Abstract
Background
Patients with non‐small‐cell lung cancer (NSCLC) receiving immunotherapy face a potential risk of developing checkpoint inhibitor‐related pneumonitis (CIP). However, there is no clear understanding of the specific link between interstitial lung abnormality (ILA) and CIP in patients with small‐cell lung cancer (SCLC). In addition, the prognosis of SCLC patients with ILA who receive chemoimmunotherapy is uncertain. Our study aimed to investigate the effect of ILA on the occurrence of CIP in SCLC patients receiving first‐line chemoimmunotherapy and to assess its relationship with prognosis.
Methods
We conducted a retrospective analysis of SCLC patients who received chemoimmunotherapy as a first‐line treatment between January 2018 and April 2024. The diagnosis of ILA was assessed by two experienced pulmonologists based on pretreatment chest computed tomography images. We investigated independent risk factors for CIP using logistic regression analysis and factors affecting PFS and OS using Cox regression analysis.
Results
A total of 128 patients with SCLC were included in the study. ILA was present in 41 patients (32.03%), and CIP occurred in 16 patients (12.50%). In multivariate logistic regression analysis, previous ILA (OR, 5.419; 95% CI, 1.574–18.652; p = 0.007) and thoracic radiation therapy (TRT) (OR, 5.259; 95% CI, 1.506–18.365; p = 0.009) were independent risk factors for CIP. ILA (HR, 2.083; 95% CI, 1.179–3.681; p = 0.012) and LDH (HR, 1.002; 95% CI, 1.001–1.002; p < 0.001) were statistically significant for increased mortality risk in multivariate Cox regression analysis.
Conclusions
In SCLC patients receiving first‐line chemoimmunotherapy, baseline ILA is a risk factor for CIP and is associated with poorer prognosis.
Keywords: checkpoint inhibitor‐related pneumonitis, chemoimmunotherapy, interstitial lung abnormality, small‐cell lung cancer, survival
To investigate the significance of pre‐existing ILA in patients with SCLC, we conducted a retrospective cohort study. Among SCLC patients receiving first‐line chemoimmunotherapy, patients with pre‐existing ILA were more likely to develop CIP and were associated with shorter PFS and OS.
INTRODUCTION
Small‐cell lung cancer (SCLC) makes up an estimated 15% of all lung cancers and is known to have a very high proliferation rate, a strong tendency to metastasize at an early stage and a poor prognosis. 1 , 2 To date, the standard treatment regimen for limited‐stage small‐cell lung cancer (LS‐SCLC) has not changed significantly in more than 20 years and mainly consists of a combination of chemotherapy and thoracic radiation therapy (TRT). 3 In extensive‐stage small‐cell lung cancer (ES‐SCLC), advances in immunotherapy have led to PD‐1/PD‐L1 inhibitors in combination with chemotherapy becoming the guideline‐recommended first‐line treatment. 4 Meanwhile, immunotherapy has expanded its use in SCLC, and prospective clinical trials have demonstrated the pivotal role of immunotherapy in patients with LS‐SCLC. 5 , 6 Immune checkpoint inhibitor‐related pneumonitis (CIP) is one of the common toxic side effects of immunotherapy and the most frequent reason for death with PD‐1/PD‐L1 inhibitors, reportedly accounting for approximately 35% of all deaths. 7 Clinical studies have shown that CIP is present in up to 19% of cases of non‐small‐cell lung cancer (NSCLC). 8 , 9 , 10 , 11 However, clinical trials of immunotherapy in lung cancer patients have largely excluded individuals diagnosed with interstitial lung disease (ILD). 12 As a result, limited clinical data are available on the occurrence of CIP following immunotherapy in lung cancer patients in combination with ILD.
According to the Fleischner guideline, interstitial lung abnormality (ILA) is defined as nondependent abnormalities involving more than 5% of any lung area on chest computed tomography (CT). ILA is defined purely radiologically as an incidental finding on chest CT in people with undiagnosed or unsuspected ILD. 13 The incidence of ILA ranges from 2% to 9% among the public at large 14 , 15 and is higher in lung cancer screening populations. 16 , 17 In lung cancer patients, ILA is also significantly associated with an increased risk of death. 17 , 18 Lung cancer patients with ILA are more likely to develop CIP after immunotherapy, according to previous studies. 19 , 20 Available studies suggest that certain histological types of lung cancer, previous lung disease, previous chest radiotherapy, and combinations with other drugs are risk factors for developing CIP. 8 , 21 , 22 Most of these studies were based on NSCLC patients, and the relationship between baseline chest radiological abnormalities and CIP in SCLC patients treated with immunotherapy is not well understood.
We investigated SCLC patients who received first‐line chemoimmunotherapy in this single‐center retrospective study. We analyzed the association of baseline ILA with the occurrence of CIP and progression‐free survival (PFS) and overall survival (OS) in SCLC patients. In addition, we also looked at the effect of the results of pulmonary function tests (PFTs), laboratory indexes, and specific imaging features on chest CT on CIP and survival.
METHODS
Study design and patient selection
We included patients with SCLC who received first‐line chemoimmunotherapy at Jinling Hospital between January 2018 and April 2024. All SCLC patients received a first‐line chemoimmunotherapy regimen consisting of etoposide plus platinum (cisplatin/carboplatin/lobaplatin/nedaplatin) in combination with an immunotherapy (serplulimab/durvalumab/tislelizumab/atezolizumab/toripalimab/adebrelimab/camrelizumab/sintilimab/penpulimab/pembrolizumab). The inclusion criteria were as follows: (1) All SCLC patients were diagnosed by cytology or histology; (2) Eastern Cooperative Oncology Group performance status (ECOG‐PS) < 2; (3) chest CT scans can be used for evaluation before immunotherapy. The exclusion criteria were as follows: (1) comorbidities with tumors elsewhere or with diseases that have a severe impact on survival; (2) patients with previous lobectomy; (3) patients with autoimmune diseases. The patient flowchart is shown in Figure S1.
Our study adhered to the tenets of the Declaration of Helsinki (as revised in 2013). It was approved by the Ethics Committee of Jinling Hospital (Registration ID: 2024DZKY‐051‐01). As this is a retrospective and observational study, we did not obtain informed consent from the selected patients. Confidentiality of patient information was maintained.
Data collection and evaluation
We collected patient information before immunotherapy, including age, sex, smoking history, ECOG‐PS, history of chronic obstructive pulmonary disease (COPD), staging of cancer, history of TRT, tumor markers, C‐reactive protein (CRP), procalcitonin (PCT), interleukin‐6 (IL‐6), fibrinogen (FIB), lactate dehydrogenase (LDH), serum albumin (ALB), neutrophil (NE), lymphocyte (Lym), monocyte (Mono), eosinophil (Eos), neutrophil/lymphocyte ratio (NLR), monocyte/lymphocyte ratio (MLR), and autoantibody. Autoantibody tests mainly include antinuclear antibodies (ANA), antineutrophil cytoplasmic antibodies (ANCA), autoimmune liver disease antibodies, antiphospholipid syndrome antibodies, and rheumatoid arthritis antibodies. A summary of positive autoantibodies in patient serum is provided in Table S1.
Before bronchodilator inhalation, we recorded baseline PFTs closest to the start of immunotherapy including inspiratory vital capacity (VC IN), expiratory vital capacity (VC EX), maximal lung capacity (VC MAX), forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), FEV1/FVC ratio, maximal expiratory flow (MEF), peak expiratory flow (PEF), maximal mid‐expiratory flow (MMEF), and maximal voluntary ventilation (MVV). We documented all values as ratios or predicted percentages adjusted for the patient's height, weight, sex, and age.
Evaluation of baseline ILA
We evaluated ILA prior to chemoimmunotherapy with a high‐resolution CT scan of the chest, 14 and our chest CT allowed us to visualize abnormal images up to one mm in diameter. ILA refers to nondependent abnormalities involving more than 5% of any lung area, including ground‐glass, reticular abnormalities, honeycombing, traction bronchiectasis, honeycomb, and non‐emphysematous cysts. 13 We also excluded patients with preclinical interstitial abnormalities identified during screening for high‐risk individuals (e.g., autoimmune diseases such as rheumatoid arthritis and scleroderma), as defined by the Fleischner Society guidelines. ILA can be divided into three subtypes: non‐subpleural, subpleural nonfibrotic, and subpleural fibrotic. Fibrosis is characterized by the presence of architectural distortion with traction bronchiectasis or honeycombing. 14 , 23 , 24 Two experienced respiratory physicians evaluated the chest CT images of all patients in turn. In case of disagreement between the two respiratory physicians, the decision was made by consensus between the two physicians. Cohen's kappa was used to assess the agreement between the two respiratory physicians.
Definition and grading of CIP
The definition and severity grading of CIP are based primarily on NCCN and ASCO guidelines, among others. 25 , 26 CIP is classified into four grades: Grade 1: asymptomatic, limited to a single lobe or <25% of the lung parenchyma; Grade 2: new/worsening symptoms involving multiple lobes and up to 25%–50% of the lung parenchyma; Grade 3: severe new symptoms involving all lobes or >50% of the lung parenchyma; and Grade 4: life‐threatening dyspnea, ARDS, and so forth. We attributed the development of pneumonia to immunotherapy, and there were no other apparent risk factors or etiologies, including infection. Two respiratory physicians documented cases of CIP that occurred during treatment, and the agreement between the two respiratory physicians was assessed retrospectively.
Statistical analysis
Continuous variables are expressed as mean ± standard deviation for those that conform to normal distribution and median (interquartile) for those that do not. Categorical variables are expressed as frequency (percentage). We used the t‐test for normal distribution and the Wilcoxon test for non‐normal distribution for continuous variables. For categorical variables, the Pearson chi‐square test was used to compare differences between the two groups.
We identified risk factors for CIP by means of univariate and multivariate logistic regression analysis. Based on the available studies on ILA and CIP, 27 we selected age, sex, smoking history, 28 ECOG‐PS, COPD, 20 cancer stage, ICI type, 29 TRT history, 30 tumor markers, CRP, PCT, IL‐6, FIB, LDH, ALB, NE, Lym, Mono, Eos, NLR, MLR, and autoantibodies for univariate analysis. In addition, PD‐L1 status has been reported as a risk factor for CIP29. However, the vast majority of SCLC patients in our study did not undergo testing. We included variables with p < 0.05 in the results of the univariate analysis in the multivariate analysis. COPD and ICI types were also included in the multivariate analysis.
PFS was calculated from the start of first‐line immunotherapy to the date of disease progression or death from any cause. OS was defined as the time from the beginning of first‐line immunotherapy to death. The end date of the survival analysis was defined as August 2, 2024. The significance of ILA and specific imaging features of ILA on chest CT in predicting PFS and OS was assessed using univariate and multivariate Cox proportional hazards models. The Kaplan–Meier (K–M) method was used to estimate PFS and OS.
SPSS version 27.0.1 and GraphPad Prism 10 were used for all statistical analysis. PASS 15.0 was utilized to perform efficacy calculations on sample contents. p‐value <0.05 was considered to be statistically significant.
RESULTS
Patient characteristics
The baseline characteristics of all patients are shown in Table 1. The study enrolled 128 patients with SCLC, including 106 patients (82.81%) with ES‐SCLC and 22 patients (17.19%) with LS‐SCLC. 41 (32.03%) of all patients developed ILA. Of all patients, 81 (63.28%) were aged ≥65 years, 110 (85.94%) were male, and 89 (69.53%) had a history of smoking. There were 75 patients (58.59%) with an ECOG‐PS score of 0 and 53 patients (41.41%) with a score of 1. A total of 43 patients (33.59%) had a history of COPD, and 36 (28.13%) were receiving TRT during their treatment. A total of 78 patients (60.94%) were treated with chemotherapy in combination with a PD‐1 inhibitor, and 50 patients (39.06%) received chemotherapy in combination with a PD‐L1 inhibitor. On univariate analysis, there were no statistically significant differences in the baseline characteristics of the SCLC patients between the ILA group and the non‐ILA group. ILA was based on specific imaging features on baseline chest CT. Representative chest CT images of ILA are shown in Figure 1a–f.
TABLE 1.
Baseline demographics and univariate analysis according to ILA status.
| Variables | Total | Group 1 (without ILA) | Group 2 (with ILA) | p value |
|---|---|---|---|---|
| Patient (n) | 128 | 87 | 41 | — |
| Age (years), n (%) | 0.230 | |||
| <65 | 47 (36.72) | 35 (40.23) | 12 (29.27) | |
| ≥65 | 81 (63.28) | 52 (59.77) | 29 (70.73) | |
| Sex, n (%) | 0.677 | |||
| Male | 110 (85.94) | 74 (85.06) | 36 (87.80) | |
| Female | 18 (14.06) | 13 (14.94) | 5 (12.20) | |
| Smoking history, n (%) | 0.149 | |||
| Never | 39 (30.47) | 23 (26.44) | 16 (39.02) | |
| Former/current | 89 (69.53) | 64 (73.56) | 25 (60.98) | |
| ECOG‐PS, n (%) | 0.122 | |||
| 0 | 75 (58.59) | 55 (63.22) | 20 (48.78) | |
| 1 | 53 (41.41) | 32 (36.78) | 11 (26.83) | |
| Comorbidities, n (%) | ||||
| COPD | 43 (33.59) | 28 (32.18) | 15 (36.59) | 0.623 |
| Staging of cancer, n (%) | 0.304 | |||
| LS‐SCLC | 22 (17.19) | 17 (19.54) | 5 (12.20) | |
| ES‐SCLC | 106 (82.81) | 70 (80.46) | 36 (87.80) | |
| ICI type | 0.693 | |||
| PD‐1 | 78 (60.94) | 52 (59.77) | 26 (63.41) | |
| PD‐L1 | 50 (39.06) | 35 (40.23) | 15 (36.59) | |
| Thoracic radiation therapy, n (%) | 36 (28.13) | 26 (29.89) | 10 (24.39) | 0.519 |
| Laboratory examination | ||||
| Cyfra21‐1 (ng/mL), median (interquartile) | 3.93 (2.48, 4.89) | 4.01 (2.48, 5.65) | 3.73 (2.42, 4.51) | 0.952 |
| NSE (μg/L), median (interquartile) | 37.51 (18.30, 101.75) | 37.19 (15.40, 100.88) | 53.60 (25.20, 173.40) | 0.150 |
| CEA (μg/L), median (interquartile) | 2.84 (1.70, 6.06) | 3.04 (1.67, 7.49) | 2.37 (1.66, 4.73) | 0.588 |
| CA125 (μ/mL), median (interquartile) | 32.19 (15.40, 140.20) | 30.50 (19.68, 96.30) | 104 (11.50, 358.40) | 0.295 |
| CRP (mg/L), median (interquartile) | 4.00 (0.60, 31.75) | 3.20 (0.50, 24.63) | 14 (1.40, 42.90) | 0.182 |
| PCT (μg/L), median (interquartile) | 0.06 (0.05, 0.15) | 0.07 (0.05, 0.14) | 0.05 (0.04, 0.26) | 0.682 |
| FIB (g/L), median (interquartile) | 4.15 (3.31, 5.03) | 3.95 (3.07, 5.02) | 4.54 (4.14, 6.24) | 0.436 |
| IL‐6 (ng/L), median (interquartile) | 12.65 (6.32, 22.23) | 11.66 (5.73, 19.81) | 18.08 (10.77, 29.95) | 0.059 |
| LDH (U/L), median (interquartile) | 235 (180.50, 328) | 218 (177.50, 305.50) | 259 (228, 407) | 0.089 |
| ALB (g/L), mean ± SD | 37.50 ± 4.28 | 37.75 ± 4.28 | 36.56 ± 4.38 | 0.647 |
| NE (×109/L), median (interquartile) | 4.04 (3.45, 5.74) | 3.80 (3.31, 5.64) | 5.16 (4.03, 7.97) | 0.255 |
| Lym (×109/L), mean ± SD | 1.60 ± 0.68 | 1.56 ± 0.64 | 1.77 ± 0.81 | 0.916 |
| Mono (×109/L), mean ± SD | 0.50 ± 0.19 | 0.49 ± 0.19 | 0.53 ± 0.20 | 0.821 |
| Eos (×109/L), median (interquartile) | 0.09 (0.06, 0.17) | 0.09 (0.04, 0.17) | 0.11 (0.08, 0.17) | 0.092 |
| NLR, median (interquartile) | 2.77 (1.82, 3.92) | 2.70 (1.78, 3.71) | 3.83 (2.32, 5.88) | 0.349 |
| MLR, median (interquartile) | 0.30 (0.24, 0.44) | 0.31 (0.24, 0.43) | 0.26 (0.22, 0.57) | 0.810 |
| Autoantibody detection, n (%) | 0.808 | |||
| Positive | 36 (28.13) | 23 (26.44) | 13 (31.71) | – |
| Negative | 21 (16.41) | 15 (17.24) | 6 (14.63) | – |
| Not tested | 71 (55.47) | 49 (56.32) | 22 (53.66) | – |
Abbreviations: ALB, serum albumin; CA125, carbohydrate antigen 125; CEA, carcinoembryonic antigen; COPD, chronic obstructive pulmonary disease; CRP, C‐reactive protein; ECOG‐PS, Eastern Cooperative Oncology Group Performance Status; Eos, eosinophil; ES‐SCLC, extensive‐stage small‐cell lung cancer; FIB, fibrinogen; ICI, immune checkpoint inhibitor; IL‐6, interleukin‐6; ILA, interstitial lung abnormality; LDH, lactate dehydrogenase; LS‐SCLC, limited‐stage small‐cell lung cancer; Lym, lymphocyte; MLR, monocyte/lymphocyte ratio; Mono, monocyte; NE, neutrophil; NLR, neutrophil/lymphocyte ratio; NSE, neuron‐specific enolase; PCT, procalcitonin; SD, standard deviation.
FIGURE 1.
Representative computed tomography images of interstitial lung abnormality. (a) Non‐subpleural ground glass attenuation. (b) Subpleural ground glass attenuation. (c) Subpleural reticular shadow. (d) Honeycombing. (e) Traction bronchiectasis. (f) Nonemphysematous cysts. (g) A 70‐year‐old man was diagnosed with extensive‐stage small‐cell lung cancer. As shown in the left figure, bilateral subpleural ground glass attenuation was present on chest computed tomography before immunotherapy. After three cycles of chemoimmunotherapy, the patient developed immune checkpoint inhibitor‐related pneumonitis, considered grade 4. Gridded shadows appeared on chest computed tomography, as shown in the right figure, presenting extensive interstitial pneumonia in both lungs.
Incidence of CIP and risk factors for it
The median length of follow‐up in the study was 690 days (569–811). A total of 16 (12.50%) SCLC patients developed CIP, of which three (18.75%) were Grade 1, six (37.50%) were Grade 2, four (25.00%) were Grade 3, and three (18.75%) were Grade 4 (Figure 2a). Figure 1g shows the representative CT image of the chest where CIP occurred. SCLC patients combined with ILA at baseline were more likely to develop CIP, and the cumulative incidence of CIP is shown in Figure 2b (HR, 12.17; 95% CI, 3.616–40.950; p < 0.001). The cumulative incidence of CIP was 6.90% in patients without ILA and 24.39% in patients with ILA.
FIGURE 2.
(a) Grading of immune checkpoint inhibitor‐related pneumonitis (CIP). (b) The cumulative incidence of CIP according to the presence or absence of combined interstitial lung abnormality (ILA). HR, hazard ratio.
Baseline data and univariate logistic regression analysis by CIP status are shown in Table 2. Among the patients who did not develop CIP, 31 (27.68%) combined with ILA. Of the patients who developed CIP, 10 (62.50%) were combined with ILA. We found that baseline ILA (OR, 4.355; 95% CI, 1.459–12.998; p = 0.008) and TRT (OR, 4.048; 95% CI, 1.377–11.901; p = 0.011) were statistically significantly correlated with the development of CIP. Specific image features and univariate analysis on chest CT by CIP status are summarized in Table 3. Of the patients with ILA, 16 (12.50%) were non‐subpleural, 21 (16.41%) were subpleural nonfibrotic, and four (3.13%) were subpleural fibrotic. 41 patients (32.03%) had ground glass attenuation on chest CT, 15 patients (11.72%) had reticular shadows, five patients (3.91%) had honeycombing, two patients (1.56%) had traction bronchiectasis, and three patients (2.34%) had nonemphysematous cysts. We found that subpleural nonfibrotic subtype (p = 0.015) and ground glass attenuation (p = 0.005) increased the risk of CIP. Table 4 presents that baseline ILA (OR, 5.419; 95% CI, 1.574–18.652; p = 0.007) and TRT (OR, 5.259; 95% CI, 1.506–18.365; p = 0.009) remained independent risk factors for CIP in multivariate logistic regression analysis. We found no association between the presence of ILA and CIP severity in SCLC patients in Table S2.
TABLE 2.
Baseline data and univariate logistic regression analysis by CIP status.
| Variables | Total | Group 1 (without CIP) | Group 2 (with CIP) | OR (95% CI) | p value |
|---|---|---|---|---|---|
| Patient (n) | 128 | 112 | 16 | — | — |
| Age (years), n (%) | |||||
| <65 | 47 (36.72) | 41 (36.61) | 6 (37.50) | — | — |
| ≥65 | 81 (63.28) | 71 (63.39) | 10 (62.50) | 0.962 (0.326, 2.842) | 0.945 |
| Sex, n (%) | |||||
| Male | 110 (85.94) | 98 (87.50) | 12 (75.00) | 0.429 (0.121, 1.515) | 0.188 |
| Female | 18 (14.06) | 14 (12.50) | 4 (25.00) | — | — |
| Smoking history, n (%) | |||||
| Never | 39 (30.47) | 30 (26.79) | 9 (56.25) | — | — |
| Former/current | 89 (69.53) | 82 (73.21) | 7 (43.75) | 0.285 (0.097, 0.832) | 0.022 |
| ECOG‐PS, n (%) | |||||
| 0 | 75 (58.59) | 66 (58.93) | 9 (56.25) | — | — |
| 1 | 53 (41.41) | 46 (41.07) | 7 (43.75) | 1.116 (0.388, 3.211) | 0.839 |
| Comorbidities, n (%) | |||||
| COPD | 43 (33.59) | 40 (35.71) | 3 (18.75) | 0.415 (0.112, 1.545) | 0.190 |
| Staging of cancer, n (%) | |||||
| LS‐SCLC | 22 (17.19) | 18 (16.07) | 4 (25.00) | — | — |
| ES‐SCLC | 106 (82.81) | 94 (83.93) | 12 (75.00) | 0.574 (0.166, 1.983) | 0.381 |
| ICI type | |||||
| PD‐1 | 78 (60.94) | 66 (58.93) | 12 (75.00) | — | — |
| PD‐L1 | 50 (39.06) | 46 (41.07) | 4 (25.00) | 0.478 (0.145, 1.576) | 0.225 |
| Thoracic radiation therapy, n (%) | 36 (28.13) | 27 (24.11) | 9 (56.25) | 4.048 (1.377, 11.901) | 0.011 |
| ILA, n (%) | 41 (32.03) | 31 (27.68) | 10 (62.50) | 4.355 (1.459, 12.998) | 0.008 |
| Laboratory examination | |||||
| Cyfra21‐1 (ng/mL), median (interquartile) | 3.93 (2.48, 4.89) | 3.93 (2.28, 4.68) | 4.30 (3.40, 6.20) | 0.996 (0.905, 1.095) | 0.927 |
| NSE (μg/L), median (interquartile) | 37.51 (18.30, 101.75) | 37.51 (18.30, 101.75) | 39.07 (20.99, 146.20) | 1.002 (0.996, 1.007) | 0.511 |
| CEA (μg/L), median (interquartile) | 2.84 (1.70, 6.06) | 3.01 (1.68, 6.57) | 2.54 (1.68, 4.48) | 0.990 (0.954, 1.028) | 0.600 |
| CA125 (μ/mL), median (interquartile) | 32.19 (15.40, 140.20) | 30.80 (15.40, 150.85) | 45.70 (14.75, 132.68) | 0.999 (0.995, 1.003) | 0.660 |
| CRP (mg/L), median (interquartile) | 4.00 (0.60, 31.75) | 3.20 (0.50, 31.75) | 8.80 (0.88, 63.00) | 1.002 (0.987, 1.018) | 0.772 |
| PCT (μg/L), median (interquartile) | 0.06 (0.05, 0.15) | 0.07 (0.05, 0.18) | 0.06 (0.04, 0.07) | 0.058 (0.000, 35.162) | 0.384 |
| FIB (g/L), median (interquartile) | 4.15 (3.31, 5.03) | 4.15 (3.16, 5.09) | 4.17 (3.54, 4.91) | 0.798 (0.513, 1.242) | 0.318 |
| IL‐6 (ng/L), median (interquartile) | 12.65 (6.32, 22.23) | 14.75 (6.32, 24.49) | 10.76 (6.68, 13.11) | 1.013 (0.982, 1.046) | 0.420 |
| LDH (U/L), median (interquartile) | 235 (180.50, 328) | 239 (177, 346.50) | 223.50 (202.75, 246) | 0.999 (0.997, 1.001) | 0.496 |
| ALB (g/L), mean ± SD | 37.50 ± 4.28 | 37.63 ± 4.20 | 38.70 ± 3.12 | 1.068 (0.936, 1.218) | 0.330 |
| NE (×109/L), median (interquartile) | 4.04 (3.45, 5.74) | 4.03 (3.41, 5.65) | 6.53 (3.67, 11.76) | 1.188 (0.963, 1.465) | 0.107 |
| Lym (×109/L), mean ± SD | 1.60 ± 0.68 | 1.57 ± 0.59 | 1.38 ± 0.71 | 0.558 (0.211, 1.472) | 0.239 |
| Mono (×109/L), mean ± SD | 0.50 ± 0.19 | 0.47 ± 0.18 | 0.50 ± 0.24 | 2.167 (0.140, 33.491) | 0.580 |
| Eos (×109/L), median (interquartile) | 0.09 (0.06, 0.17) | 0.11 (0.07, 0.18) | 0.05 (0.01, 0.08) | 0.036 (0.000, 10.795) | 0.254 |
| NLR, median (interquartile) | 2.77 (1.82, 3.92) | 2.61 (1.77, 3.76) | 6.80 (2.49, 11.03) | 1.118 (0.970, 1.289) | 0.124 |
| MLR median (interquartile) | 0.30 (0.24, 0.44) | 0.30 (0.24, 0.40) | 0.50 (0.21, 0.77) | 8.008 (0.833, 77.014) | 0.072 |
| Autoantibody detection, n (%) | |||||
| Positive | 36 (28.13) | 32 (28.57) | 4 (25.00) | 0.682 (0.201, 2.314) | 0.539 |
| Negative | 21 (16.41) | 20 (17.86) | 1 (6.25) | 0.273 (0.033, 2.247) | 0.227 |
| Not tested | 71 (55.47) | 60 (53.57) | 11 (68.75) | — | — |
Abbreviations: ALB, serum albumin; CA125, carbohydrate antigen 125; CEA, carcinoembryonic antigen; CI, confidence interval; CIP, immune checkpoint inhibitor‐related pneumonitis; COPD, chronic obstructive pulmonary disease; CRP, C‐reactive protein; ECOG‐PS, Eastern Cooperative Oncology Group Performance Status; Eos, eosinophil; ES‐SCLC, extensive‐stage small‐cell lung cancer; FIB, fibrinogen; ICI, immune checkpoint inhibitor; IL‐6, interleukin‐6; ILA, interstitial lung abnormality; LDH, lactate dehydrogenase; LS‐SCLC, limited‐stage small‐cell lung cancer; Lym, lymphocyte; MLR, monocyte/lymphocyte ratio; Mono, monocytes; NE, neutrophil; NSE, neuron‐specific enolase; NLR, neutrophil/lymphocyte ratio; OR, odds ratio; PCT, procalcitonin; SD, standard deviation.
TABLE 3.
Specific image features and univariate analysis on chest CT by CIP status.
| Variables | Total | Group 1 (without CIP) | Group 2 (with CIP) | p value |
|---|---|---|---|---|
| Patient (n) | 128 | 112 | 16 | — |
| Subcategories of ILA, n (%) | ||||
| Non‐subpleural | 16 (12.50) | 13 (11.61) | 3 (18.75) | 0.419 |
| Subpleural nonfibrotic | 21 (16.41) | 15 (13.39) | 6 (37.50) | 0.015 |
| Subpleural fibrotic | 4 (3.13) | 3 (2.68) | 1 (6.25) | 0.442 |
| Ground glass attenuation, n (%) | 0.005 | |||
| Negative | 87 (67.97) | 81 (72.32) | 6 (37.50) | |
| Positive | 41 (32.03) | 31 (27.68) | 10 (62.50) | |
| Reticular shadow, n (%) | 0.917 | |||
| Negative | 113 (88.28) | 99 (88.39) | 14 (87.50) | |
| Positive | 15 (11.72) | 13 (11.61) | 2 (12.50) | |
| Honeycombing, n (%) | 0.605 | |||
| Negative | 123 (96.09) | 108 (96.43) | 15 (93.75) | |
| Positive | 5 (3.91) | 4 (3.57) | 1 (6.25) | |
| Traction bronchiectasis, n (%) | 0.106 | |||
| Negative | 126 (98.44) | 111 (99.11) | 15 (93.75) | |
| Positive | 2 (1.56) | 1 (0.89) | 1 (6.25) | |
| Nonemphysematous cysts, n (%) | 0.270 | |||
| Negative | 125 (97.66) | 110 (98.21) | 15 (93.75) | |
| Positive | 3 (2.34) | 2 (1.79) | 1 (6.25) | |
Abbreviations: CIP, immune checkpoint inhibitor‐related pneumonitis; CT, computed tomography; ILA, interstitial lung abnormality.
TABLE 4.
Multivariate logistic regression model assessing the association of ILA with CIP.
| Variables | Multivariate analysis | |
|---|---|---|
| OR (95% CI) | p value | |
| Thoracic radiation therapy | 5.259 (1.506, 18.365) | 0.009 |
| ILA | 5.419 (1.574, 18.652) | 0.007 |
| Former/current smoking | 0.419 (0.127, 1.388) | 0.155 |
| COPD | 0.334 (0.075, 1.485) | 0.150 |
| ICI type (PD‐L1 vs. PD‐1) | 0.432 (0.113, 1.654) | 0.220 |
Abbreviations: CI, confidence interval; CIP, immune checkpoint inhibitor‐related pneumonitis; COPD, chronic obstructive pulmonary disease; ICI, immune checkpoint inhibitor; ILA, interstitial lung abnormality; OR, odds ratio.
In our study, 77 patients had pulmonary function test results before immunotherapy. No statistically significant association was found between pulmonary function test results and CIP (Table S3).
Survival analysis
SCLC patients with ILA had a median PFS of 188 days compared to 290 days in patients without ILA (HR, 2.136; 95% CI, 1.273–3.582; p = 0.004) (Figure 3a). ILA was also associated with a shorter OS, with a median OS of 340 days in SCLC patients with ILA in comparison to a median OS of 667 days in patients without ILA (HR, 2.590; 95% CI, 1.452–4.619; p = 0.001) (Figure 3b).
FIGURE 3.
(a) Kaplan–Meier curves describing progression‐free survival (PFS) in small cell lung cancer (SCLC) patients after chemoimmunotherapy by interstitial lung abnormality (ILA) status. (b) Kaplan–Meier curves describing overall survival (OS) in SCLC patients after chemoimmunotherapy by ILA status. HR, hazard ratio.
Risk factors associated with PFS and OS in the Cox proportional hazards model are shown in Table 5. In the univariate Cox regression analysis, a shorter PFS was connected with ECOG‐PS (=1) (p = 0.004), ILA (p = 0.005), NSE (p < 0.001), CA125 (p = 0.026), and LDH (p < 0.001). For factors that were significant in the univariate analysis, multivariate analysis was performed. ILA (HR, 1.976; 95% CI, 1.173–3.329; p = 0.010) and LDH (HR, 1.002; 95% CI, 1.001–1.003; p = 0.003) remained independently associated with PFS (Table 5(A)). Age (≥65) (p = 0.036), Male (p = 0.047), ECOG‐PS (=1) (p = 0.024), ES‐SCLC (p = 0.006), ILA (p = 0.002), Cyfra21‐1 (p = 0.032), NSE (p < 0.001), and LDH (p < 0.001) were linked to a shorter OS in the univariate Cox regression analysis. ILA (HR, 2.083; 95% CI, 1.179–3.681; p = 0.012) and LDH (HR, 1.002; 95% CI, 1.001–1.002; p < 0.001) were also strong independent predictors of OS after multivariate analysis (Table 5A). On chest CT, ground glass attenuation (p = 0.005) (Table 5B) and the non‐subpleural subtype of ILA (p = 0.049) (Table 5C) were found to be related to PFS. We also found that ground glass attenuation (p = 0.002), reticular shadow (p = 0.047) (Table 5B), and the non‐subpleural subtype of ILA (p < 0.001) (Table 5C) could predict worse OS.
TABLE 5.
Risk factors associated with PFS and OS in the Cox proportional hazards model.
| Progression‐free survival | Overall survival | |||||||
|---|---|---|---|---|---|---|---|---|
| Univariate analysis | Multivariate analysis | Univariate analysis | Multivariate analysis | |||||
| Variables | HR (95% CI) | p | HR (95% CI) | p | HR (95% CI) | p | HR (95% CI) | p |
| (A) | ||||||||
| Age (≥65) | 1.235 (0.799, 1.908) | 0.342 | 1.713 (1.037, 2.831) | 0.036 | 1.743 (0.993, 3.059) | 0.053 | ||
| Male | 1.524 (0.810, 2.868) | 0.191 | 2.119 (1.010, 4.446) | 0.047 | 1.853 (0.832, 4.126) | 0.131 | ||
| Former/current smoking | 1.331 (0.843, 2.101) | 0.220 | 1.594 (0.941, 2.700) | 0.083 | ||||
| ECOG‐PS (=1) | 1.830 (1.208, 2.773) | 0.004 | 1.438 (0.864, 2.393) | 0.162 | 1.713 (1.072, 2.737) | 0.024 | 1.165 (0.692, 1.961) | 0.566 |
| COPD | 1.165 (0.751, 1.809) | 0.495 | 1.199 (0.727, 1.977) | 0.476 | ||||
| ES‐SCLC | 1.620 (0.897, 2.925) | 0.110 | 3.210 (1.386, 7.434) | 0.006 | 2.400 (0.996, 5.784) | 0.051 | ||
| Thoracic radiation therapy | 1.116 (0.709, 1.755) | 0.636 | 0.646 (0.381, 1.095) | 0.105 | ||||
| ILA | 1.924 (1.222, 3.031) | 0.005 | 1.976 (1.173, 3.329) | 0.010 | 2.217 (1.349, 3.643) | 0.002 | 2.083 (1.179, 3.681) | 0.012 |
| Cyfra21‐1 | 1.030 (0.999, 1.062) | 0.062 | 1.037 (1.003, 1.072) | 0.032 | 1.024 (0.987, 1.063) | 0.199 | ||
| NSE | 1.003 (1.001, 1.005) | <0.001 | 1.000 (0.997, 1.003) | 0.818 | 1.005 (1.003, 1.007) | <0.001 | 0.998 (0.995, 1.002) | 0.341 |
| CA125 | 1.001 (1.000, 1.003) | 0.026 | 1.001 (0.999, 1.002) | 0.299 | 1.001 (1.000, 1.003) | 0.098 | ||
| CRP | 0.995 (0.987, 1.002) | 0.184 | 0.998 (0.990, 1.005) | 0.556 | ||||
| PCT | 1.399 (0.814, 2.404) | 0.224 | 1.199 (0.711, 2.022) | 0.496 | ||||
| FIB | 0.997 (0.856, 1.163) | 0.972 | 1.071 (0.906, 1.266) | 0.419 | ||||
| IL‐6 | 1.006 (0.993, 1.020) | 0.378 | 1.012 (0.999, 1.025) | 0.079 | ||||
| LDH | 1.002 (1.001, 1.002) | <0.001 | 1.002 (1.001, 1.003) | 0.003 | 1.002 (1.001, 1.002) | <0.001 | 1.002 (1.001, 1.002) | <0.001 |
| ALB | 1.041 (0.992, 1.093) | 0.102 | 1.001 (0.952, 1.053) | 0.964 | ||||
| NE | 0.954 (0.862, 1.057) | 0.367 | 0.965 (0.861, 1.082) | 0.547 | ||||
| Lym | 1.097 (0.787, 1.529) | 0.585 | 1.215 (0.854, 1.728) | 0.279 | ||||
| Mono | 0.796 (0.255, 2.481) | 0.694 | 0.659 (0.181, 2.401) | 0.527 | ||||
| Eos | 1.813 (0.377, 8.721) | 0.458 | 1.921 (0.349, 10.573) | 0.453 | ||||
| NLR | 0.943 (0.863, 1.029) | 0.188 | 0.913 (0.813, 1.026) | 0.127 | ||||
| MLR | 0.598 (0.201, 1.784) | 0.357 | 0.292 (0.073, 1.173) | 0.083 | ||||
| Autoantibodies positive | ||||||||
| Negative | 1.170 (0.629, 2.178) | 0.620 | 1.110 (0.567, 2.176) | 0.760 | ||||
| Positive | 1.546 (0.967, 2.473) | 0.069 | 1.147 (0.677, 1.943) | 0.611 | ||||
| (B) | ||||||||
| Ground glass attenuation | 1.924 (1.222, 3.031) | 0.005 | 2.217 (1.349, 3.643) | 0.002 | ||||
| Reticular shadow | 1.597 (0.843, 3.027) | 0.151 | 1.929 (1.008, 3.692) | 0.047 | ||||
| Honeycombing | 2.546 (0.924, 7.014) | 0.071 | 2.413 (0.875, 6.652) | 0.089 | ||||
| Traction bronchiectasis | 0.792 (0.110, 5.700) | 0.817 | 1.823 (0.250, 13.284) | 0.554 | ||||
| Nonemphysematous cysts | 1.587 (0.388, 6.483) | 0.520 | 3.685 (0.885, 15.349) | 0.073 | ||||
| (C) | ||||||||
| Non‐subpleural | 1.912 (1.003, 3.642) | 0.049 | 3.236 (1.618, 6.473) | <0.001 | ||||
| Subpleural nonfibrotic | 1.511 (0.875, 2.610) | 0.139 | 1.407 (0.766, 2.582) | 0.271 | ||||
| Subpleural fibrotic | 2.010 (0.631, 6.402) | 0.238 | 1.830 (0.574, 5.841) | 0.307 | ||||
Abbreviations: ALB, serum albumin; CA125, carbohydrate antigen 125; CEA, carcinoembryonic antigen; CI, confidence interval; COPD, chronic obstructive pulmonary disease; CRP, C‐reactive protein; ECOG‐PS, Eastern Cooperative Oncology Group Performance Status; Eos, eosinophil; ES‐SCLC, extensive‐stage small‐cell lung cancer; FIB, fibrinogen; HR, hazard ratio; IL‐6, interleukin‐6; LDH, lactate dehydrogenase; ILA, interstitial lung abnormality; Lym, lymphocyte; MLR, monocyte/lymphocyte ratio; Mono, monocytes; NE, neutrophil; NLR, neutrophil/lymphocyte ratio; NSE, neuron‐specific enolase; OS, overall survival; PCT, procalcitonin; PFS, progression‐free survival.
The median OS of patients without CIP was 555 days (375–735), while the median OS of patients with CIP was 477 days (132–822), and there was no statistically significant difference between the two (with CIP vs. without CIP: HR, 0.852; p = 0.638) (Figure S2A). CIP is categorized into mild (Grades 1–2) and severe (Grades 3–4). The severity of CIP (Grades 3–4 vs. Grades 1–2) was also not significantly associated with OS (HR, 2.603; 95% CI, 0.630–10.760; p = 0.186) (Figure S2B).
DISCUSSION
Chronic tobacco inhalation and increasing age are risk factors for the presence of ILA. 23 Therefore, SCLC patients are more likely to develop ILA than the general population. Currently, there are relatively few studies on SCLC patients combined with ILA. It has been shown that SCLC patients with ILA are more likely to develop ILD under chemoimmunotherapy. 31 In patients with SCLC, baseline ILA has been reported to increase the risk of radiation pneumonitis (RP). 32 , 33 However, there have been no studies on the occurrence of CIP in SCLC patients with ILA. With immunotherapy playing an increasingly important role in SCLC patients, our study is the first to evaluate the effect of ILA on the occurrence of CIP and survival in SCLC patients. We also investigated the impact of specific imaging features on chest CT on CIP and prognosis. Our study highlights the need for closer observation and monitoring of this group of SCLC patients with pre‐existing ILA.
A total of 128 patients with SCLC were enrolled in our study, and 32.03% of these patients had ILA. The prevalence of ILA in LS‐SCLC patients treated with chemoradiotherapy has been reported to be 38%, 32 which is similar to the proportion of patients with ILA in our research. The incidence of CIP in our study was 12.50%. On univariate and multivariate analyses, baseline ILA and TRT are valid predictors of the occurrence of CIP in our research. On survival analysis, baseline ILA and high levels of LDH were associated with poorer prognosis. The reason for this conclusion may be that patients with pre‐existing ILA may trigger a more severe immune response after receiving immunotherapy. Evidence shows that the immune response is associated with progressive lung inflammation and interstitial fibrosis. 34 Immunotherapy may trigger a similar pathway leading to an increase in BAL lymphocytes, predominantly CD4+ T cells, resulting in an excessive inflammatory response leading to the development of CIP. 35 However, in our study, we did not find an association between the existence of ILA and the severity of CIP, nor did we find that the presence and severity of CIP worsened the prognosis of patients with SCLC. Previous studies have reported that the presence or absence of CIP is not associated with the prognosis of lung cancer patients combined with ILA. 31 On the other hand, severe CIP has been reported to be a prognostic factor for NSCLC patients in combination with ILA. 36 More studies are needed in the future to argue for an association between CIP and the prognosis of lung cancer patients.
TRT is an indispensable treatment for SCLC patients. Radiotherapy has been reported to be a predictor of the development of CIP in NSCLC patients with ILA. 19 , 30 Our study suggests that TRT also increases the risk of CIP in SCLC patients. The possible reason for this finding is that radiotherapy may enhance the efficacy of immunotherapy for lung cancer by inducing anti‐tumor immunity by exposing tumor antigens. 37 Therefore, TRT prior to immunotherapy may increase the amount of circulating tumor antigens, leading to increased lung inflammation after immunotherapy.
Many SCLC patients have a history of smoking, and smoking is also thought to be associated with ILA. 38 Available studies are inconclusive as to whether smoking is a risk factor for developing CIP. 28 , 39 , 40 In our study, smoking was not associated with the development of CIP after multivariate analysis. We speculate that the sample size of the study may not have been large enough and that only 69.53% of the patients were smokers, so a larger sample size was needed for the analysis.
ILA and ILD can be detected clinically on the basis of abnormal PFTs. Pulmonary function detects early physiological changes in the lungs that are not apparent on chest CT but may be clinically significant in the development of CIP. 41 Conclusions regarding the effect of pretreatment PFTs and COPD on the occurrence of CIP are inconsistent. 19 , 20 , 30 In our study, they were not found to be statistically related to the incidence of CIP. Similar to previous studies, the percentage of this population in our study was relatively small. Patients with pre‐existing ILA had even fewer pretreatment PFTs. Further prospective studies are therefore needed to obtain more PFTs to better investigate the association of PFTs and COPD with CIP.
In our study, consistent with previous studies in lung cancer patients, baseline ILA was significantly related to worse PFS and OS in SCLC. 19 , 42 We also found that higher levels of LDH may be predictive of shorter PFS and OS. It has been reported that pre‐treatment peripheral blood LDH above the upper limit of normal is associated with poor clinical prognosis in NSCLC patients. 43 , 44 , 45
We found that the subpleural nonfibrotic subtype of ILA and ground glass attenuation increased the risk of developing CIP in SCLC patients and were associated with shorter PFS and OS. We also found that the reticular shadow was associated with a poorer prognosis. Huang et al. reported that the existence of ground glass opacities on pretreatment chest CT could quantitatively predict the occurrence of CIP in advanced NSCLC patients. GGOs in multiple lung lobes were identified as a risk factor for PFS. 46 It has been suggested in earlier studies that the subtypes of subpleural fibrosis and honeycombing are connected to a worse prognosis in lung cancer patients. 17 , 47 , 48 We did not find that subpleural fibrosis and honeycombing subtypes had any association with prognosis in lung cancer patients. In our research, the subtype of subpleural fibrosis was only 3.13% of all the patients, and honeycombing was only 3.91%. More patients with ILA are urgently needed to investigate the relationship between specific imaging features on chest CT and the occurrence and prognosis of CIP.
Our study has several limitations. First of all, our study was a retrospective cohort study. Some patients were lost to follow‐up, which may have led to selection bias. Confounding factors may also affect the results of our study. In addition, as a single‐center clinical study, it is uncertain whether our study can be generalized to other centers. Third, our study included only patients with good ECOG‐PS score status, which may not be representative of the entire SCLC patient population. Fourth, the sample size of our study was limited, and despite our calculation of test efficacy, it may still lack representativeness of the SCLC population. Therefore, future prospective multicenter clinical trials with larger sample sizes are needed to validate our conclusions. Finally, the diagnosis of ILA in this study was based on visual assessment by two experienced respiratory physicians. Methods for quantitative identification and monitoring of ILA using AI techniques are available, and further research is needed in this area to compare their clinical relevance.
CONCLUSION
Previous ILA and a history of TRT are risk factors for the development of CIP in SCLC patients receiving first‐line chemoimmunotherapy. Prior ILA and high levels of LDH are associated with poorer prognosis. Clinically, prospective studies with larger sample sizes are needed to validate these factors.
AUTHOR CONTRIBUTIONS
Yu Li: Conceptualization; data curation; formal analysis; investigation; methodology; project administration; software; validation; visualization; writing—original draft; writing—review and editing. Yuxin Jiang: Conceptualization; data curation; formal analysis; investigation; methodology; project administration; writing—original draft; writing—review and editing. Luyun Pan: Data curation; formal analysis; investigation; methodology. Jun Yao: Conceptualization; funding acquisition; project administration. Shuo Liang: Data curation; formal analysis; investigation; methodology. Yanjun Du: Data curation; formal analysis; investigation; methodology. Dong Wang: Conceptualization; funding acquisition; project administration; resources; supervision; validation. Hongbing Liu: Conceptualization; funding acquisition; project administration; resources; supervision; validation. Fang Zhang: Conceptualization; funding acquisition; project administration; resources; supervision; validation. Qin Wang: Conceptualization; funding acquisition; project administration; supervision; writing—review and editing. Tangfeng Lv: Conceptualization; funding acquisition; project administration; supervision; writing—review and editing. Ping Zhan: Conceptualization; methodology; funding acquisition; writing—original draft; writing—review and editing.
FUNDING INFORMATION
This work was supported by the 16th batch “Summit of the Six Top Talents” Program of Jiangsu Province (grant number WSN‐154), China Postdoctoral Science Foundation 12th batch Special fund (postdoctoral number: 45786), Jiangsu Provincial Postdoctoral Science Foundation (grant number 2018K049A), Natural Science Foundation of Jiangsu Province (grant number BK20180139), Jiangsu Provincial Health Committee Medical projects (grant number M2022110), Yancheng City Medical Science and Technology Development Program Project (grant number YK2021089), and Jiangsu Provincial Health Commission Medical Research Surface (grant number H2023025).
CONFLICT OF INTEREST STATEMENT
The authors declare no conflicts of interest.
Supporting information
Figure S1. The flowchart of the patient. CIP, immune checkpoint inhibitor‐related pneumonitis; CT, computed tomography; ECOG‐PS, Eastern Cooperative Oncology Group Performance Status; ILA, interstitial lung abnormality; ILD, interstitial lung disease; SCLC, small‐cell lung cancer.
Figure S2. (a) Kaplan–Meier curves describing OS in SCLC patients without CIP and with CIP. (b) Kaplan–Meier curves describing OS in SCLC patients with mild CIP and severe CIP. CIP, immune checkpoint inhibitor‐related pneumonitis; HR, hazard ratio; OS, overall survival; SCLC, small‐cell lung cancer.
Table S1. Summary of the positive autoantibodies in the serum of the patient.
Table S2. Baseline demographics and univariate analysis according to the severity of CIP.
Table S3. Baseline results of PFTs and risk for CIP.
Li Y, Jiang Y, Pan L, Yao J, Liang S, Du Y, et al. First‐line chemoimmunotherapy for patients with small‐cell lung cancer and interstitial lung abnormality: CIP risk and prognostic analysis. Thorac Cancer. 2024;15(34):2437–2448. 10.1111/1759-7714.15471
Yu Li andYuxin Jiang contributed equally to this work as first authors. Qin Wang, Tangfeng Lv, and Ping Zhan contributed equally to this work as corresponding authors.
Contributor Information
Qin Wang, Email: wq.026@163.com.
Tangfeng Lv, Email: bairoushui@163.com.
Ping Zhan, Email: zhanping207@163.com.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Figure S1. The flowchart of the patient. CIP, immune checkpoint inhibitor‐related pneumonitis; CT, computed tomography; ECOG‐PS, Eastern Cooperative Oncology Group Performance Status; ILA, interstitial lung abnormality; ILD, interstitial lung disease; SCLC, small‐cell lung cancer.
Figure S2. (a) Kaplan–Meier curves describing OS in SCLC patients without CIP and with CIP. (b) Kaplan–Meier curves describing OS in SCLC patients with mild CIP and severe CIP. CIP, immune checkpoint inhibitor‐related pneumonitis; HR, hazard ratio; OS, overall survival; SCLC, small‐cell lung cancer.
Table S1. Summary of the positive autoantibodies in the serum of the patient.
Table S2. Baseline demographics and univariate analysis according to the severity of CIP.
Table S3. Baseline results of PFTs and risk for CIP.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.