Dynamic changes in liver-to-spleen ratio: can it be a prognostic biomarker for chemo-immunotherapy in small cell lung cancer?

Introduction

The application of immune checkpoint inhibitors (ICIs) in immunotherapy is a major breakthrough in the field of tumor therapy, which improves the prognosis of a variety of advanced solid tumors by blocking signaling pathways such as programmed cell death protein 1 (PD-1)/programmed cell death ligand 1 (PD-L1) or cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), lifting the immune suppression in the tumor microenvironment, and activating the anti-tumor activity of T-cells (1). This includes small cell lung cancer (SCLC). SCLC is one of the most malignant subtypes of lung cancer, accounting for approximately 10–15% of lung cancer cases, and is characterized by a high degree of aggressiveness, with a 5-year survival rate of approximately 7% (2). According to the 2022 edition of the National Comprehensive Cancer Network (NCCN) Clinical Practice Guidelines for SCLC, ICIs (such as atezolizumab or durvalumab) in combination with chemotherapy have become the first-line standard of care for extensive-stage SCLC (3). However, despite the survival benefits demonstrated in pivotal trials (IMpower133 and CASPIAN) (4,5), the absolute gains in median overall survival (OS) are modest (2–3 months), a substantial proportion of patients exhibit primary resistance (approximately 30–40%), and most responders eventually develop acquired resistance within 6–12 months (6,7); chemo-immunotherapy also faces challenges of high immunotoxicity and high treatment costs (8). Consequently, there is an urgent clinical need for efficient predictive biomarkers to accurately identify the subset of patients most likely to derive significant benefit from this expensive and potential toxic therapy.

Despite the exploration of tumor mutation load (TML), PD-L1 expression, circulating tumor DNA (ctDNA), and inflammation-related indicators [such as neutrophil to lymphocyte ratio (NLR)] in the context of SCLC prognosis (9,10), a consensus on their predictive value remains limited. TML lacks a universally accepted cutoff value and its predictive performance varies considerably across SCLC subtypes and treatment settings. PD-L1 expression is generally low in SCLC, and ctDNA analysis lacks standardized clinical application. This underscores the necessity for more dimensional and reliable biomarkers, including imaging, dynamic metabolic profiling, and immune microenvironmental indicators, among others, to optimize therapeutic stratification and realize precision and individualized SCLC chemo-immunotherapy.

A growing body of evidence indicates that the pathogenesis and progression of non-alcoholic fatty liver disease (NAFLD) involve insulin resistance, oxidative stress, chronic inflammation, dysbiosis of intestinal microflora, and immune imbalance (11,12). A distinctive immune microenvironment develops during this process. On the one hand, persistent lipotoxic inflammation is found in mouse experiments to promote the accumulation of CD8⁺ PD-1⁺ T cells, which bind to PD-L1-expressing tumor cells, leading to the suppression of immune surveillance (13); on the other hand, NK cell dysfunction and a reduction of CD4⁺ T cells further weakened immune surveillance (14,15). This state may lead to the limited efficacy of ICIs in hepatocellular carcinoma (HCC) associated with NAFLD, and may even exacerbate tumor progression by enhancing the pro-inflammatory activity of CD8⁺ T cells, as shown in post hoc analyses of several phase III clinical trials. ICIs may be less effective in HCC associated with NAFLD than in HCC associated with viral hepatitis (16). Although there are differences in the pathogenesis of SCLC and HCC associated with NAFLD, the normal liver, as an important immunoregulatory hub for maintaining systemic homeostasis (17), has two sets of blood perfusion systems, and its NAFLD-associated systemic inflammation after destabilization may affect the immune response of distal tumors (such as SCLC) through circulating cytokines or immune cell migration.

The liver-to-spleen ratio (LSR) is a tool used to assess the degree of hepatic fatty infiltration for the diagnosis of NAFLD (18). It is a non-invasive, easily obtainable, and highly reproducible imaging index that can be calculated from computed tomography (CT) plain scanning. This approach obviates the necessity for contrast-enhanced scanning or liver biopsy, thereby considerably decreasing patient trauma and the cost of the examination. Concurrently, low-dose CT is regarded as the present gold standard for the screening of lung cancer patients (3). This makes LSR highly suitable for SCLC patients who require frequent follow-up and dynamic monitoring, as their follow-up chest CT often contains target levels from which LSR can be obtained.

The prognostic value of CT-based LSR varies across cancer types. In colorectal cancer, studies have yielded conflicting results regarding liver metastasis recurrence (19,20). Conversely, in gastric cancer, lower LSR was linked to favorable immunotherapy outcomes (21), while in non-SCLC, our previous study has found no such association (22). Low LSR also predicts intrahepatic recurrence in HCC (23), whereas a high LSR correlates with poor response to ipilimumab in melanoma (24). These divergent findings underscore the tumor-type specificity of LSR as a biomarker, warranting investigation in SCLC.

Therefore, the objective of this study was to assess whether the presence of NAFLD (defined by LSR) and the dynamic changes of the LSR after short- and long-term treatments indirectly reduce the susceptibility of SCLC to chemo-immunotherapy by altering the systemic immune status, with the aim of providing a new predictive tool for clinical practice. We present this article in accordance with the REMARK reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-1-0137/rc).

Methods

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Institutional Review Board of Wuhan Union Hospital (No. 0464). Verbal informed consent was obtained from all the patients. Clinical information for this study was obtained by reviewing medical records, and survival data were obtained by telephone follow-up. To ensure the anonymity of subjects, all data were anonymized.

Study design and patient selection

This study analyzed SCLC patients who received chemotherapy or chemo-immunotherapy from March 1, 2015, to March 6, 2024, at Wuhan Union Hospital. All patients underwent at least three CT scans (before treatment, after at least one cycle, and after at least two cycles of treatment). Inclusion criteria were as follows: (I) SCLC diagnosed histologically or cytologically and staged according to the NCCN Clinical Practice Guidelines in Oncology; (II) patients underwent at least 3 CT scans that included images of a portion of the right lobe of the liver and spleen; (III) patients were followed up for a minimum of 6 months and had complete follow-up data, including survival data and documentation of disease progression (PD); and (IV) age ≥18 years. The exclusion criteria were as follows: (I) combination of other primary malignant tumors or liver metastases from SCLC; (II) underwent surgical treatment; (III) patients did not undergo CT imaging evaluation before receiving treatment, or the time interval between any two consecutive imaging evaluations was less than one month; (IV) poor quality of CT images; and (V) incomplete clinical and follow-up data.

Procedure

We retrospectively collected baseline data on patients, including general patient information [age, sex, body mass index (BMI), cardiovascular risk factors, diabetes, hypertension, smoking index, drinking, performance status], laboratory data [Na, creatinine, total cholesterol, triglyceride, high-density lipoprotein, low-density lipoprotein, albumin/globulin ratio (A/G ratio), lactic dehydrogenase (LDH), alkaline phosphatase, hemoglobin, NLR, platelet to lymphocyte ratio, neuron-specific enolase (NSE), carcino-embryonic antigen (CEA)], and information of tumor and treatment (stage, treatment modality, ICIs type). The patients were divided into chemotherapy and chemo-immunotherapy cohorts according to treatment modality. All baseline information was based on the outcome of the first hospitalization.

Measurement and evaluation of LSR

We collected chest CT plain or abdominal CT plain images containing the right lobe of the liver and spleen level before the patients receiving treatment, after short-term treatment (60±30 days from treatment initiation), and long-term treatment (60±30 days from the last treatment cycle). The same parameters were applied using a 128-section CT scanner (SIEMENS SOMATOM Definition AS+, Siemens Healthcare Erlangen, Germany). The tube voltage was 120 kVp, and the tube current was adjusted automatically. A standard soft convolution kernel was used for reconstruction, with a slice thickness of 1 mm. Two independent experienced radiologists (Y.L. and Q.S.) measured the right lobe of the liver and the spleen in the mediastinal window of the same horizontal CT plain at the level of the 12th thoracic vertebra. To ensure the accuracy of the density measurements, they carefully delineated a circular region of interest (ROI) (approximately 120 mm²), avoiding bile ducts, blood vessels, artifacts, and any visible focal lesions, ensuring that only the parenchymal densities of the liver and spleen were sampled. All measurements were performed blinded to the clinical information (Figure S1). The right lobe of the liver and the spleen were each plotted with two repetitions of ROIs averaged to minimize errors, and liver attenuation and spleen attenuation were obtained, with the final average of the measurements taken by two independent radiologists used for subsequent analysis. After a 2-week clearance period, a third radiologist (L.Y.) randomly selected 50 patient images to be evaluated repeatedly by two radiologists using the same methodology. Intra- and inter-observer agreement was 0.92 [95% confidence interval (CI): 0.89–0.95] and 0.88 (95% CI: 0.82–0.92), respectively, which suggests acceptable reproducibility in intra- and inter-observer measurements (see Bland-Altman plots in Figure S2). The LSR was calculated using the measured liver attenuation and splenic attenuation. In addition, the LSR is considered to be a reliable imaging index for the diagnosis of NAFLD (18), and patients were categorized into NAFLD and non-NAFLD groups based on whether the LSR calculation was ≤1.1 at baseline. Any disagreements arising from this process were resolved through group discussion.

Follow-up and endpoints

Required follow-up information was obtained through clinical case information (including outpatient or inpatient information), CT images, and telephone follow-up. All patients were followed until March 6, 2024 or until the patient’s death. The primary outcome of this study is OS, defined as the time from the start of treatment to the patient’s death from any cause. Secondary outcomes include progression-free survival (PFS), objective response rate (ORR), and disease control rate (DCR), with PFS defined as the time from the start of treatment to PD or death. Treatment efficacy was assessed using the criteria of complete remission (CR), partial remission (PR), stable disease (SD), and PD according to RECIST standard v1.1 (25). ORR and DCR were calculated based on the proportion of patients with CR and PR and the proportion of patients with CR, PR, and SD, respectively.

Statistical analyses

For correlation analysis, patients were divided into 2×2 cohorts. We followed the following principles: LSR was defined as CT attenuation of the spleen divided by CT attenuation of the liver. Patients were categorized into those with LSR ≤1.1 (NAFLD) and those with LSR >1.1 (non-NAFLD). Baseline characteristics were compared between the NAFLD and non-NAFLD groups in the chemotherapy cohort and the chemo-immunotherapy cohort, respectively. Next, based on the change in LSR after short-term treatment as well as long-term treatment compared to LSR at baseline, the group was further categorized into decrease and non-decrease groups in each of the two phases. Continuous variables were expressed as mean (standard deviation) or median [interquartile range (IQR)] and compared using t-tests or Wilcoxon rank sum tests. For responder vs. non-responder analysis, normality was assessed by Shapiro-Wilk test. As LSR was normally distributed in all subgroups (all P>0.05), independent samples t-tests were used. Effect size was calculated using Cohen’s d. Responders were defined as CR/PR, and non-responders as SD/PD per RECIST v1.1. To further evaluate the discriminatory performance of baseline LSR for treatment response, receiver operating characteristic analysis was performed in each treatment cohort, and the area under the curve (AUC) with 95% CI was calculated. Paired t-tests were used for paired samples. Categorical variables were expressed as counts (percentages) and compared using the Pearson χ² test or Fisher’s exact test. ORR and DCR (two-sided) were compared using the Pearson Chi-squared test and Fisher’s exact test. To assess the stability of the features, intra-class correlation coefficient calculations were performed and Bland-Altman plots were plotted. We used the Kaplan-Meier method for the time-to-event analysis of OS and PFS. The log-rank test was used to compare OS and PFS in the two cohorts of chemo-immunotherapy and chemotherapy respectively, including (I) patients with and without NAFLD; (II) patients with and without LSR decrease after short-term treatment; and (III) patients with and without LSR decrease after long-term treatment in each of the cohorts. Additional sensitivity analyses were performed using three prespecified unified baseline LSR cutoffs (1.00, 1.24, and 1.35) in both treatment cohorts. Baseline LSR was also analyzed as a continuous variable using restricted cubic spline (RCS) models with four knots, adjusted for BMI, lipid profile, history of diabetes, and history of hypertension. Because these analyses were exploratory and involved multiple comparisons, P values were further adjusted using the Benjamini-Hochberg false discovery rate method. A P<0.05 was considered statistically significant. To further assess the impact of NAFLD and LSR decrease on patient survival, univariate and multivariate Cox regression models were constructed. Parameters with P<0.10 in univariate analyses were included in the multivariate Cox regression model to calculate hazard ratios (HRs) and their corresponding 95% CI. In addition, we performed exploratory subgroup analyses based on clinically relevant covariates and reported the HR and its respective 95% CI within each subgroup. Covariates for multivariable adjustment were selected a priori based on their biological relevance to LSR and NAFLD, as well as data completeness, and included BMI, lipid profile, history of diabetes, and history of hypertension. A parsimonious strategy was adopted to reduce the risk of overfitting in subgroup-based models. All statistical analyses were performed using SPSS 26.0 software (IBM Corp., Armonk, NY, USA) and R version 4.3.0 (R Foundation).

Results

Baseline character

A total of 314 patients were enrolled in this study (160 patients received chemo-immunotherapy and 154 received chemotherapy). Each of the two cohorts was categorized into two groups based on baseline status: the non-NAFLD group (chemo-immunotherapy, n=124; chemotherapy, n=113) and the NAFLD group (chemo-immunotherapy, n=36; chemotherapy, n=41). Baseline demographic characteristics are shown in Table 1. In the chemo-immunotherapy cohort, there were no significant differences between the two groups in terms of gender (P>0.99), age (P=0.25), cardiovascular risk factors (P=0.13), diabetes (P=0.13), and hypertension (P=0.97). However, the BMI was significantly higher in the NAFLD group than in the non-NAFLD group (mean: 24.71 vs. 23.49 kg/m², P=0.01), which is consistent with the reality that people with a high BMI are more likely to suffer NAFLD. Additionally, NSE levels were elevated in the NAFLD group (median: 59 vs. 34, P=0.03). In the chemotherapy cohort, none of the above metrics were significantly different between the two groups (P>0.05). ICIs were not applicable in this cohort. The mean BMI was slightly higher in the NAFLD group than in the non-NAFLD group (23.74 vs. 22.54 kg/m², P=0.057), but the difference did not reach statistical significance.

Changes in LSR

The LSR for the three phases for patients in the chemo-immunotherapy cohort were 1.23 (IQR, 1.12–1.34) at baseline, 1.24 (IQR, 1.11–1.38) for short-term treatment, and 1.25 (IQR, 1.13–1.42) for long-term treatment, with no statistically significant differences observed among phases (P>0.05); also not significant in the chemotherapy cohort (P>0.05): 1.24 (IQR, 1.10–1.36) at baseline, 1.29 (IQR, 1.12–1.42) for short-term treatment, and 1.28 (IQR, 1.15–1.40) for long-term treatment (Figure 1). There was also no statistical significance between the chemo-immunotherapy and chemotherapy cohorts at each treatment stage (P=0.34 at baseline; P=0.90 for short-term treatment; P=0.63 for long-term treatment). In the different treatment cohorts, re-categorization was based on the changes in LSR at each treatment stage, including decrease and non-decrease (Figure 2). In the chemo-immunotherapy cohort, the NAFLD group had a lower rate of LSR reduction after short-term treatment and long-term treatment than the non-NAFLD group (22.2% vs. 50.0%, P=0.003; 25.0% vs. 48.4%, P=0.01); the same trend was maintained in the chemotherapy cohort (17.1% vs. 44.2% P=0.002; 14.6% vs. 55.8%, P<0.001) (Figure 2). In addition, there was no statistically significant difference in the interval between any two consecutive CT scans in the short-term versus long-term treatment phases in the chemo-immunotherapy cohort (44.12 vs. 50.09 days, P=0.92), and similarly in the chemotherapy cohort (48.00 vs. 52.19 days, P=0.96).

Figure 1 Violin plots of the LSR in the chemo-immunotherapy cohort versus the chemotherapy cohort at different stages of treatment. LSR, liver-to-spleen ratio; NS, not significant.

Figure 2 Sankey plots of the dynamics of the LSR in the chemo-immunotherapy cohort versus the chemotherapy cohort at different stages of treatment. In the Decrease group, the two additional numerical values indicate the proportion of patients whose LSR decreased compared to baseline at the respective treatment stage; blue represents the non-NAFLD group, and red represents the NAFLD group. LSR, liver-to-spleen ratio; LT, long-term changes of LSR after treatment; NAFLD, non-alcoholic fatty liver disease; ST, short-term changes of LSR after treatment.

Tumor response

Tumor responses in the non-NAFLD and NAFLD groups between the chemo-immunotherapy cohort and chemotherapy cohort are shown in Table S1. Overall, short-term treatment outcomes were similar between the two groups. In the chemo-immunotherapy cohort, no statistically significant differences were observed between the non-NAFLD and NAFLD groups in terms of ORR (23.4% vs. 30.6%, P=0.38, Pearson) or DCR (84.7% vs. 91.7%, P=0.58, Fisher). Similarly, in the chemotherapy cohort, the ORR (29.8% vs. 30.0%, P=0.98, Pearson) and DCR (91.2% vs. 82.5%, P=0.26, Fisher) were comparable between the non-NAFLD and NAFLD groups. Baseline LSR did not differ between responders and non-responders in either cohort. In the chemo-immunotherapy cohort, mean LSR was 1.246 vs. 1.232 (P=0.71); in the chemotherapy cohort, mean LSR was 1.227 vs. 1.235 (P=0.852). Effect sizes were negligible (Cohen’s d=0.07 and 0.03, respectively). Receiver operating characteristic analysis further showed that baseline LSR had no meaningful discriminatory ability for treatment response in either cohort, with an AUC of 0.489 (95% CI: 0.375–0.605) in the chemo-immunotherapy cohort and 0.514 (95% CI: 0.415–0.618) in the chemotherapy cohort. Results are presented in Table S2.

Survival analysis

Median follow-up was 13.7 months (IQR, 8.67–24.78 months). Patients were first grouped according to baseline NAFLD status, and then re-grouped based on changes in LSR after short-term and long-term treatment compared to baseline. The OS and PFS of the above groupings were analyzed for survival separately, and Kaplan-Meier survival curves were plotted (Figures 3,4). However, we did not observe statistical differences between the non-NAFLD and NAFLD groups at baseline in the chemo-immunotherapy cohort, either in terms of PFS (6.3 vs. 6.7 months, P=0.83) or OS (15.0 vs. 16.1 months, P=0.83). Sensitivity analyses using prespecified unified baseline LSR cutoffs (1.00, 1.24, and 1.35) yielded inconsistent results across thresholds and treatment cohorts (Figures S3,S4). When baseline LSR was analyzed as a continuous variable using adjusted RCS models, an apparent association was observed only in the chemotherapy cohort, whereas no clear association was identified in the chemo-immunotherapy cohort (Figure S5). After Benjamini-Hochberg correction for multiple testing, none of these exploratory associations remained statistically significant (Table S3). Similarly, in the chemo-immunotherapy cohort, there was also no statistically significance in the groups between the non-decrease and decrease groups, either short-term treatment (PFS: 7.0 vs. 6.1 months, P=0.94; OS: 15.8 vs. 14.7 months, P=0.94) or long-term treatment (PFS: 6.7 vs. 6.1 months, P=0.98; OS: 15.8 vs. 15.1 months, P=0.91). In addition, in the chemotherapy cohort, while OS was significantly lower in the groups of non-decrease than decrease in the long-term treatment (9.7 vs. 13.3 months, P=0.03), it was also not significant between the non-NAFLD and NAFLD groups (11.7 vs. 11.1 months, P=0.11), and between the groups of non-decrease and decrease in short-term treatment (11.8 vs. 11.2 months, P=0.32). What’s more, although it did not reach a statistical difference, the analysis of PFS showed a slightly shorter PFS in the non-decrease group than in the decrease group in the short-term treatment (6.44 vs. 7.7 months, P=0.052). PFS between the non-NAFLD and NAFLD groups (6.9 vs. 6.9 months, P=0.96), and between the non-decrease group and the decrease group in the long-term treatment (6.44 vs. 8.29 months, P=0.10) did not reach statistical significance.

Figure 3 Kaplan-Meier survival curves for OS of the chemo-immunotherapy cohort versus the chemotherapy cohort at different stages of treatment. NAFLD, non-alcoholic fatty liver disease; OS, overall survival.

Figure 4 Kaplan-Meier survival curves for PFS of the chemo-immunotherapy cohort versus the chemotherapy cohort at different stages of treatment. NAFLD, non-alcoholic fatty liver disease; PFS, progression-free survival.

Cox regression analysis and subgroup analysis

In the univariate analysis of OS (Table 2), stage, LDH, alkaline phosphatase, and NSE were identified as potential risk factors for OS in the chemo-immunotherapy cohort; in the chemotherapy cohort, cardiovascular risk factors, stage, A/G ratio, LDH, NLR, NSE, CEA, long-term changes in LSR were included in the multivariate analysis. Finally, patients in the chemo-immunotherapy cohort had a significantly increased risk of death in the extensive phase compared to patients in the limited phase (adjusted HR =1.71; 95% CI: 0.98–2.97; P=0.057); for every 100-unit increase in LDH, the risk of death rose significantly by 8% (adjusted HR =1.08; 95% CI: 1.00–1.17; P=0.04); changes in alkaline phosphatase and NSE were not significantly associated with risk of death (alkaline phosphatase: adjusted HR =1.02; 95% CI: 0.99–1.04; P=0.21; NSE: adjusted HR =1.00; 95% CI: 0.97–1.03; P=0.97).

In the chemotherapy cohort, the risk of death was significantly increased for each 100-unit increase in LDH (adjusted HR =1.16; 95% CI: 1.04–1.28; P=0.008); the risk of death was also significantly higher for each 100-unit increase in CEA (adjusted HR =1.13; 95% CI: 1.02–1.26; P=0.02); patients with cardiovascular risk factors had a higher risk of death than those without but did not reach statistical significance (adjusted HR =2.19; 95% CI: 0.95–5.08; P=0.07); for each 10-unit increase in NLR, there was a trend toward a higher risk of death, but it did not reach the level of significance (adjusted HR =2.17; 95% CI: 0.99–4.73; P=0.052); patients with extensive phase had a higher risk of death than those with limited phase, but the difference was not significant (adjusted HR =1.24; 95% CI: 0.73–2.12; P=0.43); patients with reduced long-term LSR had a slightly, but not statistically significant, increased risk of death compared to those with elevated or unchanged LSR (adjusted HR =1.28; 95% CI: 0.78–2.11; P=0.33); A/G ratio (adjusted HR =0.60; 95% CI: 0.23–1.53; P=0.29) and NSE (adjusted HR =0.98; 95% CI: 0.95–1.02; P=0.39) were not significantly associated with risk of death.

In contrast, in the univariate analysis of PFS (Table 3), no potential predictors were identified within the chemo-immunotherapy cohort; in the chemotherapy cohort, the factors of stage, triglyceride, LDH, NSE, CEA, short-term changes in LSR were identified as potential predictors and further incorporated into multivariate models. Multivariable regression analysis showed a significantly increased risk of death in the chemotherapy cohort of patients with extensive phase compared to patients with limited phase (adjusted HR =2.13, 95% CI: 1.08–4.19, P=0.03). The risk of death was significantly higher for each 100-unit increase in CEA (adjusted HR =1.17, 95% CI: 1.04–1.31, P=0.01). Changes in triglyceride, LDH and NSE levels were not significantly correlated with the risk of death (P>0.05). In addition, there was no significant difference in the risk of death in patients with reduced short-term LSR compared to those with elevated or unchanged LSR (adjusted HR =0.79, 95% CI: 0.45–1.37, P=0.40).

In addition, we performed preliminary subgroup analyses to explore factors that may influence efficacy (Figures S6-S9). However, we did not find potentially positive results in the OS and PFS subgroups for either treatment modality. Furthermore, Cox regression analyses were conducted using alternative thresholds (5% and 10%) for LSR changes beyond the 0% cutoff. Consistent with the primary analysis, no significant associations with survival outcomes were observed under these thresholds (Tables S4,S5).

Discussion

Previous studies have demonstrated that NAFLD may remodel systemic immune status through mechanisms including CD8⁺ PD-1⁺ T cell accumulation, NK cell dysfunction, and elevated pro-inflammatory cytokines (12,13,26). Notably, in NAFLD-driven HCC, anti-PD-1 treatment has been associated with exacerbated CD8⁺ T cell-mediated liver injury and limited efficacy, suggesting a paradoxical effect of immunotherapy in the context of NAFLD (26-28). Given these findings, the potential impact of NAFLD on immunotherapy outcomes in other tumor types warrants investigation. In the present study, we explored this question in SCLC by dynamically monitoring LSR as a surrogate for NAFLD.

By dynamically monitoring the changes in LSR (a surrogate for NAFLD) in the comparative chemotherapy and chemo-immunotherapy cohorts, we explored for the first time the impact of dynamic changes in LSR and the prognosis of ICIs treatment in SCLC. However, we did not find regular changes in LSR values in either the chemo-immunotherapy cohort or the chemotherapy cohort. Nevertheless, if the LSR was low at baseline and met the diagnostic criteria for NAFLD in this article, the likelihood of further reduction in LSR was lower than that of non-NAFLD with either subsequent chemo-immunotherapy or chemotherapy. This may be related to the fact that there is a certain upper limit to the degree of LSR reduction, with a smaller attenuable margin.

During the survival analysis of the two treatment modalities, although LSR-defined NAFLD status, as well as LSR reduction, did not show any prognostic value at all stages of chemo-immunotherapy. We were surprised to find that in the chemo-immunotherapy cohort, LSR reduced after long-term treatment might instead serve as a protective factor in SCLC. To verify the reliability of this finding, we further performed univariable and multifactorial adjustments, the results of which did not support the conclusion. Neither chemotherapy nor chemo-immunotherapy, LSR-defined NAFLD status, nor dynamic changes in LSR, affected the prognosis of SCLC. This phenomenon is similar to the findings of our previous study in NSCLC (22), but contrasts with contradictory results in CRLM (19,20) and positive results in gastric cancer (21,29), which may suggest the presence of a tumor-type specific mechanism. We propose several mechanistic explanations for this discrepancy. First, anatomical and physiological proximity may play a role. NAFLD is a digestive system disease, and its associated chronic inflammation, local immune microenvironment suppression, and metabolic disturbances may more directly influence tumors arising from the digestive tract (e.g., gastric and colorectal cancers) through the portal circulation or local inflammatory spillover. In contrast, the systemic effects of NAFLD may be attenuated or diluted before reaching the pulmonary microenvironment. Second, divergent signaling pathway dependencies may contribute. For example, insulin resistance in NAFLD patients may promote immune escape from digestive tumors through the PI3K/AKT/mTOR pathway, whereas driver signals in SCLC (such as achaete-scute complex homologue-1, neurogenic differentiation factor 1, and so on) are weakly associated with this pathway (30,31). Consequently, metabolic alterations driven by NAFLD may have differential impacts depending on the tumor’s molecular landscape. Third, the unique immune microenvironment of SCLC may override systemic influences. The immunotherapeutic response of SCLC is mainly dependent on high tumor mutational load (32), and the local immune microenvironment is characterized by low PD-L1 expression and infiltration of immunosuppressive cells, such as regulatory T cells (Tregs) and myeloid-derived suppressor cells (MDSCs) (33-35). These features may dominate the local immune response, rendering additional systemic alterations, such as those induced by NAFLD, less impactful. Fourth, the highly aggressive biology of SCLC may diminish the relative contribution of metabolic factors. It is hypothesized that impaired hepatic metabolic function may affect ICIs clearance efficiency and increase the risk of hepatotoxicity (such as elevated transaminases) when NAFLD chronically progresses to liver fibrosis or cirrhosis (36). Whereas, the generally shorter survival in SCLC masks the possible adverse consequences of NAFLD. In contrast, slower-growing tumors may be more susceptible to systemic metabolic and inflammatory influences.

Additionally, in our study, we found that LDH could serve as an independent prognostic factor in both the SCLC chemotherapy cohort and the chemo-immunotherapy cohort, which is consistent with the findings of our previous NSCLC study (22); LDH has also been mentioned as a relatively promising prognostic assessment biomarker in other studies on the analysis of SCLC patients receiving chemotherapy (37,38). This may be due to the fact that LDH, as a key enzyme in the glycolytic pathway, catalyzes the interconversion of pyruvate and lactate, and its elevated activity reflects enhanced aerobic glycolysis (Warburg effect) in tumor cells, suggesting an active tumor metabolism, which is in line with the characteristics of the highly invasive nature of SCLC (39). It is also associated with the shaping of the tumor microenvironment, activation of oncogenic signal transduction pathways, and control of immune responses and immunosurveillance (40,41).

The negative results of this study hold important implications for clinical practice and related research fields: first, this study suggests that the impact of LSR-defined NAFLD on immunotherapy may exhibit tumor-type specificity, and it may not need to be used as an exclusion criterion for chemo-immunotherapy in SCLC treatment decision-making; second, although dynamic LSR monitoring can reflect metabolic alterations during treatment, its prognostic predictive value is limited. Scientific advancement relies not only on positive findings but also on rigorous negative conclusions, which collectively contribute to the development of more precise cancer treatment strategies.

However, the following limitations should be noted: (I) the retrospective study design inevitably introduces selection bias, and to reliably evaluate dynamic changes in LSR, we only included patients who completed ≥3 CT scans. This criterion may have excluded rapid progressors, but it provided a necessary data foundation for analyzing trajectory changes in hepatic fat metabolism, and we minimized the impact of bias through multifactorial analysis; (II) given the short survival of SCLC patients, shorter monitoring intervals were necessary to maximize the evaluable cohort, which may conflict with the reality that hepatic steatosis typically changes slowly. However, as a ratio of liver to spleen attenuation, LSR may capture subtle density alterations in the liver or spleen potentially related to treatment response. We therefore conducted these analyses as an exploratory investigation. Future studies with longer follow-up intervals and histopathological correlation are needed to clarify the biological significance of short-term LSR changes; (III) the present study is a single-center study with a relatively small sample size, and larger prospective studies are needed to validate the findings in the future; (IV) although LSR is a noninvasive and easily accessible indicator, it is not the gold standard for the diagnosis of NAFLD, nor can it quantify the degree of fatty infiltration, and liver biopsy data are needed to confirm whether NAFLD ultimately affects the efficacy of immunotherapy for SCLC.

In conclusion, NAFLD status as defined by LSR and the two dynamic changes (obtained 1–3 months apart) of LSR do not predict the prognosis of SCLC patients treated with chemo-immunotherapy, and there is no difference in the role played by chemotherapy and chemo-immunotherapy.

Conclusions

NAFLD, as defined by LSR, did not show independent prognostic value in chemotherapy and chemo-immunotherapy modalities for SCLC, a finding further supported by dynamic LSR monitoring.

Acknowledgments

The authors thank everyone who participated in and assisted with this project.

Footnote

Reporting Checklist: The authors have completed the REMARK reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-1-0137/rc

Data Sharing Statement: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-1-0137/dss

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-1-0137/prf

Funding: This study was supported by grants from National Key Research and Development Program of China (No. 2023YFE0113300), the Major Special Project for Technology Innovation of Hubei Province (No. 2023BCB014), National Natural Science Foundation of China (Nos. 82472058 and 82172034), Noncommunicable Chronic Diseases-National Science and Technology Major Project (No. 2024ZD0522802) and the Fundamental Research Funds for the Central Universities (No. 20242422).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-1-0137/coif). L.Y. reports that this study was supported by grants from the National Key Research and Development Program of China, the Major Special Project for Technology Innovation of Hubei Province, the National Natural Science Foundation of China, and the Noncommunicable Chronic Diseases-National Science and Technology Major Project. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Institutional Review Board of Wuhan Union Hospital (No. 0464). Verbal informed consent was obtained from all the patients.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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