Genetically engineered neural networks for predicting prostate cancer progression after radical prostatectomy.
about
Critical review of prostate cancer predictive toolsUsing nuclear morphometry to predict the need for treatment among men with low grade, low stage prostate cancer enrolled in a program of expectant management with curative intentPrognostic factors and reporting of prostate carcinoma in radical prostatectomy and pelvic lymphadenectomy specimens.The medical management of prostate cancer: a multidisciplinary team approach.Chromatin changes predict recurrence after radical prostatectomy.Applications of machine learning in cancer prediction and prognosisNuclear morphometry, nucleomics and prostate cancer progressionUse of machine learning methods for prediction of acute toxicity in organs at risk following prostate radiotherapy.Risk prediction models for biochemical recurrence after radical prostatectomy using prostate-specific antigen and Gleason scorePrediction of biochemical recurrence following radical prostatectomy in men with prostate cancer by diffusion-weighted magnetic resonance imaging: initial results.Quantification of large scale DNA organization for predicting prostate cancer recurrence.
P2860
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P2860
Genetically engineered neural networks for predicting prostate cancer progression after radical prostatectomy.
description
1999 nî lūn-bûn
@nan
1999年の論文
@ja
1999年論文
@yue
1999年論文
@zh-hant
1999年論文
@zh-hk
1999年論文
@zh-mo
1999年論文
@zh-tw
1999年论文
@wuu
1999年论文
@zh
1999年论文
@zh-cn
name
Genetically engineered neural ...... n after radical prostatectomy.
@en
type
label
Genetically engineered neural ...... n after radical prostatectomy.
@en
prefLabel
Genetically engineered neural ...... n after radical prostatectomy.
@en
P2093
P1433
P1476
Genetically engineered neural ...... n after radical prostatectomy.
@en
P2093
Epstein JI
Mangold LA
P304
P356
10.1016/S0090-4295(99)00328-3
P407
P577
1999-11-01T00:00:00Z