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Bimal Bhindi Christine M. Lohse Phillip J. Schulte Ross J. Mason John C. Cheville Stephen A. Boorjian Bradley C. Leibovich R. Houston Thompson 《European urology》2019,75(5):766-772
Background
Partial nephrectomy (PN) is generally favored for cT1 tumors over radical nephrectomy (RN) when technically feasible. However, it can be unclear whether the additional risks of PN are worth the magnitude of renal function benefit.Objective
To develop preoperative tools to predict long-term estimated glomerular filtration rate (eGFR) beyond 30 d following PN and RN, separately.Design, setting, and participants
In this retrospective cohort study, patients who underwent RN or PN for a single nonmetastatic renal tumor between 1997 and 2014 at our institution were identified. Exclusion criteria were venous tumor thrombus and preoperative eGFR <15 ml/min/1.73 m2.Intervention
RN and PN.Outcome measurements and statistical analysis
Hierarchical generalized linear mixed-effect models with backward selection of candidate preoperative features were used to predict long-term eGFR following RN and PN, separately. Predictive ability was summarized using marginal , which ranges from 0 to 1, with higher values indicating increased predictive ability.Results and limitations
The analysis included 1152 patients (13 206 eGFR observations) who underwent RN and 1920 patients (18 652 eGFR observations) who underwent PN, with mean preoperative eGFRs of 66 ml/min/1.73 m2 (standard deviation [SD] = 18) and 72 ml/min/1.73 m2 (SD = 20), respectively. The model to predict eGFR after RN included age, diabetes, preoperative eGFR, preoperative proteinuria, tumor size, time from surgery, and an interaction between time from surgery and age (marginal ). The model to predict eGFR after PN included age, presence of a solitary kidney, diabetes, hypertension, preoperative eGFR, preoperative proteinuria, surgical approach, time from surgery, and interaction terms between time from surgery and age, diabetes, preoperative eGFR, and preoperative proteinuria (marginal ). Limitations include the lack of data on renal tumor complexity and the single-center design; generalizability needs to be confirmed in external cohorts.Conclusions
We developed preoperative tools to predict renal function outcomes following RN and PN. Pending validation, these tools should be helpful for patient counseling and clinical decision-making.Patient summary
We developed models to predict kidney function outcomes after partial and radical nephrectomy based on preoperative features. This should help clinicians during patient counseling and decision-making in the management of kidney tumors. 相似文献104.
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Lara Feulner Hamed S. Najafabadi Simon Tanguay Janusz Rak Yasser Riazalhosseini 《Urologic oncology》2019,37(2):166-175
Background
Clear cell renal cell carcinoma (ccRCC) is known to occur across the adult lifetime traversing the spectrum of age-related organismal changes. Little is known as to how the aging process may affect the course of renal cell carcinoma (RCC) and the repertoire of genes involved.Methods
Using The Cancer Genome Atlas (n?=?436) and Cancer Genomics of the Kidney (n?=?89) datasets, we applied regression analysis to examine associations between patient age and gene expression profiles in ccRCC tumors and normal kidney tissues. Pathway enrichment analysis was performed to identify cellular process that is affected by aging in ccRCC. Moreover, connectivity mapping analysis was used to predict age-dependent response to drug treatments.Results
Our analysis revealed different age-dependent gene expression spectra in ccRCC and normal kidney tissues. These findings were significant and independently reproducible in both datasets examined. Age up-regulated genes, showing higher expression in older patients, were significantly enriched (false discovery rate <0.05) in normal tissues for pathways associated with immune response and extracellular matrix organization, whereas age up-regulated genes in tumors were enriched for metabolism and oxidation pathways. Strikingly, age down-regulated genes in normal cells were also enriched for metabolism and oxidation, while those in tumors were enriched for extracellular matrix organization. Further in silico analysis of potential drug targets predicted preferential efficacy of Phosphoinositide 3-kinase inhibitor or immunotherapy in association with age.Conclusion
We report on previously unrecognized associations between age and molecular underpinnings of RCC, including age-associated expression of genes implicated in RCC development or treatment. 相似文献106.
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《European journal of surgical oncology》2019,45(6):1039-1045
IntroductionMuch controversy exists over whether to perform lateral neck dissection (LND) on patients with papillary thyroid carcinoma (PTC). This study aimed to build predictive nomograms that could individually estimate lateral neck metastasis (LNM) risk and help determine follow up intensity.Patients and methodsUnifocal PTC patients who underwent LND between April 2012 and August 2014 were identified. Clinical and pathological variables were retrospectively evaluated using univariate and stepwise multivariate logistic regression analysis. Variables that had statistical significance in final multivariate logistic models were chosen to build nomograms, which were further corrected using the bootstrap resampling method.ResultsIn all, 505 PTC patients were eligible for analysis. Among these, 178 patients (35.2%) had lateral neck metastasis. Two nomograms were generated: nomogram (c) and nomogram (c + p). Nomogram (c) incorporated four clinical variables: age, tumor size, tumor site, and extrathyroidal extension (ETE). It had a good discriminative ability, with a C-index of 0.79 (bootstrap-corrected, 0.78). Nomogram (c + p) incorporated two clinical variables and two pathological variables: tumor size, tumor site, extranodal extension (ENE), and number of positive nodes in the central compartment. Nomogram (c + p) showed an excellent discriminative ability, with a C-index of 0.86 (bootstrap-corrected, 0.85).ConclusionTwo predictive nomograms were generated. Nomogram (c) is a clinical model, whereas nomogram (c + p) is a clinicopathological model. Each nomogram incorporates only four variables and can give an accurate estimate of LNM risk in unifocal PTC patients, which may assist clinicians in patient counseling and decision making regarding LND. 相似文献
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