Comparison of a novel predictive model with AJCC staging for predicting cancer-specific survival and overall survival in senile (≥60 years) lung adenocarcinomas

doi:10.21203/rs.3.rs-3624810/v1

Objective: We hope to develop a simple, rapid, and comprehensive predictive model that can evaluate the prognosis of elderly patients with lung adenocarcinoma(LUAD).

Methods: Basic and clinical data on 14,117 patients ≥60 years of age diagnosed with lung adenocarcinoma between 2010 and 2019 were retrospectively analyzed using the Surveillance, Epidemiology and End Results (SEER) database.Independent factors affecting patients' prognosis were identified by univariate and multivariate COX regression analyses, and Nomograms of overall survival (OS)and cancer-specific survival(CSS) at 1, 3, and 5 years were constructed based on the results of COX regression analyses.Using the Concordance-index (C-index), calibration curve, Receiver operating characteristic curve (ROC), and Decision curve analysis (DCA) to evaluate the performance of the Nomograms.We also validated our established model with a validation set of patients and finally compared it with the AJCC staging model.

Results: We included a total of 14,117 patients, which were divided into a training set and a validation set. We used the chi-square test to compare the baseline data between the two groups, which was not statistically significant (P＞0.05); we analyzed the data from the training set using Cox univariate and multivariate regression, and found that gender, ethnicity, marital status, stage, treatment, and distant metastasis were significant independent prognostic factors for OS and CSS (P<0.05);The ROC curves were used to validate the training and validation set data after the construction was completed, and the AUC for 1, 3, and 5 years all reached above 0.75, in addition to the C-index;The consistency of the calibration curves for OS and CSS is well behaved and close to the 45°reference line;The models for OS and CSS were also analyzed using DCA, showing that the net clinical benefit of the models built in this study was higher in both the training and validation sets;Finally, we used the models built for OS and CSS to compare with the AJCC staging model, and we found that both our models outperformed the AJCC staging model in terms of predictive performance.

Conclusion: This Nomogram More Accurately Predicts Prognosis in Elderly Lung Adenocarcinoma Patients.

Lung adenocarcinoma

Elderly

SEER database

Nomogram

Predictive modeling

According to global cancer statistics published in《CA Cancer J Clin》 in 2021 lung cancer is the second most common malignancy with an incidence of 11.4%, while it is the first malignancy causing death with a mortality rate of 18.0%, which is much higher than that of the second most common hepatic malignancy (8.3%)(1).About 91.7% of lung cancer patients in the U.S. are over 55 years of age, and the incidence and mortality rates of lung cancer are positively correlated with the age of the patients, which increases with age, thus making the elderly a major group of lung cancer patients(2).The most common of all pathological subtypes of lung malignancies is lung adenocarcinoma, accounting for 54.7% of all lung malignancies, and although the development of clinical therapeutic agents and individualized treatment regimens have improved the survival of many patients with lung adenocarcinoma, the 5-year survival rate for lung adenocarcinoma is only about 15%(3, 4).The results of the above data indicate that lung adenocarcinoma has become a major group of lung malignant tumors above 55 years of age, and this group has a higher mortality rate due to the presence of underlying diseases and the decline of physiological functions(5).

Providing an accurate assessment of the prognosis of these patients can improve the prognosis of the patients and the utilization of medical resources, and this prediction can provide a certain reference for clinicians to choose the treatment plan. Currently, the staging system we commonly use is the AJCC staging system, but this staging system does not provide further staging for specific groups of lung cancer, so the AJCC staging system cannot accurately predict the prognosis for some specific groups of lung cancer(6, 7).Nomogram is a novel predictive model that constructs a prognostic predictive model more in line with each lung cancer subtype group by incorporating a wide range of common clinical data, and it can be intuitively and easily demonstrated for both doctors and patients to make correct clinical decisions, and it has been widely used for a wide range of solid tumors(8).We found by searching Pubmed that there have been several relevant studies on the Nomogram of lung adenocarcinoma prognosis, whereas these studies have not examined lung adenocarcinoma patients ≥ 60 years of age in more detail(9, 10).We therefore constructed predictive models regarding the prognosis of lung adenocarcinoma patients ≥ 60 years of age using information from the SEER database.The SEER database is currently the largest tumor registry database in North America, covering approximately 30% of the North American population, and can provide us with diverse and reliable data for this study(11).We hope to assist clinicians to predict the prognosis of these patients more accurately by constructing a prediction model, which will provide a basis for individualized management of patients.

Data sources and study design

We used SEER*Stat 8.4.0.1 to obtain data on patients with lung malignancies from the February 2023 release of the SEER database, which includes tumor stage, clinicopathology, and survival information. Prior to initiating this study, we submitted a data use agreement to the SEER program and were formally granted access to the database under approval number 14038-Nov2021.SEER is a database for public research use and the personal data in the database are completely confidential, therefore ethical approval was not required for this study. The study was conducted in accordance with the Declaration of Helsinki (revised 2013).We used SEER*Stat 8.4.0.1 software to download data on lung malignant tumors, which were screened by inclusion and exclusion criteria, which were as follows:(1) age ≥ 60 years at the time of diagnosis; (2) year of diagnosis 2010–2019; (3) pathologic diagnosis of lung adenocarcinoma. The exclusion criteria were as follows:(1) patients with survival < 1 month; (2) patients with unclear clinicopathologic data.

Research variables

We screened a total of 14,117 eligible patients by the above criteria, divided these patients into training and validation sets in a ratio of 7:3, and extracted variables from the training set. Demographic data included gender (male, female), race (black, white, other race), and marital status (divorced, married, and single). Tumor characteristics included tumor location (left lung, right lung), AJCC stage (I, II, III, and IV), surgery (yes or no), radiation therapy (yes or no), chemotherapy (yes or no), and distant metastasis information (lung metastasis, bone metastasis, brain metastasis, and liver metastasis). There is also information on the patient's survival time and survival status.

Statistical Analysis

Demographics and tumor characteristics of the training and validation sets were compared using the chi-square test;Independent prognostic factors for OS and CSS were determined by univariate and multivariate Cox regression models parameterized by hazard ratios (HR) with corresponding 95% confidence intervals (CI);A Nomogram was plotted based on the established Cox regression model and used to predict 1-year, 3-year, and 5-year survival for OS and CSS;We used C-index to assess the model's discriminatory power, and the C-index thresholds for nomogram prediction accuracy were defined as low (0.50–0.70), medium (0.71–0.90), and high accuracy (> 0.90)(12);Evaluation was done using ROC and Area Under Curve(AUC), AUC of 0.50–0.70 was considered as low accuracy, between 0.71–0.90 was considered as medium accuracy and above 0.90 was considered as high accuracy(13);The calibration of the model was tested by plotting a calibration curve using bootstrap method with 1000 repetitive samples to ensure the accuracy and reliability of this model;Clinical decision curves were plotted for the model to test the clinical benefit and application value of the model;This model was validated using a validation set and finally we compared this model with the AJCC staging system.All statistical analyses were performed using R software (version 4.2.2) and P < 0.05 was considered statistically significant.

Patient characteristics

In this study, a total of 39,841 patients were screened from the SEER database, and 14,117 patients who met the inclusion criteria and those who did not meet the exclusion criteria were categorized into a training set (N = 9,881) and a validation set (N = 4,236) according to a 7:3 ratio(Fig. 1).The ratio of men to women in both the training and validation sets was about 50%;Race was predominantly White in both the training and validation sets, with a percentage of 75% or more;Regarding marital status, half of the patients were married at the time of diagnosis (55.03%); about 45% of the patients had undergone radiotherapy or chemotherapy; AJCC staging was mainly stage IV, which accounted for about 43.56% of all the stages, and most of the patients had distant metastases at the time of diagnosis;The baseline data of the two groups of patients were similar and there was no statistical difference (P > 0.05), See Table 1 for specific data.

Table 1

Clinicopathologic features of elderly patients with lung adenocarcinoma
Variable	Total (n = 14117)	Training (n = 9881)	Validation (n = 4236)	χ²	P
Sex, n (%)				0.050	0.824
Female	7332 (51.94)	5138 (52.00)	2194 (51.79)
Male	6785 (48.06)	4743 (48.00)	2042 (48.21)
Race, n (%)				0.414	0.813
Black	1128 (7.99)	799 (8.09)	329 (7.77)
Other^a	1982 (14.04)	1385 (14.02)	597 (14.09)
White	11007(77.97)	7697 (77.90)	3310 (78.14)
Marital status, n (%)				0.549	0.760
Divorced	1973 (13.98)	1392 (14.09)	581 (13.72)
Married	7768 (55.03)	5419 (54.84)	2349 (55.45)
Single	4376 (31)	3070 (31.07)	1306 (30.83)
Laterality, n (%)				0.008	0.927
Left	5817 (41.21)	4074 (41.23)	1743 (41.15)
Right	8300 (58.79)	5807 (58.77)	2493 (58.85)
AJCC StageGroup, n(%)				χ²=2.062	0.560
I	4144 (29.35)	2926 (29.61)	1218 (28.75)
II	633 (4.48)	452 (4.57)	181 (4.27)
III	3190 (22.6)	2214 (22.41)	976 (23.04)
IV	6150 (43.56)	4289 (43.41)	1861 (43.93)
Surgery, n (%)				0.382	0.536
No	10395 (73.63)	7261 (73.48)	3134 (73.98)
Yes	3722 (26.37)	2620 (26.52)	1102 (26.02)
Radiation, n (%)				0.586	0.444
No	7896 (55.93)	5506 (55.72)	2390 (56.42)
Yes	6221 (44.07)	4375 (44.28)	1846 (43.58)
Chemotherapy, n (%)				0.577	0.447
No	7354 (52.09)	5168 (52.30)	2186 (51.61)
Yes	6763 (47.91)	4713 (47.70)	2050 (48.39)
Bone metastases, n (%)				1.064	0.302
No	11309 (80.11)	7938 (80.34)	3371 (79.58)
Yes	2808 (19.89)	1943 (19.66)	865 (20.42)
Brain metastases, n (%)				1.419	0.234
No	12261 (86.85)	8560 (86.63)	3701 (87.37)
Yes	1856 (13.15)	1321 (13.37)	535 (12.63)
Liver metastases, n (%)				1.056	0.304
No	13164 (93.25)	9228 (93.39)	3936 (92.92)
Yes	953 (6.75)	653 (6.61)	300 (7.08)
Lung metastases, n (%)				0.337	0.561
No	12161 (86.14)	8501 (86.03)	3660 (86.40)
Yes	1956 (13.86)	1380 (13.97)	576 (13.60)

a=American Indian/AK Native, Asian/Pacific Islander

Independent prognostic factors for OS and CSS in elderly LUAD

Univariate COX regression analysis of variables affecting OS and CSS in the training set showed that gender, race, marital status, AJCC stage, surgery, radiotherapy, chemotherapy, and distant metastasis (bone, brain, liver, and lungs) were significant prognostic factors for OS and CSS. Multifactorial COX regression analyses were performed on these variables, and COX regression models on OS and CSS were constructed(Table 2 and Table 3).

Table 2

Univariate and multivariate analyses of OS in training set.
Variate	Univariate Analysis			Multivariate Analysis
Variate	P	HR	95%CI	P	HR	95%CI
Sex
Female	Reference			Reference
Male	< 0.001	1.30	1.24–1.36	< 0.001	1.34	1.28–1.40
Race
White	Reference			Reference
Other^a	0.318	0.97	0.91–1.03	< 0.001	0.79	0.74–0.84
Black	0.056	1.08	1.00–1.17	0.805	0.99	0.91–1.07
Marital status
Single	Reference			Reference
Divorced	< 0.001	0.89	0.83–0.95	0.014	0.91	0.85–0.98
Married	< 0.001	0.83	0.79–0.88	< 0.001	0.83	0.79–0.88
Laterality
Right	Reference				-	-
Left	0.893	1.00	0.95–1.04		-	-
AJCC Stage Group
I	Reference			Reference
II	< 0.001	1.71	1.51–1.92	< 0.001	2.54	2.25–2.87
III	< 0.001	2.75	2.57–2.94	< 0.001	2.92	2.70–3.15
IV	< 0.001	4.83	4.54–5.13	< 0.001	3.33	3.07–3.62
Surgery
Yes	Reference			Reference
No	< 0.001	4.37	4.11–4.66	< 0.001	3.18	2.95–3.43
Radiation
No	Reference			Reference
Yes	< 0.001	1.42	1.35–1.48	< 0.001	0.82	0.78–0.87
Chemotheray
No	Reference			Reference
Yes	< 0.001	1.41	1.35–1.47	< 0.001	0.52	0.49–0.55
Bone metastases
No	Reference			Reference
Yes	< 0.001	2.84	2.69–2.99	< 0.001	1.49	1.40–1.59
Brain metastases
No	Reference			Reference
Yes	< 0.001	2.52	2.37–2.68	< 0.001	1.47	1.37–1.58
Liver metastases
No	Reference			Reference
Yes	< 0.001	3.03	2.80–3.29	< 0.001	1.53	1.40–1.67
Lung metastases
No	Reference			Reference
Yes	< 0.001	2.11	1.99–2.24	0.238	1.04	0.97–1.11
a = American Indian/AK Native, Asian/Pacific Islander

Table 3

Univariate and multivariate analyses of CSS in training set.
Variate	Univariate Analysis			Multivariate Analysis
Variate	P	HR	95%CI	P	HR	95%CI
Sex
Female	Reference			Reference
Male	< 0.001	1.14	1.09–1.20	< 0.001	1.24	1.18–1.30
Race
Other^a	Reference			Reference
White	0.623	1.02	0.95–1.09	< 0.001	1.20	1.12–1.29
Black	0.050	1.11	1.01–1.23	< 0.001	1.24	1.11–1.38
Marital status
Divorced	Reference			Reference
Married	0.933	0.89	0.93–1.08	0.797	1.01	0.94–1.09
Single	0.079	1.07	0.99–1.16	0.005	1.12	1.04–1.21
Laterality
Right	Reference			-	-	-
Left	0.577	0.99	0.94–1.04	-	-	-
AJCC Stage Group
IV	Reference			Reference
I	< 0.001	0.24	0.22–0.26	< 0.001	0.27	0.24–0.29
II	< 0.001	0.40	0.35–0.45	< 0.001	0.63	0.54–0.72
III	< 0.001	0.59	0.55–0.62	< 0.001	0.80	0.75–0.86
Surgery
No	Reference			Reference
Yes	< 0.001	0.35	0.33–0.38	< 0.001	0.50	0.46–0.55
Radiation
Yes	Reference			Reference
No	< 0.001	0.92	0.87–0.96	< 0.001	1.15	1.09–1.22
Chemotheray
Yes	Reference			Reference
No	< 0.001	0.86	0.82–0.90	< 0.001	1.84	1.74–1.94
Bone metastases
Yes	Reference			Reference
No	< 0.001	0.44	0.42–0.47	< 0.001	0.71	0.66–0.76
Brain metastases
No	Reference			Reference
Yes	< 0.001	2.10	1.97–2.24	< 0.001	1.42	1.32–1.53
Liver metastases
No	Reference			Reference
Yes	< 0.001	2.41	2.21–2.62	< 0.001	1.46	1.33–1.59
Lung metastases
No	Reference			Reference
Yes	< 0.001	1.70	1.60–1.81	0.496	1.02	0.96–1.10

Table 2a = American Indian/AK Native, Asian/Pacific Islander

Patient OS and CSS prognostic Nomogram construction

Based on the results of the COX multifactorial regression analysis of the training set, the 11 variables screened were used to construct a prognostic nomogram for OS and CSS in elderly LUAD(Fig. 2).Patients' 1-year, 3-year, and 5-year survival was predicted by integrating all predictors,The total score obtained by summing the scores from each variable predicts the patient's 1-year, 3-year, and 5-year survival.

Validation of the Nomogram

By using the R software, the C-index for the OS training set and validation set were calculated as 0.753 and 0.758, and the C-indexes for the CSS training set and validation set are 0.721 and 0.730, respectively, suggesting good predictive value(Table 4).Risk scores for each of the independent prognostic factors were continued to be calculated using the R software and ROC for 1-, 3-, and 5-year survival were plotted for the training set for OS and CSS and validated using the validation set by plotting ROC for 1-, 3-, and 5-year survival;The 1-, 3-, and 5-year AUC values were 0.822, 0.840, and 0.849 for the OS training set, and 0.831, 0.839, and 0.852 for the 1-, 3-, and 5-year AUC values for the OS validation set;The 1-, 3-, and 5-year AUC values for the CSS training set were 0.784, 0.801, and 0.831, and the 1-, 3-, and 5-year AUC values for the CSS validation set were 0.794, 0.798, and 0.827(Fig. 3);The above results show the ability of this prediction model to assess the 1-, 3-, and 5-year OS and CSS prognostic survival rates of elderly LUAD patients relatively reliably.Finally, this model was validated using the Bootstrap method, setting the sampling number B = 1000, and the validation results showed that the 1-, 3-, and 5-year survival correction curves for OS and CSS were similar to the 45° reference line, suggesting a more accurate prediction(Fig. 4).

Table 4

C-index results.
	C-index
OS
Training set	0.753
Validation set	0.758
TNM staging	0.699
CSS
Training set	0.721
Validation set	0.730
TNM staging	0.668

Decision curve analysis

Decision Curve Analysis is an assessment method used to evaluate the degree of patient benefit.AUC only measures the diagnostic accuracy of the predictive model and fails to take into account the clinical utility of a particular model, which may lead to overmedication, which can be well addressed by DCA(14).In this study, DCA curves were plotted based on 1-, 3-, and 5-year survival of patients in the OS and CSS models, respectively. The results showed higher 1-, 3-, and 5-year net clinical benefit in both models established in this study(Fig. 5).

Comparison with AJCC Staging System

We used the AJCC staging system that comes with the SEER data to construct the models for OSS and CSS, and the C-indexes for the two models were calculated to be 0.699 and 0.668, respectively(Table 4);And we plotted ROC curves about OS and CSS survival, with AUC values of 0.765, 0.793, and 0.800 for 1, 3, and 5 years in OS, and 0.784, 0.801, and 0.831 for 1, 3, and 5 years in CSS, respectively(Figure.3);Finally, we plotted DCA curves regarding OS and CSS, and the net clinical benefit rate was significantly lower for the AJCC staging compared with our constructed model(Figure.4); thus, our model predicts survival in elderly LUAD more accurately than the AJCC staging system.

LUAD patients ≥ 60 years of age have now become the type of lung malignancy with the highest proportion of lung malignancies(2, 3).However, there are few previous studies on this group of patients, and 33% of the 248 phase III clinical trials on non-small cell lung cancer from 1980–2010 excluded patients > 65 years of age, so older patients with LUAD are missing both in terms of treatment and prediction of prognosis(15).We hope that by using the huge data in the SEER data to construct an accurate prognostic model for this group of patients, we can better help patients and clinicians make the right choices.Gender, race, marital status, AJCC stage, surgery, radiotherapy, chemotherapy, and distant metastases (bone, brain, liver, and lung) were found to be significant prognostic factors for OS and CSS in this study we conducted by using the COX multifactorial regression model, and a Nomogram was constructed using these prognostic factors.

It has been previously believed that men are more likely than women to have a higher mortality rate from pulmonary malignancies, and in our present study, men had an approximately 30% increased risk of disease progression and death compared to women.A cohort study in Sweden by Cecilia Radkiewicz et al(16).showed that among lung adenocarcinoma subtypes, male patients had a worse prognosis and shorter survival, which is the same result as in our study.An analysis of five phase III clinical trials on chemotherapy for non-small cell lung cancer found that in platinum-based chemotherapy regimens, female patients with lung adenocarcinoma achieved higher ORR and longer survival, as the prognosis of lung adenocarcinoma in females was better than that of males, but this gender difference was not reflected in squamous carcinoma(17).In addition, Carmen Behrens et al(18).pointed out that the number of tumor-associated immune-infiltrating cells in female lung adenocarcinoma patients was higher than that in male lung adenocarcinoma patients, and that such tumor-associated immune-infiltrating cells could bring about better efficacy of treatment with immune checkpoint inhibitors. These two points may be the reason why the prognosis of female lung adenocarcinoma patients is better than that of male patients, and the prognostic differences between genders can be further verified by grouping and comparing the variable of gender using the SEER database.Race is also an important indicator of lung cancer prognosis, and some previous studies have concluded that there is no effect of different races on the prognosis of lung cancer, although the multivariate Cox proportional risk model has been used in most of the previous studies, which ignores the existence of competing risks, thus resulting in unexpected results(19, 20).Whereas the same multivariate Cox proportional risk model was used to construct the Nomogram in our study, we found that there was a difference in survival between races, which is different from previous studies.Differences in marital status among demographic indicators also affect the prognosis of patients with lung adenocarcinoma, and many previous studies have been conducted in this regard.A study based on the SEER database by Dechang Zhao et al(21).showed that single and unmarried patients may have a worse prognosis compared to married individuals and still obtained the same results after correcting for baseline information using the propensity to match score.Another study, also based on the SEER database, on the effect of marital status on survival in lung cancer patients also showed that married patients can have higher cancer-specific survival rates(22).The two studies mentioned above have the same results as those conducted by us, and we speculate that the main reason for this is that married patients may receive more support and care from their family members to help them have a more optimistic mindset, and these may affect the immune microenvironment, which may reduce the mortality rate of the patients(23, 24).

In our modeling, I also incorporated some of the more common clinical factors that affect prognosis. These factors mainly include AJCC staging, surgery, radiotherapy, chemotherapy, and distant metastasis.First of all, the AJCC staging has been revised many times to predict the prognosis of lung malignancies very accurately(25).However, this staging system is not perfect, and the advances in genetic testing and molecularly targeted drug therapy for lung adenocarcinoma in recent years have led to a better prognosis for lung adenocarcinoma than for other types of tumors, which is not taken into account in the AJCC staging system, and the same stage of lung malignant tumors may result in a different prognosis due to different pathologic types(26, 27).That is why we developed this prognostic prediction model. But we still incorporate the AJCC staging system in our model because it still has good prognostic predictive power in the same type of pathology, so we incorporate it.The second independent influencing factor included in our model that affects the prognosis of LUAD in the elderly is the treatment-related factors; current treatments for lung adenocarcinoma include surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy, but unfortunately only surgery, chemotherapy, and radiotherapy are available in the SEER database.It is clear that surgery improves the prognosis of patients with lung adenocarcinoma, but the risk of surgery increases significantly in the elderly, so many clinicians try to avoid surgical operations in this group of patients, but with the improvement of surgical operation techniques and anesthesia techniques, the mortality rate due to surgery has been reduced to an acceptable range, so that surgery also improves the patient's prognosis in the elderly(28).A retrospective study on the benefit of surgery for lung cancer in the elderly by Hideomi Ichinokawa(29) and others showed that surgery can be equally beneficial in the elderly, and that differences in staging may lead to a different prognosis in the elderly, which also applies in middle-aged lung cancer and is not unique to elderly patients.When lung adenocarcinoma develops to middle or late stage, only chemotherapy and radiotherapy can be used to prolong the survival of patients in previous treatments. There have been many previous clinical trials showing that platinum-based chemotherapy regimens are more effective in prolonging patient survival and improving survival treatments relative to supportive care, and with the development of third-generation chemotherapeutic agents, such as paclitaxel and gemcitabine, many patients can still benefit from chemotherapy in second- and third-line treatments(30).Radiotherapy, on the other hand, plays a great role in both radical and palliative treatment of non-small cell lung cancer, and can be used for postoperative adjuvant therapy and palliative treatment of advanced metastases, all of which can prolong the overall survival of patients(31).These are also consistent with our model, which is unfortunately a shortcoming given that non-small cell lung cancer has now entered to the era of immunotherapy and targeted therapies, and has even reached the stage of removing chemotherapy from the first line of treatment in intermediate to advanced LUAD, which is missing from the SEER data.Finally, data related to distant metastasis of LUAD were included in our model, and it is well known that patients with metastatic LUAD have a worse prognosis compared to those without metastasis(32).In an observational study conducted in Sweden, a total of 17,431 patients who died of lung cancer were included, and the data showed that brain metastasis (47%), bone metastasis (39%), liver metastasis (35%), and respiratory metastasis (22%) were common sites of metastasis from pulmonary malignancies, and that the median survival after diagnosis of non-metastatic lung cancer was 13 months compared with 5 months for metastatic lung cancer, with only 3 months for patients with hepatic metastases(33).In our study although the baseline baseline material showed a low percentage of patients with metastases, distant metastases were found to be an independent influence on the prognosis of elderly patients with LUAD after screening by COX regression modeling, which is in line with the above retrospective study.

We constructed a predictive model that is more consistent with the prognosis of elderly lung adenocarcinoma patients by using information from the SEER data, and our model is significantly superior to AJCC staging.But this model still has some shortcomings:First, because of missing data within the SEER database, we excluded a portion of the data, which could lead to biased results;Second, our model is missing some factors that are readily available and may be associated with the prognosis of LUAD in the elderly, such as family history, tumor markers, driver mutation status, immunotherapy and targeted therapy information, which we were not able to incorporate because they are not recorded in the SEER database;Finally, the data for this study were all from the SEER database and were not validated against an external population. Therefore a subsequent validation with external data from multiple centers is needed.

In this study we conducted a population-based study using the SEER database to predict OS and CSS in elderly LUAD patients by creating a Nomogram, and this Nomogram was demonstrated to provide a more accurate prediction of survival in elderly LUAD patients through multiple validation methods.

Authors’ contributions

Feiyang Li and Fang Li: Collection of materials and manuscript writing. Dong Zhao: supervised the manuscript.All authors read and approved the final manuscript. Feiyang Li and Fang Li contributed equally to this work and are co-first authors. All authors contributed to the article and approved the submitted version.

Conflict of Interest Disclosures

The authors declare that they have no competing interests.

Funding/Support

No specific funding sources were used for this work.

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No competing interests reported.

Comparison of a novel predictive model with AJCC staging for predicting cancer-specific survival and overall survival in senile (≥60 years) lung adenocarcinomas

Status:

Version 1

Abstract

Figures

Introduction

Materials and methods

Data sources and study design

Research variables

Statistical Analysis

Results

Patient characteristics

Independent prognostic factors for OS and CSS in elderly LUAD

Patient OS and CSS prognostic Nomogram construction

Validation of the Nomogram

Decision curve analysis

Comparison with AJCC Staging System

Discussion

Conclusions

Declarations

References

Additional Declarations

Status:

Version 1