Pretreatment prognostic nutritional index as a prognostic factor in lung cancer: Review and meta-analysis

Wang, Zhongtao; Wang, Yongjun; Zhang, Xinmei; Zhang, Tingting

doi:10.1016/j.cca.2018.08.030

Cited by 111 publications

(106 citation statements)

References 41 publications

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“…Importantly, following a milestone study by Onodera et al, further investigations disclosed that PNI is associated with survival outcomes in various malignancies. 4,[14][15][16][17][27][28][29][30] Critically, while past examinations have meticulously researched the relationship among the PNI and NSCLC, these studies used eligibility criteria that included patients with a considerable degree of heterogeneity in terms of disease stage, histological subtypes and treatments. [31][32][33] Such non-uniformities restrict the judicial interpretation of the actual prognostic worth of PNI in this patient population.…”

Section: Discussionmentioning

confidence: 99%

“…[31][32][33] Such non-uniformities restrict the judicial interpretation of the actual prognostic worth of PNI in this patient population. 4 Therefore, we investigated the prognostic ability of PNI in a relatively homogeneous group comprised exclusively of stage IIIB NSCLC patients treated with exclusive CRT. Our most noticeable finding was the noteworthy relationship between PNI ≤ 40.5 and poor median OS (16.8 vs 36.7 months; P < 0.001) relative to patients with PNI > 40.5.…”

Section: Discussionmentioning

confidence: 99%

“…36 Accordingly, accessible previous literature and the current data altogether indicate that PNI has robust prognostic utility. 4 Although the underlying exact mechanism remains unclear, two ingredients of PNI, namely albumin and absolute lymphocyte count 37 are well-recognized measures of nutrition, immunity, and systemic inflammation. 27 On the other hand, based on the equation used to calculate PNI (10 × albumin + 0.005 × ALC); the resultant score is affected basically by the albumin levels, rather than the ALC.…”

Section: Discussionmentioning

confidence: 99%

“…1,2 Despite the fact that well-established conventional prognostic tools (ie, tumor-node-metastasis [TNM], histological subtype, and genetic biomarkers) may stratify patients into significantly distinct outcome groups, unexpected recurrences or CRT resistance can still occur, which are common obstacles for the treatment approach and confer a relatively worse prognosis contrasted to patients presenting with indistinguishable TNM stages. 3,4 Moreover, the high cost and inconvenience of reliable genetic biomarker detection restrict the use of customized treatments based on such genetic biomarkers, especially in countries with limited incomes. 4 These limitations may cause difficulties in predicting treatment responses, which can lead to insufficient disease management (eg, using less aggressive forms of therapy) or overtreatment, resulting in either treatment failure or treatmentrelated toxicity, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…3,4 Moreover, the high cost and inconvenience of reliable genetic biomarker detection restrict the use of customized treatments based on such genetic biomarkers, especially in countries with limited incomes. 4 These limitations may cause difficulties in predicting treatment responses, which can lead to insufficient disease management (eg, using less aggressive forms of therapy) or overtreatment, resulting in either treatment failure or treatmentrelated toxicity, respectively. 5 Therefore, novel practical prognostic tools with a lower cost are required to anticipate host treatment responses and improve patient selection for customized therapies in a more accurate manner.…”

Section: Introductionmentioning

confidence: 99%

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<p>Low Prognostic Nutritional Index Predicts Poor Clinical Outcomes in Patients with Stage IIIB Non-small-cell Lung Carcinoma Undergoing Chemoradiotherapy</p>

Özdemir

Topkan

Mertsoylu

et al. 2020

CMAR

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Purpose: To investigate the prognostic utility of the prognostic nutritional index (PNI) in stage IIIB non-small-cell lung carcinoma (NSCLC) patients undergoing concurrent chemoradiotherapy (CRT). Methods: A total of 358 stage IIIB NSCLC patients who received a total dose of 60-66 Gy (2 Gy/fraction) radiotherapy and ≥1 cycle(s) of platinum-based chemotherapy were analyzed. The receiver operating curve analysis was utilized to identify the optimal PNI cutoff value demonstrating a significant connection with the overall survival (OS), locoregional progression-free survival (LRPFS), and progression-free survival (PFS). Results: At a median follow-up time of 22.5 months (range: 2.4-123.5), 30.2% and 14% of the patients were still alive and free of disease progression, respectively.The median OS, LRPFS, and PFS were 25.2 [95% confidence interval (CI): 36.3-46.6 months], 15.4 (95% CI: 26.6-35.3 months), and 10.7 (95% CI: 36.8-69.9 months), individually, for the whole study accomplice. The ROC analysis revealed an optimum rounded cutoff that associated meaningfully with each of the OS [area under the curve (AUC): 84.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

<p>Low Prognostic Nutritional Index Predicts Poor Clinical Outcomes in Patients with Stage IIIB Non-small-cell Lung Carcinoma Undergoing Chemoradiotherapy</p>

Özdemir

Topkan

Mertsoylu

et al. 2020

CMAR

View full text Add to dashboard Cite

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Prognostic nutritional index combined with NLR to construct a survival prediction model and decision analysis of patients with muscle‐invasive bladder cancer after surgery

et al. 2023

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Objectives To build a nomogram prediction model, assess its predictive ability, and perform a survival decision analysis on patients with muscle‐invasive bladder cancer (MIBC) to study risk factors affecting overall survival (OS). Methods A retrospective analysis was performed on the clinical information of 262 patients with MIBC who underwent radical cystectomy (RC) at the Urology Department of the Second Affiliated Hospital of Kunming Medical University between July 2015 and August 2021. The final model variables that were included were chosen using single‐factor stepwise Cox regression, optimal subset regression, and LASSO regression + cross‐validation with the minimum AIC value. The next step was to do a multivariate Cox regression analysis. The establishment of a nomogram model by fitting and the screening out of independent risk factors impacting the survival of patients with MIBC having radical resection. Receiver Activity Characteristic curves, C‐index, and a calibration plot evaluated the prediction accuracy, validity, and clinical benefit of the model. The 1‐, 3‐, and 5‐year survival rates were then computed for each risk factor using a Kaplan–Meier survival analysis. Results 262 eligible patients in total were enrolled. With a median follow‐up of 32 months, the follow‐up period ranged from 2 to 83 months. 171 cases (65.27%) survived while 91 cases (34.73%) perished. Age (HR = 1.06 [1.04; 1.08], p = 0.001), preoperative hydronephrosis (HR = 0.69 [0.46, 1.05], p = 0.087), T stage (HR = 2.06 [1.09, 3.93], p = 0.027), lymphovascular invasion (LVI, HR = 1.73 [1.12, 2.67], p = 0.013), prognostic nutritional index (PNI, HR = 1.70 [1.09, 2.63], p = 0.018), and neutrophil‐to‐lymphocyte ratio (NLR, HR = 0.52 [0.29, 0.93)], p = 0.026) were independent risk factor for the survival of bladder cancer patients. Create a nomogram based on the aforementioned findings, and then draw the 1‐year, 3‐year, and 5‐year OS receiver operating characteristic curves by the nomogram. The AUC values were 0.811 (95% CI [0.752, 0.869]), 0.814 (95% CI [0.755, 0.873]), and 0.787 (95% CI [0.708, 0.865]), respectively, and the calibration plot matched the predicted value well. The 1‐year, 3‐year, and 5‐year decision curve analyses were higher than the ALL line and None line at threshold values of >5%, 5%–70%, and 20%–70% indicating that the model has good clinical applicability. The calibration plot for the Bootstrap 1000‐time resampled validation model was similar to the actual value. Patients with preoperative combination hydronephrosis, higher T‐stage, combined LVI, low PNI, and high NLR had worse survival, according to Kaplan–Meier survival analysis for each variable. Conclusions This study might conclude that PNI and NLR were separate risk factors that affect a patient's OS after RC for MIBC. The prognosis of bladder cancer may be predicted by PNI and NLR, but additional confirmation in randomized controlled trials is required.

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Predictive role of the prognostic nutritional index in patients with pancreatic ductal adenocarcinoma who underwent neoadjuvant chemoradiotherapy followed by curative pancreatic resection: A retrospective study using prospectively collected data

Suto,

Matsukawa,

Ando

et al. 2024

J Hepato Biliary Pancreat

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BackgroundDespite a strong association between nutritional indices and disease prognosis, evidence regarding the evaluation of nutritional indices after preoperative treatment for pancreatic ductal adenocarcinoma (PDAC) is insufficient. We evaluated the clinical significance of the prognostic nutritional index (PNI) in patients with resectable (R‐) and borderline resectable (BR‐) PDAC who received neoadjuvant chemoradiotherapy (NACRT) followed by pancreatic resection.MethodsWe assessed 153 patients with R‐ and BR‐PDAC who underwent NACRT followed by curative resection between 2009 and 2022. We evaluated the association between preoperative PNI after NACRT and short‐ and long‐term outcomes.ResultsThe median preoperative PNI value after NACRT was 42.1, and the optimal cutoff value from the time‐dependent receiver operating characteristic curve was 38.6. The low PNI group (PNI < 38.6, n = 44) exhibited significantly worse inflammatory parameters, surgical outcomes, and prognoses than the high PNI group (PNI ≥ 38.6, n = 109). Multivariate analysis identified preoperative PNI ≤ 38.6 (hazard ratio [HR]: 2.32, 95% confidence interval [CI]: 1.00–5.38, p = .049), blood loss ≥1642 mL (HR: 3.05, 95% CI: 1.65–5.64, p < .001), node positive pathology (HR: 2.10, 95% CI: 1.32–3.34, p = .002), and lack of postoperative adjuvant chemotherapy (HR: 3.55, 95% CI: 2.05–6.15, p < .001) as significant predictors of overall survival.ConclusionsFor patients with R‐ and BR‐PDAC receiving preoperative treatment, it is imperative to closely monitor their nutritional status when determining the optimal surgical procedure timing.

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Pretreatment prognostic nutritional index as a prognostic factor in lung cancer: Review and meta-analysis

Cited by 111 publications

References 41 publications

<p>Low Prognostic Nutritional Index Predicts Poor Clinical Outcomes in Patients with Stage IIIB Non-small-cell Lung Carcinoma Undergoing Chemoradiotherapy</p>

<p>Low Prognostic Nutritional Index Predicts Poor Clinical Outcomes in Patients with Stage IIIB Non-small-cell Lung Carcinoma Undergoing Chemoradiotherapy</p>

Prognostic nutritional index combined with NLR to construct a survival prediction model and decision analysis of patients with muscle‐invasive bladder cancer after surgery

Predictive role of the prognostic nutritional index in patients with pancreatic ductal adenocarcinoma who underwent neoadjuvant chemoradiotherapy followed by curative pancreatic resection: A retrospective study using prospectively collected data

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