BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

183 related articles for article (PubMed ID: 12473391)

  • 1. A combined neural network and decision trees model for prognosis of breast cancer relapse.
    Jerez-Aragonés JM; Gómez-Ruiz JA; Ramos-Jiménez G; Muñoz-Pérez J; Alba-Conejo E
    Artif Intell Med; 2003 Jan; 27(1):45-63. PubMed ID: 12473391
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Nonlinear discriminant analysis and prognostic factor classification in node-negative primary breast cancer using probabilistic neural networks.
    Le Goff JM; Lavayssière L; Rouëssé J; Spyratos F
    Anticancer Res; 2000; 20(3B):2213-8. PubMed ID: 10928180
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Artificial Intelligence Systems as Prognostic and Predictive Tools in Ovarian Cancer.
    Enshaei A; Robson CN; Edmondson RJ
    Ann Surg Oncol; 2015 Nov; 22(12):3970-5. PubMed ID: 25752894
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A prognostic model that makes quantitative estimates of probability of relapse for breast cancer patients.
    De Laurentiis M; De Placido S; Bianco AR; Clark GM; Ravdin PM
    Clin Cancer Res; 1999 Dec; 5(12):4133-9. PubMed ID: 10632351
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A demonstration that breast cancer recurrence can be predicted by neural network analysis.
    Ravdin PM; Clark GM; Hilsenbeck SG; Owens MA; Vendely P; Pandian MR; McGuire WL
    Breast Cancer Res Treat; 1992; 21(1):47-53. PubMed ID: 1391974
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Artificial neural networks improve the accuracy of cancer survival prediction.
    Burke HB; Goodman PH; Rosen DB; Henson DE; Weinstein JN; Harrell FE; Marks JR; Winchester DP; Bostwick DG
    Cancer; 1997 Feb; 79(4):857-62. PubMed ID: 9024725
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Predicting factors for survival of breast cancer patients using machine learning techniques.
    Ganggayah MD; Taib NA; Har YC; Lio P; Dhillon SK
    BMC Med Inform Decis Mak; 2019 Mar; 19(1):48. PubMed ID: 30902088
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Advanced integrated technique in breast cancer thermography.
    Ng EY; Kee EC
    J Med Eng Technol; 2008; 32(2):103-14. PubMed ID: 17852648
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Time-dependent estimates of recurrence and survival in colon cancer: clinical decision support system tool development for adjuvant therapy and oncological outcome assessment.
    Steele SR; Bilchik A; Johnson EK; Nissan A; Peoples GE; Eberhardt JS; Kalina P; Petersen B; Brücher B; Protic M; Avital I; Stojadinovic A
    Am Surg; 2014 May; 80(5):441-53. PubMed ID: 24887722
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Omission of histologic grading from clinical decision making may result in overuse of adjuvant therapies in breast cancer: results from a nationwide study.
    Lundin J; Lundin M; Holli K; Kataja V; Elomaa L; Pylkkänen L; Turpeenniemi-Hujanen T; Joensuu H
    J Clin Oncol; 2001 Jan; 19(1):28-36. PubMed ID: 11134192
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Impact of censoring on learning Bayesian networks in survival modelling.
    Stajduhar I; Dalbelo-Basić B; Bogunović N
    Artif Intell Med; 2009 Nov; 47(3):199-217. PubMed ID: 19833488
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Improvement of breast cancer relapse prediction in high risk intervals using artificial neural networks.
    Jerez JM; Franco L; Alba E; Llombart-Cussac A; Lluch A; Ribelles N; Munárriz B; Martín M
    Breast Cancer Res Treat; 2005 Dec; 94(3):265-72. PubMed ID: 16254686
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A novel method for predicting kidney stone type using ensemble learning.
    Kazemi Y; Mirroshandel SA
    Artif Intell Med; 2018 Jan; 84():117-126. PubMed ID: 29241659
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Artificial neural networks applied to survival prediction in breast cancer.
    Lundin M; Lundin J; Burke HB; Toikkanen S; Pylkkänen L; Joensuu H
    Oncology; 1999 Nov; 57(4):281-6. PubMed ID: 10575312
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Who should not undergo breast conservation?
    Nijenhuis MV; Rutgers EJ
    Breast; 2013 Aug; 22 Suppl 2():S110-4. PubMed ID: 24074770
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Artificial neural networks for cancer research: outcome prediction.
    Burke HB
    Semin Surg Oncol; 1994; 10(1):73-9. PubMed ID: 8115788
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Predicting the Survival of Gastric Cancer Patients Using Artificial and Bayesian Neural Networks.
    Korhani Kangi A; Bahrampour A
    Asian Pac J Cancer Prev; 2018 Feb; 19(2):487-490. PubMed ID: 29480983
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A fuzzy logic based-method for prognostic decision making in breast and prostate cancers.
    Seker H; Odetayo MO; Petrovic D; Naguib RN
    IEEE Trans Inf Technol Biomed; 2003 Jun; 7(2):114-22. PubMed ID: 12834167
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Tailored follow-up for early breast cancer patients: a prognostic index that predicts locoregional recurrence.
    van Nes JG; Putter H; van Hezewijk M; Hille ET; Bartelink H; Collette L; van de Velde CJ;
    Eur J Surg Oncol; 2010 Jul; 36(7):617-24. PubMed ID: 20558026
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Artificial neural network for the joint modelling of discrete cause-specific hazards.
    Biganzoli EM; Boracchi P; Ambrogi F; Marubini E
    Artif Intell Med; 2006 Jun; 37(2):119-30. PubMed ID: 16730963
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.