Association between lateral pterygoid muscle index as a predictor for sarcopenia and overall survival in non-small cell lung cancer patients with brain metastasis

Abstract

Background & aims

Sarcopenia has emerged as a significant prognostic factor for lung cancer. However, it remains unclear how the lateral pterygoid muscle index (LPMI) calculated based on the lateral pterygoid muscle area correlates with sarcopenia in lung cancer patients with brain metastasis (BM). This study aims to determine whether LPMI can serve as a predictor for sarcopenia in lung cancer patients with BM and assess its utility in predicting survival.

Methods

This study retrospectively analyzed the initial brain magnetic resonance imaging (MRI) parameters of non-small cell lung cancer (NSCLC) patients with BM at our hospital between March 2017 and June 2024. We examined the correlation between LPMI and the lumbar skeletal muscle index (LSMI) at the third lumbar vertebra (L3). After adjusting for key prognostic factors, we performed a multivariate Cox regression analysis of overall survival (OS) to evaluate the prognostic significance of LPMI.

Results

A total of 223 patients were included. The LPMI was positively correlated with LSMI in NSCLC patients with BM (r = 0.618, p < 0.001). Multivariate linear regression analysis indicated LPMI was associated with the risk of sarcopenia (β = 0.427,p < 0.001). The optimal cut-off value of LPMI for identifying NSCLC with sarcopenia was 120.06. Multivariate logistic regression showed an increased blood urea nitrogen/creatinine ratio (BUN/Cr) was associated with lower LPMI values (OR = 1.014, p = 0.013). Patients with an LPMI thicker than the median had a longer OS compared to those with an LPMI thinner than the median (9 months vs. 14.5 months, p = 0.011). Multivariate Cox analysis showed a thicker LPMI measurement was a predictor of longer OS (HR = 0.993,p = 0.036).

Conclusions

Our results indicate that LPMI has the potential to serve as an alternative diagnostic indicator for sarcopenia in NSCLC patients with BM and as an independent predictor of OS.

Introduction

Metastatic brain tumors are the most prevalent intracranial malignancy in adults [1], with an annual incidence over ten times higher than that of primary brain tumors [2]. Approximately 15–30% of cancer patients will eventually develop brain metastasis (BM), with non-small cell lung cancer (NSCLC) being the most frequently identified primary tumor [3]. NSCLC comprises 85% of primary lung cancers, and notably, 30–50% of NSCLC patients will develop BM [4]. Although advances in the treatment and management of BM in NSCLC, patients generally face a poor prognosis [5]. In the absence of treatment, the median survival time is approximately 1 month, which extends to 2 months with glucocorticoid therapy and 2.4–4.8 months with whole brain radiation therapy.

Cancer cachexia is a multifactorial syndrome characterized by persistent skeletal muscle mass loss from an underlying disease, which significantly impairs quality of life and often indicates a poor prognosis [6]. This condition cannot be fully reversed through nutritional support [7]. Sarcopenia, a key aspect of cancer cachexia, involves degenerative skeletal muscle loss and decreased muscle strength [8]. Sarcopenia has emerged as an objectively measurable indicator of frailty and poor outcome in various types of cancer [9–11]. Currently, the most widely adopted method for assessing sarcopenia is through the skeletal muscle index (SMI), which is determined by measuring the cross-sectional area (CSA) of several muscles at the third lumbar vertebra (L3) level on computed tomography (CT) [12, 13]. The CSA at the L3 has been shown to correlate with total body muscle mass and is therefore frequently used as the gold standard for evaluating sarcopenia in numerous studies [14, 15]. However, lung cancer patients with BM often do not undergo imaging at the L3 level during initial diagnosis or subsequent treatment. Recently published studies have identified a strong correlation between lumbar skeletal muscle CSAs and temporal muscle thickness (TMT) derived from routine diagnostic brain magnetic resonance imaging (MRI). Notably, this suggests that craniofacial muscles, like lumbar muscles, can serve as reliable surrogate markers for skeletal muscle mass estimation [16–18]. Despite the relatively small diameter of craniofacial muscles, it is crucial to adhere strictly to predefined anatomical landmarks and take precise measurements on enlarged MR images to enhance measurement accuracy.

As a key craniofacial muscle, the lateral pterygoid muscle (LPM) is easily evaluable via routine MRI, which offers distinct advantages for patients with intracranial neoplastic lesions. Currently, there is a lack of research on the connection between the area of the LPM and sarcopenia. Therefore, we hypothesize that the lateral pterygoid muscle index (LPMI), defined as the ratio of LPM area to patient height squared, serves as both an easily measurable and reliable surrogate marker for patient frailty. In this study, we aimed to investigate whether there is a prognostic relationship between LPMI and overall survival (OS) in an independent cohort of NSCLC patients with BM.

Materials and methods

Study design and patient selection

From March 2017 to June 2024, medical records of NSCLC patients with BM were retrieved from the dedicated brain metastasis database of the First Affiliated Hospital of Xi’an Jiaotong University, with a total of 249 patients included. Based on a specific formula ) ², the sample size for inclusion was calculated. Subsequently, 26 patients (10.4% of the initial cohort) were excluded from further analysis due to various reasons: missing information on sex, age, height, or weight (n = 12); unavailability of MRI examinations (n = 3); and inadequacy of MRI examinations due to motion artifacts (n = 6) or incomplete depiction of both LPMs (n = 5). Clinical data on patient characteristics, diagnostic procedures for primary tumor identification, the clinical course of the disease, and survival times were retrospectively evaluated through a comprehensive chart review.

Analysis of lateral pterygoid muscle area on brain MRI

Image assessment and measurements were performed on our institutional Picture Archiving and Communication System (PACS). In this study, two radiologists with over five years of experience used the Radiomics module in 3D Slice software to delineate regions of interest (ROI) on contrast-enhanced T1-weighted MR imaging (T1WI) sequences, excluding interference from motion artifacts and image resolution issues. The extracted imaging features underwent standardized preprocessing, which involved normalizing them to a range of (0,1) using the min-max normalization method. Radiologist 1 placed the ROI and extracted the imaging features once and repeated the process one week later to assess intra-observer consistency. Radiologist 2 performed the ROI placement and feature extraction only once, and the measurement was compared with Radiologist 1’s first measurement to assess inter-observer consistency. The intraclass correlation coefficient (ICC) was calculated for standardized features extracted by both radiologists and by the same radiologist twice, with a minimum ICC requirement of 0.8. The measurements were taken at the level of the mandibular condyle, and the maximum CSA on both sides were summed and divided by 2 to obtain a mean CSA per patient. The CSA of the LPM was normalized for the square of the patient’s height in meters and reported as the LPMI.

Analysis of cross-sectional area on abdominal computed tomography

CT was utilized to assess the CSA of the lumbar skeletal muscles. Specifically, skeletal muscle cross-sectional area (cm²) was quantified at the L3 vertebral level on two consecutive transverse slices where both vertebral spines were clearly visualized. The average surface area of these two consecutive slices was used for the analysis. The preprocessing outputs were thoroughly reviewed by an experienced CT analyst, who manually corrected and reran the measurements as necessary to ensure accuracy. The SMI was defined as the muscle area at L3 (in square centimeters) divided by the square of the patient’s height in meters [19]. This allowed for a standardized comparison of muscle mass across different individuals.

Clinical variables

Standard demographic and clinical data were collected from electronic case notes, including age, sex, body mass index (BMI), hematological results, radiological findings related to BM, survival status, and/or date of death attributed to a pulmonary origin. Prior to initiating the study, we will reach out to all patients included in this retrospective analysis to secure their consent and obtain a signed written informed consent form. This study was approved by the Medical Ethics Review Committee of The First Affiliated Hospital of Xi’an Jiaotong University.

Statistical analysis

Continuous variables are presented as mean ± standard deviation (SD) or medians (interquartile range, IQR) as appropriate, while categorical variables are presented as numbers and percentages. Comparisons between groups were conducted using the independent-samples Student’s t-test for normally distributed continuous variables, the Mann-Whitney U test for non-normally distributed variables, and either the Fisher exact test or chi-square test for categorical variables. Pearson and Spearman correlation tests were used to evaluate the strength of relationships between continuous and ranked variables, respectively. Multiple linear regression models were constructed to determine independent influencing factors associated with the L3 SMI. The optimal cut-off value for LPMI, predictive of sarcopenia, was determined using receiver operating characteristic (ROC) curves and the area under the curve (AUC). Survival analyses were performed using Kaplan-Meier estimation with the log-rank test, as well as the Cox proportional hazards regression model. Both univariate and multivariate analyses were conducted, generating hazard ratios (HRs) and 95% confidence intervals (CIs). A p-value < 0.05 was considered statistically significant in the multivariable analysis. Statistical analyses were performed using GraphPad Prism 8 (GraphPad Software, La Jolla, CA) and IBM SPSS Statistics version 25 (IBM, Armonk, NY).

Results

Patient characteristics

A total of 223 patients (57.4% male; median age: 67 year [IQR, 59.00–71.00 years]) were included in this study. At baseline, the median serum levels of carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), and cytokeratin-19-fragment (CY21-1) were 5.82 ng/mL (IQR, 2.89–37.12), 22.00 ng/mL (IQR, 16.18−39.00), and 5.21 ng/mL (IQR, 2.33–22.38), respectively. The mean (SD) LSMI and LPMI were 666.59 (130.18) and 135.94 (30.33), respectively. Table 1 presents a summary of subject recruitment and baseline characteristics.

Table 1.

Baseline characteristics of the enrolled patients

Characteristics	Value or no. of patients
Age, years	67.00 (59.00–71.00)
Sex, n (%)
Female	95 (42.6%)
Male	128 (57.4%)
BMI, kg/m2
Female	22.18 ± 3.35
Male	22.67 ± 3.01
WBC, ×109/L	6.20 (4.90–8.50)
Neutrophils, ×109/L	4.40 (3.20–5.80)
Lymphocytes,×109/L	1.30 (0.80–1.70)
NLR	3.64 (2.15–5.50)
BUN/Cr	81.50 ± 32.22
Alb, g/L	39.70 (36.00−42.30)
Hgb, g/L	126.40 ± 18.47
LSMI	666.59 ± 130.18
LPMI	135.94 ± 30.33
CEA (ng/mL)	5.82 (2.89–37.12)
NSE (ng/mL)	22.00 (16.18−39.00)
CY21−1 (ng/mL)	5.21 (2.33–22.38)

Values presented as mean ± SD, median (25-75th percentiles), or %

BMI Body mass index, WBC White blood cells, NLR Neutrophil to lymphocyte ratio, Hgb Hemoglobin, BUN Blood urea nitrogen, Cr Creatinine, Alb Albumin, CEA Carcinoembryonic antigen, NSE Neuron specific enolase, CY21-1 Cytokeratin-19-fragment, LSMI Lumbar skeletal muscle index, LPMI Lateral pterygoid muscle index

Correlation analysis between clinical and radiological parameters and L3 lumbar muscle mass

Correlations between various demographic, clinical and laboratory variables and L3 SMI level are shown in Table 2. Age at the diagnosis of brain metastases exhibited a low negative association with mean L3 SMI (r = −0.291, p< 0.001). Notably, a statistically significant correlation was identified between the LPMI and the L3 SMI (r = 0.618, p< 0.001), as illustrated in Fig. 1. Additionally, the results indicated a positive correlation with L3 SMI and BMI (r = 0.530, p< 0.001), albumin (Alb; r = 0.310, p< 0.001), and hemoglobin (Hgb; r = 0.285, p< 0.001). Conversely, negative correlations were observed between L3 SMI and CY21-1 (r = −0.379, p< 0.001) and NSE (r = −0.161, p = 0.016). For all other tested variables, the correlations were not statistically significant (all p > 0.05).

Table 2.

Pearson or spearman’s correlation analysis between LSMI and other laboratory parameters

	LSMI
	r	p-value
Age	−0.291	< 0.001
Sex	0.128	0.056
BMI	0.530	< 0.001
WBC	−0.030	0.660
Neutrophils	−0.060	0.370
Lymphocytes	0.080	0.233
NLR	−0.055	0.418
BUN/Cr	−0.122	0.068
Alb	0.310	< 0.001
Hgb	0.285	< 0.001
LPMI	0.618	< 0.001
CEA	−0.050	0.458
NSE	−0.161	0.016
CY21−1	−0.379	< 0.001

BMI Body mass index, WBC White blood cells, NLR Neutrophil to lymphocyte ratio, Hgb Hemoglobin, BUN Blood urea nitrogen, Cr Creatinine, Alb Albumin, CEA Carcinoembryonic antigen, NSE Neuron specific enolase, CY21-1 Cytokeratin-19-fragment, LSMI Lumbar skeletal muscle index, LPMI Lateral pterygoid muscle index

Fig. 1.

Representative images for evaluating muscle mass of the lateral pterygoid and lumbar skeletal muscles on MRI and CT images (n = 223). A, B A 72-year-old male patient with an OS of 7 months (LPMI = 97.15; LSMI = 510.91), and (C, D) a 69-year-old male patient with an OS of 26 months (LPMI = 209.73; LSMI = 776.73). LPMI, lateral pterygoid muscle index. LSMI, lumbar skeletal muscle index. OS, overall survival

Analysis of the association between LPMI and L3 SMI in NSCLC patients with BM

To further investigate whether LPMI retains its association with L3 SMI independently of other clinical variables, we conducted a multivariate analysis using linear regression. The results demonstrated that LPMI maintained a significant association with LSMI (β = 0.427, p< 0.001; Table 3), even after considering factors such as age, BMI, blood biochemistry, and tumor markers.

Table 3.

Multivariate linear regression analysis of the association between LPMI and LSMI

	β	S.E.	t	95% CI	p-value
Constant		89.588	0.679	−115.764 to 237.401	0.498
Age	−0.055	0.672	−1.049	−2.030 to 0.620	0.296
BMI	0.351	2.222	6.494	10.053–18.814	< 0.001
Alb	0.019	1.342	0.326	−2.208 to 3.084	0.745
Hgb	0.072	0.399	1.270	−0.280 to 1.293	0.206
LPMI	0.427	0.232	7.911	1.376–2.289	< 0.001
NSE	0.008	0.123	0.150	−0.223 to 0.260	0.881
CY21−1	−0.065	0.164	−1.240	−0.525 to 0.120	0.216

β Standardized coefficient, S.E. Standard error, CI Confidence interval, BMI Body mass index, Hgb Hemoglobin, Alb Albumin, NSE Neuron specific enolase, CY21-1 Cytokeratin-19-fragment, LSMI Lumbar skeletal muscle index, LPMI Lateral pterygoid muscle index

ROC curve for evaluating the sensitivity and specificity of the prediction model

To establish the optimal cut-off point for sarcopenia identification, the Youden’s index was utilized, which maximizes the combined sensitivity and specificity of the biomarker by analyzing the ROC curve [20]. An LPMI cut-off value of ≤ 120.06 was determined as the most effective threshold for identifying sarcopenia, yielding a sensitivity of 88.2% and a specificity of 61.0%. To investigate whether combining LPMI with tumor markers such as NSE and CY21-1 could further enhance their predictive capability for sarcopenia, a comprehensive analysis was conducted (Fig. 2). Although the prognostic performance of the combined model was slightly superior than that of the LPMI prediction model (AUC: 0.823 vs. 0.812), this difference did not achieve statistical significance.

Fig. 2.

The performance of various models (LPMI alone and combined models) in predicting LSMI measurements (n = 223). NSE, neuron specific enolase. CY21-1, cytokeratin-19-fragment. LPMI, lateral pterygoid muscle index. LSMI, lumbar skeletal muscle index. FPR, false positive rate. TPR, true positive rate

Comparison of clinical parameters between low-LPMI and high-LPMI groups

Patients were divided into two groups based on the mean value of LPMI, with 127 patients classified as “low-LPMI” and 96 as “normal-LPMI”. A significant difference was observed between the two groups in terms of age (p = 0.018), BMI (p = 0.003), NLR (p = 0.020), LSMI (p< 0.001), Alb levels (p = 0.006), Hgb levels (p = 0.001), blood urea nitrogen/creatinine ratio (BUN/Cr, p< 0.001), and CY21-1 (p< 0.001). Detailed findings are presented in Table 4.

Table 4.

Differences between normal and low LPMI groups in NSCLC patients

Characteristics	Normal LPMI (n = 96)	Low LPMI (n = 127)	p-value
Age, years	62.94 ± 10.56	66.18 ± 9.69	0.018
Sex, n (%)			0.806
Female	40 (41.7%)	55 (43.3%)
Male	56 (58.3%)	72 (56.7%)
BMI, kg/m2	23.19 ± 3.31	21.92 ± 2.94	0.003
WBC, ×109/L	6.05 (4.52–8.10)	6.30 (5.00–9.00)	0.082
Neutrophils, ×109/L	4.15 (2.82–5.70)	4.50 (3.30–6.30)	0.073
Lymphocytes,×109/L	1.30 (0.80–1.87)	1.20 (0.80–1.60)	0.265
NLR	3.16 (1.87–5.20)	3.91 (2.63–6.28)	0.020
BUN/Cr	72.49 ± 30.59	88.30 ± 31.85	< 0.001
Alb, g/L	40.59 ± 4.26	38.64 ± 6.31	0.006
Hgb, g/L	131.05 ± 17.71	122.88 ± 18.32	0.001
LSMI	735.82 ± 111.99	614.26 ± 118.29	< 0.001
CEA (ng/mL)	5.75 (2.42–25.29)	6.29 (3.07–45.66)	0.077
NSE (ng/mL)	19.89 (15.16–38.48)	22.94 (17.35–42.16)	0.121
CY21−1 (ng/mL)	3.78 (2.05–8.61)	9.34 (3.34–34.56)	< 0.001

Values presented as mean ± SD, median (25-75th percentiles), or %

BMI Body mass index, WBC White blood cells, NLR Neutrophil to lymphocyte ratio, Hgb Hemoglobin, BUN Blood urea nitrogen, Cr Creatinine, Alb Albumin, CEA Carcinoembryonic antigen, NSE Neuron specific enolase, CY21-1 cytokeratin-19-fragment, LSMI Lumbar skeletal muscle index, LPMI Lateral pterygoid muscle index, NSCLC Non-small cell lung cancer

Variables with a p-value of < 0.05 in the univariate analysis were selected for inclusion in the multivariate analysis to determine factors associated with low LPMI. The results of this analysis indicated that LSMI [odds ratio (OR) = 0.990, 95% CI = 0.987–0.994; p< 0.001] and BUN/Cr (OR = 1.014, 95% CI = 1.003–1.025; p = 0.013) were independently associated with the decreased LPMI. Detailed information is presented in Table 5.

Table 5.

Multivariate logistic regression analysis of clinical variables related to low LPMI in patients with advanced NSCLC

Variables	B	OR	95% CI	p-value
Constant	4.430	83.918		0.070
Age	0.015	1.015	0.982–1.050	0.371
BMI	0.072	1.075	0.955–1.210	0.233
NLR	0.032	1.033	0.952–1.119	0.437
LSMI	−0.010	0.990	0.987–0.994	<0.001
Alb	−0.018	0.982	0.921–1.047	0.585
Hgb	−0.006	0.994	0.974–1.013	0.532
BUN/Cr	0.014	1.014	1.003–1.025	0.013
CY21−1	0.001	1.001	0.993–1.009	0.868

B Regression coefficient, OR Odds ratio, CI Confidence interval, BMI Body mass index, NLR Neutrophil to lymphocyte ratio, Alb Albumin, Hgb Hemoglobin, BUN Blood urea nitrogen, Cr Creatinine, CY21-1 Cytokeratin-19-fragment, LSMI Lumbar skeletal muscle index, LPMI Lateral pterygoid muscle index, NSCLC Non-small cell lung cancer

LPMI and overall survival

The median OS for the entire group was 12.0 (95% CI: 5.0–21.0) months (follow up range: 0–67 months). Within this population, patients with high LPMI had a median OS of 14.5 months (95% CI = 12.0–16.0), compared to 9.0 months (95% CI = 7.0–12.0) for those with low LPMI (log-rank p = 0.011; Fig. 3). To evaluate the impact of other factors on the prognostic value of LPMI, we conducted univariate and multivariate Cox proportional hazards regression analyses, with inclusion of variables such as age, BMI, performance status (PS), treatment modality, LSMI, and additional hematological test findings. The multivariate analysis of OS identified LPMI as a significant independent prognostic factor (HR = 0.993, 95% CI = 0.987−1.000; p = 0.036). The results of both univariate and multivariate analyses for predictors of OS are summarized in Table 6.

Fig. 3.

Comparison of OS in NSCLC patients with BM for high and low LPMI groups using Kaplan-Meier survival curves analysis (n = 223). OS, overall survival. NSCLC, non-small cell lung cancer. BM, brain metastasis. LPMI, lateral pterygoid muscle index

Table 6.

Univariate and multivariate Cox regression analysis of risk factors and radiological muscle measurements on OS

Variates	Univariate Analysis		Multivariate Analysis
	HR (95% CI)	p-value	HR (95% CI)	p-value
Age	1.015 (1.000−1.030)	0.051	1.015 (0.999–1.032)	0.070
BMI	0.977 (0.936–1.019)	0.281	0.968 (0.916–1.023)	0.247
NLR	1.007 (0.975–1.040)	0.683	0.978 (0.943–1.014)	0.231
Hgb	0.993 (0.986−1.000)	0.063	0.996 (0.987–1.005)	0.375
Alb	0.974 (0.949–0.999)	0.041	0.978 (0.951–1.007)	0.135
BUN/Cr	0.999 (0.995–1.004)	0.766	0.998 (0.993–1.003)	0.538
CY21−1	1.001 (0.998–1.003)	0.545	1.000 (0.997–1.003)	0.956
LSMI	1.000 (0.999–1.001)	0.695	1.001 (1.000−1.003)	0.079
LPMI	0.995 (0.990−1.000)	0.043	0.993 (0.987−1.000)	0.036
PS	1.210 (1.090–1.343)	<0.001	1.219 (1.095–1.356)	<0.001
Treatment type	1.033 (0.851–1.255)	0.740	1.032 (0.846–1.259)	0.756

HR Hazard ratio, CI Confidence interval, BMI Body mass index, NLR Neutrophil to lymphocyte ratio, Alb Albumin, Hgb Hemoglobin, BUN Blood urea nitrogen, Cr Creatinine, CY21-1 Cytokeratin-19-fragment, LSMI Lumbar skeletal muscle index, LPMI Lateral pterygoid muscle index, PS Performance status, OS Overall survival

Discussions

Lung cancer patients are at a higher risk of developing sarcopenia compared to those with other types of cancer [21–23]. Reliable measurements of whole-body skeletal muscle mass (SMM) can be obtained by assessing abdominal CT images at the L3 level. However, in NSCLC patients with BM, scans at this lumbar level are not frequently performed. Previously studies have shown a good correlation between the CSA of muscles at the L3 level and those at the C3 level in the evaluation of SMM [24–26]. However, this method can be time-consuming, and on routine cranial MR images, these muscles are often only partially depicted. Therefore, we investigated the relationship between lateral pterygoid muscle parameters and those at the L3 level, as well as explored the association between LPMI and patient survival.

We found the muscle mass of the lateral pterygoid muscle correlated well with the L3 level and has the potential to emerge as a novel and effective index for assessing sarcopenia. The cut-off value for defining the sensitivity of LPMI in predicting decreased LSMI was determined using ROC curve analysis. The predictive performance could be enhanced by incorporating serum tumor marker variables such as NSE and CY21-1. Multivariate analysis revealed that a low LPMI was an independent adverse predictor for OS. More importantly, LPMI is independent of other clinical factors, suggesting that it can provide additional prognostic value. Despite the p-value being close to 0.05, this finding holds promise for aiding in patient stratification and enabling better identification of those at higher risk of poorer survival. This can serve as a crucial basis for clinical decision-making, such as enabling closer monitoring of these patients or providing more aggressive treatment strategies. The predictive role of LPMI points to a potential biological link with patients’ survival status, offering clues for further investigating its relationships with tumor progression, treatment response, and other factors, as well as new targets for future therapeutic strategies. Thus, we conclude that LPMI measurement holds clinical utility for prognostic assessment of BM patients, supporting the formulation of tailored follow-up and monitoring plans and enabling effective patient stratification in clinical trials. To the best of our knowledge, this is the first report on the predictive value of LPMI in the context of sarcopenia.

This study has demonstrated the significant role of LPMI as an independent prognostic factor for OS assessment in patients with NSCLC and brain metastases. However, in practical clinical application and promotion, LPMI still encounters several challenges and unresolved issues. Due to variations in MRI machine hardware, scanning parameters, and the diversity of image post-processing software, LPMI measurements may vary across different machines. To address this, it is recommended to establish a unified MRI scanning protocol, encompassing machine type, scanning sequence, and image resolution, as well as developing standardized image post-processing software to ensure comparability of LPMI data obtained from various machines. In clinical practice, factors such as patient position, respiratory status, and scanning time can affect the measurement results of LPMI. Therefore, detailed operational guidelines should be formulated to specify standard positioning, respiratory control, and scanning time points for LPMI measurement. Providing necessary training and guidance to patients can further reduce measurement errors. While this study focuses on patients with NSCLC and brain metastases, the potential applications of LPMI extend beyond this. Future research will explore the prognostic value of LPMI in other tumor types, such as breast cancer and prostate cancer, as well as its potential as an independent prognostic factor for patients with brain metastases from various tumors. Measuring LPMI requires professional imaging techniques and post-processing software support, which may not be available in all medical institutions. Therefore, strengthening collaboration with medical institutions and research organizations is crucial to promote LPMI measurement techniques and software, while providing necessary technical training and support. Compared to traditional methods for diagnosing sarcopenia, such as bioimpedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA) scans, LPMI measurement based on MRI images eliminates the need for additional invasive examinations, ensuring patient safety. MRI images offer high resolution and clear soft tissue contrast, enabling accurate quantification of the lateral pterygoid muscle area. This provides an objective and precise assessment method, thereby minimizing errors introduced by subjective judgments. In summary, despite the challenges faced by LPMI in clinical application, its unique advantages and potential clinical value cannot be overlooked. Through technological optimization, clinical promotion, and integration with existing technologies, LPMI is poised to play a significant role in individualized treatment and prognosis assessment for cancer patients in the future.

Surprisingly, our findings revealed that the BUN/Cr ratio had a notable impact on LPMI measurements. While serum Cr levels have previously been reported as an indicator of sarcopenia [27], there is currently no report on the relationship between the BUN/Cr ratio and muscle mass in cancer patients. Although the BUN itself has not been established as directly related to skeletal muscle mass, exploring its clinical role in NSCLC patients is valuable. Our results suggest that the BUN/Cr ratio, derived from BUN-corrected Cr, could potentially predict the mass of the LPM in NSCLC patients. Therefore, we hypothesize that the serum BUN/Cr ratio may serve as a predictive factor for sarcopenia, given its significant association with LPMI. We aim to test these hypotheses in future research endeavors.

Cancer cachexia is a multifactorial metabolic syndrome characterized by systemic inflammation, and muscle and adipose tissue wasting, resulting in weight loss despite adequate nutritional support [28]. Our study supports this notion by demonstrating that low levels of BMI and Alb are associated with malnutrition and increased sarcopenia. These findings align with existing literature, which indicates that malnutrition and sarcopenia are poor prognostic factors in various cancers [29–31]. However, to our knowledge, this association has not been previously explored in NSCLC patients with BM. This underscores the importance of closely monitoring patients with nutritional risks and administering oral nutritional supplements to enhance protein intake, thereby supporting muscle mass. Future research should include additional prospective studies to validate and further support these findings.

A key aspect of our study is that all images were analyzed utilizing tools integrated within our institution’s standard clinical PACS application. This ensures a seamless implementation within the clinical setting, as it necessitates no additional hardware or software, and images can be promptly reconstructed. Furthermore, our study gathered comprehensive data on potential confounding variables and accounted for them in the models. Lastly, we acquired long-term survival data, enabling us to observe pertinent outcome indicators.

The results of our research indicate a potential prognostic value of LPMI in predicting OS in NSCLC with BM. However, it is important to acknowledge certain limitations in our study. Primarily, as it was retrospective, our research could not account for all potential confounding factors that might influence the outcomes. To address this and further clarify our findings, a prospective study is required. In future research, we aim to broaden our dataset by incorporating other types of brain tumors, including both metastatic tumors originating from other cancers and primary brain tumors such as gliomas. This expansion will allow us to explore the potential association between LPMI and prognosis across a wider range of brain tumor types. Additionally, to validate our findings and mitigate potential biases stemming from our study’s retrospective and oligo-institutional nature, prospective multicenter studies with larger sample sizes may be necessary. We acknowledge that the limited sample size and data availability in our study may have contributed to some degree of underpowering in our analysis, potentially obscuring subtle associations between LPMI and some of the included factors. Thus, we highlight the importance of future studies with expanded sample sizes and broader datasets to further explore the relationship between LPMI and relevant factors in NSCLC patients with brain metastases more in-depth and conclusively.

Understanding the relationship between the decreased muscle mass and cancer prognosis may lead to identification of novel and specific therapeutic targets, leading to effective treatment strategies. Cancer cachexia, characterized by increased catabolic processes resulting from inflammation-mediated metabolic changes, mainly affects solid tumor patients and involves muscle protein breakdown. Counteracting muscle atrophy emerges as a pivotal therapy for cancer cachexia, as patients with this condition are unable to regain muscle mass through nutritional intake alone. Recognizing the association between survival and skeletal muscle mass may unveil additional therapeutic avenues, such as exercise training [32], nutritional supplements [33], and pharmacotherapy with myostatin inhibitors [34]. Given the significant clinical and financial burden that muscle mass loss poses on cancer patients and society, it is crucial to devise strategies to prevent muscle mass loss, or even to maintain and possibly enhance muscle mass.

Conclusions

Our research findings indicate that LPMI holds potential prognostic significance for OS in NSCLC patients with brain metastases, functioning as a valuable predictor of survival outcomes. LPMI measurement allows for more precise identification of patients with sarcopenia, which in turn supports the refinement of treatment strategies and recruitment for clinical trials. However, it is essential to acknowledge the limitations of our study, including a relatively small sample size and the potential presence of unmeasured confounding factors. Consequently, further research, especially external validation in larger, multicenter cohorts, is essential to establish the clinical utility and validity of LPMI as a prognostic biomarker.

Abbreviations

Alb

Albumin

BM

Brain metastasis

BMI

Body mass index

BUN

Blood urea nitrogen

CEA

Carcinoembryonic antigen

Cr

Creatinine

CSA

Cross-sectional area

CY21-1

Cytokeratin-19-fragment

Hgb

Hemoglobin

ICC

Intraclass correlation coefficient

LPMI

Lateral pterygoid muscle index

LSMI

Lumbar skeletal muscle index

MRI

Magnetic resonance imaging

NLR

Neutrophil to lymphocyte ratio

NSCLC

Non-small cell lung cancer

NSE

Neuron specific enolase

OS

Overall survival

PS

Performance status

ROI

Regions of interest

TMT

Temporal muscle thickness

WBC

White blood cells

Authors’ contributions

Hengxing Gao : Conceptualization, Data curation, Investigation, Methodology, Resources, Validation, Writing-original draft. Xuexue Zou : Data curation, Methodology, Project administration, Resources, Supervision. Jiejun Zhou and Meng Fan : Data curation, Project administration, Resources. Mingwei Chen : Conceptualization, Data curation, Formal analysis, Investigation, Resources, Validation, Writing-original draft, Writing-review & editing.

Funding

This study was supported by the Key Research and Development Projects of Shaanxi Province (Grant number 2024SF-GJHX-41).

Data availability

All data generated or analyzed during this study are included in this published article.

Declarations

Ethics approval and consent to participate

This study was approved by the the Medical Ethics Review Committee of The First Affiliated Hospital of Xi’an Jiaotong University (Approval No. XJTU1AF2021LSK-339). The informed consent was obtained from all subjects and/or their legal guardian(s).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.