Predictive Effect of Prognostic Nutritional Index on Lymph Node Regression Rate in Patients with Locally Advanced Nasopharyngeal Carcinoma Undergoing Concurrent Chemoradiotherapy

Abstract

Objective

Determining reliable predictive indicators of therapeutic efficacy for patients with nasopharyngeal carcinoma (NPC) can help select those who will benefit the most from treatment. This research assessed the predictive significance of the prognostic nutritional index (PNI) in patients with locally advanced nasopharyngeal carcinoma (LANPC) receiving concurrent chemoradiotherapy (CCRT).

Methods

A retrospective analysis was performed on 128 patients with LANPC who underwent CCRT. The PNI was calculated using peripheral blood values, the optimal cut-off value of the PNI was determined using the receiver operating characteristic (ROC) curve, and the patients were categorized into low- and high-PNI groups. The Mann–Whitney U test and Pearson's chi-square test were employed to test the differences between groups. Univariate and multivariate logistic regression analyses were used to determine the predictors of a good response to CCRT.

Results

The optimal cut-off value for PNI was 51.95. The regression rates of the cervical lymph nodes (CLNs) and total lymph nodes (TLNs) were higher in the high-PNI group compared to the low-PNI group (CLNs 78.67% and 65.91%; TLNs 78.56% and 67.60% respectively). Multivariate logistic regression showed that the PNI served as an independent predictor of CCRT efficacy in patients with LANPC.

Conclusion

The PNI is a non-invasive, low-cost, and easy-to-use indicator in clinical practice for patients with LANPC undergoing CCRT. Patients with LANPC and low PNI require attention to ensure early diagnosis of residual disease and timely rescue treatment. These findings may help develop treatment strategies and clinical risk stratification.

Keywords

locally advanced nasopharyngeal carcinoma concurrent chemoradiotherapy prognostic nutritional index efficacy biomarker

Introduction

Nasopharyngeal carcinoma (NPC) is one of the most common malignant head and neck tumors. The disease has distinct epidemiological characteristics. More than 80% of the cases occur in Asia, especially in southern China and Southeast Asia.¹ Due to the lymphatic network in the nasopharynx, the lymph node metastasis rate of NPC is high. Studies have found that 85-94.5% of patients with NPC have lymph node metastasis at the time of diagnosis.^2,3 At present, concurrent chemoradiotherapy (CCRT) is the standard treatment for locally advanced nasopharyngeal carcinoma (LANPC), but about 15%-30% of patients will still have distant metastasis after standard initial treatment.^4,5 The prognosis of metastatic NPC is poor, with a median progression-free survival (PFS) of 7 months and a 5-year overall survival (OS) of less than 20%.⁶

Tumor response to treatment is a critical prognostic indicator. Li et al⁷ found that local or regional residual tumor is a significant predictor of early recurrence and patients experiencing early recurrence have a worse prognosis than patients with late recurrence. Consequently, it is crucial to predict the response of tumors to treatment accurately in clinical decision-making processes. The therapeutic effect on tumors should be evaluated as early as possible, to identify residual tumors and provide timely interventions. Unfortunately, predicting the tumor's response to treatment based on clinicopathological information before therapy is challenging.⁸ There are considerable individual differences in the response of patients with NPC to treatment. Currently, the therapeutic effect is mainly evaluated by imaging; however, imaging examinations are expensive and have some contraindications. The Epstein-Barr (EB) virus is an effective predictive marker. However, the EB virus level is not elevated in some patients with NPC; therefore, additional therapeutic response biomarkers need to be explored.

Patients with cancer often have accompanying malnutrition, which is closely related to the progression of their condition.⁹ Studies have shown that patients with malnutrition are less tolerant to adverse drug reactions during chemotherapy, which impacts their abilities to continue with the chemotherapy and leads to a poor chemotherapy response.¹⁰ The prognostic nutritional index (PNI) serves as a simple method for evaluating the nutritional status of patients.¹¹ Among the many objective immune nutritional indicators, the PNI is the most attractive immune nutritional biomarker. The PNI is calculated based on serum albumin concentration and peripheral blood lymphocyte count, initially introduced to evaluate the risk of surgery in patients undergoing gastrointestinal surgery.¹² As a prognostic biomarker, the PNI has been widely studied in esophageal cancer,¹³ non-small cell lung cancer,¹⁴ liver cancer,¹⁵ and other tumors. It has been found that the PNI can predict the treatment response of rectal cancer,¹⁶ hepatocellular carcinoma,¹⁷ locally advanced cervical cancer,^18,19 advanced gastric cancer,²⁰ non-small cell lung cancer,²¹ advanced cancer,²² breast cancer²³ and many other cancers. However, the predictive effect of the PNI on the treatment response of patients with LANPC receiving CCRT remains unclear. In this study, we evaluated the predictive value of the PNI for the efficacy of CCRT in patients with LANPC.

Material and Methods

Patients

Retrospective collection of clinical data was conducted for 128 patients diagnosed with LANPC between January 2019 and December 2020. The reporting of this study conforms to STROBE guidelines.²⁴ All the patient details had been de-identified.

The selection criteria included: (1) patients aged between 18 and 75 years; (2) NPC confirmed by histopathology, with imaging evaluation revealing stage III-IVA; (3) with local lymph node metastases (4) Receive CCRT; (5) Eastern Cooperative Oncology Group (ECOG) performance status ≤ 2; (6) no concurrent severe immune diseases; (7) heart, liver, kidney, bone marrow and other functions are normal, and can tolerate radiotherapy and chemotherapy; (8) there is no history of other malignant tumors. The exclusion criteria were as: (1) incomplete medical records, including imaging, laboratory, and pathological information; (2) severe heart, liver, and kidney dysfunction; (3) any systemic infection that may affect the PNI value; (4) pregnant and lactating women; (5) patients with autoimmune diseases; and (6) patients with a previous nasopharyngeal mass or cervical lymph node resection.

Treatment

All the patients received CCRT. The target delineation followed the international guideline for the delineation of the clinical target volumes (CTV) for NPC,²⁵ and radiotherapy process was under the guidance of Report 50 and Report 62 of International Commission on Radiation Units and Measurements. Nasopharynx gross tumor volume (GTVnx) includes primary NPC foci and enlarged retropharyngeal lymph nodes (RLNs), while lymph node gross tumor volume (GTVnd) includes enlarged cervical lymph nodes (CLNs) detected by imaging and palpation. The Clinical target volume-1(CTV1) is a 5-10 mm (or 1-3 mm if close to the brainstem or spinal cord) outward expansion of the GTVnx, to cover the neighboring areas around the primary tumor lesion that are highly likely to be invaded or metastasized. The Clinical target volume-2 (CTV2) was outward expanded 5-10 mm of CTV1, including skull base, cavernous sinus, parapharyngeal space, pterygopalatine fossa, posterior 1/3 of nasal cavity, posterior 1/3 of maxillary sinus and cervical lymph node drainage area.The prescribed doses for GTVnx, GTVnd, CTV1, and CTV2 were 68-74 Gy, 66-70 Gy, 60-66 Gy, and 50-56 Gy, respectively, five times a week for 6-7 weeks, 30-33f. The concurrent chemotherapy regimens included cisplatin (80-100 mg/m²), nedaplatin (80 mg/m²), 21 days as a cycle, or cisplatin 40 mg/m², once a week.

Data Collection

Baseline, clinicopathological, and treatment-related data, and laboratory indicators for the enrolled patients were obtained from their medical records. These included age, sex, histopathological type, tumor node metastasis (TNM) classification, smoking and drinking status, ECOG performance status, albumin level, peripheral blood lymphocyte count, history, and treatment plan.

Evaluation of Lymph Node Volume and Treatment Response

Images were captured using a 1.5 T magnetic resonance imaging (MRI) scanner. All patients received conventional and enhanced MRI of the nasopharynx and neck before and after CCRT. The T2 plain scan-weighted axial MRI image data were transferred to the Varian Eclipse radiotherapy treatment planning system in DICOM format. Positive metastatic CLNs and RLNs were then delineated layer by layer. One month after CCRT, residual RLNs and CLNs were delineated on T2-weighted axial MRI images.The contouring was verified by two medical professionals (a trained radiation oncologist and a radiological expert) following the principle of consensus. If there was disagreement between them, further discussions were held by an expert team composed of two radiation oncology specialists and one chief radiologist. The Eclipse system was utilized to automatically and accurately calculate lymph node volume using the following formula:

Total volume of lymph nodes = volume of retropharyngeal lymph nodes (RLNs) + volume of CLNs.

The volumes of lymph nodes prior to and following radiotherapy were designated as LNV_before and LNV_after, respectively. The regression rate of the lymph nodes was computed using the following equation:

Lymph node regression rate = \frac{({LNV}_{before} - {LNV}_{after})}{LN V_{before}} \times 100 % .

²⁶

According to the rate of lymph node regression, treatment response was divided into good and poor. Good response was characterized by a lymph node regression rate> the median regression rate, and poor response was indicated by a lymph node regression rate ≤ the median regression rate.

Definition of PNI

Peripheral venous blood was collected one week before treatment to determine serum albumin and lymphocyte counts. The PNI was computed using the following formula: 10 × albumin (g/dL) + 0.005 × total lymphocyte count (/mm³).²⁷ The optimal cut-off value was determined using the receiver operating curve (ROC). Based on this optimal cut-off value, the enrolled patients were categorized into two groups: the high-PNI group (PNI > optimal cut-off value) and the low-PNI group (PNI ≤ optima cut-off value).

Statistical Analysis

All statistical analyses were conducted using IBM SPSS Statistics version 25 (IBM SPSS, Turkey). The normal distribution of the data was tested using the Shapiro–Wilk test. For continuous variables, differences between groups were tested using the Mann–Whitney U test, while for categorical variables, the Pearson chi-square test was employed. The optimal cut-off value for continuous data was determined using the ROC curve. A multivariate analysis was performed using a backward stepwise logistic regression model. P < .05 was considered statistically significant.

Results

The Best Cut-off Value of PNI

To identify the most suitable cut-off value for continuous variables, we developed a ROC curve and assessed the area under the curve (AUC) to gauge the predictive power of the PNI for therapeutic efficacy (Figure 1). The optimal cut-off value of PNI was 51.95, with an AUC of 0.624 (95% CI: 0.534-0.708, sensitivity: 70.31%, specificity: 57.81%). Based on the optimal critical value of PNI, patients were categorized into two groups: high-PNI group (PNI > 51.95) and low-PNI group (PNI ≤ 51.95), with 56 cases in the high-PNI group, while 72 cases in the low-PNI group.

Figure 1.

Receiver Operating Characteristic Curve of PNI. Abbreviation: PNI, Prognostic Nutritional index; AUC, Area Under the Curve.

Patient Characteristics

A total of 128 patients with LANPC were included in our study. The clinical features of the patients are showed in Table 1.Most of the participants were less than 60 years old (114/128, 89.06%). 94 males (73.44%), 118 patients were T3-4(92.19%),93 patients were N2-3(72.66%), 85 patients were stage IVa(66.41%). Most of the pathological types were undifferentiated non-keratinizing carcinoma(118/128, 92.19%).

Table 1.

Patient Characteristics.

Characteristics	N (128)	Percentage (%)
Age
＜60	114	89.06
≥60	14	10.94
Sex
Male	94	73.44
Female	34	26.56
ECOG
0	77	60.16
1	51	39.84
T stage
T1-2	10	7.81
T3-4	118	92.19
N stage
N0-1	35	27.34
N2-3	93	72.66
Clinical stage^a
III	43	33.59
IVa	85	66.41
Histological type
WHO I-II	10	7.81
WHO III	118	92.19
Smoking status
No	81	63.28
Yes	47	36.72
Alcohol status
No	94	73.44
Yes	34	26.56
Hypertension
No	121	94. 53
Yes	7	5.74
Diabetes
No	121	94.53
Yes	7	5.47

TNM staging followed the eighth edition of the American Joint Commission on Cancer (AJCC) staging system.

Abbreviation: PNI, prognostic nutritional index; ECOG, Eastern Cooperative Oncology Group; WHO, World Health Organization.

Comparison of Treatment Efficacy Between low- and High-PNI Groups

All patients underwent CCRT. After treatment, plain head and neck scans and enhanced MRI were performed to evaluate therapeutic effects. The results are presented in Table 2. There were no notable variations in the volumes of the RLNs, CLNs, and TLNs between the high-PNI and low-PNI groups before CCRT (P > .05). The regression rates of RLNs in the high-PNI and low-PNI groups were 70.43% and 68.50%, respectively, with no significant difference observed (P = .291). The regression rates of CLNs were 78.67% and 65.91%, respectively, indicating a statistically significant difference (P = .017). Similarly, the TLNs regression rates were 78.56% and 67.60%, also showing a statistically significant difference (P = .018). Overall, the regression rate of lymph nodes in the low-PNI group was lower compared to the high-PNI group.

Table 2.

Treatment Efficacy Between Low-PNI and High-PNI Groups.

	High-PNI Median (IQR)	Low-PNI Median (IQR)	P
RLNs
RNV_before (cm³)	4.66 (2.91, 7.66)	4.67 (2.26, 7.16)	.878
RNV_after(cm³)	1.00 (0.47, 2.09)	1.31 (0.53, 2.94)	.296
Regression rates of RLNs(%)	70.43 (56.97, 86.50)	68.50 (51.35, 77.51)	.219
CLNs
CNV_before(cm³)	24.54 (12.00, 36.31)	17.60 (10.43, 32.46)	.395
CNV_after(cm³)	4.66 (2.69, 7.49)	5.36 (3.59, 8.82)	.186
Regression rates of CLNs(%)	78.67 (57.67, 83.56)	65.91 (57.14, 78.28)	.017*
TLNs
TNV_before(cm³)	28.46 (15.36, 43.10)	23.50 (16.64, 36.90)	.387
TNV_after(cm³)	5.66 (3.60, 9.64)	7.39 (4.40, 11.58)	.202
Regression rates of TLNs(%)	78.56 (59.41, 83.52)	67.60 (57.91, 77.99)	.018*

Abbreviation: IQR, interquartile Range; RLNs, retropharyngeal lymph nodes; RNV, volume of retropharyngeal lymph nodes; CLNs, cervical lymph nodes; CNV, volume of cervical lymph nodes; TLNs, total lymph nodes; TNV, volume of total lymph nodes.

*Statistically significant.

Univariate and Multivariate Analysis of the Efficacy of CCRT for LANPC

To investigate the factors influencing the efficacy of CCRT for LANPC, a univariate logistic regression analysis was conducted using the clinicopathological features along with laboratory indicators (Table 3). The therapeutic efficacy was significantly associated with the ECOG score (P = .020) and the PNI (P = .002). In order to correct the potential confounding factors, we included indicators with P ≤ .1 (including clinical stage, ECOG score, and PNI) in the multivariate logistic regression analysis. Multivariate logistic regression analysis results showed that the PNI was an independent predictor of efficacy in patients with LANPC receiving CCRT (adjusted odds ratio, 3.069; 95% CI, 1.438-6.553, P = .004).

Table 3.

Univariate and Multivariate Analysis of Factors Affecting the Therapeutic Effect of NPC.

	Univariate analysis		Multivariate analysis
Variable	OR (95%CI)	P	OR (95%CI)	P
Sex (female vs male)	1.174 (0.535-2.575)	.689
Age (≥60 vs＜60)	0.518 (0.163-1.641)	.263
Histologic type (I-II vs III)	1.552 (0.416-5.783)	.513
T stage (T3-4 vs T1-2)	0.400 (0.099-1.624)	.200
N stage (N2-3 vs N0-1)	1.485 (0.678-3.251)	.323
Clinical stage (IVa vs III)	0.529 (0.251-1.115)	.094	0.555 (0.249-1.235)	.149
Smoking status (yes vs no)	0.623 (0.302-1.286)	.201
ECOG score (1 vs 0)	2.368 (1.145-4.897)	.020*	2.568 (1.188-5.554)	.017*
Alcohol status (yes vs no)	0.522 (0.234-1.164)	.112
Hypertension (yes vs no)	0.738 (0.158-3.437)	.698
PNI (>51.95 vs ≤51.95)	3.246 (1.563-6.738)	.002*	3.069 (1.438-6.553)	.004*
Diabetes (yes vs no)	2.627 (0.491-14.069)	.259

Abbreviation: OR, odds ratio; CI, Confidence Interval.

*Statistically significant.

Discussion

Accurate and early prediction of treatment failure in cancer patients is the key to optimizing treatment strategies. CCRT is the primary treatment option for LANPC. The tumor response to CCRT varies from person to person. Some patients do not benefit from standard CCRT and are unnecessarily exposed to cytotoxic drugs. The accurate prediction of tumor response to treatment is very important in clinical decision-making. Therefore, it is urgent to find a dependable approach to precisely predict the efficacy of CCRT in patients with LANPC. Lymph node metastasis is one of the important prognostic indicators of NPC. Mao et al²⁸ found that the primary tumor volume is not a factor affecting the efficacy of radiotherapy in patients with locally advanced head and neck squamous cell carcinoma (LAHNSCC). While the volume of metastatic lymph nodes is a factor affecting the efficacy of radiotherapy in patients with LAHNSCC. The sensitivity and accuracy of metastatic lymph nodes in evaluating the efficacy are better than those of primary tumors. Therefore, evaluating the changes of metastatic lymph nodes volume after treatment is helpful to evaluate the treatment response. However, in the past, the size of lymph nodes was usually assessed by measuring the minimum or maximum transverse diameter, which may not take into account changes in lymph node size in other dimensions, such as the longitudinal dimension. And in elliptical or asymmetric tumors, the maximum diameter obtained through one-dimensional measurement cannot fully display the tumor burden.²⁹ With the advancement of imaging technology, volume can theoretically describe solid tumors more accurately, and volume is unaffected by the presence of any intervening normal tissue or non-invasive disease. Study have shown that the average error of three-dimensional measurement of CLNs volume is lower than that of two-dimensional measurement.³⁰ The predictive value of the maximum tumor diameter for the true volume of a lesion is poor.^31,32 Johnson et al³³ found that the correlation between tumor volume and tumor control exceeded the predictive ability of AJCC staging. However, Because of the irregular shape of NPC, measurement of a single-dimensional diameter may be difficult and inconsistent. The lymph nodes are easier to measure, and the measurement error is relatively smaller. Therefore, we choose metastatic lymph nodes as monitoring indicators. Strongin et al have explored the predictive effect of primary tumor volume on the prognosis of LAHNSCC receiving radiotherapy and chemotherapy.³⁴ And Yuan et al explored the relationship between the volume of CLNs and the prognosis of NPC.³⁵ However, no studies have been seen on the changes in the volume of metastatic lymph nodes to reflect the therapeutic effect.To the best of our knowledge, this study is the first to investigate the predictive effect of PNI on treatment response of patients with LANPC receiving CCRT based on three-dimensional measurements to quantify the volume change of metastatic lymph nodes. This three-dimensional measurement provides a better assessment than one- or two-dimensional measurements used in other studies. Moreover, PNI is non-invasive, low-cost and easy to be applied in clinical practice.

Regardless of the tumor stage, nutrition plays a crucial role in the outcomes of cancer treatment.³⁶ The PNI provides a comprehensive assessment of the patient's nutritional and immune status, which is strongly related to the body's capacity to remove tumor cells and decrease the risk of local recurrence.³⁷ The PNI reflects the degree of tumor progression.^38,39 Our study found that for patients with a PNI > 51.95, the regression rates of CLNs and TLNs were higher than those patients with a PNI ≤ 51.95. Multivariate analysis suggested that the PNI served as an independent predictor of therapeutic efficacy in patients with LANPC undergoing CCRT. Miao et al⁴⁰ found that CCRT alone cannot achieve satisfactory results in patients with stage II-IVb NPC with PNI ≤ 52.0, and further research is needed to optimize the treatment plan. Salati et al⁴¹ found that the PNI was a predictor of treatment response to gemcitabine combined with platinum in cholangiocarcinoma. In contrast to the high-PNI group, patients in the low-PNI group exhibited a poorer response to treatment. Hua et al⁴² reported that patients with T2N1 breast cancer and a high PNI were more likely to benefit from radiotherapy. Our research reached conclusions consistent with those of previous studies. For patients with a low PNI, the efficacy of CCRT is poor, and strong individualized treatment, such as targeted or immune therapy, may be needed to improve the therapeutic effect.

The PNI showed a positive correlation with both nutritional indicators and the immune status of patients, serving as a comprehensive reflection of the two. The predictive value of the PNI for therapeutic efficacy can be explained by two dimensions: nutritional status and body immunity. The host's immune response to cancer primarily relies on systemic lymphocytes. Lymphocytopenia may be a manifestation of tumor-induced immunosuppression and a driver of tumor progression.²³ Lymphocytes are crucial in preventing cancer spread through their role in initiating cytotoxic immune responses and inhibiting cancer cell proliferation, invasion, and metastasis.⁴³ A low systemic lymphocyte count may lead to tumor advancement.⁴⁴ In addition, nutritional status plays a decisive role in the onset and evolution of tumors,⁴⁵ and the nutritional condition of patients plays a crucial role in influencing tumor development.⁴⁶ Malnutrition may change protein binding, damage the activity of cytochrome P450 3A, and lead to increased toxicity and reduced efficacy of chemotherapy by increasing the catabolic processes and inducing acute-phase reactions.⁴⁷ A low nutritional status can promote the proliferation of circulating tumor cells.⁴⁸ Albumin is the main protein in human plasma and the most direct indicator of human nutritional condition.⁴⁹ Hypoproteinemia indicates impaired immune function, which results in poor anti-cancer treatment.⁵⁰

A randomized study showed that whole-course nutritional management can improve treatment resistance and short-term prognosis in patients with esophageal cancer undergoing chemoradiotherapy.⁵¹ Thus, our research indicates that screening high-risk patients with poor response to treatment using the PNI could be used to guide clinicians in developing personalized treatment and nutritional support plans and may be of great significance for improving the prognosis of patients with LANPC. The PNI is easy to obtain, low-cost, non-invasive, and easily accepted by patients. Patients with lower PNI values should be closely monitored to ensure early diagnosis of residual or recurrent disease and initiation of appropriate salvage therapy. Therefore, we recommend that the PNI be included in the routine evaluation of patients with NPC before treatment to better stratify patients, and to identify those with a low PNI who may need individualized treatment plans and close follow-up. In the long term, this can increase the clinical benefits by reducing treatment failure and providing timely salvage treatment. This finding provides an important reference for the potential risk and positive significance of individualized clinical treatment of patients with NPC with different PNI levels and provides a basis for further studies of the effect of nutritional intervention on radiotherapy and chemotherapy response in LANPC.

This study had some limitations. First, the sample size is small. Second, the patients were from a single center, which may have produced a selection bias. Third, because there was no standard critical value, we calculated the critical value of the PNI alone, as in other studies. AUC = 0.624 was in the middle level and did not reach the excellent (AUC > 0.8) or good (AUC > 0.7) level, which clearly reflected that the current indicators as an independent clinical prediction tool had limited overall ability to distinguish and may lead to inappropriate clinical decision-making. However, as an exploratory study, this study has good sensitivity (70.31%), which provides a certain idea for predicting the efficacy of CCRT for LANPC, and has certain reference value. And our results should be verified using data from other centers. In the future, we will increase the sample size and use other methods to determine the best cut-off value to verify and improve the prediction performance. At the same time, we plan to conduct a forward-looking study to assess if immunonutritional support can improve therapeutic efficacy in patients with a low PNI.

Conclusion

Our study showed that the PNI is a non-invasive, low-cost, and easy-to-use indicator in clinical practice for patients with LANPC undergoing CCRT. Patients with LANPC and low PNI levels require special attention to ensure the early diagnosis of residual disease and timely rescue treatment. For such patients, combined immunotherapy or targeted therapy may improve efficacy. These findings may help develop treatment approaches and clinical risk stratification to avoid unnecessary toxicity and resource use in patients who are unlikely to benefit from treatment.

Footnotes

ORCID iD

Jun Bie

Ethical Considerations

This research was reviewed and approved by the Institutional Review Board of the First Affiliated Hospital of Guangxi Medical University (approval number:2024-E0831). The study adhered to the ethical guidelines outlined in the 1964 Declaration of Helsinki and its later revisions. As this was a retrospective study, the review committee did not require written informed consent

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by grants from the Project of Bureau of Science & Technology Nanchong City (20SXQT0257), Major project of Sichuan Science and Technology Department (2023YFS0473), the National Natural Science Foundation of China (No.82260469,82060019).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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