Show simple item record

Urinary bladder cancer staging in CT urography using machine learning

dc.contributor.authorGarapati, Sankeerth S.
dc.contributor.authorHadjiiski, Lubomir
dc.contributor.authorCha, Kenny H.
dc.contributor.authorChan, Heang‐ping
dc.contributor.authorCaoili, Elaine M.
dc.contributor.authorCohan, Richard H.
dc.contributor.authorWeizer, Alon
dc.contributor.authorAlva, Ajjai
dc.contributor.authorParamagul, Chintana
dc.contributor.authorWei, Jun
dc.contributor.authorZhou, Chuan
dc.date.accessioned2017-12-15T16:47:44Z
dc.date.available2019-01-07T18:34:36Zen
dc.date.issued2017-11
dc.identifier.citationGarapati, 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.issn0094-2405
dc.identifier.issn2473-4209
dc.identifier.urihttps://hdl.handle.net/2027.42/139956
dc.publisherAmerican Cancer Society Inc.
dc.publisherWiley Periodicals, Inc.
dc.subject.othermachine learning
dc.subject.otherradiomics
dc.subject.othersegmentation
dc.subject.otherbladder cancer staging
dc.subject.othercomputerâ aided diagnosis
dc.subject.otherCT urography
dc.subject.otherfeature extraction
dc.subject.otherclassification
dc.titleUrinary bladder cancer staging in CT urography using machine learning
dc.typeArticleen_US
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelMedicine (General)
dc.subject.hlbtoplevelHealth Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/139956/1/mp12510.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/139956/2/mp12510_am.pdf
dc.identifier.doi10.1002/mp.12510
dc.identifier.sourceMedical Physics
dc.identifier.citedreferenceRumelhart DE, Hinton GE, Williams RJ. Learning Internal Representation by Error Propagation, Parallel Distributed Processing. Cambridge, MA: MIT Press; 1986.
dc.identifier.citedreferenceBabjuk 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.citedreferenceAJCC Cancer Staging Handbook, 8th ed. Chicago, IL: American Joint Committee on Cancer; 2016.
dc.identifier.citedreferenceHerr HW, Donat SM. Quality control in transurethral resection of bladder tumours. BJU Int. 2008; 102: 1242 â 1246.
dc.identifier.citedreferenceMeeks 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.citedreferenceFagg 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.citedreferenceRaghavan 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.citedreferenceHuguet 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.citedreferenceFritsche 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.citedreferenceTurker 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.citedreferenceShariat 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.citedreferenceACR Manual on Contrast Media. ACR Committee on Drugs and Contrast Media; 2016.
dc.identifier.citedreferenceHadjiiski 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.citedreferenceHadjiiski 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.citedreferenceSahiner 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.citedreferenceDasarathy BR, Holder EB. Image characterizations based on joint grayâ level runâ length distributions. Pattern Recog Lett. 1991; 12: 497 â 502.
dc.identifier.citedreferenceWay 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.citedreferenceChan 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.citedreferenceLachenbruch PA. Discriminant Analysis. New York: Hafner Press; 1975.
dc.identifier.citedreferenceTatsuoka MM. Multivariate Analysis, Techniques for Educational and Psychological Research, 2nd edn. New York: Macmillan; 1988.
dc.identifier.citedreferenceVapnik VN. Statistical Learning Theory. New York: Wiley; 1998.
dc.identifier.citedreferenceBurges CJC. A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc. 1998; 2: 121 â 167.
dc.identifier.citedreferenceHo TK. The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell. 1998; 20: 832 â 844.
dc.identifier.citedreferenceWitten 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.citedreferenceJaccard P. The distribution of the flora in the alpine zone. New Phytol. 1912; 11: 37 â 50.
dc.identifier.citedreferenceMetz CE. ROC methodology in radiologic imaging. Invest Radiol. 1986; 21: 720 â 733.
dc.identifier.citedreferenceMetz 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.citedreferenceMetz ROC Software. University of Chicago Medical Center Department of Radiology, see http://metz-roc.uchicago.edu/MetzROC/software.
dc.identifier.citedreferenceLitjens G, Kooi T, Bejnordi BE, et al. A Survey on Deep Learning in Medical Image Analysis. arXiv:1702.05747; 2017.
dc.identifier.citedreferenceGreenspan 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.citedreferenceCha 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.citedreferenceCha 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.citedreferenceAmerican Cancer Society. Cancer Facts & Figures 2017. Atlanta: American Cancer Society Inc.; 2017.
dc.identifier.citedreferenceBladder Cancer Advocacy Network, www.bcan.org/facts; 2017, Bladder Cancer Facts (2017).
dc.identifier.citedreferenceChang 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.citedreferenceWitjes JA, Comperat E, Cowan NC, et al. Guidelines on muscleâ invasive and metastatic bladder cancer, European association of urology; 2016.
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.