ, Bellmunt J, Bochner BH, et al. A systematic review of neoadjuvant and adjuvant chemotherapy for muscleâ invasive bladder cancer. Eur Urol. 2012; 62: 523 â 533. | |
dc.identifier.citedreference | Fagg SL, Dawsonedwards P, Hughes MA, Latief TN, Rolfe EB, Fielding JWL. CISâ Diamminedichloroplatinum (DDP) as initial treatment of invasive bladder cancer. Br J Urol. 1984; 56: 296 â 300. | |
dc.identifier.citedreference | Raghavan D, Pearson B, Coorey G, et al. Intravenous CISâ platinum for invasive bladder cancer â safety and feasibility of a new approach. Med J Aust. 1984; 140: 276 â 278. | |
dc.identifier.citedreference | Huguet J, Crego M, Sabate S, Salvador J, Palou J, Villavicencio H. Cystectomy in patients with high risk superficial bladder tumors who fail intravesical BCG therapy: preâ cystectomy prostate involvement as a prognostic factor. Eur Urol. 2005; 48: 53 â 59. | |
dc.identifier.citedreference | Fritsche HM, Burger M, Svatek RS, et al. Characteristics and outcomes of patients with clinical T1 grade 3 urothelial carcinoma treated with radical cystectomy: results from an international cohort. Eur Urol. 2010; 57: 300 â 309. | |
dc.identifier.citedreference | Turker P, Bostrom PJ, Wroclawski ML, et al. Upstaging of urothelial cancer at the time of radical cystectomy: factors associated with upstaging and its effect on outcome. BJU Int. 2012; 110: 804 â 811. | |
dc.identifier.citedreference | Shariat SF, Palapattu GS, Karakiewicz PI, et al. Discrepancy between clinical and pathologic stage: impact on prognosis after radical cystectomy. Eur Urol. 2007; 51: 137 â 151. | |
dc.identifier.citedreference | ACR Manual on Contrast Media. ACR Committee on Drugs and Contrast Media; 2016. | |
dc.identifier.citedreference | Hadjiiski LM, Chan Hâ P, Caoili EM, Cohan RH, Wei J, Zhou C. Autoâ initialized cascaded level set (AIâ CALS) segmentation of bladder lesions on multiâ detector row CT urography. Acad Radiol. 2013; 20: 148 â 155. | |
dc.identifier.citedreference | Hadjiiski LM, Sahiner B, Chan Hâ P, Petrick N, Helvie MA, Gurcan MN. Analysis of temporal change of mammographic features: computerâ aided classification of malignant and benign breast masses. Med Phys. 2001; 28: 2309 â 2317. | |
dc.identifier.citedreference | Sahiner B, Chan Hâ P, Petrick N, Helvie MA, Goodsitt MM. Computerized characterization of masses on mammograms: the rubber band straightening transform and texture analysis. Med Phys. 1998; 25: 516 â 526. | |
dc.identifier.citedreference | Dasarathy BR, Holder EB. Image characterizations based on joint grayâ level runâ length distributions. Pattern Recog Lett. 1991; 12: 497 â 502. | |
dc.identifier.citedreference | Way TW, Hadjiiski LM, Sahiner B, et al. Computerâ aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours. Med Phys. 2006; 33: 2323 â 2337. | |
dc.identifier.citedreference | Chan Hâ P, Wei D, Helvie MA, et al. Computerâ aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space. Phys Med Biol. 1995; 40: 857 â 876. | |
dc.identifier.citedreference | Lachenbruch PA. Discriminant Analysis. New York: Hafner Press; 1975. | |
dc.identifier.citedreference | Tatsuoka MM. Multivariate Analysis, Techniques for Educational and Psychological Research, 2nd edn. New York: Macmillan; 1988. | |
dc.identifier.citedreference | Vapnik VN. Statistical Learning Theory. New York: Wiley; 1998. | |
dc.identifier.citedreference | Burges CJC. A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc. 1998; 2: 121 â 167. | |
dc.identifier.citedreference | Ho TK. The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell. 1998; 20: 832 â 844. | |
dc.identifier.citedreference | Witten IH, Frank E, Hall MA, Pal CJ, The WEKA Workbench. Online Appendix for Data Mining: Practical Machine Learning Tools and Techniques. Burlington, MA: Morgan Kaufmann; 2016. | |
dc.identifier.citedreference | Jaccard P. The distribution of the flora in the alpine zone. New Phytol. 1912; 11: 37 â 50. | |
dc.identifier.citedreference | Metz CE. ROC methodology in radiologic imaging. Invest Radiol. 1986; 21: 720 â 733. | |
dc.identifier.citedreference | Metz CE, Herman BA, Shen JH. Maximumâ likelihood estimation of receiver operating characteristic (ROC) curves from continuouslyâ distributed data. Stat Med. 1998; 17: 1033 â 1053. | |
dc.identifier.citedreference | Metz ROC Software. University of Chicago Medical Center Department of Radiology, see http://metz-roc.uchicago.edu/MetzROC/software. | |
dc.identifier.citedreference | Litjens G, Kooi T, Bejnordi BE, et al. A Survey on Deep Learning in Medical Image Analysis. arXiv:1702.05747; 2017. | |
dc.identifier.citedreference | Greenspan H, van Ginneken B, Summers RM. Deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans Med Imaging. 2016; 35: 1153 â 1159. | |
dc.identifier.citedreference | Cha KH, Hadjiiski L, Samala RK, Chan HP, Caoili EM, Cohan RH. Urinary bladder segmentation in CT urography using deepâ learning convolutional neural network and level sets. Med Phys. 2016; 43: 1882 â 1896. | |
dc.identifier.citedreference | Cha KH, Hadjiiski LM, Chan Hâ P, et al. Bladder cancer treatment response assessment using deep learning in CT with transfer learning. Proc SPIE. 2017; 10134: 101341 â 101346. | |
dc.identifier.citedreference | American Cancer Society. Cancer Facts & Figures 2017. Atlanta: American Cancer Society Inc.; 2017. | |
dc.identifier.citedreference | Bladder Cancer Advocacy Network, www.bcan.org/facts; 2017, Bladder Cancer Facts (2017). | |
dc.identifier.citedreference | Chang SS, Boorjian SA, Chou R, et al. Diagnosis and treatment of nonâ muscle invasive bladder cancer: AUA/SUO guideline. J Urol. 2016; 196: 1021 â 1029. | |
dc.identifier.citedreference | Witjes JA, Comperat E, Cowan NC, et al. Guidelines on muscleâ invasive and metastatic bladder cancer, European association of urology; 2016. | |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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