A nomogram for predicting the overall survival in non-small cell lung cancer patients with liver metastasis

doi:10.21203/rs.3.rs-1949924/v1

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A nomogram for predicting the overall survival in non-small cell lung cancer patients with liver metastasis

https://doi.org/10.21203/rs.3.rs-1949924/v1

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Liver metastasis (LM) is correlated with dismal prognosis for non-small cell lung cancer (NSCLC) patients, which remains a clinical concern. Here, we developed a practical model for predicting the clinical survival of these patients by using the cases from the Surveillance, Epidemiology, and End Results (SEER) database. In total, 2367 NSCLC patients with liver metastasis were recruited to establish the nomogram. Sex, age, race, grade, T stage, bone metastasis, brain metastasis, surgical resection, and chemotherapy were identified as independent predictors. By incorporating all these variables, the nomogram showed a robust discrimination with a concordance index (C-index) of 0.700 (95% CI 0.684–0.716) for the training dataset and 0.677 (95%CI 0.653–0.701) for the validation dataset. Excellent agreements between the predicted and observed overall survival were exhibited by calibration plots. Moreover, the decision curve analysis (DCA) and the risk classification system revealed favorable clinical applicability of the nomogram. This is the first study to build the prediction model focusing on the overall survival of NSCLC patients with LM. It provides a potential tool to promote personalized treatment strategies and guide future studies.

liver metastasis

nomogram

non-small cell lung cancer

SEER

overall survival

As the first leading cause of tumor-related death worldwide, lung cancer is still a severe threat to global health(1). Of all lung cancer cases, more than 85% are the non-small cell lung cancer (NSCLC)(2). Among them, up to 57% present with metastatic lesions at diagnosis(1). As distant metastasis contributes to a large portion of NSCLC-related death, the prognosis of metastatic NSCLC patients remains dismal(1, 3). Although sites of distant metastases are equivalent in the AJCC stage system, previous study revealed that the survival of patients varies with sites of metastases(4). Compared with other metastatic lesions, liver metastasis (LM) is the most lethal site with a median survival of only 3–5 months(3–5).

Despite the enormous progress of novel therapeutic modalities including molecular targeted therapy and immunotherapy in NSCLC, the clinical outcome of patients with LM has only marginally improved. A multicenter study reported that LM was the negative predictive factor in NSCLC patients harboring epidermal growth factor receptor (EGFR) mutation and receiving tyrosine kinase inhibitors (TKIs) therapy(6). Similarly, Meng and colleagues revealed that EGFR-TKIs failed to improve the clinical outcomes in the setting of liver metastasis(7). Besides, results from multiple studies indicated a resistance to immunotherapy in NSCLC patients with LM(8–10). This may be due to macrophage-mediated T cell elimination according to a subsequent study(11). Therefore, it is necessary to predict the individualized survival of this unique group of patients. A refined predicting model may propose a tool to develop more optimized therapeutic strategies.

It is obvious that the TNM staging system does not sufficiently predict the outcomes of individuals. Previous evidence showed that some demographic factors including histology, age, and race could also serve as independent predictors of survival in NSCLC patients(12–14). However, limited studies explicitly focus on the clinical outcomes of NSCLC patients in the metastatic setting. Moreover, there is an absent ideal tool for predicting survival. Nomograms, in recent decades, have been served as a means to improve cancer prognostication(15). Based on statistical regression models, the nomograms provide more accessible results by integrating diverse determinant variables and graphically depicting them with intuitive graphs. Sporadic reports have created the nomograms for predicting the outcomes in metastatic NSCLC(16, 17), whereas the nomogram for the prediction of overall survival (OS) specifically in LM has not been established.

Herein we propose to construct an accurate and efficient nomogram for NSCLC with LM based on records extracted from the Surveillance, Epidemiology, and End Results (SEER) database. With this tool, improved clinical decision-making and patient-clinician communication can be achieved.

Patient selection and data elements

Metastatic lung cancer cases diagnosed between 2010 and 2015 were selected from the SEER database using the SEER*Stat software (v8.3.5). Overall, 144,302 records were recruited. Among them, 9462 patients met the inclusion criteria: lung cancer is the first primary tumor; cases without other malignant cancers; NSCLC confirmed by histology; liver metastasis. Subtypes of histology included in the study as follows: adenocarcinoma (ADC; ICD-O-3: 8140, 8141, 8144, 8250, 8251, 8255, 8260, 8310, 8323, 8480), squamous cell carcinoma (SCC; ICD-O-3: 8070–8075, 8083, 8084), and other NSCLC (ICD-O-3: 8012, 8013, 8030–8035). In contrast, cases ineligible for analysis were excluded: patients with an overall survival time of 0 month, and patients with incomplete information (blanks, unknown, or N/A). Ultimately, 2367 patients were included for analysis. Figure 1 summarized the detailed screening process of data collection. The study was performed in accordance with the Declaration of Helsinki (as revised in 2013). As public and anonymous data were used, according to the ethics guidelines, neither informed consent nor approval of an ethics committee was required.

In total, fifteen variables including clinical and pathological characteristics were extracted and retrospectively reviewed in this study. Specifically, clinical factors consist of sex, age, race, marital status, metastatic lesion, and treatment modalities (surgery, radiotherapy, chemotherapy). Pathological factors included primary site, histology, pathological grade, and TNM stage (based on the 7th edition of the American Joint Committee on Cancer staging). The optimal cut-off point for age was determined based on clinical consideration and prior reports. Time of overall survival (OS) was counted as the period between diagnosis and death from any cause or last follow-up.

Nomogram construction and evaluation

We randomly split all eligible patients into the training and validation cohort (7:3). Univariate and multivariate Cox regression analyses were applied in the training cohort to determine parameters for generating the nomogram. The discriminative ability of the final nomogram was quantified by the concordance index (C-index). The consistency between predicted and actual 3-, 6-, and 12-month OS was displayed in the form of calibration curves. All validations for the model were performed using 1,000-resample bootstrapping. Besides, the decision curve analysis (DCA) was carried out to assess the net benefit of the nomogram(18). Based on the sum-scores of individuals on the nomogram, we further divided cases into high-score group and low-score group with the median score as the cut-off value. To compare the OS between different risk groups, he Kaplan-Meier curves and log-rank tests were applied.

Statistical analysis

Variables with P < 0.05 in the multivariate analysis were collected as the independent prognostic factors integrated into the nomogram, which was constructed by R software with survival and rms packages. Additionally, the Kaplan-Meier survival analysis and log-rank test were used to evaluate and compare the survival time. R software (version 4.1.0) and SPSS software (version 25.0.0, IBM) were conducted for statistical analyses. All results with two-sided P < 0.05 were deemed statistically significant.

Patient demographics

In total, 2367 records of NSCLC with liver metastasis diagnosed between 2010 and 2015 were enrolled in the final analysis. Specifically, 1677 cases were included in the training set, while 690 cases were included in the validation set. Baseline demographic features of patients in three cohorts are summarized in Table 1. The patients in this study were mainly male (59.3%), and most cases were married (83.1%) and white (77.3%). The median diagnostic age was 66 years (range 18–94). Adenocarcinoma (ADC) accounted for the largest proportion (63.2%), followed by squamous cell carcinoma (SCC; 32.1%). Apart from the liver metastasis, 54.2%, 26.7%, and 36.7% of patients also presents with metastasis of bone, brain, and lung, respectively. Regarding treatments, the vast majority of patients did not receive primary tumor resection (96.6%). Meanwhile, 45.4% of patients underwent radiotherapy, and 64.1% of patients had chemotherapy.

Table 1

Baseline features of patients.
Variable	Total cohort, n (%)	Training cohort, n (%)	Validation cohort, n (%)
Gender
Male	1403 (59.3)	997 (59.5)	406 (58.8)
Female	964 (40.7)	680 (40.5)	284 (41.2)
Age
< 65	1127 (47.6)	785 (46.8)	342 (49.6)
≥ 65	1240 (52.4)	892 (53.2)	348 (50.4)
Race
White	1829 (77.3)	1288 (76.8)	541 (78.4)
Black	326 (13.8)	239 (14.3)	87 (12.6)
Others	212 (8.9)	150 (8.9)	62 (9.0)
Primary site
Upper lobe	1228 (51.9)	851 (50.7)	377 (54.6)
Middle lobe	117 (4.9)	87 (5.2)	30 (4.3)
Lower lobe	660 (27.9)	484 (28.9)	176 (25.5)
Main bronchus	115 (4.9)	85 (5.1)	30 (4.3)
Overlapping lesion	247 (10.4)	170 (10.1)	77 (11.3)
Histology
Adenocarcinoma	1497 (63.2)	1062 (63.3)	435 (63.0)
SCC	759 (32.1)	531 (31.7)	228 (33.0)
Others	111 (4.7)	84 (5.0)	27 (4.0)
Grade
Well differentiated (Grade I)	102 (4.3)	70 (4.2)	32 (4.6)
Moderately differentiated (Grade II)	638 (27.0)	463 (27.6)	175 (25.4)
Poorly differentiated (Grade III)	1570 (66.3)	1104 (65.8)	466 (67.5)
Undifferentiated (Grade IV)	57 (2.4)	40 (2.4)	17 (2.5)
T stage
T1	173 (7.3)	118 (7.0)	55 (8.0)
T2	611 (25.8)	450 (26.8)	161 (23.3)
T3	682 (28.8)	483 (28.8)	199 (28.8)
T4	901 (38.1)	626 (37.4)	275 (39.9)
N stage
N0	437 (18.5)	319 (19.0)	118 (17.1)
N1	197 (8.3)	140 (8.4)	57 (8.3)
N2	1192 (50.4)	837 (49.9)	355 (51.4)
N3	541 (22.9)	381 (22.7)	160 (23.2)
Metastasis
Bone	1284 (54.2)	919 (54.8)	365 (52.9)
Brain	631 (26.7)	440 (26.2)	191 (27.7)
Lung	868 (36.7)	593 (35.4)	275 (39.9)
Marital status
Unmarried	400 (16.9)	296 (17.7)	104 (15.1)
Married	1967 (83.1)	1381 (82.3)	586 (84.9)
Surgery
No	2287 (96.6)	1622 (96.7)	665 (96.4)
Yes	80 (3.4)	55 (3.3)	25 (3.6)
Radiotherapy
No	1292 (54.6)	917 (54.7)	375 (54.3)
Yes	1075 (45.4)	760 (45.3)	315 (45.7)
Chemotherapy
No	850 (35.9)	604 (36.0)	246 (35.7)
Yes	1517 (64.1)	1073 (64.0)	444 (64.3)

Among the total cohort, the median follow-up were 4.0 months (IQR 2–11 months) for all patients and 23 months (IQR 16–35 months) for alive cases. The median OS was 5 months (IQR 2–11 months). At the last follow-up, the death rate was 94.3%. Further, the 3-, 6-, and 12-month survival rates were 66.8%, 44.0%, and 22.6%, respectively.

Independent prognostic factors for OS

As shown in Table 2, eleven characteristics proved to be significantly associated with OS according to the univariate analysis. Subsequently, these factors were enrolled into a further multivariate analysis. Nine variables (gender, age, race, grade, T stage, bone metastasis, brain metastasis, surgery, and chemotherapy) turned out to be the independent risk predictors for OS (all P < 0.05).

Table 2

Cox regression analysis in the training cohort.
Variables	Univariate Cox regression		Multivariate Cox regression
Variables	HR (95% CI)	p-value	HR (95% CI)	p-value
Gender
Male	reference		reference
Female	0.849 (0.767–0.939)	0.001	0.899 (0.809–0.998)	0.046
Age
< 65	reference		reference
≥ 65	1.187 (1.075–1.311)	< 0.001	1.134 (1.024–1.257)	0.016
Race
White	reference		reference
Black	1.026 (0.891–1.181)	0.725	0.982 (0.850–1.134)	0.803
Others	0.723 (0.604–0.866)	< 0.001	0.803 (0.669–0.964)	0.019
Primary site
Main bronchus	reference
Upper lobe	0.847 (0.675–1.063)	0.151
Middle lobe	0.752 (0.553–1.023)	0.069
Lower lobe	0.803 (0.635–1.016)	0.068
Overlapping lesion	0.929 (0.712–1.212)	0.586
Histology
Adenocarcinoma	reference		reference
SCC	1.182 (1.061–1.316)	0.002	1.112 (0.989–1.251)	0.076
Others	1.433 (1.144–1.797)	0.002	1.251 (0.979-1.600)	0.073
Grade
Well differentiated (Grade I)	reference		reference
Moderately differentiated (Grade II)	1.411 (1.072–1.858)	0.014	1.213 (0.916–1.606)	0.178
Poorly differentiated (Grade III)	1.838 (1.408–2.399)	< 0.001	1.565 (1.191–2.055)	0.001
Undifferentiated (Grade IV)	2.582 (1.711–3.859)	< 0.001	1.889 (1.223–2.917)	0.004
T stage
T1	reference		reference
T2	1.302 (1.055–1.606)	0.014	1.172 (0.945–1.454)	0.149
T3	1.270 (1.031–1.565)	0.025	1.138 (0.919–1.408)	0.235
T4	1.443 (1.176–1.769)	< 0.001	1.356 (1.100–1.670)	0.004
N stage
N0	reference
N1	0.978 (0.796–1.202)	0.832
N2	1.049 (0.918–1.199)	0.481
N3	1.126 (0.966–1.312)	0.131
Bone metastasis
No	reference		reference
Yes	1.306 (1.182–1.443)	< 0.001	1.403 (1.263–1.558)	< 0.001
Brain metastasis
No	reference		reference
Yes	1.140 (1.020–1.275)	0.021	1.199 (1.055–1.364)	0.005
Lung metastasis
No	reference
Yes	1.044 (0.941–1.157)	0.418
Marital status
Unmarried	reference
Married	0.983 (0.863–1.120)	0.800
Surgery
No	reference		reference
Yes	0.512 (0.380–0.690)	< 0.001	0.602 (0.445–0.814)	0.001
Radiotherapy
No	reference		reference
Yes	1.116 (1.011–1.233)	0.030	1.049 (0.938–1.173)	0.403
Chemotherapy
No	reference		reference
Yes	0.425 (0.382–0.472)	< 0.001	0.414 (0.371–0.461)	< 0.001

Nomogram construction and validation

Based on the results above, a nomogram integrating all independent risk factors was created for the prediction of OS. By calculating the total scores located on the scales, the 3-, 6-, and 12-month survival probabilities of patients can be estimated (Fig. 2). As illustrated in the nomogram, chemotherapy contributed the largest proportion to the OS, followed by grade and surgical resection. Bone metastasis and T stage presented a moderate impact on survival. On the contrary, gender and age showed a relatively small effect on survival.

What is more, the nomogram exhibited good discriminative ability and accuracy at predicting OS. The C-index was 0.700 (95% CI 0.684–0.716) in the training dataset and 0.677 (95%CI 0.653–0.701) in the validation dataset. Meanwhile, the calibration curves confirmed an excellent consistency between the observed and predicted survival probabilities in both cohorts (Fig. 3).

Clinical value of the nomogram

Decision curve analysis (DCA) evaluated the net benefit of patients in both cohorts, indicating a favorable clinical value of the proposed nomogram (Fig. 4). Moreover, the total points of individuals were calculated based on the nomogram, and all patients were classified into two risk groups. The Kaplan-Meier survival analysis indicated that patients with lower scores achieved a significantly longer survival time than patients with higher scores (P < 0.0001), providing solid evidence of the strong predictive ability of the model (Fig. 5).

In spite of a relatively low frequency (3–7%)(5, 19, 20), liver metastasis (LM) is considered as the worst prognostic factor among all metastatic sites in NSCLC patients(3, 21). The estimated OS of these patients is usually less than 6 months(4, 5, 9). Thus, predicting individual survival is critical to personalize treatment strategies and improve physician-patient communications. In this study, a nomogram was built to assess OS probabilities of NSCLC patients with LM. Further analysis confirmed the predictive ability and clinical value of the model, which is of great significance for stratifying risk groups.

Our study revealed that sex, race, age, grade, and T stage were independent predictors of overall survival, which is consistent with prior studies(4, 16, 17). Notably, in addition to these classical risk factors, we found that in NSCLC patients with LM, the presence of bone or brain metastasis was significantly correlated with poor outcomes, whereas the impact of lung metastasis in the same group is negligible. Several reports have noted that the outcome among different metastatic sites varies(3, 21). Yang et al(22) pointed out that the prognosis in NSCLC patients with multiorgan metastases was substantially more unfavorable than those with single organ metastasis. All the evidence strongly supports metastatic heterogeneity in NSCLC. In terms of histologic types, our results found that the prognosis was better in adenocarcinoma than other subtypes of NSCLC, which was in line with earlier findings(16, 17). Nevertheless, histology surprisingly did not remain significantly statistically in the multivariate analysis. It has been reported that the survival benefit for adenocarcinoma was only observed in recent decades, indicating the increasing impact of molecular targeted therapy in adenocarcinoma patients(23). Unfortunately, recent studies demonstrated that LM was a negative prognostic factor in NSCLC patients receiving EGFR-TKIs therapy. Compared to NSCLC patients with an absent of LM, the treatment response was dramatically poorer in those with the presence of LM(6, 7). Therefore, the discrepancy in our study could be attributed to the liver resistance to the molecular target therapy.

As shown in the nomogram, chemotherapy contributed the most to the overall survival. In our study, the patients received chemotherapy had a much better prognosis than those without chemotherapy (7 months vs. 2 months, P < 0.001). Similarly, prior studies suggested that chemotherapy made a large contribution to the survival outcomes for NSCLC patients with brain or bone metastasis(24–26). Moreover, given that LM was significantly related to dismal prognosis in patients had targeted therapy and immunotherapy(6–11), chemotherapy still plays an essential role in the setting of liver metastasis.

The significance of local treatment in patients with metastatic NSCLC remains controversial. Current study revealed a survival benefit in patients with surgically treated NSCLC with LM. Of patients received resection of primary tumor, 71% treated with chemotherapy and 33% treated with radiotherapy, indicating that local resection was only a part of the multimodal therapy in most cases. David and colleagues also reported that the median OS time was significantly increased with surgical procedures in NSCLC patients with metastasis(27). Besides, they also stated that surgery might be considered as part of multimodality therapy, given the crucial role of systemic therapies. Of note, not all patients can benefit from surgery. Various individual and tumor-related risk factors should be taken into consideration when surgical treatment is performed in a multidisciplinary setting (28–30). Interestingly, our study found that radiotherapy, as another option of local treatment strategies, was not able to independently predict the OS in NSCLC patients with LM (P = 0.403). The role of radiotherapy in metastatic NSCLC has not been fully explored. Our data indicated that radiotherapy alone may have a modest impact on prognosis. However, it should be noted that the prognosis of NSCLC patients with LM is unfortunately dismal with a median OS of only 5 months. There might not be much of a chance to prove the value of radiotherapy in long-term prediction, due to a relatively long course of the treatment. Hence, Wallace and colleagues suggested that shorter courses of radiotherapy should be considered in patients with limited prognosis(31). Further, preclinical studies reported that radiotherapy induced T-cell infiltration and increased tumor antigen release, which leading to an enhancement of antitumor immune response(32–34). As a result, Corrao and colleagues implied that the survival outcomes in NSCLC patients with LM might be improved when radiotherapy combined with immunotherapy(35). Besides, for patients with severe pain or complications, radiotherapy may still be an option of palliative care.

Unlike bone or brain metastasis, limited data exist to look at liver metastasis of NSCLC. To date, this is the first study to establish a prediction model focusing on the overall survival of NSCLC patients with LM. By using this practical tool, physicians might be able to identify the subgroup patients that require more intensive treatments and needs closer attention.

Nevertheless, limitations should be admitted in this study. First, there was an inevitable selection bias resulting from its retrospective nature. Second, some detailed information on treatments, such as chemotherapy regimens, radiation dose, targeted therapy, immunotherapy, was not available in the SEER database. Similarly, the data of gene mutations, other metastatic sites including adrenal metastasis, performance status cannot be obtained. Third, our nomogram was only validated internally with a relatively small size cohort. An independent external cohort with a larger sample size is still required for future application. Finally, given that all enrolled records were from the United States, perspective global data is warranted to apply our model to the Asian population and patients worldwide.

Author contributions:

(I) Conception and design: TX, CL, and DF

(II) Administrative support: FM and XL

(III) Provision of study materials or patients: DF and ZC

(IV) Collection and assembly of data: TX, CL, and ZC

(V) Data analysis and interpretation: TX, CL, and DF

(VI) Manuscript writing: All authors

(VII) Final approval of manuscript: All authors

Acknowledgments: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form. The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Data availability: All data generated or analysed during this study are included in the supplementary information file.

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No competing interests reported.

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A nomogram for predicting the overall survival in non-small cell lung cancer patients with liver metastasis

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Abstract

Figures

Introduction

Methods

Patient selection and data elements

Nomogram construction and evaluation

Statistical analysis

Results

Patient demographics

Independent prognostic factors for OS

Nomogram construction and validation

Clinical value of the nomogram

Discussion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1