Development and validation of a novel nomogram for predicting distant metastasis-free survival among breast cancer patients

Optimized Nomogram for Nasopharyngeal Carcinoma Prognosis Prediction in Younger Patients (Aged 18-59): Development and Validation

PURPOSE

To develop a nomogram model for the predicted overall survival (OS) in patients aged 18 to 59 years with nasopharyngeal carcinoma (NPC) and assess the value of the clinical application.

METHODS

In total, 1334 registers of NPC patients from 2010 to 2015 were retrieved from the Surveillance, Epidemiology, and End Results database. Univariate and multivariate Cox analysis were used to screen out independent risk factors affecting patients. Cox analysis predicted OS for patients with NPC at 3, 5, and 8 years. Nomogram performance was validated using the concordance index (C-index), receiver operating characteristic, calibration curve, and decision curve analysis (DCA).

RESULTS

Age, sex, race, marital, histological type, tumor size, AJCC stage, and radiotherapy were independent risk factors. The C-index of the nomogram was 0.69 [95% confidence interval (CI): 0.68-0.71] for the training set, and the C-index of the AJCC stage was 0.63 (95% CI: 0.62-0.65), both statistically significant (P < .01). The area under the curve for the nomogram at these intervals (0.755, 0.729, and 0.729, respectively) was higher than that of the AJCC stage (0.667, 0.646, and 0.646, respectively), indicating better predictive accuracy. The calibration curves revealed a high degree of agreement between the observation and the prediction. Compared to the American Joint Committee on Cancer (AJCC) stage, DCA showed better clinical utility.

CONCLUSION

The nomogram as novel predictor for nasopharyngeal carcinoma patients' survival.

Developing a nomogram model and prognostic analysis of nasopharyngeal squamous cell carcinoma patients: a population-based study

BACKGROUND

Nasopharyngeal squamous cell carcinoma (NPSCC) is a common histo-logical subtype of nasopharyngeal cancer with a generally poor prognosis. The aim of this study is to identify factors affecting the survival prognosis of NPSCC patients and develop a specialized nomogram model.

METHODS

We extracted clinical data of 1235 diagnosed cases of NPSCC from the SEER database using SEER*Stat software. Univariate and multivariate Cox proportional hazards regression analyses were conducted to explore clinical factors that impact the prognosis of NPSCC patients. Based on significant independent factors, we developed a nomogram to predict the 1, 3, and 5 years overall survival rates. The discriminative and predictive abilities of the nomogram were evaluated using C-index, calibration curve, area under the curve (AUC), and receiver operating characteristic curve. We evaluated the clinical value of the nomogram using decision curve analysis (DCA) and clinical impact curve (CIC).

RESULTS

We performed a cohort analysis on 846 patients with nasopharyngeal cancer in the training cohort. Multivariate Cox regression analysis revealed age, race, marital status, primary tumor, radiation therapy, chemotherapy, SJCC stage, primary tumor size, Lung metastasis and brain metastasis as independent prognostic factors for NPSCC patients, which we used to construct the nomogram prediction model. The C-index of the training cohort was 0.737. The ROC curve analysis indicated that the AUC of the OS rate at 1, 3, and 5 years in the training cohort was > 0.75. The calibration curves of the two cohorts showed good consistency between the predicted and observed results. DCA and CIC demonstrated that the nomogram prediction model had good clinical benefits.

CONCLUSIONS

The nomogram risk prediction model for NPSCC patient survival prognosis, constructed in this study, has exhibited excellent predictive capability. This model can be employed for swift and precise assessment of individualized survival prognosis. It can offer valuable guidance to clinical physicians in diagnosing and treating NPSCC patients.

Predicting Patterns of Distant Metastasis in Breast Cancer Patients following Local Regional Therapy Using Machine Learning

Up to 30% of breast cancer (BC) patients will develop distant metastases (DM), for which there is no cure. Here, statistical and machine learning (ML) models were developed to estimate the risk of site-specific DM following local-regional therapy. This retrospective study cohort included 175 patients diagnosed with invasive BC who later developed DM. Clinicopathological information was collected for analysis. Outcome variables were the first site of metastasis (brain, bone or visceral) and the time interval (months) to developing DM. Multivariate statistical analysis and ML-based multivariable gradient boosting machines identified factors associated with these outcomes. Machine learning models predicted the site of DM, demonstrating an area under the curve of 0.74, 0.75, and 0.73 for brain, bone and visceral sites, respectively. Overall, most patients (57%) developed bone metastases, with increased odds associated with estrogen receptor (ER) positivity. Human epidermal growth factor receptor-2 (HER2) positivity and non-anthracycline chemotherapy regimens were associated with a decreased risk of bone DM, while brain metastasis was associated with ER-negativity. Furthermore, non-anthracycline chemotherapy alone was a significant predictor of visceral metastasis. Here, clinicopathologic and treatment variables used in ML prediction models predict the first site of metastasis in BC. Further validation may guide focused patient-specific surveillance practices.

Pectoralis muscle predicts distant metastases in breast cancer by deep learning radiomics

BACKGROUND

Sarcopenia is associated with a poor prognosis in patients with breast cancer (BC). Currently, there are few quantitative assessments carried out between muscle biomarkers and distant metastasis using existing methods.

PURPOSE

To assess the predictive value of the pectoralis muscle for BC distant metastasis, we developed a deep learning radiomics nomogram model (DLR-N) in this study.

MATERIAL AND METHODS

A total of 493 patients with pathologically confirmed BC were registered. Image features were extracted from computed tomography (CT) images for each patient. Univariate and multivariate Cox regression analyses were performed to determine the independent prognostic factors for distant metastasis. The DLR-N was built based on independent prognostic factors and CT images to predict distant metastases. The model was assessed in terms of overall performance, discrimination, calibration, and clinical value. Finally, the predictive performance of the model was validated using the testing cohort.

RESULTS

The developed DLR-N combined multiple radiomic features and clinicopathological factors and demonstrated excellent predictive performance. The C-index of the training cohort was 0.983 (95% confidence interval [CI] = 0.969-0.998) and the C-index of the testing cohort was 0.948 (95% CI = 0.917-0.979). Decision curve analysis (DCA) showed that patients could benefit more from incorporating multimodal radiomic features into clinicopathological models.

CONCLUSIONS

DLR-N verified that there were biomarkers at the level of the fourth thoracic vertebra (T4) that affected distant metastasis. Multimodal prediction models based on deep learning could be a potential method to aid in the prediction of distant metastases in patients with BC.

Prediction of bone metastasis risk of early breast cancer based on nomogram of clinicopathological characteristics and hematological parameters

Objectives

The purpose of this study was to determine the independent risk factors for bone metastasis in breast cancer and to establish a nomogram to predict the risk of bone metastasis in early stages through clinicopathological characteristics and hematological parameters.

Methods

We selected 1042 patients with breast cancer from the database of Shandong Cancer Hospital for retrospective analysis, and determined independent risk factors for bone metastatic breast cancer (BMBC). A BMBC nomogram based on clinicopathological characteristics and hematological parameters was constructed using logistic regression analysis. The performance of the nomograph was evaluated using the receiver operating characteristic (ROC) and calibration curves. The clinical effect of risk stratification was tested using Kaplan-Meier analysis.

Results

BMBC patients were found to be at risk for eight independent risk factors based on multivariate analysis: age at diagnosis, lymphovascular invasion, pathological stage, pathological node stage, molecular subtype, platelet count/lymphocyte count, platelet count * neutrophil count/lymphocyte count ratio, Systemic Immunological Inflammation Index, and radiotherapy. The prediction accuracy of the BMBC nomogram was good. In the training set, the area under the ROC curve (AUC) was 0.909, and in the validation set, it was 0.926, which proved that our model had good calibration. The risk stratification system can analyze the risk of relapse in individuals into high- and low-risk groups.

Conclusion

The proposed nomogram may predict the possibility of breast cancer bone metastasis, which will help clinicians optimize metastatic breast cancer treatment strategies and monitoring plans to provide patients with better treatment.

Options of locoregional therapy for primary foci of breast cancer influence the rate of nonregional lymph node metastasis in N2-N3 status patients: a SEER database analysis

OBJECTIVE

We aim to use the SEER database to discuss the effect of various surgical methods of primary foci and other influencing factors on the nonregional lymph node (NRLN) metastasis in invasive ductal carcinoma (IDC) patients.

METHODS

Clinical information of IDC patients used in this study was obtained from the SEER database. The statistical analyses used included a multivariate logistic regression model, the chi-squared test, log-rank test and propensity score matching (PSM).

RESULTS

243,533 patients were included in the analysis. 94.3% of NRLN patients had a high N positivity (N3) but an equal distribution in T status. The proportion of operation type, especially BCM and MRM, differed significantly between the N0-N1 and N2-N3 groups in the NRLN metastasis group and nonmetastasis group. Age > 80 years, positive PR, modified radical mastectomy (MRM)/radical mastectomy (RM) and radiotherapy for primary tumor were shown to be protective factors for NRLN metastasis, and higher N positivity was the most significant risk factors. N2-N3 patients receiving MRM had a lower metastasis to NRLN than those receiving BCM (1.4% vs 3.7%, P < 0.001), while this relevance was not discovered in N0-N1 patients. In N2-N3 patients, a better OS was observed in MRM group than BCM group (P < 0.001).

CONCLUSION

MRM exerted a protective effect on NRLN metastasis compared to BCM in N2-N3 patients but not N0-N1 patients. This implies the need for more consideration when choosing the operation methods of primary foci in patients with high N positivity.

Implications for surveillance for breast cancer patients based on the internally and externally validated BRENDA-metastatic recurrence score

PURPOSE

Although the incidence of distant relapse is decreasing, 20-30% of patients with early breast cancer die of metastasis. The aim of this study is to characterize patients with metastasis-free survival(MFS) less than 5 years, to analyze the most probable site of metastases according to the internally and externally validated BRENDA-score. The BRENDA-score is a combination of the biological subtype and clinical staging.

METHOD

3832 patients with primary diagnosis of breast cancer and either distant metastatic recurrence within 5 years or MFS ≥ 5 years were assigned to this study. Patients were classified for metastatic recurrence according to the BRENDA-score. 1765 patients were in a validation set. Statistical methods were Kaplan-Meier curves, Cox regression analysis, Exhausted CHAID, likelihood-ratio tests and the Nearest Neighbor Estimation method.

RESULTS

There was a significant(p < 0.001) difference between the Kaplan-Meier MFS-functions of M0-patients stratified by BRENDA-score. The BRENDA score outperforms intrinsic subtypes and the Nottingham prognostic score. It fits the original data and the validation set equally well (p = 0.179).There was a significant(p < 0.001) difference between mean BRENDA-Index for patients with MFS < 5y(21.0 ± 9.0) and patients with MFS ≥ 5y(mean BRENDA-Index 11.7 ± 8.2). 55.6% of the very high risk patients(BRENDA-Index ≥ 27) had metastases within 5 years. The most likely primary metastatic site was bone(30%) followed by liver(19%) and lung(18%). The discriminatory ability(areas under the time dependent ROC curve) of the BRENDA score is good to acceptable for the first 5 years. In the very low/low risk (intermediate, high/very high) risk group 50% of all metastases were diagnosed within 26 months. Guideline adherence had a highly significant influence on outcome independent of the risk group.

CONCLUSION

The evaluation showed that the BRENDA-Score is a robust predictive tool for breast cancer recurrence and site of metastases in the first five years after diagnosis. It outperforms intrinsic subtypes and the Nottingham prognostic score. The BRENDA-score could be a tool for a risk orientated and targeted follow up.

The biomarkers related to immune infiltration to predict distant metastasis in breast cancer patients

Background: The development of distant metastasis (DM) results in poor prognosis of breast cancer (BC) patients, however, it is difficult to predict the risk of distant metastasis. Methods: Differentially expressed genes (DEGs) were screened out using GSE184717 and GSE183947. GSE20685 were randomly assigned to the training and the internal validation cohort. A signature was developed according to the results of univariate and multivariate Cox regression analysis, which was validated by using internal and external (GSE6532) validation cohort. Gene set enrichment analysis (GSEA) was used for functional analysis. Finally, a nomogram was constructed and calibration curves and concordance index (C-index) were compiled to determine predictive and discriminatory capacity. The clinical benefit of this nomogram was revealed by decision curve analysis (DCA). Finally, we explored the relationships between candidate genes and immune cell infiltration, and the possible mechanism. Results: A signature containing CD74 and TSPAN7 was developed according to the results of univariate and multivariate Cox regression analysis, which was validated by using internal and external (GSE6532) validation cohort. Mechanistically, the signature reflect the overall level of immune infiltration in tissues, especially myeloid immune cells. The expression of CD74 and TSPAN7 is heterogeneous, and the overexpression is positively correlated with the infiltration of myeloid immune cells. CD74 is mainly derived from myeloid immune cells and do not affect the proportion of CD8+T cells. Low expression levels of TSPAN7 is mainly caused by methylation modification in BC cells. This signature could act as an independent predictive factor in patients with BC (p = 0.01, HR = 0.63), and it has been validated in internal (p = 0.023, HR = 0.58) and external (p = 0.0065, HR = 0.67) cohort. Finally, we constructed an individualized prediction nomogram based on our signature. The model showed good discrimination in training, internal and external cohort, with a C-index of 0.742, 0.801, 0.695 respectively, and good calibration. DCA demonstrated that the prediction nomogram was clinically useful. Conclusion: A new immune infiltration related signature developed for predicting metastatic risk will improve the treatment and management of BC patients.

Comparison of the Characteristics and Prognosis Between Very Young Women and Older Women With Breast Cancer: A Multi-Institutional Report From China

Purpose

Our understanding of breast cancer in very young women (≤35 years old) remains limited. We aimed to assess the clinicopathological characteristics, molecular subtype, and treatment distribution and prognosis of these young patients compared with patients over 35 years.

Methods

We retrospectively analyzed non-metastatic female breast cancer cases treated at three Chinese academic hospitals between January 1, 2008, and December 31, 2018. Local recurrence-free survival (LRFS), disease-free survival (DFS), and overall survival (OS) were compared between different age groups and stratified with distinct molecular subtypes.

Results

A total of 11,671 women were eligible for the final analyses, and 1,207 women (10.3%) were ≤35 years at disease onset. Very young breast cancer women were more likely to be single or childless, have higher-grade disease, have more probability of lymphovascular invasion (LVI) in tumor and triple-negative subtype, and be treated by lumpectomy, chemotherapy especially more anthracycline- and paclitaxel-based chemotherapy, endocrine therapy plus ovarian function suppression (OFS), anti-HER2 therapy, and/or radiotherapy than older women (P < 0.05 for all). Very young women had the lowest 5-year LRFS and DFS among all age groups (P < 0.001 for all). When stratified by molecular subtype, very young women had the worst outcomes vs. women from the 35~50-year-old group or those from >50-year-old group for hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2−) subtype, including LRFS, DFS, and OS (P < 0.05 for all). In terms of LRFS and DFS, multivariate analyses showed similar results among the different age groups.

Conclusion

Our study demonstrated that very young women with breast cancer had higher-grade tumors, more probability of LVI in tumor, and more triple-negative subtype, when compared with older patients. They had less favorable survival outcomes, especially for patients with the HR+/HER2− subtype.

Multi-Omics Profiling Suggesting Intratumoral Mast Cells as Predictive Index of Breast Cancer Lung Metastasis

Breast cancer lung metastasis has a high mortality rate and lacks effective treatments, for the factors that determine breast cancer lung metastasis are not yet well understood. In this study, data from 1067 primary tumors in four public datasets revealed the distinct microenvironments and immune composition among patients with or without lung metastasis. We used multi-omics data of the TCGA cohort to emphasize the following characteristics that may lead to lung metastasis: more aggressive tumor malignant behaviors, severer genomic instability, higher immunogenicity but showed generalized inhibition of effector functions of immune cells. Furthermore, we found that mast cell fraction can be used as an index for individual lung metastasis status prediction and verified in the 20 human breast cancer samples. The lower mast cell infiltrations correlated with tumors that were more malignant and prone to have lung metastasis. This study is the first comprehensive analysis of the molecular and cellular characteristics and mutation profiles of breast cancer lung metastasis, which may be applicable for prognostic prediction and aid in choosing appropriate medical examinations and therapeutic regimens.

BRENDA-Score, a Highly Significant, Internally and Externally Validated Prognostic Marker for Metastatic Recurrence: Analysis of 10,449 Primary Breast Cancer Patients

Simple Summary

The BRENDA-Score provides an easy to use tool for clinicians to estimate the risk of recurrence in primary breast cancer. The algorithm has been validated via a second independent database and provides five recurrence risk groups. This grouping helps clinicians to encourage high risk patients to undergo the recommended treatment.

Abstract

Background Current research in breast cancer focuses on individualization of local and systemic therapies with adequate escalation or de-escalation strategies. As a result, about two-thirds of breast cancer patients can be cured, but up to one-third eventually develop metastatic disease, which is considered incurable with currently available treatment options. This underscores the importance to develop a metastatic recurrence score to escalate or de-escalate treatment strategies. Patients and methods Data from 10,499 patients were available from 17 clinical cancer registries (BRENDA-project. In total, 8566 were used to develop the BRENDA-Index. This index was calculated from the regression coefficients of a Cox regression model for metastasis-free survival (MFS). Based on this index, patients were categorized into very high, high, intermediate, low, and very low risk groups forming the BRENDA-Score. Bootstrapping was used for internal validation and an independent dataset of 1883 patients for external validation. The predictive accuracy was checked by Harrell’s c-index. In addition, the BRENDA-Score was analyzed as a marker for overall survival (OS) and compared to the Nottingham prognostic score (NPS). Results: Intrinsic subtypes, tumour size, grading, and nodal status were identified as statistically significant prognostic factors in the multivariate analysis. The five prognostic groups of the BRENDA-Score showed highly significant (p < 0.001) differences regarding MFS:low risk: hazard ratio (HR) = 2.4, 95%CI (1.7–3.3); intermediate risk: HR = 5.0, 95%CI.(3.6–6.9); high risk: HR = 10.3, 95%CI (7.4–14.3) and very high risk: HR = 18.1, 95%CI (13.2–24.9). The external validation showed congruent results. A multivariate Cox regression model for OS with BRENDA-Score and NPS as covariates showed that of these two scores only the BRENDA-Score is significant (BRENDA-Score p < 0.001; NPS p = 0.447). Therefore, the BRENDA-Score is also a good prognostic marker for OS. Conclusion: The BRENDA-Score is an internally and externally validated robust predictive tool for metastatic recurrence in breast cancer patients. It is based on routine parameters easily accessible in daily clinical care. In addition, the BRENDA-Score is a good prognostic marker for overall survival. Highlights: The BRENDA-Score is a highly significant predictive tool for metastatic recurrence of breast cancer patients. The BRENDA-Score is stable for at least the first five years after primary diagnosis, i.e., the sensitivities and specificities of this predicting system is rather similar to the NPI with AUCs between 0.76 and 0.81 the BRENDA-Score is a good prognostic marker for overall survival.

Construction of a MicroRNA-Based Nomogram for Prediction of Lung Metastasis in Breast Cancer Patients

The lung is one of the most common sites of distant metastasis in breast cancer (BC). Identifying ideal biomarkers to construct a more accurate prediction model than conventional clinical parameters is crucial. MicroRNAs (miRNAs) data and clinicopathological data were acquired from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database. miR-663, miR-210, miR-17, miR-301a, miR-135b, miR-451, miR-30a, and miR-199a-5p were screened to be highly relevant to lung metastasis (LM) of BC patients. The miRNA-based risk score was developed based on the logistic coefficient of the individual miRNA. Univariate and multivariate logistic regression selected tumor node metastasis (TNM) stage, age at diagnosis, and miRNA-risk score as independent predictive parameters, which were used to construct a nomogram. The Cancer Genome Atlas (TCGA) database was used to validate the signature and nomogram. The predictive performance of the nomogram was compared to that of the TNM stage. The area under the receiver operating characteristics curve (AUC) of the nomogram was higher than that of the TNM stage in all three cohorts (training cohort: 0.774 vs. 0.727; internal validation cohort: 0.763 vs. 0.583; external validation cohort: 0.925 vs. 0.840). The calibration plot of the nomogram showed good agreement between predicted and observed outcomes. The net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision-curve analysis (DCA) of the nomogram showed that its performances were better than that of the TNM classification system. Functional enrichment analyses suggested several terms with a specific focus on LM. Subgroup analysis showed that miR-30a, miR-135b, and miR-17 have unique roles in lung metastasis of BC. Pan-cancer analysis indicated the significant importance of eight predictive miRNAs in lung metastasis. This study is the first to establish and validate a comprehensive lung metastasis predictive nomogram based on the METABRIC and TCGA databases, which provides a reliable assessment tool for clinicians and aids in appropriate treatment selection.

Profiles of alternative splicing landscape in breast cancer and their clinical significance: an integrative analysis based on large-sequencing data

Background

Alternative splicing (AS) is closely correlated with the initiation and progression of carcinoma. The systematic analysis of its biological and clinical significance in breast cancer (BRCA) is, however, lacking.

Methods

Clinical data and RNA-seq were obtained from the TCGA dataset and differentially expressed AS (DEAS) events between tumor and paired normal BRCA tissues were identified. Enrichment analysis was then used to reveal the potential biological functions of DEAS events. We performed protein-protein interaction (PPI) analysis of DEAS events by using STRING and the correlation network between splicing factors (SFs) and AS events was constructed. The LASSO Cox model, Kaplan-Meier and log-rank tests were used to construct and evaluate DEAS-related risk signature, and the association between DEAS events and clinicopathological features were then analyzed.

Results

After strict filtering, 35,367 AS events and 973 DEAS events were detected. DEAS corresponding genes were significantly enriched in pivotal pathways including cell adhesion, cytoskeleton organization, and extracellular matrix organization. A total of 103 DEAS events were correlated with disease free survival. The DEAS-related risk signature stratified BRCA patients into two groups and the area under curve (AUC) was 0.754. Moreover, patients in the high-risk group had enriched basel-like subtype, advanced clinical stages, proliferation, and metastasis potency.

Conclusions

Collectively, the profile of DEAS landscape in BRCA revealed the potential biological function and prognostic value of DEAS events.

Development and validation of a nomogram in survival prediction among advanced breast cancer patients

Background

The overall survival (OS) among patients with advanced breast cancer (ABC) varies greatly. Although molecular subtype is known as the most important factor in OS differentiation, significant differences in OS among patients with the same molecular subtype still occur, leading to the need for a more accurate prognostic prediction model. This study aimed to develop a prediction model (nomogram) based on current diagnosis and treatment to predict the OS of newly diagnosed ABC patients in China.

Methods

From the institution’s database, we collected data of 368 ABC patients from Sun Yat-sen Memorial Hospital (national hospital) as a training set to establish a nomogram with prognostic risk factors that calculated the predicted probability of survival. Nomograms were independently validated with 278 patients with ABC from two other institutions using the concordance index (C-index), calibration plots and risk group stratifications.

Results

The initial primary tumor stage, molecular subtype, disease-free survival (DFS), presence of brain metastasis, and the tumor burden of metastasis disease (local recurrence, oligo-metastatic disease, or multiple-metastatic disease) were included in the prognostic nomogram. The nomogram had a C-index of 0.77 and 0.71 in the training and the validation sets, respectively. The nomogram was able to stratify patients into different risk groups, respectively (HR 6.81, 95% CI: 4.69 to 9.89, P<0.001). In the lower risk score group (risk score <11), there was no significant difference between the OS with chemotherapy and hormone therapy (HR 0.81, 95% CI: 0.44 to 1.47, P=0.48).

Conclusions

We have constructed a novel prediction nomogram that can guide the physicians to select personalized treatment options. Furthermore, our study is the first to add oligo-metastatic disease and primary endocrine/trastuzumab resistance into the prognostic models.

Clinicopathological characteristics and prediction of cancer-specific survival in large cell lung cancer: a population-based study

Background

To describe the demographic and clinical characteristics of large cell lung cancer (LCLC) with a population-based database and to find the prognosis factors of cancer-specific survival (CSS) for these patients; also, to develop a nomogram to independently validate and predict the CSS for LCLC based on the identified prognosis factors.

Methods

We extracted the LCLC patient’s information from the Surveillance, Epidemiology, and End Results (SEER) database [2005–2014] and summarized the characteristics of the extracted factors. We used Cox proportional hazards regression to find the prognosis factors for LCLC patients and to develop the nomogram based on these in a split train cohort from the extracted data. The validation of the developed nomograms was performed in an independent validation cohort from the extracted data, in which the C-index and the average of the time-dependent area under the receiver operating characteristic curve (time-dependent AUC) for CSS in 1-year, 3-year, and 5-year CSS was calculated. The calibration curves were drawn to visualize the performance of the established nomogram.

Results

As a result, 4,936 patients with LCLC were identified from the SEER database. Nearly half of LCLC patients were diagnosed with stage IV; only approximately 20% of patients underwent surgery. The prognosis factors that influenced the LCLC patients included age, sex, American Joint Committee on Cancer (AJCC) stage, race, surgery, tumor size, and marital status. The calculated C-index was 0.701±0.01, and the mean time-dependent AUC for in 1-year, 3-year, and 5-year CSS was 0.88. The calibrated curve showed that the gap between the predicted and observed values for 1-year, 3-year, and 5-year CSS was small.

Conclusions

Sex, age, race, marital status, AJCC stage, surgery, and tumor size were shown to all be the independent prognostic factors of CSS in LCLC. The established nomogram can provide more precise evaluation for the survival of LCLC patients and help the clinicians in the individual management of patients.