Abstract
Background
Although neoadjuvant immunochemotherapy (nICT) demonstrates considerable promise in improving survival rates for patients with non-small cell lung cancer (NSCLC), responses among individual patients can vary significantly, and not all patients attain optimal therapeutic outcomes. Our research seeks to discover a simple and cost-effective indicator for the dynamic evaluation of NSCLC patients’ responses to nICT.
Methods
We enrolled 188 patients with surgically resectable NSCLC who received two to four cycles of nICT from January 1, 2021, and December 31, 2023, at Zhejiang Cancer Hospital. Subsequently, we analyzed the performance of the relative change in forced expiratory volume in one second (r-FEV1) and the relative change in carbon monoxide transfer factor (r-TLCO) as predictors of nICT efficacy. We employed binary logistic regression to assess the relationship between predictors and major pathological response (MPR). Kaplan-Meier (K-M) analysis and Cox regression were utilized to identify factors that predict prognosis. The primary endpoint of this study was event-free survival (EFS).
Results
The male sex [odds ratios (OR) 8.86, 95% confidence interval (CI): 2.45–32.06, P<0.001], smoking history (OR 3.92, 95% CI: 2.01–7.64, P<0.001), radiological response evaluation (complete response/partial response vs. stable disease/progressive disease, OR 0.30, 95% CI: 0.16–0.55, P<0.001), and histological type (lung squamous cell carcinoma vs. lung adenocarcinoma, OR 4.96, 95% CI: 2.51–9.81, P<0.001) were found to be significantly associated with non-MPR. In the K-M analysis, we observed that low r-FEV1 was associated with a shorter EFS (log-rank P=0.01). Additionally, multivariable Cox analysis indicated that low r-FEV1 (HR 2.13, 95% CI: 1.01–4.50, P=0.047) remained significantly correlated with shorter EFS.
Conclusions
This study demonstrates that low r-FEV1 is a predictor of non- MPR and shorter EFS in patients with locally advanced NSCLC undergoing nICT. These findings support the use of r-FEV1 as a clinically actionable marker for predicting the efficacy of nICT. The integration of routine lung function tests (LFTs), particularly r-FEV1, into standard pretreatment evaluation protocols has the potential to improve risk stratification and inform therapeutic decision-making for patients with locally advanced NSCLC.
Keywords: Non-small cell lung cancer (NSCLC), lung function, neoadjuvant immunochemotherapy (nICT)
Highlight box.
Key findings
• Relative change in forced expiratory volume in one second may serve as a dynamic monitoring parameter for evaluating radiological response in neoadjuvant immunochemotherapy (nICT), and has the potential to enhance the precision of treatment response assessment when combined with other predictive biomarkers.
What is known and what is new?
• Current predictive biomarkers for nICT therapeutic response face intrinsic limitations like pseudo-progression phenomena and dynamic monitoring constraints.
• This study reports that pulmonary function parameters permit longitudinal assessment during treatment, enabling real-time personalized optimization of therapeutic regimens.
What is the implication, and what should change now?
• These findings expand the repertoire of predictive biomarkers for nICT response. Subsequent research should integrate multimodal predictors to develop higher-accuracy predictive models.
Introduction
Lung cancer is the leading cause of cancer-related mortality, with a five-year relative survival rate of merely 25% (1,2). Non-small cell lung cancer (NSCLC) accounts for 85% of all lung cancer cases (3,4). Recent interim analyses of the KEYNOTE-671 trial along with data from the CheckMate series studies presented at American Society of Clinical Oncology (ASCO) 2024, demonstrate that neoadjuvant immunochemotherapy (nICT) significantly improves overall survival (OS), as well as the rates of pathological complete response (pCR)/major pathological response (MPR), and R0 resection in NSCLC (5-8). Despite the increasing adoption of nICT, individual differences in treatment response result in some patients not achieving satisfactory outcomes (6).
Clinical trials have indicated a significant correlation between programmed death-ligand 1 (PD-L1) expression levels and MPR rates (9). Furthermore, recent randomized controlled trials (RCTs) have demonstrated that the integration of PD-L1 expression analysis with tumor microenvironment (TME) assessments can act as a prognostic indicator for the outcomes of neoadjuvant immunotherapy in patients with advanced NSCLC (9,10). Additionally, research suggests that patients with unresectable or metastatic tumors may benefit from immune checkpoint inhibitors (ICIs) when a high tumor mutational burden (TMB) is identified in tissue samples (11,12). Circulating tumor cells (CTCs) have also been shown to reflect the effectiveness of nICT (13). The complexity, cost, and incomplete predictive power of these treatments have prompted calls for new methods to predict the effectiveness of nICT.
Lung function tests (LFTs) play a multifaceted role in the perioperative management of lung cancer. Lung function serves as a key indicator of overall pulmonary health, reflecting levels of airway obstruction, ventilation capacity, and the efficiency of gas exchange between the alveoli and the bloodstream (14). The forced expiratory volume in one second (FEV1) is a critical measurement of pulmonary ventilatory function, with its percentage of the predicted value utilized to assess the severity of airflow limitation (15). The degree of airflow limitation is essential for determining surgical tolerance in patients with lung cancer. Furthermore, airflow limitation has been confirmed as an independent risk factor for postoperative respiratory failure in patients undergoing lung cancer surgery (16).
Previous studies have demonstrated that nICT can influence lung function in patients with NSCLC (17,18). Notably, LFTs are relatively simple and can be performed repeatedly throughout treatment, facilitating dynamic monitoring of radiological response. Given these advantages, LFTs may serve as a valuable tool for evaluating treatment response in patients with resectable NSCLC. In this study, we conducted a retrospective analysis to investigate the relationship between lung function indices and the efficacy of nICT. We present this article in accordance with the REMARK reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-485/rc).
Methods
Patient data collection and organization
The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study received approval from the Ethics Committee of Zhejiang Cancer Hospital (No. IRB-2024-328), and individual consent for this retrospective analysis was waived. This investigation retrospectively compiled data from patients with NSCLC who were initially admitted to Zhejiang Cancer Hospital between January 1, 2021, and December 31, 2023. Eligible patients were aged 18 to 75 years and had clinical stage T1–4, N0–2, M0 NSCLC, as defined by the eighth edition criteria of the American Joint Committee on Cancer tumor, node and metastasis (TNM) classification (19). Moreover, genetic testing revealed that none of the patients enrolled in our study harbored mutations indicative of eligibility for targeted therapy. The neoadjuvant chemotherapeutic and immunotherapeutic agents used in this study are detailed in Tables S1,S2. Patients were considered potentially resectable following a multidisciplinary discussion. Surgery was performed 3 to 4 weeks after the completion of 2 to 4 cycles of nICT. A total of 188 patients who met the enrollment criteria were included in the study, and the enrollment process is illustrated in Figure 1.
Figure 1.
Study enrollment process. M, metastasis; N, node; NSCLC, non-small cell lung cancer; SCLC, small cell lung cancer; T, tumor.
All patients underwent LFTs both before and after neoadjuvant treatment, with results measured as FEV1 and carbon monoxide transfer factor (TLCO). The relative changes in lung function measurements before and after treatment (r-FEV1, r-TLCO) were defined as the ratio of each post-treatment value to its corresponding pre-treatment value. Additionally, patient information—including sex, age, body mass index (BMI, obtained within one week prior to the first neoadjuvant treatment), smoking history, histological type, clinical T stage (cT), clinical N stage (cN), radiological response [assessed using contrast-enhanced chest computed tomography (CT) scans, which were conducted and interpreted by board-certified thoracic radiologists according to RECIST 1.1 criteria (20)], and pathological response—was collected from medical records.
Patient characteristics, including age, sex, BMI, tumor size, and clinical stage, were collected according to the REMARK checklist for observational studies.
Assessment of MPR and clinical staging
Specialized pathologists employed standard hematoxylin-eosin staining to assess the proportion of residual tumors at the primary tumor site. The classification of MPR and non-MPR was based on the patients’ pathological responses to nICT. Patients exhibiting 10% or less residual tumors after surgery were categorized into the MPR group, while the remaining patients were assigned to the non-MPR group. Routine follow-up examinations were conducted as per the established schedule, with tumor CT imaging performed every three months during the first year, every four months in the second year, and every six months thereafter. The final follow-up date for patients was updated in August 2024. The primary endpoint of the study was event-free survival (EFS), defined as the duration from the initiation of nICT until disease progression, in situ or distant recurrence, the emergence of secondary primary tumors, or death from any cause.
Statistical analyses
Statistical analyses were conducted using R (version 4.4.2). Patients with incomplete data, including missing lung function and MPR values, were excluded from the final efficacy analysis. Patient characteristics were described using demographic and clinical variables, including age, sex, BMI, smoking history, and baseline tumor parameters. Differences in these characteristics were evaluated using the Chi-squared test and the Fisher-Freeman-Halton exact test. T-test was employed to analyze changes in lung function indices before and after neoadjuvant treatment. Receiver operating characteristic (ROC) curves were generated to calculate the optimal cut-off values for lung function indicators (Table S3). Based on this cutoff value, patients were categorized into high or low groups according to their FEV1 and TLCO levels, respectively. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using a binary logistic regression model to identify predictors of MPR. EFS was calculated and compared among patients using Kaplan-Meier (K-M) survival analysis and log-rank tests. Both univariable and multivariable Cox regression analyses were employed to identify factors predicting prognosis in the nICT cohort. All statistical tests were two-sided, with a significance level set at P<0.05.
Results
Patient characteristics
This study involved a total of 188 patients, comprising 73 in the non-MPR group and 115 in the MPR group. The demographic and clinical characteristics of these patients are summarized in Table 1, revealing no significant differences in age, BMI, or tumor location between the two groups. However, the MPR group exhibited a higher proportion of male patients (97.39% vs. 80.82%) and a lower proportion of female patients (2.61% vs. 19.18%) compared to the non-MPR group (P<0.001). Additionally, a greater percentage of patients in the MPR group had a history of smoking (82.61% vs. 54.79%, P<0.001). The MPR group also demonstrated a higher percentage of complete response (CR) and partial response (PR) (71.30% vs. 42.47%, P<0.001). Conversely, the non-MPR group exhibited a smaller pre-CT tumor size (73.97% vs. 26.03%, P=0.04) compared to the MPR group. In terms of histological types, lung squamous cell carcinoma (LUSC) accounted for 84.35% of the MPR group, in contrast to 52.05% in the non-MPR group (P<0.001).
Table 1. Baseline characteristics of NSCLC patients enrolled in the study.
| Variables | Non-MPR (n=73) | MPR (n=115) | P |
|---|---|---|---|
| Sex | <0.001 | ||
| Female | 14 (19.18) | 3 (2.61) | |
| Male | 59 (80.82) | 112 (97.39) | |
| Age (years) | 0.49 | ||
| <65 | 37 (50.68) | 51 (44.35) | |
| ≥65 | 36 (49.32) | 64 (55.65) | |
| Smoking history | <0.001 | ||
| Never smoking | 33 (45.21) | 20 (17.39) | |
| Smoker or ex-smoker | 40 (54.79) | 95 (82.61) | |
| BMI | 0.30 | ||
| Normal weight | 48 (65.75) | 85 (73.91) | |
| Under/overweight | 25 (34.25) | 30 (26.09) | |
| Pre-CT tumor size | 0.04 | ||
| <50 mm | 54 (73.97) | 67 (58.26) | |
| ≥50 mm | 19 (26.03) | 48 (41.74) | |
| Clinical stage | 0.02 | ||
| I | 2 (2.74) | 11 (9.57) | |
| II | 33 (45.21) | 31 (26.96) | |
| III | 38 (52.05) | 73 (63.48) | |
| Radiological response | <0.001 | ||
| CR/PR | 31 (42.47) | 82 (71.30) | |
| SD/PD | 42 (57.53) | 33 (28.70) | |
| Tumor location | 0.37 | ||
| LLL | 13 (17.81) | 23 (20.00) | |
| LUL | 19 (26.03) | 31 (26.96) | |
| RLL | 19 (26.03) | 21 (18.26) | |
| RML | 0 (0.00) | 5 (4.35) | |
| RUL | 22 (30.14) | 35 (30.43) | |
| Histological type | <0.001 | ||
| LUAD | 35 (47.95) | 18 (15.65) | |
| LUSC | 38 (52.05) | 97 (84.35) | |
| r-FEV1 | 1.02±0.11 | 1.10±0.21 | <0.001 |
| r-TLCO | 0.99±0.30 | 1.01±0.41 | 0.66 |
| Surgery | >0.99 | ||
| Lobectomy | 72 (98.63) | 112 (97.39) | |
| Pneumonectomy | 1 (1.37) | 3 (2.61) | |
| Surgery methods | 0.32 | ||
| Thoracotomy | 20 (27.40) | 23 (20.00) | |
| Video-assisted thoracoscopic surgery | 53 (72.60) | 92 (80.00) |
Continuous variables are expressed as mean ± standard deviation; categorical variables are presented as n (%). BMI, body mass index; CR, complete response; CT, computed tomography; LLL, left lower lobe; LUL, left upper lobe; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; MPR, major pathological response; NSCLC, non-small cell lung cancer; PD, progressive disease; PR, partial response; r-FEV1, relative change in forced expiratory volume in one second; RLL, right lower lobe; RML, right middle lobe; RUL, right upper lobe; r-TLCO, relative change in carbon monoxide transfer factor; SD, stable disease.
Association of lung function results with MPR
Table S4 presents the changes in lung function indices before and after nICT. The FEV1 exhibited a significant decrease of −0.11 (95% CI: −0.15, −0.07; P<0.001). In contrast, the TLCO showed a significant increase of 0.38 (95% CI: 0.03, 0.72; P=0.03). The distribution characteristics of r-FEV1 and r-TLCO in the MPR and non-MPR groups are illustrated in Figure 2. Also, in accordance with the guidelines of the American Thoracic Society (ATS) and the European Respiratory Society (ERS) (21), we used an FEV1/FVC ratio of 70% as the cut-off value to categorize patients into high and low groups and generated a pie chart. We found that only 11.70% of the subjects had an FEV1/FVC ratio below 70% before nICT (Figure S1).
Figure 2.
Distribution characteristics of lung function indices in the MPR group and the non-MPR group. ns, not significant; **, P<0.01. MPR, major pathological response; r-FEV1, relative change in forced expiratory volume in one second; r-TLCO, relative change in carbon monoxide transfer factor.
In the univariable logistic regression analysis, male sex (OR 8.86, 95% CI: 2.45–32.06, P<0.001), smoking history (OR 3.92, 95% CI: 2.01–7.64, P<0.001), pre-CT tumor size <50 mm (OR 2.04, 95% CI: 1.07–3.86, P=0.03), radiological response (CR/PR vs. SD/PD, OR 0.30, 95% CI: 0.16–0.55, P<0.001), histological type [LUSC vs. lung adenocarcinoma (LUAD), OR 4.96, 95% CI: 2.51–9.81, P<0.001], and low r-FEV1 (OR 0.40, 95% CI: 0.22–0.74, P=0.004) were significantly associated with non-MPR (Table 2). In the multivariable logistic regression analysis, histological type was correlated with MPR (LUSC vs. LUAD, OR 2.44, 95% CI: 1.09–5.47, P=0.03) (Table S5).
Table 2. Univariable regression analyses of MPR in NSCLC patients receiving nICT.
| Variables | Univariable | |
|---|---|---|
| OR (95% CI) | P value | |
| Sex: male vs. female | 8.86 (2.45–32.06) | <0.001 |
| Age: ≥65 vs. <65 years | 1.29 (0.72–2.32) | 0.40 |
| Smoking history: ever vs. never | 3.92 (2.01–7.64) | <0.001 |
| BMI: normal weight vs. under/overweight | 0.68 (0.36–1.28) | 0.23 |
| Pre-CT tumor size: <50 vs. ≥50 mm | 2.04 (1.07–3.86) | 0.03 |
| Clinical stage: III vs. I/II | 1.60 (0.88–2.90) | 0.12 |
| Tumor location: LL/ML vs. UL | 1.05 (0.58–1.90) | 0.87 |
| Radiological response: SD/PD vs. CR/PR | 0.30 (0.16–0.55) | <0.001 |
| Histological type: LUSC vs. LUAD | 4.96 (2.51–9.81) | <0.001 |
| r-FEV1: low vs. high | 0.40 (0.22–0.74) | 0.004 |
| r-TLCO: low vs. high | 0.70 (0.36–1.34) | 0.28 |
BMI, body mass index; CI, confidence interval; CR, complete response; CT, computed tomography; LL, lower lobe; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; ML, middle lobe; MPR, major pathological response; nICT, neoadjuvant immunochemotherapy; NSCLC, non-small cell lung cancer; OR, odds ratio; PD, progressive disease; PR, partial response; r-FEV1, relative change in forced expiratory volume in one second; r-TLCO, relative change in carbon monoxide transfer factor; SD, stable disease; UL, upper lobe.
Association of lung function results with prognosis
Patients with low r-FEV1 exhibited significantly shorter EFS compared to those with high r-FEV1 (log-rank P=0.01, Figure 3A). In contrast, no significant difference in EFS was observed between patients with high and low r-TLCO (log-rank P=0.97, Figure 3B). In the univariable Cox analysis, low r-FEV1 was consistently associated with shorter EFS (HR 2.50, 95% CI: 1.21–5.14, P=0.01). Moreover, MPR was linked to longer EFS (HR 0.51, 95% CI: 0.27–0.96, P=0.04), and histological type was significantly associated with EFS (LUSC vs. LUAD, HR 0.44, 95% CI: 0.23–0.84, P=0.01). In the multivariable Cox analysis, low r-FEV1 (HR 2.13, 95% CI: 1.01–4.50, P=0.047) remained significantly associated with shorter EFS (Table 3).
Figure 3.
The relationship between the r-FEV1 (A) and r-TLCO (B) with patients’ event-free survival. EFS, event-free survival; r-FEV1, relative change in forced expiratory volume in one second; r-TLCO, relative change in carbon monoxide transfer factor.
Table 3. Univariable and multivariable Cox analyses of EFS in NSCLC patients who have received nICT.
| Variables | Univariable | Multivariable | |||
|---|---|---|---|---|---|
| HR (95% CI) | P | HR (95% CI) | P | ||
| Sex: male vs. female | 0.51 (0.21–1.22) | 0.13 | – | – | |
| Age: ≥65 vs. <65 years | 1.18 (0.62–2.24) | 0.61 | – | – | |
| Smoking history: ever vs. never | 0.79 (0.40–1.54) | 0.48 | – | – | |
| BMI: normal weight vs. under/overweight | 1.43 (0.73–2.80) | 0.30 | – | – | |
| Clinical stage: III vs. I/II | 1.32 (0.67–2.58) | 0.42 | – | – | |
| Tumor location: LL/ML vs. UL | 1.02 (0.53–1.93) | 0.96 | – | – | |
| Pre-CT tumor size: <50 vs. ≥50 mm | 0.53 (0.25–1.13) | 0.10 | – | – | |
| Radiological response: SD/PD vs. CR/PR | 1.49 (0.79–2.82) | 0.22 | – | – | |
| Histological type: LUSC vs. LUAD | 0.44 (0.23–0.84) | 0.01 | 0.55 (0.27–1.08) | 0.08 | |
| MPR: yes vs. no | 0.51 (0.27–0.96) | 0.04 | 0.74 (0.37–1.50) | 0.41 | |
| r-FEV1: low vs. high | 2.50 (1.21–5.14) | 0.01 | 2.13 (1.01–4.50) | 0.047 | |
| r-TLCO: low vs. high | 1.02 (0.50–2.05) | 0.97 | – | – | |
BMI, body mass index; CI, confidence interval; CR, complete response; CT, computed tomography; EFS, event-free survival; HR, hazard ratio; LL, lower lobe; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; ML, middle lobe; MPR, major pathological response; nICT, neoadjuvant immunochemotherapy; NSCLC, non-small cell lung cancer; PD, progressive disease; PR, partial response; r-FEV1, relative change in forced expiratory volume in one second; r-TLCO, relative change in carbon monoxide transfer factor; SD, stable disease; UL, upper lobe.
Discussion
Building on the findings of existing research, we aim to identify the correlation between lung function and the efficacy of nICT. Recent studies have emphasized the potential benefits of nICT for patients with locally advanced NSCLC (22-24). However, the response to nICT can vary significantly among patients, and the identification of predictive markers for treatment efficacy remains a critical challenge (25). Lung function, as a key indicator of overall health and potential treatment tolerance, has been suggested to influence outcomes in patients with thoracic malignancies (26). Specifically, compromised lung function caused by checkpoint Inhibitor-related pneumonitis may affect the effectiveness of nICT (27). Given these considerations, we propose that dynamic changes in lung function during nICT may serve as valuable indicators of treatment response and long-term outcomes.
Our study investigates the relationship between dynamic changes in lung function indicators and therapeutic outcomes in NSCLC patients undergoing nICT. Among the baseline characteristics of the patients, we identified sex, smoking history, radiological response, and histological type as factors correlated with MPR rates. Specifically, male patients and those with a smoking history were more likely to achieve MPR. Additionally, patients with pre-CT tumor size <50 mm had lower MPR rates. These findings suggest that certain patient characteristics may influence the response to nICT, aligning with a previous study indicating that specific patient demographics and clinical features can impact treatment outcomes (28).
Reduced r-FEV1 was significantly associated with decreased MPR rates and inferior EFS, while r-TLCO showed no correlation with survival outcomes. These findings suggest that diminished r-FEV1 may serve as a predictive marker for nICT efficacy, highlighting its utility for dynamic monitoring during therapy to guide personalized treatment strategies. The significant decrease in FEV1 observed post-treatment (−0.11, 95% CI: −0.15, −0.07; P<0.001) indicates that changes in lung function, specifically ventilatory function, may reflect the underlying tumor response to therapy. This is supported by the fact that patients with locally advanced NSCLC often present with tumors that obstruct their airways, thereby impairing ventilatory function (29). Previous studies have found that nICT has the potential to reduce tumor size, which could, in turn, improve ventilatory function (30,31). Our radiological response also demonstrated a significant relationship with MPR, further supporting the ventilatory function in patients with locally advanced NSCLC is associated with the tumor-reducing effects of nICT.
Currently, there is no consensus on the optimal number of cycles for nICT. However, the chemotherapy drugs used in combination with nICT can lead to adverse reactions in some patients (32). Therefore, evaluating treatment efficacy after two cycles of nICT is crucial for determining whether to proceed with subsequent neoadjuvant therapy (33). LFTs can enhance the accuracy of this evaluation. While radiological methods are commonly employed to assess efficacy, they may be subject to inherent errors, and some patients might exhibit pseudoprogression, characterized by transient radiographic progression without true disease advancement (34). Our study confirms that assessing pre- and post-treatment FEV1 is essential for a dynamic evaluation of nICT efficacy. LFTs provide valuable insights into patients’ responses to nICT. These findings underscore the importance of integrating LFTs into the routine evaluation of patients undergoing nICT, potentially improving the precision of treatment response monitoring.
There are several limitations in this study. First, as all cases were obtained from Zhejiang Cancer Hospital, the single-center design may yield findings that reflect the specific patient population of this institution, potentially limiting the generalizability of the results. Second, as a retrospective study, it is susceptible to bias, and the absence of long-term follow-up further restricts the scope of the findings. Third, we did not collect the treatment protocols for adjuvant therapy after surgery. Future research should prioritize validating our findings in larger, multicenter cohorts and investigating the underlying mechanisms that link changes in lung function to treatment response. Additionally, future studies should incorporate long-term follow-up to gain insights into disease progression and long-term outcomes. In addition, our study excluded patients who did not undergo surgery following neoadjuvant treatment. Subsequent investigations can further evaluate cases of this patient group, assessing their biological characteristics and potential therapeutic implications.
In conclusion, meticulous monitoring and management of lung function are essential. We anticipate that future research will involve a comprehensive analysis of lung function, CT imaging, immunohistochemistry, and other relevant factors to develop a more precise predictive model for the efficacy of nICT. This model will serve as a more robust tool for individualized treatment in clinical practice.
Conclusions
Our study investigated the dynamic changes in lung function among patients with locally advanced NSCLC undergoing nICT and their correlation with treatment outcomes. We found that patients with lower r-FEV1 exhibited significantly lower MPR rates and shorter EFS. These findings underscore the potential of lung function as a predictive marker for treatment response in nICT. Our results advocate for the integration of LFTs into the routine evaluation of patients receiving nICT, to better tailor treatment strategies and enhance patient outcomes.
Supplementary
The article’s supplementary files as
Acknowledgments
None.
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 received approval from the Ethics Committee of Zhejiang Cancer Hospital (No. IRB-2024-328), and individual consent for this retrospective analysis was waived.
Footnotes
Reporting Checklist: The authors have completed the REMARK reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-485/rc
Funding: This work was supported by the Zhejiang Traditional Chinese Medicine co-construction project (GZY-ZJ-KJ-23004) and National Key Scientific Program of China (2022YFA1304500).
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-485/coif). The authors have no conflicts of interest to declare.
Data Sharing Statement
Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-485/dss
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