An imaging-based approach predicts clinical outcomes in prostate cancer through a novel support vector machine classification

Oncotarget

Volumn 7, Issue 47, 2016, Pages 78140-78151

  Zhang, Yu Dong ^a   Wang, Jing ^b   Wu, Chen Jiang ^a   Bao, Mei Ling ^a   Li, Hai ^a   Wang, Xiao Ning ^a   Tao, Jun ^a   Shi, Hai Bin ^a  

^a THE FIRST AFFILIATED HOSPITAL OF NANJING MEDICAL UNIVERSITY   (China)

^b CFDA   (China)

Author keywords

Biochemical recurrence; MRI; Prostate cancer; Radical prostatectomy; Support vector machine

Indexed keywords

ADULT; AGED; APPARENT DIFFUSION COEFFICIENT; ARTICLE; CANCER PROGNOSIS; CLINICAL FEATURE; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC IMAGING; DIAGNOSTIC TEST ACCURACY STUDY; DISEASE ASSOCIATION; FOLLOW UP; GLEASON SCORE; HISTOPATHOLOGY; HUMAN; HUMAN TISSUE; IMAGE ANALYSIS; INTERMETHOD COMPARISON; MAJOR CLINICAL STUDY; MALE; NUCLEAR MAGNETIC RESONANCE IMAGING; PREDICTION; PROSTATE CANCER; PROSTATECTOMY; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; CASE REPORT; NOMOGRAM; PREDICTIVE VALUE; PROGNOSIS; PROSTATE TUMOR; STATISTICAL MODEL; TREATMENT OUTCOME;

AGED; HUMANS; LOGISTIC MODELS; MALE; NOMOGRAMS; PREDICTIVE VALUE OF TESTS; PROGNOSIS; PROSTATECTOMY; PROSTATIC NEOPLASMS; SUPPORT VECTOR MACHINE; TREATMENT OUTCOME;

EID: 84999041068     PISSN: None     EISSN: 19492553     Source Type: Journal    
DOI: 10.18632/oncotarget.11293     Document Type: Article

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