Can Pre-Operative Neutrophil-to-Lymphocyte Ratio (NLR) Help Predict Non-Metastatic Renal Carcinoma Recurrence after Nephrectomy? (UroCCR-61 Study)

Allenet, C.; Klein, Clément; Rouget, Benjamin; Margue, G.; Capon, G.; Blanc, Peggy; Estrade, V.; Bladou, Franck; Robert, G.; Bernhard, Jean-Christophe

doi:10.3390/cancers14225692

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…As the only clinical predictor included in our nomogram model, NLR is also a well-known prognostic biomarker in various solid tumors, such as RCC, lung cancer, gastric cancer, etc. [ 26 , 27 ].…”

Section: Discussionmentioning

confidence: 99%

CT features based preoperative predictors of aggressive pathology for clinical T1 solid renal cell carcinoma and the development of nomogram model

Wang,

Dong,

et al. 2024

BMC Cancer

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Background We aimed to identify preoperative predictors of aggressive pathology for cT1 solid renal cell carcinoma (RCC) by combining clinical features with qualitative and quantitative CT parameters, and developed a nomogram model. Methods We conducted a retrospective study of 776 cT1 solid RCC patients treated with partial nephrectomy (PN) or radical nephrectomy (RN) between 2018 and 2022. All patients underwent four-phase contrast-enhanced CT scans and the CT parameters were obtained by two experienced radiologists using region of interest (ROI). Aggressive pathology was defined as patients with nuclear grade III-IV; upstage to pT3a; type II papillary renal cell carcinoma (pRCC), collecting duct or renal medullary carcinoma, unclassified RCC or sarcomatoid/rhabdoid features. Univariate and multivariate logistic analyses were used to determine significant predictors and develop the nomogram model. To evaluate the accuracy and clinical utility of the nomogram model, we used the receiver operating characteristic (ROC) curve, calibration plot, decision curve analysis (DCA), risk stratification, and subgroup analysis. Results Of the 776 cT1 solid RCC patients, 250 (32.2%) had aggressive pathological features. The interclass correlation coefficient (ICC) of CT parameters accessed by two reviewers ranged from 0.758 to 0.982. Logistic regression analyses showed that neutrophil-to-lymphocyte ratio (NLR), distance to the collecting system, CT necrosis, tumor margin irregularity, peritumoral neovascularity, and RER-NP were independent predictive factors associated with aggressive pathology. We built the nomogram model using these significant variables, which had an area under the curve (AUC) of 0.854 in the ROC curve. Conclusions Our research demonstrated that preoperative four-phase contrast-enhanced CT was critical for predicting aggressive pathology in cT1 solid RCC, and the constructed nomogram was useful in guiding patient treatment and postoperative follow-up.

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Section: Discussionmentioning

confidence: 99%

CT features based preoperative predictors of aggressive pathology for clinical T1 solid renal cell carcinoma and the development of nomogram model

Wang,

Dong,

et al. 2024

BMC Cancer

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“…The association of inflammatory markers with poor prognosis has been demonstrated in several cancers 19 and the neutrophil-to-lymphocyte ratio (NLR) is often used as a prognostic biomarker 20 . In kidney cancer, its predictive value has been evaluated several times [21][22][23] .…”

Section: Discussionmentioning

confidence: 99%

UroPredict: Machine learning model on real-world data for prediction of kidney cancer recurrence (UroCCR-120)

Margue,

Ferrer,

Etchepare

et al. 2024

npj Precis. Onc.

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Renal cell carcinoma (RCC) is most often diagnosed at a localized stage, where surgery is the standard of care. Existing prognostic scores provide moderate predictive performance, leading to challenges in establishing follow-up recommendations after surgery and in selecting patients who could benefit from adjuvant therapy. In this study, we developed a model for individual postoperative disease-free survival (DFS) prediction using machine learning (ML) on real-world prospective data. Using the French kidney cancer research network database, UroCCR, we analyzed a cohort of surgically treated RCC patients. Participating sites were randomly assigned to either the training or testing cohort, and several ML models were trained on the training dataset. The predictive performance of the best ML model was then evaluated on the test dataset and compared with the usual risk scores. In total, 3372 patients were included, with a median follow-up of 30 months. The best results in predicting DFS were achieved using Cox PH models that included 24 variables, resulting in an iAUC of 0.81 [IC95% 0.77–0.85]. The ML model surpassed the predictive performance of the most commonly used risk scores while handling incomplete data in predictors. Lastly, patients were stratified into four prognostic groups with good discrimination (iAUC = 0.79 [IC95% 0.74–0.83]). Our study suggests that applying ML to real-world prospective data from patients undergoing surgery for localized or locally advanced RCC can provide accurate individual DFS prediction, outperforming traditional prognostic scores.

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“…Among others, high NLR has been associated with an adverse OS in many solid tumors [ 35 , 36 ], and an elevated NLR has been negatively associated with OS and progression-free survival (PFS) in renal cell carcinoma (RCC) [ 37 ]. It is of major interest that pre-operative NLR has recently been demonstrated to be an inexpensive and easily accessible prognostic biomarker for non-metastatic RCC [ 38 ]. Consistently, our data show that high-baseline NLR (>3) was more common among cancer patients who did not survive at the cut-off date of the NUTRIONCO database (64.5%, Table 5 ), with a statistically significant association with the outcome “patient’s death” ( p < 0.0001).…”

Section: Discussionmentioning

confidence: 99%

The Impact of NUTRItional Status at First Medical Oncology Visit on Clinical Outcomes: The NUTRIONCO Study

Muscaritoli

Modena

Valerio

et al. 2023

Cancers

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Malnutrition affects up to 75% of cancer patients and results from a combination of anorexia and metabolic dysregulation. Metabolic and nutritional abnormalities in cancer patients can lead to cachexia, a multifactorial syndrome characterized by involuntary loss of skeletal muscle mass, systemic inflammation and increased protein catabolism. Cancer cachexia negatively affects patients’ outcomes, response to anticancer treatments, quality of life, and survival. However, risk of malnutrition, and cachexia are still under-recognized in cancer patients. The Prevalence of Malnutrition in Oncology (PreMiO) study revealed that 51% of patients already had nutritional deficiencies at their first medical oncology visit. Here, we report the results of the subsequent retrospective, observational NUTRItional status at first medical oncology visit ON Clinical Outcomes (NUTRIONCO) study, aimed at assessing the impact of baseline nutritional and non-nutritional variables collected in the PreMiO study on the clinical outcomes of the same patients followed up from August 2019 to October 2021. We have highlighted a statistically significant association between baseline variables and patient death, rehospitalization, treatment toxicity, and disease progression at follow-up. We found a higher overall survival probability in the well-nourished general study population vs. malnourished patients (p < 0.001). Of major interest is the fact that patient stratification revealed that malnutrition decreased survival probability in non-metastatic patients but not in metastatic patients (p < 0.001). Multivariate analysis confirmed that baseline malnutrition (p = 0.004) and VAS score for appetite loss (p = 0.0104), in addition to albumin < 35 g/L (p < 0.0001) and neutrophil/lymphocyte ratio > 3 (p = 0.0007), were independently associated with the death of non-metastatic patients at follow-up. These findings highlight the importance of proactive, early management of malnutrition and cachexia in cancer patients, and in particular, in non-metastatic patients, from the perspective of a substantial improvement of their clinical outcomes.

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Can Pre-Operative Neutrophil-to-Lymphocyte Ratio (NLR) Help Predict Non-Metastatic Renal Carcinoma Recurrence after Nephrectomy? (UroCCR-61 Study)

Cited by 7 publications

References 21 publications

CT features based preoperative predictors of aggressive pathology for clinical T1 solid renal cell carcinoma and the development of nomogram model

CT features based preoperative predictors of aggressive pathology for clinical T1 solid renal cell carcinoma and the development of nomogram model

UroPredict: Machine learning model on real-world data for prediction of kidney cancer recurrence (UroCCR-120)

The Impact of NUTRItional Status at First Medical Oncology Visit on Clinical Outcomes: The NUTRIONCO Study

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