A preoperative nomogram predicting the pseudocapsule status in localized renal cell carcinoma.

Renal cell carcinoma (RCC) nomogram pseudocapsule invasion

Journal

Translational andrology and urology
ISSN: 2223-4691
Titre abrégé: Transl Androl Urol
Pays: China
ID NLM: 101581119

Informations de publication

Date de publication:
Apr 2020
Historique:
entrez: 19 5 2020
pubmed: 19 5 2020
medline: 19 5 2020
Statut: ppublish

Résumé

Tumor enucleation (TE) surgery for localized renal cell carcinoma (RCC) relies on a complete peritumoral pseudocapsule (PC). Study objective was to develop a preoperative model to predict PC status. The prediction model was developed in a cohort that consisted of 170 patients with localized RCC, and data was gathered from 2010 to 2015. Multivariable logistic regression analysis and R were used to generate this prediction model. The statistical performance was assessed with respect to the calibration, discrimination, and clinical usefulness. The prediction model incorporated the systemic inflammatory markers [neutrophil-lymphocyte ratio (NLR); albumin-globulin ratio (AGR)], CT imaging features (tumor size and necrosis), and clinical risk factors (BMI). The model showed good discrimination, with a C-index of 0.85 (0.78-0.91), and good calibration (P=0.60). The sensitivity and specificity were 62% and 94% respectively. Decision curves and clinical impact curve demonstrated that the current model was clinically useful. We constructed a model that incorporated both the systematic inflammatory markers and clinical risk factors. It can be conveniently used to preoperatively predict the individualized risk of PC invasion and identify the best candidates to receive TE surgery.

Sections du résumé

BACKGROUND BACKGROUND
Tumor enucleation (TE) surgery for localized renal cell carcinoma (RCC) relies on a complete peritumoral pseudocapsule (PC). Study objective was to develop a preoperative model to predict PC status.
METHODS METHODS
The prediction model was developed in a cohort that consisted of 170 patients with localized RCC, and data was gathered from 2010 to 2015. Multivariable logistic regression analysis and R were used to generate this prediction model. The statistical performance was assessed with respect to the calibration, discrimination, and clinical usefulness.
RESULTS RESULTS
The prediction model incorporated the systemic inflammatory markers [neutrophil-lymphocyte ratio (NLR); albumin-globulin ratio (AGR)], CT imaging features (tumor size and necrosis), and clinical risk factors (BMI). The model showed good discrimination, with a C-index of 0.85 (0.78-0.91), and good calibration (P=0.60). The sensitivity and specificity were 62% and 94% respectively. Decision curves and clinical impact curve demonstrated that the current model was clinically useful.
CONCLUSIONS CONCLUSIONS
We constructed a model that incorporated both the systematic inflammatory markers and clinical risk factors. It can be conveniently used to preoperatively predict the individualized risk of PC invasion and identify the best candidates to receive TE surgery.

Identifiants

pubmed: 32420152
doi: 10.21037/tau.2020.01.26
pii: tau-09-02-462
pmc: PMC7214989
doi:

Types de publication

Journal Article

Langues

eng

Pagination

462-472

Informations de copyright

2020 Translational Andrology and Urology. All rights reserved.

Déclaration de conflit d'intérêts

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/tau.2020.01.26). XZ serves as an unpaid editorial board member of Translational Andrology and Urology from Mar 2019 to Feb 2021. The other authors have no conflicts of interest to declare.

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Auteurs

Jiao Hu (J)

Department of Urology, Xiangya Hospital, Central South University, Changsha 410008, China.

Jinbo Chen (J)

Department of Urology, Xiangya Hospital, Central South University, Changsha 410008, China.

Huihuang Li (H)

Department of Urology, Xiangya Hospital, Central South University, Changsha 410008, China.

Tongchen He (T)

Department of Urology, Xiangya Hospital, Central South University, Changsha 410008, China.

Hao Deng (H)

Department of Urology, Xiangya Hospital, Central South University, Changsha 410008, China.

Guanghui Gong (G)

Department of Pathology, Xiangya Hospital, Central South University, Changsha 410008, China.

Yu Cui (Y)

Department of Urology, Xiangya Hospital, Central South University, Changsha 410008, China.

Peihua Liu (P)

Department of Urology, Xiangya Hospital, Central South University, Changsha 410008, China.

Wenbiao Ren (W)

Department of Urology, Xiangya Hospital, Central South University, Changsha 410008, China.

Xu Zhou (X)

Reproductive Medicine Center, Xiangya Hospital, Central South University, Changsha 410008, China.

Chao Li (C)

Department of Urology, Xiangya Hospital, Central South University, Changsha 410008, China.

Xiongbing Zu (X)

Department of Urology, Xiangya Hospital, Central South University, Changsha 410008, China.

Classifications MeSH