Construction of a Prognostic Model for Extensive-Stage Small Cell Lung Cancer Patients Undergoing Immune Therapy in Real-World Settings and Prediction of Treatment Efficacy Based on Response Status at Different Time Points

doi:10.21203/rs.3.rs-4006710/v1

Background and purpose

In recent years, with the clinical application of programmed cell death protein-1 (PD-1) represented by serplumab and programmed cell death ligand-1 (PD-L1) represented by durvalumab, immune checkpoint inhibitors (ICIs) have been used in patients with extensive-stage small cell lung cancer (ES-SCLC). clinical applications, immune checkpoint inhibitors (ICIs) have shown significant efficacy in patients with extensive-stage small cell lung cancer (ES-SCLC), but not all patients are beneficiaries of immunotherapy. Immunomarkers such as PD-L1 expression and tumor mutational burden (TMB), which are good predictors in a variety of malignancies, have been found not to be predictive in small cell lung cancer (SCLC). With the in-depth study of SCLC subtypes, SCLC-Y/SCLC-I molecular subtypes have been recognized as potential immunotherapeutic markers. However, the predictive efficacy of a single marker is limited, so a comprehensive predictive model is needed to achieve precision immunotherapy. National and international studies have found that certain basic clinical characteristics of patients and peripheral blood markers correlate with the prognosis of ES-SCLC immunotherapy. The aim of this study was to establish a model for predicting the prognosis of immunotherapy in ES-SCLC patients using basic clinical characteristics and peripheral hematological indicators of patients, and to explore the potential characteristics of long-term survival of patients, to provide guidance for individualized treatment of patients, and to provide corresponding strategies for clinical immunotherapy.

Methods

This study utilized a retrospective research method, investigating patients with ES-SCLC who received PD-1/PD-L1 inhibitor treatment at Harbin Medical University Cancer Hospital from March 1, 2019, to October 31, 2022. The research data were randomly divided into a training set and a validation set in a 7:3 ratio. By conducting univariate and multivariate Cox regression analyses, variables related to the overall survival (OS) of patients were identified and used to develop a model. The model was visualized through Kaplan-Meier curves. The discriminative ability of the model was evaluated using Harrell's C-index, time-dependent receiver operating characteristic curve (tROC), and time-dependent area under curve (tAUC). The calibration of the model was assessed using calibration curves. Furthermore, the clinical utility of the model is assessed using Decision Curve Analysis (DCA). Patients are stratified into risk groups using percentile segmentation, and survival curves for Overall Survival (OS) and Progression-Free Survival (PFS) at different risk levels and milestone time points are plotted using the Kaplan-Meier method. The Chi-square test is used to compare differences between groups. Statistical analysis is performed using R 4.1.2 and SPSS 26.

Results

This study included a total of 113 patients with ES-SCLC who received immunotherapy. Based on the patients' clinical characteristics and hematological indicators, we conducted a series of studies. Firstly, we established a model to predict the prognosis of ES-SCLC patients undergoing immunotherapy, with 79 patients used for model development and 34 patients for model validation. Through univariate and multivariate Cox regression analyses, six variables were identified as being associated with poorer overall survival (OS) in patients: liver metastasis (P=0.001), bone metastasis (P=0.013), neutrophil-to-lymphocyte ratio (NLR) < 2.14 (P=0.005), poor Lung Immune Prognostic Index (LIPI) assessment (P<0.001), Prognostic Nutritional Index (PNI) < 51.03 (P=0.002), and lactate dehydrogenase (LDH) ≥ 146.5 (P=0.037). The model established based on the aforementioned variables demonstrates good discriminability, with Harrell’s C-index of 0.85 (95% CI: 0.76-0.93) for the training set and 0.88 (95% CI: 0.76-0.99) for the validation set. The AUC values corresponding to 12 months, 18 months, and 24 months in the training set's tROC curve are 0.754, 0.848, and 0.819, respectively, while in the validation set, they are 0.858, 0.904, and 0.828, respectively. The tAUC curves indicate that, in both the training and validation sets, the overall tAUC is >0.7 with little fluctuation over time. Calibration plots show the model's good calibration, and the DCA decision curves indicate the model's practical clinical application value. Based on the predicted risk scores in the scatter plot for patients in the training set, patients are categorized into low-risk (0-69 points), medium-risk (70-162 points), and high-risk (≥163 points) groups. In the training set, 52 patients died, with a median OS of 15.0 months and a median PFS of 7.8 months. Compared to the high-risk group, the median Overall Survival (OS) for the medium-risk group was 24.5 months (HR=0.47, P=0.038), and the median OS for the low-risk group was not reached (HR=0.14, P=0.007). Compared to the high-risk group, the median Progression-Free Survival (PFS) for the medium-risk group was 12.7 months (HR=0.45, P=0.026), and the median PFS for the low-risk group was not reached (HR=0.12, P=0.004). In the validation set, 25 patients died, with a median OS of 13.8 months and a median PFS of 6.9 months. Compared to the high-risk group, the median OS for the medium-risk group was 16.8 months (HR=0.47, P=0.047), and the median OS for the low-risk group was not reached (HR=0.40, P=0.001). Compared to the high-risk group, there was no significant improvement in the median PFS for the medium-risk group (HR=0.56, P=0.189), while the median PFS for the low-risk group was significantly extended (HR=0.12, P=0.002). Secondly, we observed that in the real world, patients with ES-SCLC who have undergone immunotherapy demonstrated a median OS (Overall Survival) of 19.5 months for responders, compared to 11.9 months for non-responders at the 6-week mark (P=0.033). At 12 and 20 weeks, the overall survival duration of responders was 20.7 months and 20.7 months, respectively, while for non-responders, it was 11.9 months and 11.7 months (P=0.044 and P=0.015). Additionally, the median PFS (Progression-Free Survival) of responders was significantly prolonged, being 10.6 months at both 6 and 20 weeks, compared to 6.4 months and 6.3 months for non-responders (P=0.036 and P=0.028). At the 12-week time point, the PFS for responders was 9.2 months, while it was 6.3 months for non-responders (P=0.069). Finally, we found that in the real world, ES-SCLC (Extensive-Stage Small Cell Lung Cancer) patients without liver metastasis (P=0.002), bone metastasis (P=0.001), a total number of metastatic organs <2 (P=0.002), and LDH (Lactate Dehydrogenase) ≤ ULN (Upper Limit of Normal) (P=0.09) are more likely to become long-term survivors (LTS) after receiving immunotherapy.

Conclusion

First, this study constructed a new prognostic model based on basic patient clinical characteristics and peripheral blood indices, which can be a good predictor of the prognosis of immunotherapy in ES-SCLC patients. Second, in the real world, the response status at milestone time points (6, 12, and 20 weeks) can be a good indicator of long-term survival in ES-SCLC patients receiving immunotherapy. Finally, patients with no liver metastases or bone metastases, total metastatic organ count <2 and LDH ≤ULN were more likely to have long-term survival before ES-SCLC patients received immunotherapy.

Small cell lung cancer

prognostic model

Immunotherapy

Long term Survivors

Recently, the treatment of small cell lung cancer has shown significant success with both international Programmed cell death ligand-1(PD-L1) inhibitors (such as atezolizumab and durvalumab) and localized Programmed cell death protein-1 (PD-1)

/PD-L1 inhibitors (such as serplumab and adebrelimab) ^[1–4] Despite these Immune checkpoint inhibitors (ICIs) agents surpassing the constraints of traditional chemotherapy, immunotherapy does not benefit all patients. Consequently, identifying suitable immune indicators for precise screening of immunotherapy recipients has emerged as a significant concern for medical professionals.

Exploring biomarkers for immunotherapy is challenging because of the complex immune traits of small cell lung cancer (SCLC). Research indicates that certain conventional biomarkers, such as PD-L1 expression and Tumor mutational burden (TMB), are not significantly related to the success of immuno-combination chemotherapy in SCLC, and have not yet been proven to predict efficacy^{[2, 5, 6]}. As research into small cell lung cancer variants have grown, molecular variants of SCLC-Y/SCLC-I have surfaced as promising indicators for SCLC immunotherapy^[7]. Nonetheless, these biomarkers typically require invasive tests dependent on sufficient tumor tissue samples and sophisticated genome sequencing or histopathology methods, and their high cost restricts their clinical use. Furthermore, the forecasting power of individual markers is restricted, necessitating an increased clinical demand for affordable, non-intrusive, and readily available biomarkers to collectively determine the effectiveness of immunotherapy in SCLC treatment. Consequently, the creation of an all-encompassing predictive model has emerged as a key research avenue for achieving precise immunotherapy.

Latest research^{[8, 9]} has identified various predictive elements in SCLC, including the Eastern Cooperative Oncology Group Performance Status (ECOG) score, LDH level, and the effectiveness of initial treatments. Furthermore, the overall inflammatory reaction is crucial for determining the clinical outcomes of patients with SCLC ^[10]. Reports ^[10–12] indicate that certain blood-related inflammatory indicators, including Neutrophil-to-lymphocyte ratio (NLR), Prognostic nutritional index (PNI), and Lung immune prognostic index (LIPI), might influence the longevity of SCLC. This practical study retrospectively examined the survival data, inflammation indicators before treatment, and various clinicopathological features of 113patients with ES-SCLC at Harbin Medical University's associated tumor hospital. Initially, we developed a comprehensive forecasting model to estimate the survival rates of patients with ES-SCLC undergoing immunotherapy. Additionally, we confirmed the link between the response to treatment at actual milestone intervals (such as 6, 12, and 20 weeks) and prolonged survival of ES-SCLC patients undergoing immunotherapy. Finally, we analyzed the differences in clinical features between ES-SCLC long-term survivors (LTS) undergoing immunotherapy and those who did not survive long (non-LTS survivors). This research aimed to offer insights for tailoring treatments to patients with ES-SCLC, thereby enhancing clinical immunotherapy approaches.

2.1 object of study

The data for the study were gathered from 113 individuals with advanced SCLC who received initial treatment with PD-1/PD-L1 inhibitors between March 1, 2019, and October 31, 2022, at the Affiliated Cancer Hospital of Harbin Medical University. The final date for follow-up was October 31, 2023. Below are the criteria for inclusion and exclusion, along with the screening flowchart (Fig. 1).

2.1.1 Inclusion criteria

(1) Patients with SC LC diagnosed at an extensive stage by histopathology and imaging at our institution according to the 8th edition of the TNM staging criteria for lung cancer developed by the American Joint Committee on Cancer in conjunction with the American Legion Lung Cancer Association's development of the Stage II staging method.

(2) Patients Received PD-1/PD-L1 inhibitor treatment at our institution with ≥ 2 cycles of treatment.

(3) Hematology tests at our institution within one week prior to immunotherapy.

(4) Must have at least one measurable lesion as the target lesion

(5) Complete medical record information at the time of initial immunotherapy at our hospital.

2.1.2 Exclusion criteria

(1) Patients with an unclear pathologic type, pathologic reports showing mixed tissue components, or concurrent lung cancer of other pathologic types.

(2) Other systemic malignancies.

(3) Patients who were treated for limited-stage SCLC when receiving PD-1/PD-L1 and then progressed to extensive-stage SCLC.

(4) Test reports of blood biochemistry and blood routine within one week before immunotherapy without the His system in our hospital.

(5) Autoimmune system diseases, acute infections, or severe liver and kidney diseases.

(6) No complete medical record for the first time receiving immunotherapy.

(7) Receiving two or more ICIs simultaneously.

(8) History of immunomodulator application within three months prior to immunotherapy.

2.2 Gathering and Defining Clinical Data

2.2.1 Basic Clinical Characteristics

Basic clinical characteristics were collected at the time of the first use of ICI, including age, sex, ECOG score, height, weight, smoking history, family history of malignancy, presence of co-liver, bone metastases, and brain metastases, number of metastatic organs, presence of superior vena cava syndrome, and time of first use of ICI, etc. Based on the above data, the relevant composite index was calculated as follows:

(1) Body mass index (BMI) = weight/height2 (weight in kilograms, height in meters)

(2) Smoking index = number of cigarettes smoked per day × number of years smoked.

2.2.2 Laboratory-related indicators

Patient lab-related metrics, including white blood cell counts, platelet counts, lymphocytes, and neutrophils in routine blood cell tests, as well as low LDH, serum albumin, sodium, and chloride ions in blood biochemistry, were collected two weeks before ICI was first used. Based on the above data, relevant composite indices were calculated as follows:

(1) Neutrophil lymphocyte ratio (NLR) = neutrophil count (10⁹/L)/lymphocyte count (10⁹/L)

(2) Derive d neutrophil-lymphocyte ratio (dNLR) = Neutrophil Count (10⁹/L) / (leukocyte-neutrophil count) (10⁹/L)

(3) Platelet to lymphocyte ratio (PLR) = platelet count (10⁹/L)/lymphocyte count (10⁹/L)

(4) Prognostic Nutritional Index (PNI) = 10 × albumin (g/L) + 0.05 x lymphocyte count (10⁹/L)

(5) Evaluating the Lung Immune Prognostic Index (LIPI) is based on these prognostic measures: Derived Neutrophil-to-Lymphocyte Ratio (dNLR) at or above 3 and LDH at or above the normal upper limit. The patients were divided into three categories based on these factors: good (0 factors), medium (1 factor), and poor (2 factors).

2.3 Ending indicators

Patient efficacy was evaluated using the Solid Tumor Efficacy Criteria version 1.1. The study primarily aimed to determine Overall Survival (OS), defined as the time from the initial use of immune checkpoint inhibitors (ICIs) by patients until death from any cause or the follow-up limit. Additionally, progression-free survival (PFS), defined as the period from the first ICI administration to disease progression or the patient's death due to any cause or follow-up limit, was a secondary objective. Patient response statuses were recorded at different times using the following definition of response status:

(1) Complete Response (CR): All tumor target lesions disappeared, no new lesions appeared, and tumor markers were normal for at least 4 weeks.

(2) Partial Response (PR): The sum of the largest diameters of the tumor target lesions was reduced by ≥ 30% for at least 4 weeks.

(3) Stable Disease (SD): the sum of the largest diameters of the target lesions of the tumor decreased but did not reach the level of PR or increased but did not reach the level of Progressive Disease (PD).

(4) Progressive Disease (PD):≥20% increase in the sum of the largest diameters of tumor target lesions or the appearance of new lesions.

2.4 Model construction and assessment methods

2.4.1 Model construction

The study entailed the random allocation of 113 advanced small cell lung cancer patients in a 7:3 ratio, with 79 patients (accounting for 7/10 of the total) used to build the model (training set) and 34 patients (3/10 of the total) to validate the model (validation set). Factors associated with the prognosis of immunotherapy were analyzed using one-way Cox analysis (P < 0.05). Furthermore, these crucial elements were integrated into the complex Cox regression analysis in conjunction with relevant studies to fine-tune the choice of variables for modeling. Following the selection of these variables, a model was developed to forecast the survival likelihood of patients with advanced-stage small cell lung cancer undergoing immunotherapy, and column line graphs were created to estimate the survival chances at 12, 18, and 24 months.

2.4.2 Model assessment methods

The model's differentiation assessment used Harrell's C-index and time-variant subject work characteristic curves (tROC), including their area under the curve (tAUC), calibration through curve plots, and further validation for discrimination and calibration in the validation set. The clinical efficacy of the model was assessed using Decision Curve Analysis (DCA). Patients underwent risk categorization through percentile division, and the link between this categorization and overall survival (OS) was examined using survival curves.

2.5 Statistical methods

The Cox regression method was used for both single-variable and multiple-variable analyses. A range of interpolation methods was employed to fill the data voids. By employing the Kaplan-Meier method, survival trajectories for OS and PFS were charted across various risk tiers and response stages at distinct intervals. The chi-square test was used to evaluate differences in survival duration across patient categories. Statistical analyses were performed using R 4.1.2 and SPSS 26, where a p-value below 0.05 signifies a statistically significant difference.

3.1 Baseline patient characteristics

The study included 113 participants with advanced lung cancer, and Table 1 displays the fundamental features of the training, validation, and patient groups. The clinical characteristics are shown in Fig. 2. Among the patients, the mean age was 61 years; 78 (69%) were male; 38 (34%) had a smoking rate of 400 or more; 14 (12%) had a family history of tumors; all patients were in good health, with an ECOG score under 2 in 109 (96%) instances; 7 (6%) had superior vena cava syndrome; 29 (26%) had liver metastases; 38 (34%) had bone metastases; 24 (21%) had brain metastases; and 86 (76%) had fewer than two organ counts. By the follow-up date, 74 (65%) patients had died, and 39 (35%) survived.

Table 1

Baseline characteristics of patients
Characteristic	Train set, N = 79(%)	Validation set, N = 34 (%)	Pooled cohor, N = 113 (%)	Characteristic	Train set, N = 79(%)	Validation set, N = 34 (%)	Pooled cohor, N = 113 (%)
Gender				PNI
Femal	24(30%)	9(26%)	35 (31%)	< 51.03	57(72%)	24(71%)	81 (72%)
Male	55(70%)	25(76%)	78 (69%)	≥ 51.03	22 (28%)	10(29%)	32 (28%)
Age				LDH
≥ 65	50(63%)	28(82%)	78 (69%)	< 146.5	3(4%)	1(3%)	4 (4%)
< 65	29(37%)	6(18%)	35 (31%)	≥ 146.5	76(96%)	33(97%)	109 (96%)
BMI				Albumin
< 24	41(52%)	21(62%)	62 (55%)	<40	30(38%)	12(35%)	42 (37%)
≥ 24	38(48%)	13(38%)	51 (45%)	≥ 40	49(62%)	22(65%)	71 (63%)
Smoking index				Hyponatremia
<400	52(66%)	23(68%)	75 (66%)	No	55(70%)	26(76%)	81 (72%)
≥ 400	27(34%)	11(32%)	38 (34%)	Yes	24(30%)	8(24%)	32 (28%)
ECOG				Hypochloremia
< 2	77(97%)	32(94%)	109 (96%)	No	63(80%)	30(88%)	93 (82%)
≥ 2	2(3%)	2(7%)	4 (4%)	Yes	16(20%)	4(12%)	20 (18%)
Family history of tumors				Number of distant organ metastases
No	70(89%)	29(85%)	99 (88%)	< 3	59(75%)	27(79%)	86 (76%)
Yes	9(11%)	5(15%)	14 (12%)	≥ 3	20(25%)	7(21%)	27 (24%)
Superior vena cava syndrome				Liver metastases
No	74(94%)	32(94%)	106 (94%)	No	61(77%)	23(68%)	84 (74%)
Yes	5(6%)	2(6%)	7 (6%)	Yes	18(23%)	11(32%)	29 (26%)
NLR				Bone metastases
<2.14	20(25%)	6(18%)	26 (23%)	No	53(67%)	22(65%)	75 (66%)
≥ 2.14	59(75%)	28(82%)	87 (77%)	Yes	26(33%)	12(35%)	38 (34%)
PLR				Brain metastases
< 209.36	52(66%)	30(88%)	82 (73%)	No	64(81%)	25(74%)	89 (79%)
≥ 209.36	27(34%)	4(12%)	31 (27%)	Yes	15(19%)	9(26%)	24 (21%)
LIPI
Good	43(54%)	20(59%)	63 (56%)
Medium	34(43%)	10(29%)	44 (39%)
Poor	2(3%)	4(12%)	6 (5%)

3.2 Optimal cut-off values for continuity variables in SCLC patients

Leveraging relevant studies and clinical significance, threshold values were used to convert continuous variables into binary variables. The threshold values for serum albumin, hyponatremia, and hypochloremia represented the lowest normal reference in our blood biochemistry tests (serum albumin = 40 g/L, hyponatremia NA⁺ < 137 mmol/L, hypochloremia Cl⁻ < 99 mmol/L), smoking index of 400, Eastern (Cooperative Oncology Group ECOG) score of 2, number of metastatic organs of 2, and BMI threshold of 24. The ideal threshold values for NLR, PLR, LDH, and PNI were determined by employing the subject's work characteristic curve (ROC), the related area under the curve (AUC), and Jordon's index (Fig. 3), setting LDH at 146.5, NLR at 2.14, PLR at 209.36, and PNI at 51.03.

3.3 Filtering variable results

In this study, the Cox regression analysis was used for variable screening. Initially, our findings indicated that a blood test NLR < 2.14 and LIPI deemed subpar, along with a BMI < 24, familial tumor history, liver and bone metastases, and the count of distant organ metastases ≥ 2 in clinical features correlated with reduced overall survival (OS) as per univariate Cox regression analysis (P < 0.05) (Table 2). Meanwhile, relevant literature and clinical experience have shown that LDH and PNI are also associated with the prognosis of patients with ES-SCLC. Following multifactorial Cox regression analysis of the aforementioned factors, six indicators linked to a worse prognosis were ultimately excluded: liver and bone metastases, NLR < 2.14, LIPI rated as poor, PNI < 51.03, and LDH ≥ 146.5 (Fig. 4).

Table 2

Univariate analysis
Characteristic	Hazard Ratio(95%CI)	P value
Gender
Male	1
Female	0.697(0.416–1.165)	0.169
BMI
< 24	1
≥ 24	0.592(0.365–0.959)	0.033
Family history of tumors
No	1
Yes	2.171(1.157–4.073)	0.016
Number of distant organ metastases
< 2	1
≥ 2	2.206(1.332–3.652)	0.002
Liver metastases
No	1
Yes	2.808(1.729–4.56)	0
Bone metastases
No	1
Yes	2.664(1.661–4.273)	0
Brain metastases
No	1
Yes	0.717(0.385–1.333)	0.293
NLR
<2.14	1
≥ 2.14	0.505(0.304–0.837)	0.008
PLR
< 209.36	1
≥ 209.36	0.795(0.467–1.356)	0.401
LIPI
Good	1
Medium	1.601(0.994–2.582)	0.054
Poor	3.932(1.531–10.141)	0.005
PNI
< 51.03	1
≥ 51.03	1.553(0.901–2.679)	0.113
LDH
< 146.5	1
≥ 146.5	0.396(0.142–1.102)	0.076

3.4 Model construction

A column plot of the probability of predicting patient survival at 12, 18, and 24 months was constructed based on the six variables included in the final model (Fig. 5). The scores for each variable on the column line graph were as follows: 45 for liver metastasis, 38 for bone metastasis, 56 for NLR < 2.14, 0 for LIPI assessment as good, 22 for medium, 99 for poor, 55 for PNI < 51.03, and 87 for LDH ≥ 146.5 (refer to Table 3). The overall patient score represents the aggregate of their individual scores for each variable, with the respective forecasted survival probabilities for 12-month, 18-month, and 24-month periods available in the column line graph based on these scores.

Table 3 The risk scoring system

3.5 Evaluation of model effectiveness and validation

This study evaluated the accuracy of the model predictions using differentiation and calibration methods. Harrell's C-index, tROC curve, and tAUC curve were used to evaluate the discriminative ability of the model. For the training and validation datasets, the scores of Harrell's C-index were 0.85 (95% CI: 0.76–0.93) and 0.88 (95% CI: 0.76–0.99) respectively, indicating successful model differentiation. Figures (Fig. 6A and B) indicate that for 12, 18, and 24 months, the AUC values of the training set were 0.754, 0.848, and 0.819, respectively, compared to 0.858, 0.904, and 0.828, respectively, in the validation set. All AUC values surpassed 0.8, except for the 12-month AUC in the training set, which also demonstrated the notable differentiation of the model. The tAUC graphs in Figures (Fig. 6C, D) depict time as the horizontal coordinate and AUC values as the vertical coordinate in both the training and validation sets, with the overall tAUC exceeding 0.7, showing slight fluctuations over time, signifying the effective discrimination and stability of the prognostic model.

This study assessed and verified the model's calibrations at 12, 18, and 24 months using the validation set (Fig. 7). The horizontal coordinates in the plots represent the predicted chance of patient deaths, while the vertical coordinates show the actual probability of these deaths. The diagonal line indicates the correspondence between the forecast and actual probabilities. Observations revealed that the predicted probability diverged from the actual occurrence probability at 18 and 24 months in the training group, and at 24 months in the validation group. During these periods, the model infers a 20–40% higher chance of patient mortality when the probability of death hovers near 70%, likely due to the following reasons: First, the follow-up duration of the study was 12 months; then, there was a lack of sufficient long-term data at 18 and 24 months to validate the model's forecasting accuracy. Additionally, the small sample size in this study could have led to variable predictive results. Furthermore, the short survival period of individuals with advanced-stage small-cell lung cancer could lead to a decrease in the accuracy of predicting long-term survival. Moreover, the accurate alignment of the model with the real likelihood of occurrence suggests superior calibration capability.

3.6 Assessment of Actual Needs for Clinical Decision-Making Models

This research involved charting DCA decision trajectories derived from the one-year survival likelihoods of patients to assess the model's clinical effectiveness. In this study, as shown in the figure (Fig. 8), in this study, we denote the horizontal coordinate as the threshold probability, whereas the vertical coordinate represents the difference in the net benefit rate of the model between true positives and false positives. The two polar scenarios are represented by solid gray and red lines; the one-year total patient mortality is shown by a solid gray line, indicating a net benefit rate of zero, the one-year survival of all patients by a solid red line, and the model's net benefit by a solid blue line. The figure reveals that the model curves are positioned above the two extreme curves in both the training and validation sets, within a threshold probability of 1.0, suggesting its clinical applicability.

3.7 Survival analysis of patients with different risk stratification

Patients in the training cohort were categorized according to their expected risk scores in the column-line diagram using the 25th and 75th percentile brackets and divided into low-risk (0–69), medium-risk (70–162), and high-risk (≥ 163) groups. Among the 79 participants in the training cohort, 52 (66%) died and 67 (85%) exhibited disease progression based on the monitoring date. The average total survival period stood at 15.0 months (95% CI: 11.9–20.7), and the median duration without disease progression was 7.8 months (95% CI: 6.4–12.0). A significant increase in overall survival was observed in both the intermediate-risk (Median overall survival time(mOS), 24.5 months; HR = 0.47, 95%CI: 0.23–0.96, P = 0.038) and low-risk (mOS, NA; HR = 0.14, 95%CI: 0.03–0.58, P = 0.007) groups, as opposed to the high-risk (mOS, 12.3 months). Additionally, there was a significant increase in median progression-free survival (mPFS) in both the intermediate-risk (mPFS: 12.7 months; HR = 0.45, 95%CI: 0.23–0.91, P = 0.026) and low-risk categories (mPFS:NA; HR = 0.12, 95%CI: 0.03–0.50, P = 0.004) than in the high-risk category (mPFS,6.2 months) (Table 4, Fig. 9). Among the 34 participants in the validation group, 25 (74%) succumbed, and 31 (91%) showed disease advancement by the date of follow-up. The average total survival time (OS) was recorded at 13.8 months (95% CI: 10.4–20.7), while the median duration of progression-free survival (PFS) was 6.9 months (95% CI: 4.8–11.5). There was a notable extension of OS in both the intermediate-risk (mOS, 16.8 months; HR = 0.47, 95%CI: 0.17–0.99, P = 0.047) and low-risk (mOS, NA; HR = 0.40, 95%CI: 0.03–0.42, P = 0.001) groups than in the high-risk group (mOS, 7.9 months). Furthermore, when contrasted with the high-risk cohort (mPFS,4.1 months), the intermediate-risk group showed no notable enhancement in median PFS (mPFS: 6.65 months; HR = 0.56, 95%CI: 0.24–1.33, P = 0.189), while the low-risk group exhibited a considerable extension in median PFS (mPFS:NA; HR = 0.12, 95%CI: 0.03–0.44, P = 0.002) (Table 5, Fig. 10).

Table 4

**OS and PFS based on the risk stratification in train set**
			OS			PFS
Groups	N (%)	mOS(m)	HR (95%CI)	P	mPFS(m)	HR (95%CI)	P
High	50(63%)	12.3	1		6.2	1
Medium	10(13%)	24.5	0.47(0.23–0.96)	0.038	12.7	0.45(0.23–0.91)	0.026
Low	19(24%)	NA	0.14(0.03–0.58)	0.007	NA	0.12(0.03–0.50)	0.004

Table 5

**PFS based on the risk stratification in test set**
			OS			PFS
Groups	N (%)	mOS(m)	HR (95%CI)	P	mPFS(m)	HR (95%CI)	P
High	10(29%)	7.9	1		4.1	1
Medium	10(29%)	16.8	0.47(0.17–0.99)	0.047	6.65	0.56(0.24–1.33)	0.189
Low	14(42%)	NA	0.40(0.03–0.42)	0.001	NA	0.12(0.03–0.44)	0.002

3.8 The correlation between milestone response status and survival

The study further investigated how patients' responses at key intervals (6, 12, and 20 weeks) correlate with the survival rates of patients treated with immune checkpoint inhibitors at these intervals, as depicted in Fig. 11. Responders in this study were defined as patients who had CR or PR at each milestone time point; all other patients were defined as non-responders. Six weeks after the use of immune checkpoint inhibitors, 52 patients were assessed as having PR, with 46% responding.The correlation between their response and overall survival (OS) is depicted in Fig. 12A. Post six weeks of combined immune checkpoint inhibitor and chemotherapy, responders showed a notable long-term survival benefit compared to non-responders (median survival time of 19.5 months vs 11.9 months, P = 0.033). The patterns observed in the preceding studies at 12 and 20 weeks (refer to Fig. 12B, C) aligned with those at 6 weeks, showing that responders experienced notably extended survival periods than non-responders (mOS 20.7 months vs 11.9 months; 20.7 months vs 11.7 months, respectively), evidenced by significance levels of P = 0.044 and P = 0.015, respectively.

Additionally, there is a link between the patients' reaction state and progression-free survival (PFS) at key moments. As shown from the figure (Fig. 13), at 6 and 20 weeks of treatment with immune checkpoint inhibitors in combination with chemotherapy, the progression-free survival time (PFS) was significantly longer in responders than in non-responders, with a median progression-free survival time of 10.6 months versus 6.4 months and 10.6 months versus 6.3 months, respectively. The observed variances held statistical significance, as indicated by p-values of 0.036 and 0.028, respectively. Moreover, at the 12-week mark, the progression-free survival (PFS) for those who responded was 9.2 months (95% CI:4.9–13.6), in contrast to the 6.3 months (95% CI:4.2–8.4) for non-responders, suggesting a tendency for extended survival, yet this difference was not statistically significant (P = 0.069).

3.9 Exploratory Analysis of the Potential Clinical Characteristics of LTS

Furthermore, our research delved into the potential characteristics of LTS in advanced-stage small cell lung cancer treatment. The findings indicate a one-year overall survival (OS) rate of 58.4% after immunotherapy in patients with advanced-stage small cell lung cancer, aligning closely with the 1-year OS rates observed in numerous extensive clinical trials both domestically and internationally, as detailed in Table 6. This encompassed 52% in the IMpower133 study's A + CE treatment group, 52.8% in the CASPIAN study's D + EP group, and 62.9% and 60.7% in the immuno-combination chemotherapy groups of the CAPSTONE-1, CAPSTONE-1, and ASTRUM-005 studies, respectively, in the immunocombination chemotherapy group. The 18-month survival rate in this study was 33.6%, closely mirroring the results of the CASPIAN and IMpower133 studies. The research results showed a survival rate of 17.7% over two years, which is slightly lower than the rates observed in other clinical trials. This might be attributed to the small number of participants and short follow-up period in this study. However, the aforementioned data still suggest that the study is truly dependable.

Table 6

OS rate of the various studies
	IMpower133	CASPIAN	CAPSTONE-1	ASTRUM-005	This study
12-month OS rate	52%	52.8%	62.9%	60.7%	58.4%
18-month OS rate	34%	32%	45%	45%	33.6%
24-month OS rate	22%	22.9%	31.3%	43.1%	17.7%

Investigating the clinical characteristics of patients with prolonged survival times, along with the median overall survival (OS) from earlier research (12.9 months in CASPIAN, 12.3 months in IMpower133, 15 months in ASTRUM-005, and 15.3 months in CAPSTONE-1), we classified patients with immunocheckpoint inhibitors according to their survival periods exceeding 12 months. The classification relied on a 12-month period, and the chi-squared test was applied to both groups of patients, as shown in Fig. 14.Patients lacking initial liver or bone metastases and having fewer than two metastatic organs generally experienced extended survival times (P = 0.002, 0.001, and 0.002, respectively).

Simultaneously, our study indicated that individuals with LDH < ULN showed improved survival rates post-immune checkpoint inhibitor therapy (P = 0.09). Moreover, patients with NLR ≥ 2.14, albumin ≥ 40 g/L, and superior LIPI scores tended toward an enhanced immune response, although the differences were not statistically significant (P = 0.058, 0.074, and 0.081, respectively).

The use of immune checkpoint inhibitors for small-cell lung cancer treatment has resulted in many clinical trials demonstrating the significant efficacy of immune-combination chemotherapy in treating advanced small-cell lung cancer ^[1–4]. However, not all patients benefited from immunotherapy. Furthermore, the inflexible structure of clinical trials strictly follows certain inclusion and exclusion criteria, which may limit the practical use of their results in real-life situations, owing to differences in patient disease characteristics and other conditions relative to clinical trials. In the era of precision therapy, assessing the impact of immunotherapy on patients with advanced small-cell lung cancer is vital, along with capturing the characteristics of those receiving immunotherapy. This helps identify patients more precisely and provides better guidance for customized treatment. Medical professionals must precisely identify the group receiving immunotherapy based on suitable biomarkers. Traditional biomarkers for immunotherapy, such as PD-L1 expression and TMB assays, are expensive, time-consuming, and ineffective in forecasting results in ES-SCLC^[4–6]. As a result, it is vital to identify predictive indicators that are accurate, economical, and easily accessible for the early detection of the potential advantages of immunotherapy. This research led to the creation of a column-line graphical representation to forecast the survival rates of patients with advanced-stage small-cell lung cancer undergoing treatment with immune checkpoint inhibitors. The model utilized clinicopathological traits and selected inflammatory markers, demonstrating its strong predictive capability.

Owing to the malignancy of small cell lung cancer and the risk of early stage distant metastases, some patients progress to a more advanced stage at the time of diagnosis. Metastasis frequently occurs in the liver, bones, and the brain. A variety of important clinical investigations, such as the IMpower133 study ^[1] and the CAPSTONE-1 study ^[4], have noted an increase in survival rates of advanced-stage small-cell lung cancer patients without liver metastases when treated with immune-combination chemotherapy. Furthermore, a backward-looking analysis of 236 ES-SCLC patients^[9] pinpointed liver metastasis as an independent prognostic factor for survival in advanced small-cell lung cancer patients receiving initial chemotherapy with ICIs (HR = 2.08; 95% CI:1.3–3.82; P = 0.018). Similarly, our study showed that ES-SCLC patients had a grim outlook if liver metastases were present prior to initiating immune combination treatment, acting as an independent risk factor (HR = 2.59; 95% CI:1.45–4.60; P = 0.001). It is possible that liver metastases weaken the body's defense against tumors by diminishing the presence of circulating CD8⁺ T cells following the interaction of activated T cells with macrophages. The observed immunosuppressive condition could originate from liver metastases and may not be mitigated by ICI therapies ^[13]. Apart from liver metastases, our research suggests a connection between bone metastases and poorer patient outcomes (HR = 1.99; 95% CI:1.16–3.41; P = 0.013). However, the impact of bone metastases on the prognosis of small-cell lung cancer patients receiving immune checkpoint inhibitor therapy is still debated. Studies ^{[14, 15]} show that patients with SCLC who have bone metastases prior to undergoing immune-combination therapy have a significantly reduced average survival rate compared to those without these metastases (P < 0.05). An increase in bone-related events linked to bone metastases, including bone pain, hypercalcemia, pathological fractures, and spinal cord compression disorders, might significantly affect the quality of life of patients, thereby altering immunotherapy outcomes. However, studies^[9] revealed that bone metastasis alone did not predict overall survival in ES-SCLC patients undergoing initial chemotherapy with ICIs (HR = 1.15; 95% CI:0.71–1.88; P = 0.572). This implies that other elements may obscure the predictive significance of bone metastases in ICI therapy, necessitating additional research to clarify this link.

In addition to clinical features, such as liver and bone metastases, markers linked to blood play a vital role in forecasting small-cell lung cancer because of their easy accessibility and ease of use. Various immunoinflammatory markers, including specific hematological markers, are extensively employed in evaluating the prognosis of small cell lung cancer immunotherapy, as they mirror the body's inflammatory and immune conditions.

Studies suggest a connection between NLR, often signaling the body's inflammatory and immune states, and results in patients receiving immunotherapy for various cancers^{[16, 17]}. However, the relationship between the NLR and SCLC prognosis remains controversial. In a backward-looking study^[10] of 612 SCLC patients receiving initial chemotherapy (22 on PD-L1 inhibitors in conjunction with chemotherapy), NLR emerged as an independent predictive factor in SCLC cases (P = 0.004). Furthermore, our findings indicate that patients with SCLC and an NLR ≥ 2.14 or higher generally had a better outlook post-ICI treatment (HR = 0.42; 95% CI:0.23–0.77; P = 0.005). Additionally, two other investigations^{[18, 19]} focusing on SCLC patients reported similar findings. However, further analysis of 53 ES-SCLC patients treated with atezolizumab and chemotherapy in the NCT03041311 trial [12] showed that NLR was an unreliable predictor for ES-SCLC patients (P = 0.69). Additionally, a backward-looking study ^[20] with 234 SCLC patients determined that the NLR before starting platinum-based chemotherapy was not a standalone predictor of OS in patients with ES-SCLC. In conclusion, the connection between NLR and SCLC prognosis remains a topic of discussion and requires further investigation for confirmation and understanding. Consequently, additional research is essential to thoroughly evaluate NLR's predictive value of the NLR in patients with SCLC, aiming to offer more precise and dependable advice for clinical prognostic evaluations.

LDH, pivotal in transforming pyruvate into lactate, plays a key role in the glycolysis of cancerous cells, rendering LDH concentration a frequent marker of tumor cell activity^{[10, 21]}. The findings of our study indicate that patients with ES-SCLC, starting with an LDH level of 146.5 U/L or higher, tended to show a lower positive reaction to immunotherapy (P = 0.037). However, the precise mechanism connecting LDH with immunotherapy is still unclear, with hypotheses suggesting that increased LDH levels leading to lactic acid production lessens the ability of T cells and NK cells to generate NFAT in acidic environments, causing a decrease in interferon gamma release and a weakened ability of lymphocytes to destroy tumor cells^[22]. Furthermore, multiple studies suggest that LDH serves as a negative sign for patients with SCLC, potentially forecasting brain and bone metastasis in SCLC patients ^{[23, 24]}. Hence, merging serum LDH measurements with additional metrics is vital for evaluating ES-SCLC risk categorization and future outlooks.

Several studies have discovered^{[21, 25]} that in small cell lung cancer, the combination of lactate dehydrogenase (LDH) and the neutrophil-to-lymphocyte ratio (dNLR), known as LIPI, has been identified as an independent prognostic factor. Based on prognostic factors, the LIPI is categorized into three groups: good (0 factors), intermediate (1 factor), and poor (2 factors). Prognostic factors included a dNLR ≥ 3 and an LDH level ≥ the upper limit of normal. This study found that among patients with ES-SCLC receiving combination immunotherapy, those with a poor LIPI score had a significantly increased risk of adverse prognosis compared to patients with a good LIPI score (HR = 8.79; 95% CI: 3.05–25.29; P < 0.001). Recently, scholars in China conducted a large multicenter retrospective analysis targeting patients with SCLC receiving first-line atezolizumab/durvalumab combined with standard chemotherapy^[12]. The results also revealed a significant correlation between the LIPI and survival prognosis (P < 0.05). LIPI, a composite assessment indicator that combines the predictive capabilities of LDH and dNLR, has demonstrated good immunopredictive power for SCLC in multiple studies. It also has advantages such as minimal invasiveness and lower difficulty of acquisition, and is expected to become a common clinical predictive tool for SCLC immunotherapy in the future.

Albumin, recognized as a secure immunogenic protein, is frequently linked to malnutrition and widespread inflamation owing to its low levels. Studies have shown that albumin plays an inhibitory role in the systemic inflammatory response and may contribute to tumor progression^[26]. The prognostic nutritional index (PNI), encompassing albumin and lymphocytes, serves as an essential metric that accurately reflects the connection between a patient's nutritional status and the overall inflammatory response. Recently, the PNI has been transformed into an innovative measure for evaluating lung cancer and other tumor prognoses. A prior study of 112 men with stage III SCLC identified a link between lower predictive nutritional indices and adverse results for these individuals (P = 0.009)^[11]. Furthermore, a separate investigation of patients with limited-stage small cell lung cancer revealed that those with a higher PNI often had a more favorable prognosis (P = 0.018) ^[27]. The cut-off value of PNI in this study was 51.03, which is consistent with previous studies (45.15-52.525)^{[11, 27, 28]}. The findings of our study reveal that people with a PNI exceeding 51.03 generally hold a more positive view (HR = 0.36; 95% CI:0.19–0.69; P = 0.002), corroborating previous studies suggesting that ES-SCLC sufferers with sufficient nutrition are more likely to benefit from immunotherapy. Additionally, malnutrition is notably prevalent in several cancers, including lung cancer ^[29], characterized by a complex interaction between malnutrition, tumor proliferation, and metastasis, which leads to malnutrition in patients, and the spread of tumors exacerbates malnutrition, forming an unyielding cycle. The present research indicates that these mechanisms might be linked to weakened immune responses, inflammatory reactions, leptin concentrations, and tumor cell autophagy in cancer sufferers ^[30–32].

In conclusion, elements such as liver and bone metastases, NLR under 2.14, a reduced LIPI score, PNI < 51.03, and LDH exceeding 146.5 collectively suggest a negative outlook for individuals with advanced-stage small-cell lung cancer before receiving immunological combination treatment. The utilization of these elements led to the creation of a predictive model. Through examination of column-line graphs, patient risk scores were ascertained, indicating enhanced post-immunotherapy results and higher survival probabilities at 12, 18, and 24 months for those with lower scores. For this model, Harrell's C-index was recorded at 0.84 (95% CI: 0.75–0.92) during training and 0.88 (95% CI: 0.76–0.99) in the validation group. Forecasts for survival at 12, 18, and 24 months revealed AUC values of 0.778, 0.908, and 0.845, respectively, in the training phase and 0.808, 0.844, and 0.824, respectively, in the validation phase, signifying the model's effectiveness in differentiation. Consequently, this model aids healthcare professionals in more precisely forecasting the survival chances of patients with advanced small-cell lung cancer post-immunotherapy and supports the creation of tailored treatment approaches.

In the past, scientists have attempted to develop novel predictive models for small-cell lung cancer patient survival rates. In a specific multicenter retrospective analysis ^[33], 2309 patients with SCLC from Shandong province and another 2309 from the same province were included, creating a model to predict outcomes based on their location, sex, age, TNM stage, and whether they underwent surgery, chemotherapy, or radiotherapy. The findings revealed that the model's time-variant AUC for forecasting 1-, 3-, and 5-year overall survival in the training and validation groups were 0.699, 0.683, and 0.683, respectively, and 0.698, 0.698, and 0.639, respectively. The advantage of this model is its ability to incorporate a significant number of samples, thereby boosting the authenticity and reliability of its results. However, this approach has drawbacks, such as the omission of certain inflammatory markers and other laboratory markers, along with missing aspects such as immunotherapy methods. Additionally, the critical area under the curve (AUC) for predicting survival rates over 1, 3, and 5 years remained below 0.7 in both training and validation data, suggesting relatively low accuracy. In a subsequent backward-looking study^[10], 612 patients with small cell lung cancer were analyzed to create a model predicting outcomes, focusing on their initial C-reactive protein-to-albumin ratio, NLR, hyponatremia, and the success of early chemotherapy and stage. Harrell's C index for the training and validation datasets of this model was 0.666 and 0.747, respectively. The value of this model stems from its incorporation of a wide range of samples and inflammatory markers, facilitating an in-depth analysis of patient outcomes. However, it presents some constraints, especially the diminished Harrell’s C-index in both the training and validation cohorts, indicating reduced predictive accuracy. Moreover, the model overlooks elements, such as the type of immunotherapy used, potentially reducing its predictive strength.

In contrast to the previously mentioned models, our method primarily aims to predict the progression of advanced small cell lung cancer in patients receiving immune-combination therapy, enhancing its applicability in current clinical environments. Furthermore, it ameliorates a range of laboratory indicators such as NLR, LIPI, LDH, and PNI, reflecting the inflammatory and nutritional states of patients and correlating them with their prognosis. Additionally, we incorporated clinical indicators such as liver and bone metastases for an in-depth predictive study. Finally, the AUC values for Harrell's C index and our model's survival predictions at different times in both the training and validation datasets exceeded those of the previously mentioned models, indicating our model's enhanced accuracy and discrimination capabilities. However, our model has drawbacks, especially its small sample size. Although it falls short of the sample size used in previous models, our model provides precise and useful forecasts. Generally, this approach offers specific advantages in predicting the survival probability of patients with advanced cell lung cancer patients after immunological combination therapy. The current model can be enhanced and validated by incorporating external validation and enlarging the sample size to boost its predictive power and practicality.

At the 2022 World Conference on Lung Cancer (WCLC), Johal ^[8] investigated the link between the health of responders and their prognosis, focusing on participants who underwent initial durvalumab and chemotherapy during the key stages (weeks 6, 12, and 20) of the CASPIAN study. Their findings indicated a significant increase in both progression-free survival (PFS) and overall survival (OS) for responders, in contrast to non-responders (all P < 0.001). Remarkably, by the 12th week, the median overall survival (mOS) for responders stood at 16.7 months (14.4–21.5), in contrast to 8.0 months (5.7–8.7) for non-responders. Months (mPFS) of 5.7 months (5.0–7.0), as opposed to 1.8 months (1.7–1.8) for those who did not respond. The research revealed a notable link between the status of responses at key time points and prognosis in SCLC patients undergoing initial durvalumab combination chemotherapy, where responders exhibited markedly improved PFS and OS compared to non-responders.

Based on these findings, this study delved deeper into the connection between response levels and ES-SCLC prognosis at key real-world moments. Observations showed a marked increase in overall survival (OS) among individuals who reacted to the use of immune checkpoint inhibitors at weeks 6, 12, and 20, unlike those who did not respond. Notably, the mPFS was extended more in responders than in non-responders at weeks 6 and 20. By the 12-week mark, a tendency for extension was observed; however, the PFS showed no notable statistical variance between those who responded and those who did not (mPFS: 9.2 months vs. 6.3 months, P = 0.069). The findings of our study indicated that during the key phases of administering immune checkpoint inhibitors for treating small cell lung cancer, those who responded showed a marked increase in both overall and progression-free survival rates compared to those who did not respond. Contrary to the preliminary findings of the CASPIAN study, this study found no significant statistical differences in progression-free survival (PFS) between those who responded and those who did not, only at the 12-week mark. However, during other crucial periods, both overall survival (OS) and PFS durations were much longer in responders than in non-responders. This suggests that in real-world situations, the response to treatment during key phases can precisely predict the results of immunotherapy in patients with advanced small cell lung cancer patients. Therefore, a swift assessment of patient reactions assists healthcare providers in selecting and adjusting treatment strategies more efficiently, improving patient longevity, and reducing their financial burden. While this research corroborates the preliminary findings of the CASPIAN study in a practical environment, more extensive multi-institutional studies are essential to validate the predictive power of response status at key junctures for ES-SCLC prognosis and to investigate additional potential determinants.

The remarkable effectiveness of immune checkpoint inhibitors in managing advanced small cell lung cancer has revived patients' hopes for extended survival. Various scientists ^[34–36] are currently investigating the link between clinical symptoms and extended survival in patients with ES-SCLC. The research by Stephen V. Liu and team^[34] revealed that ES-SCLC patients initially receiving atezolizumab and chemotherapy, characterized by an ECOG score of 0 (OR = 1.8, P = 0.03), LDH ≤ ULN (OR = 1.8, P = 0.03), and a reduced number of metastatic sites (OR = 0.8, P = 0.03), showed an increased likelihood of sustained long-term survival (LTS). Additionally, our study assessed the link between the clinical traits of these patients and LTS, categorizing those who lived for 12 months or more after immune checkpoint inhibitor treatment as LTS. Research has revealed that people lacking liver or bone metastases prior to immune-combination treatment and with a reduced number of metastatic organs (p-values of 0.002, 0.001, and 0.002, respectively) showed comparable findings in the IMpower133 study ^[1] and CAPSTONE-1 study ^[4], indicating a tendency for better overall survival in those without liver metastases treated with atezolizumab or adebrelimab along with chemotherapy (HR = 0.64, 95% CI: 0.45–0.90; HR = 0.61, 95% CI: 0.46–0.81). The study also revealed a significant increase in survival rates among patients with initial LDH ≤ ULN (P = 0.09), consistent with the findings of the CAPSTONE-1 study (OS HR = 0.59, 95% CI: 0.42–0.82).

The limitations of our study are mainly due to its backward-looking approach, which makes it difficult to address possible biases and confounding factors, such as the likelihood of recall bias in patients previously treated with immune checkpoint inhibitors. Furthermore, the number of patients was notably small (113), which indicates a limited sample size. Additionally, the diversity of recipients of immune checkpoint inhibitors, which could affect prognosis outcomes, was not considered. Moreover, the monitoring period was only 12 months, in contrast to numerous studies that described long-term survival ranging from 18 months to 5 years. Finally, gene expression analysis and analysis of small cell lung cancer subtypes were not performed in this study to explore the differences between long-term and non-long-term survival in patients. Therefore, forthcoming comprehensive, multi-institutional, forward-looking studies are crucial to externally validate our model and explore its significance for patients with early stage small cell lung cancer undergoing immunotherapy in different cancer centers. These investigations aimed to more thoroughly evaluate prognostic elements and pinpoint personalized treatment approaches.

6.1 This study, based on patients' clinical characteristics and hematological indicators, developed a model to predict the prognosis of patients with extensive-stage small cell lung cancer (ES-SCLC) undergoing immunotherapy. This prognostic model incorporated six indicators: liver metastasis, bone metastasis, NLR, LIPI, PNI, and LDH. It demonstrates good discriminative ability and calibration, with nomogram scores able to predict the survival probability of patients with ES-SCLC during immunotherapy.

6.2 In the real world, the response status at milestone time points (6, 12, and 20 weeks) can serve as a good indicator of long-term survival for patients with ES-SCLC receiving immunotherapy. This allows for early survival predictions during the treatment process, providing a reference for clinical screening of the population that benefits from immunotherapy.

6.3 For ES-SCLC patients receiving immunotherapy, individuals lacking liver or bone metastases, having no more than two metastatic organs, and an initial LDH ≤ ULN have a higher probability of attaining prolonged survival.

Data availability

More detailed data are available from the corresponding authors upon reasonable request.

Acknowledgements

The authors acknowledge the contributors for uploading meaningful datasets, and the Third Affiliated Hospital of Harbin Medical University for technical support.

Funding

This study was supported by the National Cancer Center Climbing Fund(NO. NCC201908B12)

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Consent for publication

Written informed consent was obtained from patients’ parents for the inclusion of any potentially identifiable images or data in this article.

Consent to Participate

Prior to their inclusion in this study, written informed consent was obtained from all human participants. The nature and purpose of the study, as well as the potential risks and benefits, were explained to each participant, ensuring their voluntary participation.

Ethical approval

The research involving human participants was reviewed and approved by the Ethics Committee of The Third Affiliated Hospital of Harbin Medical University.

Supplementary Information

No.

Author Contribution

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by JunJie Dang. The first draft of the manuscript was written by JunJie Dang and all authors commented on previous versions of the manuscript. The project was guided by LiHua Shang. All authors read and approved the final manuscript.

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No competing interests reported.

Construction of a Prognostic Model for Extensive-Stage Small Cell Lung Cancer Patients Undergoing Immune Therapy in Real-World Settings and Prediction of Treatment Efficacy Based on Response Status at Different Time Points

Status:

Version 1

Abstract

Figures

1 Introduction

2 Materials and methods