Image-based clinical decision support for transrectal ultrasound in the diagnosis of prostate cancer: Comparison of multiple logistic regression, artificial neural network, and support vector machine

European Radiology

Volumn 20, Issue 6, 2010, Pages 1476-1484

  Lee, Hak Jong ^a,b,c   Hwang, Sung Il ^a,b,c   Han, Seok Min ^d   Park, Seong Ho ^e   Kim, Seung Hyup ^c   Cho, Jeong Yeon ^c   Seong, Chang Gyu ^b   Choe, Gheeyoung ^a  

^a Seoul National University Bundang Hospital   (South Korea)

^b Seoul National University   (South Korea)

^c Seoul National University Hospital   (South Korea)

^d Samsung Advanced Institute of Technology (SAIT)   (South Korea)

^e University of Ulsan College of Medicine   (South Korea)

Author keywords

Artificial neural network; Multiple logistic regression; Prostate cancer; Support vector machine; US transrectal

Indexed keywords

PROSTATE SPECIFIC ANTIGEN;

ADULT; AGE DISTRIBUTION; AGED; ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; DECISION SUPPORT SYSTEM; DIAGNOSTIC ACCURACY; DIAGNOSTIC VALUE; DIGITAL RECTAL EXAMINATION; HUMAN; HUMAN TISSUE; IMAGE ANALYSIS; INTERMETHOD COMPARISON; MAJOR CLINICAL STUDY; MALE; MULTIVARIATE LOGISTIC REGRESSION ANALYSIS; PRIORITY JOURNAL; PROBABILITY; PROSTATE BIOPSY; PROSTATE CANCER; RECEIVER OPERATING CHARACTERISTIC; SUPPORT VECTOR MACHINE; TASK PERFORMANCE; TRANSRECTAL ULTRASONOGRAPHY;

ADULT; AGED; AGED, 80 AND OVER; ARTIFICIAL INTELLIGENCE; DECISION SUPPORT SYSTEMS, CLINICAL; DECISION SUPPORT TECHNIQUES; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; LOGISTIC MODELS; MALE; MIDDLE AGED; PATTERN RECOGNITION, AUTOMATED; PROSTATIC NEOPLASMS; RECTUM; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; ULTRASONOGRAPHY;

EID: 77952093742     PISSN: 09387994     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00330-009-1686-x     Document Type: Article

References (52)

1. C Stephan K Jung H Cammann, et al. 2002 An artificial neural network considerably improves the diagnostic power of percent free prostate-specific antigen in prostate cancer diagnosis: results of a 5-year investigation Int J Cancer 99 466 473 10.1002/ijc.10370 1:CAS:528:DC%2BD38XjsFagur0%3D 11992419 (Pubitemid 34495864)
2. WJ Catalona JP Richie FR Ahmann, et al. 1994 Comparison of digital rectal examination and serum prostate specific antigen in the early detection of prostate cancer: results of a multicenter clinical trial of 6,630 men J Urol 151 1283 1290 1:STN:280:DyaK2c3htVKgsA%3D%3D 7512659
3. SH Woolf 1995 Screening for prostate cancer with prostate-specific antigen. An examination of the evidence N Engl J Med 333 1401 1405 10.1056/NEJM199511233332107 1:STN:280:DyaK28%2FktlKntw%3D%3D 7477122
4. WJ Catalona DS Smith TL Ratliff, et al. 1991 Measurement of prostate-specific antigen in serum as a screening test for prostate cancer N Engl J Med 324 1156 1161 1:STN:280:DyaK3M7oslSksA%3D%3D 1707140
5. CG Arcangeli DK Ornstein DW Keetch GL Andriole 1997 Prostate-specific antigen as a screening test for prostate cancer. The United States experience Urol Clin North Am 24 299 306 10.1016/S0094-0143(05)70376-1 1:STN:280: DyaK2s3ntFaktA%3D%3D 9126227 (Pubitemid 27169254)
6. JP Richie WJ Catalona FR Ahmann, et al. 1993 Effect of patient age on early detection of prostate cancer with serum prostate-specific antigen and digital rectal examination Urology 42 365 374 10.1016/0090-4295(93)90359-I 1:STN:280:DyaK2c%2FisFCgsA%3D%3D 7692657 (Pubitemid 23314326)
7. WJ Catalona DS Smith DK Ornstein 1997 Prostate cancer detection in men with serum PSA concentrations of 2.6 to 4.0 ng/mL and benign prostate examination. Enhancement of specificity with free PSA measurements Jama 277 1452 1455 10.1001/jama.277.18.1452 1:CAS:528:DyaK2sXjsFejtrc%3D 9145717
8. N Lawrentschuk n Fleshner 2009 the role of magnetic resonance imaging in targeting prostate cancer in patients with previous negative biopsies and elevated prostate-specific antigen levels BJU Int 103 730 733 10.1111/j.1464-410X.2008.08205.x 19154475
9. H Suzuki A Komiya N Kamiya, et al. 2006 Development of a nomogram to predict probability of positive initial prostate biopsy among Japanese patients Urology 67 131 136 10.1016/j.urology.2005.07.040 16413348 (Pubitemid 43069271)
10. K Kamoi RJ Babaian 1999 Advances in the application of prostate-specific antigen in the detection of early-stage prostate cancer Semin Oncol 26 140 149 1:STN:280:DC%2BD3c%2FmsVGqtw%3D%3D 10597725
11. WJ Catalona AW Partin KM Slawin, et al. 1998 Use of the percentage of free prostate-specific antigen to enhance differentiation of prostate cancer from benign prostatic disease: a prospective multicenter clinical trial Jama 279 1542 1547 10.1001/jama.279.19.1542 1:STN:280:DyaK1c3ms12gtw%3D%3D 9605898 (Pubitemid 28233009)
12. H Suzuki K Akakura T Igarashi, et al. 2004 Clinical usefulness of serum antip53 antibodies for prostate cancer detection: a comparative study with prostate specific antigen parameters J Urol 171 182 186 10.1097/01.ju. 0000101501.54931.4a 1:CAS:528:DC%2BD3sXps1yqsbY%3D 14665872 (Pubitemid 37549119)
13. PB Snow DS Smith WJ Catalona 1994 Artificial neural networks in the diagnosis and prognosis of prostate cancer: a pilot study J Urol 152 1923 1926 1:STN:280:DyaK2M%2FhvVyitA%3D%3D 7523737
14. C Stephan H Cammann A Semjonow, et al. 2002 Multicenter evaluation of an artificial neural network to increase the prostate cancer detection rate and reduce unnecessary biopsies Clin Chem 48 1279 1287 1:CAS:528: DC%2BD38XlslKktro%3D 12142385
15. PI Karakiewicz S Benayoun MW Kattan, et al. 2005 Development and validation of a nomogram predicting the outcome of prostate biopsy based on patient age, digital rectal examination and serum prostate specific antigen J Urol 173 1930 1934 10.1097/01.ju.0000158039.94467.5d 15879784 (Pubitemid 40675214)
16. FK Chun A Briganti M Graefen, et al. 2007 Development and external validation of an extended 10-core biopsy nomogram Eur Urol 52 436 444 10.1016/j.eururo.2006.08.039 17010505 (Pubitemid 46963425)
17. P Finne R Finne C Bangma, et al. 2004 Algorithms based on prostate-specific antigen (PSA), free PSA, digital rectal examination and prostate volume reduce false-positive PSA results in prostate cancer screening Int J Cancer 111 310 315 10.1002/ijc.20250 1:CAS:528:DC%2BD2cXlvFWntr8%3D 15197788 (Pubitemid 38924712)
18. J Walz M Graefen FK Chun, et al. 2006 High incidence of prostate cancer detected by saturation biopsy after previous negative biopsy series Eur Urol 50 498 505 10.1016/j.eururo.2006.03.026 16631303 (Pubitemid 44189057)
19. RK Nam A Toi LH Klotz, et al. 2007 Assessing individual risk for prostate cancer J Clin Oncol 25 3582 3588 10.1200/JCO.2007.10.6450 1:CAS:528: DC%2BD2sXhtVeqt7bE 17704405 (Pubitemid 47372595)
20. FJ Bianco Jr 2006 Nomograms and medicine Eur Urol 50 884 886 10.1016/j.eururo.2006.07.043 16973258 (Pubitemid 44488713)
21. T Loch I Leuschner C Genberg, et al. 1999 Artificial neural network analysis (ANNA) of prostatic transrectal ultrasound Prostate 39 198 204 10.1002/(SICI)1097-0045(19990515)39:3<198::AID-PROS8>3.0.CO;2-X 1:STN:280:DyaK1M3msFakug%3D%3D 10334109 (Pubitemid 29203407)
22. C cortes V vapnik 1995 Support-vector networks Mach Learn 20 173 297
23. GD Carlson CB Calvanese AW Partin 1998 An algorithm combining age, total prostate-specific antigen (PSA), and percent free PSA to predict prostate cancer: results on 4298 cases Urology 52 455 461 10.1016/S0090-4295(98)00205-2 1:STN:280:DyaK1czpvVahug%3D%3D 9730460 (Pubitemid 28399914)
24. JA Eastham R May JL Robertson O Sartor MW Kattan 1999 Development of a nomogram that predicts the probability of a positive prostate biopsy in men with an abnormal digital rectal examination and a prostate-specific antigen between 0 and 4 ng/mL Urology 54 709 713 10.1016/S0090-4295(99)00213-7 1:STN:280:DyaK1MvjvV2msw%3D%3D 10510933 (Pubitemid 29449435)
25. SR Potter W Horniger M Tinzl G Bartsch AW Partin 2001 Age, prostate-specific antigen, and digital rectal examination as determinants of the probability of having prostate cancer Urology 57 1100 1104 10.1016/S0090- 4295(01)00980-3 1:STN:280:DC%2BD387hslOlsQ%3D%3D 11377318 (Pubitemid 32455579)
26. M Ohori P Swindle 2002 Nomograms and instruments for the initial prostate evaluation: the ability to estimate the likelihood of identifying prostate cancer Semin Urol Oncol 20 116 122 10.1053/suro.2002.32520 12012297
27. Jiang L, Manry MT (2008) Nonlinear networks for classification. ftp://ftp.simtel.net/pub/simtelnet/msdos/calculte. Accessed 13 Mar 2008
28. E Comak A Arslan I Turkoglu 2007 A decision support system based on support vector machines for diagnosis of the heart valve diseases Comput Biol Med 37 21 27 10.1016/j.compbiomed.2005.11.002 16426598 (Pubitemid 44584252)
29. Chang C-C, Lin C-J (2008) LIBSVM-A library for support vector machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm. Accessed 1 Jul 2008
30. MW Kattan 2002 Nomograms. Introduction Semin Urol Oncol 20 79 81 12030248
31. CR Porter EJ Gamito ED Crawford, et al. 2005 Model to predict prostate biopsy outcome in large screening population with independent validation in referral setting Urology 65 937 941 10.1016/j.urology.2004.11.049 15882727 (Pubitemid 40692986)
32. JE Oesterling WH Cooner SJ Jacobsen HA Guess MM Lieber 1993 Influence of patient age on the serum PSA concentration. An important clinical observation Urol Clin North Am 20 671 680 1:STN:280:DyaK2c%2FpvVOlsw%3D%3D 7505975
33. HB Carter JD Pearson EJ Metter, et al. 1992 Longitudinal evaluation of prostate-specific antigen levels in men with and without prostate disease Jama 267 2215 2220 10.1001/jama.267.16.2215 1:STN:280:DyaK383htFOrtQ%3D%3D 1372942
34. TD Richardson JE Oesterling 1997 Age-specific reference ranges for serum prostate-specific antigen Urol Clin North Am 24 339 351 10.1016/S0094-0143(05) 70381-5 1:STN:280:DyaK2s3ntFarsw%3D%3D 9126232 (Pubitemid 27169259)
35. MC Benson IS Whang CA Olsson DJ McMahon WH Cooner 1992 The use of prostate specific antigen density to enhance the predictive value of intermediate levels of serum prostate specific antigen J Urol 147 817 821 1:STN:280:DyaK387mslChsQ%3D%3D 1371555
36. MW Kattan JA Eastham TM Wheeler, et al. 2003 Counseling men with prostate cancer: a nomogram for predicting the presence of small, moderately differentiated, confined tumors J Urol 170 1792 1797 10.1097/01.ju.0000091806. 70171.41 14532778 (Pubitemid 37254930)
37. EW Steyerberg MJ Roobol MW Kattan TH van der Kwast HJ de Koning FH Schroder 2007 Prediction of indolent prostate cancer: validation and updating of a prognostic nomogram J Urol 177 107 112 10.1016/j.juro.2006.08.068 1:STN:280:DC%2BD28jitF2nsg%3D%3D 17162015 (Pubitemid 44879468)
38. A Errejon ED Crawford J Dayhoff, et al. 2001 Use of artificial neural networks in prostate cancer Mol Urol 5 153 158 10.1089/10915360152745821 1:STN:280:DC%2BD38%2FmsVeksw%3D%3D 11790276 (Pubitemid 34038835)
39. T Anagnostou M Remzi B Djavan 2003 Artificial neural networks for decision-making in urologic oncology Rev Urol 5 15 21 16985612
40. RJ Babaian H Miyashita RB Evans EI Ramirez 1992 The distribution of prostate specific antigen in men without clinical or pathological evidence of prostate cancer: relationship to gland volume and age J Urol 147 837 840 1:STN:280:DyaK387mslChug%3D%3D 1371558
41. RJ babaian H Fritsche A Ayala, et al. 2000 Performance of a neural network in detectong prostate cancer in the prostate-specific antigen reflex range of 2.5 to 4.0 ng/mL Urology 56 1000 1006 10.1016/S0090-4295(00)00830-X 1:STN:280:DC%2BD3M7hsFekuw%3D%3D 11113747
42. M Remzi T Anagnostou V Ravery, et al. 2003 An artificial neural network to predict the outcome of repeat prostate biopsies Urology 62 456 460 10.1016/S0090-4295(03)00409-6 12946746 (Pubitemid 36999868)
43. M Remzi B Djavan S Hruby, et al. 2001 Use of anewly developed artificial neural network(ANN) to select patients for repeat prostatic biopsies Eur Urol 39 A106
44. M Han PB Snow JM Brandt AW Partin 2001 Evaluation of artificial neural networks for the prediction of pathologic stage in prostate carcinoma Cancer 91 1661 1666 10.1002/1097-0142(20010415)91:8+<1661::AID-CNCR1180>3.0.CO;2-5 1:STN:280:DC%2BD3M3hvFWgug%3D%3D 11309765
45. E Byvatov U Fechner J Sadowski G Schneider 2003 Comparison of support vector machine and artificial neural network systems for drug/nondrug classification J Chem Inf Comput Sci 43 1882 1889 1:CAS:528:DC%2BD3sXns1Wmt74%3D 14632437
46. NF Giiler S Kocer 2005 Use of support vector machines and neural network in diagnosis of neuromuscular disorders J Med Syst 29 271 284 10.1007/s10916-005-5187-4 (Pubitemid 40931930)
47. A Chu h Ahn B Halwan, et al. 2008 A decision support system to facilitate management of patients with acute gastrointestinal bleeding Artif Intell Med 42 247 259 10.1016/j.artmed.2007.10.003 18063351
48. ME Mavroforakis HV Georgiou N Dimitropoulos D Cavouras S Theodoridis 2006 Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers Artif Intell Med 37 145 162 10.1016/j.artmed.2006.03.002 16716579 (Pubitemid 43816282)
49. RW Veltri M Chaudhari MC Miller EC Poole GJ O'Dowd AW Partin 2002 Comparison of logistic regression and neural net modeling for prediction of prostate cancer pathologic stage Clin Chem 48 1828 1834 1:CAS:528: DC%2BD38XnsV2gsLs%3D 12324513
50. J Cho S Kim S Lee 2000 Peripheral hypoechoic lesions of the prostate: evaluation with color and power Doppler ultrasound Eur Urol 37 443 448 10.1159/000020166 1:STN:280:DC%2BD3c3ivFKmtg%3D%3D 10765075
51. HJ Lee KG Kim SE Lee, et al. 2006 Role of transrectal ultrasonography in the prediction of prostate cancer: artificial neural network analysis J Ultrasound Med 25 815 821 16798891
52. F Rabbani N Stroumbakis B Kava M Cookson W Fair 1998 Incidence et clinical significance of false-negative sextant prostate biopsies J Urol 159 1247 1250 10.1016/S0022-5347(01)63574-2 1:STN:280:DyaK1c7mslaltg%3D%3D 9507846 (Pubitemid 28172807)