Prognostic nutritional index and serum lactate dehydrogenase predict the prognosis of nasopharyngeal carcinoma patients who received intensity-modulated radiation therapy

Zhang, Chunxia; Zhan, Zhouwei; Fang, Yunxiang; Ruan, Yuanyuan; Lin, Mingan; Dai, Zhisen; Zhang, Yanping; Yang, Shanshan; Xiao, Shuxiang; Chen, Bijuan

doi:10.1007/s00432-023-05485-5

Cited by 6 publications

(4 citation statements)

References 49 publications

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“…The prognostic nutritional index, which assesses both nutritional and immunological status, has emerged as a notable candidate. The prognostic significance of the PNI has been demonstrated by a series of studies, but the vast majority of these have relied solely on single-timepoint pre-treatment PNI measurements [ 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. To the best of our knowledge, this is the first study to utilize longitudinal PNI data for outcome prediction in patients with nasopharyngeal carcinoma.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…The PNI is determined by assessing the serum albumin levels and the total lymphocyte count in the peripheral blood and has demonstrated prognostic value in various types of malignancies, including lung cancer, esophageal cancer, gastric cancer, colorectal cancer, pancreatic cancer, liver cancer, as well as head and neck cancer [ 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. For patients with NPC, increasing evidence has demonstrated that a low PNI is associated with poorer survival outcomes in both the short and long term [ 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. Previous research has predominantly focused on the pre-treatment PNI, although some studies have also examined the post-treatment levels [ 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

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A Joint Model Based on Post-Treatment Longitudinal Prognostic Nutritional Index to Predict Survival in Nasopharyngeal Carcinoma

Hsiao,

Wang,

et al. 2024

Cancers

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Background: a low PNI in patients with NPC is linked to poor survival, but prior studies have focused on single-timepoint measurements. Our study aims to employ joint modeling to analyze longitudinal PNI data from each routine visit, exploring its relationship with overall survival. Methods: In this retrospective study using data from the Chang Gung Research Database (2007–2019), we enrolled patients with NPC undergoing curative treatment. We analyzed the correlation between patient characteristics, including the PNI, and overall survival. A joint model combining a longitudinal sub-model with a time-to-event sub-model was used to further evaluate the prognostic value of longitudinal PNI. Results: A total of 2332 patient were enrolled for the analysis. Separate survival analyses showed that longitudinal PNI was an independent indicator of a reduced mortality risk (adjusted HR 0.813; 95% CI, 0.805 to 0.821). Joint modeling confirmed longitudinal PNI as a consistent predictor of survival (HR 0.864; 95% CI, 0.850 to 0.879). An ROC analysis revealed that a PNI below 38.1 significantly increased the risk of 90-day mortality, with 90.0% sensitivity and 89.6% specificity. Conclusions: Longitudinal PNI data independently predicted the overall survival in patients with NPC, significantly forecasting 90-day survival outcomes. We recommend routine PNI assessments during each clinic visit for these patients.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Joint Model Based on Post-Treatment Longitudinal Prognostic Nutritional Index to Predict Survival in Nasopharyngeal Carcinoma

Hsiao,

Wang,

et al. 2024

Cancers

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“…A low PNI is indicative of a decline in both peripheral blood lymphocyte count and albumin levels, strongly hinting at suboptimal nutritional status and a weakened immune system in patients. A study conducted within the same institution has firmly established the predictive value of PNI in NPC patients [22]. Additionally, a retrospective analysis on newly diagnosed metastatic nasopharyngeal carcinoma patients has further bolstered the argument that PNI serves as a superior nutritional predictor in comparison to BMI [23].…”

Section: Discussionmentioning

confidence: 99%

A nomogram based on nutritional and inflammatory parameters to predict DMFS and identify beneficiaries of adjuvant chemotherapy in IVA-stage nasopharyngeal carcinoma

pan,

Chen,

Hong

et al. 2024

BMC Cancer

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Objective This study aims to develop a nomogram integrating inflammation (NLR), Prognostic Nutritional Index (PNI), and EBV DNA (tumor burden) to achieve personalized treatment and prediction for stage IVA NPC. Furthermore, it endeavors to pinpoint specific subgroups that may derive significant benefits from S-1 adjuvant chemotherapy. Methods A total of 834 patients diagnosed with stage IVA NPC were enrolled in this study and randomly allocated into training and validation cohorts. Multivariate Cox analyses were conducted to identify independent prognostic factors for constructing the nomogram. The predictive and clinical utility of the nomogram was assessed through measures including the AUC, calibration curve, DCA, and C-indexes. IPTW was employed to balance baseline characteristics across the population. Kaplan-Meier analysis and log-rank tests were utilized to evaluate the prognostic value. Results In our study, we examined the clinical features of 557 individuals from the training cohort and 277 from the validation cohort. The median follow-up period was 50.1 and 49.7 months, respectively. For the overall cohort, the median follow-up duration was 53.8 months. The training and validation sets showed 3-year OS rates of 87.7% and 82.5%, respectively. Meanwhile, the 3-year DMFS rates were 95.9% and 84.3%, respectively. We created a nomogram that combined PNI, NRI, and EBV DNA, resulting in high prediction accuracy. Risk stratification demonstrated substantial variations in DMFS and OS between the high and low risk groups. Patients in the high-risk group benefited significantly from the IC + CCRT + S-1 treatment. In contrast, IC + CCRT demonstrated non-inferior 3-year DMFS and OS compared to IC + CCRT + S-1 in the low-risk population, indicating the possibility of reducing treatment intensity. Conclusions In conclusion, our nomogram integrating NLR, PNI, and EBV DNA offers precise prognostication for stage IVA NPC. S-1 adjuvant chemotherapy provides notable benefits for high-risk patients, while treatment intensity reduction may be feasible for low-risk individuals.

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Machine learning‐derived prognostic signature for progression‐free survival in non‐metastatic nasopharyngeal carcinoma

Zuo,

Ma,

Yan

et al. 2024

Head & Neck

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BackgroundEarly detection of high‐risk nasopharyngeal carcinoma (NPC) recurrence is essential. We created a machine learning‐derived prognostic signature (MLDPS) by combining three machine learning (ML) models to predict progression‐free survival (PFS) in patients with non‐metastatic NPC.MethodsA cohort of 653 patients with non‐metastatic NPC was divided into a training (n = 457) and validation (n = 196) dataset (7:3 ratio). The study included clinicopathological characteristics, hematologic markers, and MRI findings in three machine learning models—random forest (RF), extreme gradient boosting (XGBoost), and least absolute shrinkage and selection operator (LASSO)—to predict progression‐free survival (PFS). A Venn diagram identified the overlapping signatures from the three ML algorithms. Cox proportional hazard analysis determined the MLDPS for PFS.ResultsThe RF, XGBoost, and LASSO algorithms identified six consensus factors from the 33 signatures. Cox proportional hazards analysis showed that the MLDPS includes age, lymphocyte count, number of positive lymph nodes, and regional lymph node density. Additionally, MLDPS effectively stratified prognosis, with low‐risk individuals showing better PFS than high‐risk individuals (p < 0.001).ConclusionMLDPS, based on clinicopathological characteristics, hematologic markers, and MRI findings, is crucial for guiding clinical management and personalizing treatments for patients with non‐metastatic NPC.

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Prognostic nutritional index and serum lactate dehydrogenase predict the prognosis of nasopharyngeal carcinoma patients who received intensity-modulated radiation therapy

Cited by 6 publications

References 49 publications

A Joint Model Based on Post-Treatment Longitudinal Prognostic Nutritional Index to Predict Survival in Nasopharyngeal Carcinoma

A Joint Model Based on Post-Treatment Longitudinal Prognostic Nutritional Index to Predict Survival in Nasopharyngeal Carcinoma

A nomogram based on nutritional and inflammatory parameters to predict DMFS and identify beneficiaries of adjuvant chemotherapy in IVA-stage nasopharyngeal carcinoma

Machine learning‐derived prognostic signature for progression‐free survival in non‐metastatic nasopharyngeal carcinoma

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