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[Analysis of influential factors for prostate biopsy and establishment of logistic regression model for prostate cancer].
Li, Yonglin; Tang, Zhengyan; Qi, Lin; Chen, Zhi; Li, Dongjie; Zeng, Mingqiang; Xue, Ruizhi; Peng, Chuan.
Afiliación
  • Li Y; Department of Urology, Xiangya Hospital, Central South University, Changsha 410008, China.
  • Tang Z; Department of Urology, Xiangya Hospital, Central South University, Changsha 410008, China.
  • Qi L; Department of Urology, Xiangya Hospital, Central South University, Changsha 410008, China.
  • Chen Z; Department of Urology, Xiangya Hospital, Central South University, Changsha 410008, China.
  • Li D; Department of Urology, Xiangya Hospital, Central South University, Changsha 410008, China.
  • Zeng M; Department of Urology, Xiangya Hospital, Central South University, Changsha 410008, China.
  • Xue R; Department of Urology, Xiangya Hospital, Central South University, Changsha 410008, China.
  • Peng C; Department of Urology, Xiangya Hospital, Central South University, Changsha 410008, China.
Zhong Nan Da Xue Xue Bao Yi Xue Ban ; 40(6): 651-6, 2015 Jun.
Article en Zh | MEDLINE | ID: mdl-26164515
ABSTRACT

OBJECTIVE:

To establish logistic regression model for prostate cancer and provide basis for prostate biopsy.


METHODS:

A total of 117 cases of prostate biopsy were retrospectively analyzed in chronological sequence. All cases were assigned into a model group (n=78) and a validation group (n=39). Logistic regression model was established and its value was estimated by receiver operating characteristic (ROC) curve. 


RESULTS:

Digital rectal examination(DRE), transrectal ultrasound(TRUS), MRI, prostate-specific antigen density (PSAD), and free PSA/total PSA (fPSA/tPSA) were the influential factors for prostate biopsy (P<0.01). The established logistic regression model for prostate cancer by regression coefficient was logit P=-2.362+2.561×DRE+1.747×TRUS+2.901×MRI+1.126×PSAD-2.569×fPSA/tPSA and area under curve was 0.907. When the cutoff aimed at 0.12, the sensitivity and specificity were 81.80% and 89.30%, respectively.


CONCLUSION:

Logistic regression model for prostate cancer can provide sufficient basis for prostate biopsy. Prostate biopsy should be performed when P value is more than 0.12.
Asunto(s)

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Neoplasias de la Próstata / Biopsia / Modelos Logísticos Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans / Male Idioma: Zh Revista: Zhong Nan Da Xue Xue Bao Yi Xue Ban Asunto de la revista: MEDICINA Año: 2015 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Neoplasias de la Próstata / Biopsia / Modelos Logísticos Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans / Male Idioma: Zh Revista: Zhong Nan Da Xue Xue Bao Yi Xue Ban Asunto de la revista: MEDICINA Año: 2015 Tipo del documento: Article País de afiliación: China