Literature DB >> 16150630

Computer technology in detection and staging of prostate carcinoma: a review.

Yanong Zhu1, Stuart Williams, Reyer Zwiggelaar.   

Abstract

After two decades of increasing interest and research activity, computer-assisted diagnostic approaches are reaching the stage where more routine deployment in clinical practice is becoming a possibility [Kruppinski, E.A., 2004. Computer-aided detection in clinical environment: Benefits and challenges for radiologists. Radiology 231, 7-9]. This is particularly the case in the analysis of mammographic images [Helvie, M.A., Hadjiiski, L., Makariou, E., Chan, H.P., Petrick, N., Sahiner, B., Lo, S.C., Freedman, M., Adler, D., Bailey, J., Blane, C., Hoff, D., Hunt, K., Joynt, L., Klein, K., Paramagul, C., Patterson, S.K., Roubidoux, M.A., 2004. Sensitivity of noncommercial computer-aided detection system for mammographic breast cancer detection: pilot clinical trial. Radiology 231, 208-214] and in the detection of pulmonary nodules [Reeves, A.P., Kostis, W.J., 2000. Computer-aided diagnosis for lung cancer. Radiol. Clin. North Am. 38, 497-509]. However, similar approaches can be applied more widely with the promise of increasing clinical utility in other areas. We review how computer-aided approaches may be applied in the diagnosis and staging of prostatic cancer. The current status of computer technology is reviewed, covering artificial neural networks for detection and staging, computerised biopsy simulation and computer-assisted analysis of ultrasound and magnetic resonance images.

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Mesh:

Year:  2005        PMID: 16150630     DOI: 10.1016/j.media.2005.06.003

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  6 in total

1.  A fast automatic recognition and location algorithm for fetal genital organs in ultrasound images.

Authors:  Sheng Tang; Si-ping Chen
Journal:  J Zhejiang Univ Sci B       Date:  2009-09       Impact factor: 3.066

2.  Questioning context: a set of interdisciplinary questions for investigating contextual factors affecting health decision making.

Authors:  Andrea Charise; Holly Witteman; Sarah Whyte; Erica J Sutton; Jacqueline L Bender; Michael Massimi; Lindsay Stephens; Joshua Evans; Carmen Logie; Raza M Mirza; Marie Elf
Journal:  Health Expect       Date:  2010-10-28       Impact factor: 3.377

3.  Ultrasound imaging and segmentation of bone surfaces: A review.

Authors:  Ilker Hacihaliloglu
Journal:  Technology (Singap World Sci)       Date:  2017-03-31

4.  Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning.

Authors:  Yanrong Guo; Yaozong Gao; Yeqin Shao; True Price; Aytekin Oto; Dinggang Shen
Journal:  Med Phys       Date:  2014-07       Impact factor: 4.071

5.  Comparing Three Data Mining Methods to Predict Kidney Transplant Survival.

Authors:  Leila Shahmoradi; Mostafa Langarizadeh; Gholamreza Pourmand; Ziba Aghsaei Fard; Alireza Borhani
Journal:  Acta Inform Med       Date:  2016-11-01

6.  Cluster Analysis of Cell Nuclei in H&E-Stained Histological Sections of Prostate Cancer and Classification Based on Traditional and Modern Artificial Intelligence Techniques.

Authors:  Subrata Bhattacharjee; Kobiljon Ikromjanov; Kouayep Sonia Carole; Nuwan Madusanka; Nam-Hoon Cho; Yeong-Byn Hwang; Rashadul Islam Sumon; Hee-Cheol Kim; Heung-Kook Choi
Journal:  Diagnostics (Basel)       Date:  2021-12-22
  6 in total

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