The prognostic analysis of different metastatic patterns in extensive-stage small-cell lung cancer patients: a large population-based study

Metastatic pattern and prognosis in patients with lung adenosquamous carcinoma: A surveillance, epidemiology, and end results-based population study

Background

Lung adenosquamous carcinoma (ASC) is a rare tumor with high invasive and metastatic potential. Few studies have explored metastatic patterns in patients with advanced-stage ASC.

Methods

Patients diagnosed with ASC in the Surveillance, Epidemiology, and End Results database from 2010 to 2015 were selected. Descriptive statistics were obtained to characterize the metastatic sites of the study participants. The Kaplan-Meier method was applied to compare survival curves among patients with different metastatic patterns. Cox regression analysis was performed to evaluate risk factors for metastasis.

Results

A total of 858 eligible patients with ASC were enrolled; the mean age was 71.5 years (standard deviation ± 7.8 years). There was a slightly higher proportion of male patients (54.0 %). A total of 63.2 % of patients harbored single-site metastasis (median OS: 5 months), 23.6 % of patients had two-site metastasis (median OS: 4 months), and approximately 10 % of patients harbored three or more sites metastasis (median OS: 3 months). Bone (56.9 %) was the most frequent site of metastasis (median OS: 4 months), followed by lung metastasis (37.6 %, median OS: 5 months), liver metastasis (22.1 %, median OS: 5 months), and brain metastasis (21.4 %, median OS: 4 months). Chemotherapy decreased the risk of death by 70 % (HR = 0.296, 95 % CI 0.241-0.363), 70 % (HR = 0.302, 95 % CI 0.224-0.406), 78 % (HR = 0.218, 95 % CI 0.151-0.314), and 70 % (HR = 0.302, 95 % CI 0.231-0.396) in patients harboring bone, liver, brain and lung metastases, respectively. The brain increased the risk of death by 50 % (HR = 1.501, 95 % CI 1.209-1.865), 64 % (HR = 1.644, 95 % CI 1.126-2.402), and 128 % (HR = 2.284, 95 % CI 1.653-3.157) in patients harboring bone, liver and lung metastases, respectively.

Conclusion

Patients with advanced-stage ASC have unfavorable outcomes. Early detection and aggressive treatment can improve patients outcomes.

Prognostic factors in extensive-stage small cell lung cancer patients with organ-specific metastasis: unveiling commonalities and disparities

BACKGROUND

This study aimed to identify shared and distinct prognostic factors related to organ-specific metastases (liver, lung, bone, and brain) in extensive-stage small cell lung cancer (ES-SCLC) patients, then construct nomograms for survival prediction.

METHODS

Patient data for ES-SCLC were from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2019. Kaplan-Meier analysis was applied to estimate overall survival (OS), and Cox regression was used to identify prognostic factors. A Venn diagram was utilized to distinguish common and unique prognostic factors among the variables assessed. These identified prognostic factors were used to formulate a nomogram, and its predictive accuracy and reliability were evaluated using C-indexes, calibration curves, and receiver operating characteristic (ROC) curves.

RESULTS

A total of 24,507 patients diagnosed with ES-SCLC exhibiting metastases to the liver, lung, bone, and brain were included. The 6-month, 1-year, and 2-year OS rates were 46.1%, 19.7%, and 5.0%, respectively. Patients with liver metastasis demonstrated the most unfavorable prognosis, with a 1-year OS rate of 14.5%, while those with brain metastasis had a significantly better prognosis with a 1-year OS rate of 21.6%. The study identified seven common factors associated with a poor prognosis in ES-SCLC patients with organ-specific metastases: older age, male sex, unmarried status, higher T stage, presence of other metastases, and combination radiotherapy and chemotherapy. Furthermore, specific prognostic factors were identified for patients with metastasis to the liver, bone, and brain, including paired tumors, lack of surgical treatment at the primary site, and household income, respectively. To facilitate prognostic predictions, four nomograms were developed and subsequently validated. The performance of these nomograms was assessed using calibration curves, C-indexes, and the area under the curve (AUC), all of which consistently indicated good predictive accuracy and reliability.

CONCLUSIONS

Patients diagnosed with ES-SCLC with organ-specific metastases revealed shared and distinct prognostic factors. The nomograms developed from these factors demonstrated good performance and can serve valuable clinical tools to predict the prognosis of ES-SCLC patients with organ-specific metastases.

Development and validation of prognostic models for small cell lung cancer patients with liver metastasis: a SEER population-based study

BACKGROUND

This study was to establish and validate prediction models to predict the cancer-specific survival (CSS) and overall survival (OS) of small-cell lung cancer (SCLC) patients with liver metastasis.

METHODS

In the retrospective cohort study, SCLC patients with liver metastasis between 2010 and 2015 were retrospectively retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into the training group and testing group (3: 1 ratio). The Cox proportional hazards model was used to determine the predictive factors for CSS and OS in SCLC with liver metastasis. The prediction models were conducted based on the predictive factors. The performances of the prediction models were evaluated by concordance indexes (C-index), and calibration plots. The clinical value of the models was evaluated by decision curve analysis (DCA).

RESULTS

In total, 8,587 patients were included, with 154 patients experiencing CSS and 154 patients experiencing OS. The median follow-up was 3 months. Age, gender, marital status, N stage, lung metastases, multiple metastases surgery of metastatic site, chemotherapy, and radiotherapy were independent predictive factors for the CSS and OS of SCLC patients with liver metastasis. The prediction models presented good performances of CSS and OS among patients with liver metastasis, with the C-index for CSS being 0.724, whereas the C-index for OS was 0.732, in the training set. The calibration curve showed a high degree of consistency between the actual and predicted CSS and OS. DCA suggested that the prediction models provided greater net clinical benefit to these patients.

CONCLUSION

Our prediction models showed good predictive performance for the CSS and OS among SCLC patients with liver metastasis. Our developed nomograms may help clinicians predict CSS and OS in SCLC patients with liver metastasis.

The Role of Exosome-Derived microRNA on Lung Cancer Metastasis Progression

The high mortality from lung cancer is mainly attributed to the presence of metastases at the time of diagnosis. Despite being the leading cause of lung cancer death, the underlying molecular mechanisms driving metastasis progression are still not fully understood. Recent studies suggest that tumor cell exosomes play a significant role in tumor progression through intercellular communication between tumor cells, the microenvironment, and distant organs. Furthermore, evidence shows that exosomes release biologically active components to distant sites and organs, which direct metastasis by preparing metastatic pre-niche and stimulating tumorigenesis. As a result, identifying the active components of exosome cargo has become a critical area of research in recent years. Among these components are microRNAs, which are associated with tumor progression and metastasis in lung cancer. Although research into exosome-derived microRNA (exosomal miRNAs) is still in its early stages, it holds promise as a potential target for lung cancer therapy. Understanding how exosomal microRNAs promote metastasis will provide evidence for developing new targeted treatments. This review summarizes current research on exosomal miRNAs' role in metastasis progression mechanisms, focusing on lung cancer.

Feasibility and long-term outcomes of post-chemotherapy-based consolidation radiotherapy in extensive stage small-cell lung cancer

Background

The target definition of consolidation radiotherapy (RT) for extensive stage small-cell lung cancer (ES-SCLC) has not been standardized. This study aimed to demonstrate the feasibility of post-chemotherapy based consolidation RT in ES-SCLC.

Methods

All ES-SCLC patients without initial brain metastases who completed ≥ 4 cycles of systemic therapy at Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University from 2012 to 2021 were included in this retrospective study. We correlated the site of first recurrence to the post-chemotherapy-based radiation volume (small-field). Relapse pattern, progression-free survival (PFS) and overall survival (OS) were compared between those received and did not receive consolidation RT.

Results

A total of 152 patients were followed up for a median of 31.7 months (interquartile range [IQR], 23.9-39.6 months). The median PFS and OS of the cohort were 8.3 months (IQR, 6.1-11.2 months) and 16.2 months (IQR, 9.9-24.9 months), respectively. Thoracic consolidation RT served not only as an independent prognostic factor for improved PFS in the entire cohort, but also significantly prolonged OS in the subgroup without synchronous liver metastases. Small-field consolidation RT markedly reduced in-field recurrences (hazard ratio [HR], 0.28 [95% CI, 0.12-0.38]; P < 0.001) without increasing out-of-field recurrences (HR, 0.40 [95% CI, 0.13-1.16]; P = 0.080). No relapse was observed at the margin of the targets. Treatment-related toxicities were moderate, with grade 3 acute radiation pneumonia, radiation esophagitis, and bone marrow suppression rates of 8.3%, 3.1%, and 12.5%, respectively. No grade 5 toxicity occurred.

Conclusion

Small-field consolidation RT based on post-chemotherapy volume is safe and can significantly improve local control in ES-SCLC.

Individual risk and prognostic value prediction by machine learning for distant metastasis in pulmonary sarcomatoid carcinoma: a large cohort study based on the SEER database and the Chinese population

Background

This study aimed to develop diagnostic and prognostic models for patients with pulmonary sarcomatoid carcinoma (PSC) and distant metastasis (DM).

Methods

Patients from the Surveillance, Epidemiology, and End Results (SEER) database were divided into a training set and internal test set at a ratio of 7 to 3, while those from the Chinese hospital were assigned to the external test set, to develop the diagnostic model for DM. Univariate logistic regression was employed in the training set to screen for DM-related risk factors, which were included into six machine learning (ML) models. Furthermore, patients from the SEER database were randomly divided into a training set and validation set at a ratio of 7 to 3 to develop the prognostic model which predicts survival of patients PSC with DM. Univariate and multivariate Cox regression analyses have also been performed in the training set to identify independent factors, and a prognostic nomogram for cancer-specific survival (CSS) for PSC patients with DM.

Results

For the diagnostic model for DM, 589 patients with PSC in the training set, 255 patients in the internal and 94 patients in the external test set were eventually enrolled. The extreme gradient boosting (XGB) algorithm performed best on the external test set with an area under the curve (AUC) of 0.821. For the prognostic model, 270 PSC patients with DM in the training and 117 patients in the test set were enrolled. The nomogram displayed precise accuracy with AUC of 0.803 for 3-month CSS and 0.869 for 6-month CSS in the test set.

Conclusion

The ML model accurately identified individuals at high risk for DM who needed more careful follow-up, including appropriate preventative therapeutic strategies. The prognostic nomogram accurately predicted CSS in PSC patients with DM.

Construction of the prognostic model for small-cell lung cancer based on inflammatory markers: A real-world study of 612 cases with eastern cooperative oncology group performance score 0-1

OBJECTIVES

This research aimed to explore the relationship between pre-treatment inflammatory markers and other clinical characteristics and the survival of small-cell lung cancer (SCLC) patients who received first-line platinum-based treatment and to construct nomograms for predicting overall survival (OS) and progression-free survival (PFS).

METHODS

A total of 612 patients diagnosed with SCLC between March 2008 and August 2021 were randomly divided into two cohorts: a training cohort (n = 459) and a validation cohort (n = 153). Inflammatory markers, clinicopathological factors, and follow-up information of patients were collected for each case. Cox regression was used to conduct univariate and multivariate analyses and the independent prognostic factors were adopted to develop the nomograms. Harrell's concordance index (C-index) and time-dependent receiver operating characteristic curve were used to verify model differentiation, calibration curve was used to verify consistency, and decision curve analysis was used to verify the clinical application value.

RESULTS

Our results showed that baseline C-reactive protein/albumin ratio, neutrophil/lymphocyte ratio, NSE level, hyponatremia, the efficacy of first-line chemotherapy, and stage were independent prognostic factors for both OS and PFS in SCLC. In the training cohort, the C-index of PFS and OS was 0.698 and 0.666, respectively. In the validation cohort, the C-index of PFS and OS was 0.727 and 0.747, respectively. The nomograms showed good predictability and high clinical value. Also, our new clinical models were superior to the US Veterans Administration Lung Study Group (VALG) staging for predicting the prognosis of SCLC.

CONCLUSIONS

The two prognostic nomograms of SCLC including inflammatory markers, VALG stage, and other clinicopathological factors had good predictive value and could individually assess the survival of patients.

Deep learning of bone metastasis in small cell lung cancer: A large sample-based study

Introduction

Bone is a common metastatic site for small cell lung cancer (SCLC). Bone metastasis (BM) in patients have are known to show poor prognostic outcomes. We explored the epidemiological characteristics of BM in SCLC patients and create a new deep learning model to predict outcomes for cancer-specific survival (CSS) and overall survival (OS).

Materials and Methods

Data for SCLC patients diagnosed with or without BM from 2010 to 2016 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox proportional hazards regression models were used to evaluate the effects of prognostic variables on OS and CSS. Through integration of these variables, nomograms were created for the prediction of CSS and OS rates at 3-month,6- month,and 12-month. Harrell's coordination index, calibration curves,and time- dependent ROC curves were used to assess the nomograms' accuracy. Decision tree analysis was used to evaluate the clinical application value of the established nomogram.

Results

In this study, 4201 patients were enrolled. Male sex, tumor size 25 but <10, brain and liver metastases, as well as chemotherapy were associated with a high risk for BM. Tumor size, Age, N stage, gender, liver metastasis, radiotherapy as well as chemotherapy were shown to be prognostic variables for OS, and the prognostic variables for CSS were added to the tumor number in addition. Based on these results, nomograms for CSS and OS were established separately. Internal as well as external validation showed that the C-index, calibration cuurve and DCA had good constructive correction effect and clinical application value. Decision tree analysis further confirmed the prognostic factors of OS and CSS.

Discussion

The nomogram and decision tree models developed in this study effectively guided treatment decisions for SCLC patients with BM. The creation of prediction models for BM SCLC patients may be facilitated by deep learning models.

125I Radiotherapy combined with metronomic chemotherapy may boost the abscopal effect, leading to complete regression of liver metastasis in an SCLC patient with a 58.5-month OS: a case report

The liver is the most common and lethal metastatic site in patients with extensive-stage small-cell lung cancer (ES-SCLC), and median survival with current standard treatment is only 9-10 months from diagnosis. Clinical observations show that a complete response (CR) is extremely rare in ES-SCLC patients with liver metastasis. Moreover, to the best of our knowledge, complete regression of liver metastasis induced by the abscopal effect, boosted primarily by permanent radioactive iodine-125 seeds implantation (PRISI), combined with a low-dose metronomic temozolomide (TMZ) regimen, has not been recorded. Here, we present the case of a 54-year-old male patient who developed multiple liver metastases from ES-SCLC after multiple lines of chemotherapy. The patient was given partial PRISI therapy (two out of six tumor lesions; 38 iodine-125 seeds in one dorsal lesion and 26 seeds in one ventral lesion), which was combined with TMZ metronomic chemotherapy (50 mg/m²/day, days 1-21, every 28 days). The abscopal effect was observed for 1 month after PRISI treatment. After about 1 year, all the liver metastases had completely disappeared, and the patient experienced no relapse. The patient eventually died of malnutrition caused by a non-tumor intestinal obstruction and had an overall survival of 58.5 months after diagnosis. PRISI combined with TMZ metronomic chemotherapy might be considered a potential therapy to trigger the abscopal effect in patients with liver metastases.

The prognosis analysis of organ metastatic patterns in lung large cell neuroendocrine carcinoma: A population-based study

Lung large cell neuroendocrine carcinoma (LCNEC) is a rare and highly aggressive malignancy with a dismal prognosis. This study was designed to depict patterns of distant organ metastatic and to analyze prognosis of LCNEC patients. We gathered data from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. We conducted the Kaplan-Meier method to calculate overall survival (OS) and compare different variables. Cox proportional hazards regression models in univariate and multivariate analyses were employed to further explore prognostic factors. A total of 1335 LCNEC patients were eventually selected from the SEER database, of which 348 patients (26.0%) had single organ metastasis and 197 patients (14.8%) had multiple metastases. Our study indicates that patients with single organ metastasis generally have a poor prognosis, with a median OS of 8 months for both lung and brain metastasis with 1-year survival rates of 33% and 29% respectively. Patients with multiple metastases exhibited the worst prognosis, with a median OS of only 4 months and a 1-year OS of 8%. Multivariate analysis revealed that age, T stage, N stage, chemotherapy and radiation in metastatic patients were independently associated with OS. In conclusion, LCNEC exhibits a high metastatic rate when diagnosed. The most common metastatic organ is the brain in single-site metastatic patients. Patients with single or multiple metastases exhibit a significantly worse prognosis than those with non-organ metastases. In the group of single organ metastases, patients with brain and lung metastases had a better prognosis than those with bone and liver metastases.

A prognostic nomogram to predict survival in elderly patients with small-cell lung cancer: a large population-based cohort study and external validation

BACKGROUND

Age is an independent prognostic factor for small cell lung cancer (SCLC). We aimed to construct a nomogram survival prediction for elderly SCLC patients based on the Surveillance, Epidemiology, and End Results (SEER) database.

METHODS

A total of 2851 elderly SCLC patients from the SEER database were selected as a primary cohort, which were randomly divided into a training cohort and an internal validation cohort. Additionally, 512 patients from two institutions in China were identified as an external validation cohort. We used univariate and multivariate to determine the independent prognostic factors and establish a nomogram to predict survival. The value of the nomogram was evaluated by calibration plots, concordance index (C-index) and decision curve analysis (DCA).

RESULTS

Ten independent prognostic factors were determined and integrated into the nomogram. Calibration plots showed an ideal agreement between the nomogram predicted and actual observed probability of survival. The C-indexes of the training and validation groups for cancer-specific survival (CSS) (0.757 and 0.756, respectively) based on the nomogram were higher than those of the TNM staging system (0.631 and 0.638, respectively). Improved AUC value and DCA were also obtained in comparison with the TNM model. The risk stratification system can significantly distinguish individuals with different survival risks.

CONCLUSION

We constructed and externally validated a nomogram to predict survival for elderly patients with SCLC. Our novel nomogram outperforms the traditional TNM staging system and provides more accurate prediction for the prognosis of elderly SCLC patients.

Correlation between tumor diameter, distant metastasis site, and survival in extensive stage small cell lung cancer

Background

Small cell lung cancer (SCLC) is a malignant disease that spreads quickly. There is limited research on the relationship between tumor diameter and distant metastatic patterns in extensive stage small cell lung cancer (ES-SCLC). This study aimed to investigate the relationship between tumor diameter, distant metastasis site, and survival in extensive stage small cell lung cancer.

Method

Patients over the age of 18 who applied to Ankara Atatürk Chest Diseases and Thoracic Surgery Training and Research Hospital with the diagnosis of small cell lung cancer and distant organ metastasis between January 2015 and December 2019 were retrospectively analyzed.

Results

The study comprised a total of 178 patients, with 12 women (6.7%) and 166 men (93.3%) participating. The patient was followed for a period of 1 to 36 months, with a median value of 7 months. The univariate model showed that pancreatic metastasis, single metastasis, tumor diameter, and tumor N stage had a significant (p = 0.003, p = 0.001, p = 0.013, p = 0.001, respectively) effect on survival. The N stage III group’s expected life expectancy [6.8 months (5.8–7.7)] was considerably (p = 0.000) lower than the N stage I–II groups [11.2 months (8.8–13.4)]. The predicted life expectancy for the group with pancreatic metastasis [4.1 months (2.6–5.5)] was significantly (p = 0.001) shorter than that of the group without pancreatic metastasis [8.9 months (7.6–10.1)]. The predicted life expectancy for the group with tumor size > 7 cm [6.7 months (5.4–8.0)] was significantly shorter than that of the group with tumor size of 0–3 cm [10.9 months (7.3–14.6)] (p = 0.019) and 3–7 cm [9.2 months (7.5–11)] (p = 0.023).

Conclusion

The authors of this study found that pancreatic metastasis, single metastasis, tumor diameter, and tumor N stage can be used as independent predictive factors for the survival of SCLC patients.

The prognostic risk stratification model for metastatic small-cell lung cancer: An analysis of the SEER database

Distant metastases of small-cell lung cancer (DM-SCLC) is an important factor in the selection of treatment strategies. In this study, we established a nomogram to predict DM-SCLC and determine the benefit of radiotherapy (RT) for DM-SCLC. We analyzed DM-SCLC prognosis based on surveillance, epidemiology, and end result database (SEER) data. A comprehensive and practical nomogram that predicts the overall survival (OS) of DM-SCLC was constructed and the results were compared with the 7th edition of the American Joint Committee on Cancer (AJCC) TNM stage system. A concordance index (C-index) and receiver operating characteristic plot were generated to evaluate the nomogram discrimination. The calibration was evaluated with a calibration plot, and its effectiveness was evaluated by a decision curve analysis (DCA). A score was assigned to each variable, and a total score was established for the risk stratification model. A total of 13,403 DM-SCLC patients were included. Eight characteristic variables were identified as independent prognostic variables. The C-index of the validation and training cohorts was 0.716 and 0.734, respectively. The area under the receiver operating characteristic curve (AUC) values of the nomogram used to predict 1-, 2-, and 3-year OS were 0.751, 0.744, and 0.786 in the validation cohorts (0.761, 0.777, 0.787 in the training cohorts), respectively. The calibration curve of 1-, 2-, 3-year survival rates showed that the prediction of the nomogram was in good agreement with the actual observation. The nomogram exhibited higher clinical utility after evaluation with the 1-, 2-, 3-year DCA compared with the AJCC stage system. A predictive nomogram and risk stratification model have been constructed to evaluate the prognosis of DM-SCLC effectively and accurately. This nomogram may provide a reference for prognosis stratification and treatment decisions.

Development and validation of a prognostic nomogram for bone metastasis from lung cancer: A large population-based study

Background

Bone is one of the most common metastatic sites of advanced lung cancer, and the median survival time is significantly shorter than that of patients without metastasis. This study aimed to identify prognostic factors associated with survival and construct a practical nomogram to predict overall survival (OS) in lung cancer patients with bone metastasis (BM).

Methods

We extracted the patients with BM from lung cancer between 2011 and 2015 from the Surveillance, Epidemiology, and End Result (SEER) database. Univariate and multivariate Cox regressions were performed to identify independent prognostic factors for OS. The variables screened by multivariate Cox regression analysis were used to construct the prognostic nomogram. The performance of the nomogram was assessed by receiver operating characteristic (ROC) curve, concordance index (C-index), and calibration curves, and decision curve analysis (DCA) was used to assess its clinical applicability.

Results

A total of 7861 patients were included in this study and were randomly divided into training (n=5505) and validation (n=2356) cohorts using R software in a ratio of 7:3. Cox regression analysis showed that age, sex, race, grade, tumor size, histological type, T stage, N stage, surgery, brain metastasis, liver metastasis, chemotherapy and radiotherapy were independent prognostic factors for OS. The C-index was 0.723 (95% CI: 0.697-0.749) in the training cohorts and 0.738 (95% CI: 0.698-0.778) in the validation cohorts. The AUC of both the training cohorts and the validation cohorts at 3-month (0.842 vs 0.859), 6-month (0.793 vs 0.814), and 1-year (0.776 vs 0.788) showed good predictive performance, and the calibration curves also demonstrated the reliability and stability of the model.

Conclusions

The nomogram associated with the prognosis of BM from lung cancer was a reliable and practical tool, which could provide risk assessment and clinical decision-making for individualized treatment of patients.

Prognostic nomogram and novel risk-scoring system for small cell lung cancer with different patterns of metastases

OBJECTIVE

This research is aimed to develop the prognostic nomogram and novel risk-scoring system for small cell lung cancer (SCLC) with different patterns of metastases.

METHODS

Data on SCLC patients were extracted from the 2010-2015 Surveillance, Epidemiology, and End Results (SEER) database. This nomogram prognostic model was confirmed in the validation cohort. C-index and calibration curve were used to evaluate the accuracy of nomogram model. The predictive capability and net benefit of nomogram was estimated by decision curve analysis (DCA). The cut-off point of the risk stratification system based on nomogram was assessed by X-tile analysis.

RESULTS

Our Cox model indicated that age, gender, American Joint Committee on Cancer (AJCC) stage, metastases, chemotherapy, radiation and surgery were independent predictors for OS in SCLC patients. The C-index value of nomogram integrating significant variables for predicting OS in SCLC patients was 0.752 in SEER training set and 0.748 in SEER validation set, respectively. However, the TNM stage only had C-indexes of 0.464 and 0.472 for predicting OS, respectively. The nomogram prognostic model in this study showed higher C-indexes than those in the TNM stage. The C-index value and high quality of calibration plots indicate that the predictive ability of our nomogram model was of great superiority. DCA showed the nomogram had good clinical value. SCLC patients were further divided into low-risk and high-risk group according to nomogram predicted scores.

CONCLUSION

Our nomogram model that integrated significant factors can aid as an individualized clinical predictive tool in SCLC patients.

Machine Learning for the Prediction of Synchronous Organ-Specific Metastasis in Patients With Lung Cancer

Background

This study aimed to develop an artificial neural network (ANN) model for predicting synchronous organ-specific metastasis in lung cancer (LC) patients.

Methods

A total of 62,151 patients who diagnosed as LC without data missing between 2010 and 2015 were identified from Surveillance, Epidemiology, and End Results (SEER) program. The ANN model was trained and tested on an 75/25 split of the dataset. The receiver operating characteristic (ROC) curves, area under the curve (AUC) and sensitivity were used to evaluate and compare the ANN model with the random forest model.

Results

For distant metastasis in the whole cohort, the ANN model had metrics AUC = 0.759, accuracy = 0.669, sensitivity = 0.906, and specificity = 0.613, which was better than the random forest model. For organ-specific metastasis in the cohort with distant metastasis, the sensitivity in bone metastasis, brain metastasis and liver metastasis were 0.913, 0.906 and 0.925, respectively. The most important variable was separate tumor nodules with 100% importance. The second important variable was visceral pleural invasion for distant metastasis, while histology for organ-specific metastasis.

Conclusions

Our study developed a “two-step” ANN model for predicting synchronous organ-specific metastasis in LC patients. This ANN model may provide clinicians with more personalized clinical decisions, contribute to rationalize metastasis screening, and reduce the burden on patients and the health care system.

Heterogeneity of neuroendocrine transcriptional states in metastatic small cell lung cancers and patient-derived models

Molecular subtypes of small cell lung cancer (SCLC) defined by the expression of key transcription regulators have recently been proposed in cell lines and limited number of primary tumors. The clinical and biological implications of neuroendocrine (NE) subtypes in metastatic SCLC, and the extent to which they vary within and between patient tumors and in patient-derived models is not known. We integrate histology, transcriptome, exome, and treatment outcomes of SCLC from a range of metastatic sites, revealing complex intra- and intertumoral heterogeneity of NE differentiation. Transcriptomic analysis confirms previously described subtypes based on ASCL1, NEUROD1, POU2F3, YAP1, and ATOH1 expression, and reveal a clinical subtype with hybrid NE and non-NE phenotypes, marked by chemotherapy-resistance and exceedingly poor outcomes. NE tumors are more likely to have RB1, NOTCH, and chromatin modifier gene mutations, upregulation of DNA damage response genes, and are more likely to respond to replication stress targeted therapies. In contrast, patients preferentially benefited from immunotherapy if their tumors were non-NE. Transcriptional phenotypes strongly skew towards the NE state in patient-derived model systems, an observation that was confirmed in paired patient-matched tumors and xenografts. We provide a framework that unifies transcriptomic and genomic dimensions of metastatic SCLC. The marked differences in transcriptional diversity between patient tumors and model systems are likely to have implications in development of novel therapeutic agents.

Value of pretreatment serum lactate dehydrogenase as a prognostic and predictive factor for small-cell lung cancer patients treated with first-line platinum-containing chemotherapy

BACKGROUND

The current study aimed to evaluate the serum pretreatment lactate dehydrogenase (LDH) and overall survival (OS) in small cell lung cancer (SCLC) patients who received first-line platinum-containing chemotherapy.

METHODS

A total of 234 SCLC patients, who received first-line platinum-based chemotherapy between 2013 and 2018, were retrospectively analyzed. The data of hematological characteristics, age, gender, ECOG score, staging, metastatic site, smoking history, chemotherapy cycle, thoracic radiotherapy and hyponatremia were collected. Overall survival was calculated using the Kaplan-Meier method. The statistically significant factors in the univariate analysis were selected for the multivariate COX model analysis.

RESULTS

Age, ECOG score, stage, thoracic radiotherapy, hyponatremia, liver metastasis, brain metastasis, bone metastasis, LDH, NSE and neutrophil-to-lymphocyte ratio (NLR) were closely correlated to OS in the univariate analysis. Furthermore, the multivariate analysis revealed that age (<65 years), ECOG score (<2 points), limited-stage (LD), thoracic radiotherapy and LDH <215.70 U/L were the independent prognostic factors for survival. The median OS time was worse for patients with LDH ≥215.70 U/L. In the subgroup analysis, LDH ≥215.70 U/L was significant for survival in both limited and extensive disease. Patients who achieved CR + PR in the first-line treatment had lower initial LDH levels. It was found that the pretreatment LDH increased the incidence of patients with liver metastasis.

CONCLUSIONS

Positive independent prognostic factors for SCLC patients were age < 65 years old, ECOG score < 2 points, LD-SCLC, and pretreatment LDH <215.70 U/L. These factors may be useful for stratifying patients with SCLC for treatment approaches.

KEY POINTS

SIGNIFICANT FINDINGS OF THE STUDY: Age < 65 years old, ECOG score < 2 points, LD-SCLC, and pretreatment LDH <215.70 U/L are the positive independent prognostic factors for SCLC patients.

WHAT THIS STUDY ADDS

The current study provided more references for SCLC diagnosis and treatment and determined more factors for stratifying patients with SCLC for treatment approaches.

Construction and Validation of a Novel Nomogram to Predict the Overall Survival of Patients With Combined Small Cell Lung Cancer: A Surveillance, Epidemiology, and End Results Population-Based Study

INTRODUCTION

Combined small cell lung cancer (C-SCLC) represents a rare subtype of all small cell lung cancer cases, with limited studies investigated its prognostic factors. The aim of this study was to construct a novel nomogram to predict the overall survival (OS) of patients with C-SCLC.

METHODS

In this retrospective study, a total of 588 C-SCLC patients were selected from the Surveillance, Epidemiology, and End Results database. The univariate and multivariate Cox analyses were performed to identify optimal prognostic variables and construct the nomogram, with concordance index (C-index), receiver operating characteristic curves, and calibration curves being used to evaluate its discrimination and calibration abilities. Furthermore, decision curve analysis (DCA), integrated discrimination improvement (IDI), and net reclassification index (NRI) were also adopted to assess its clinical utility and predictive ability compared with the classic TNM staging system.

RESULTS

Seven independent predictive factors were identified to construct the nomogram, including T stage, N stage, M stage, brain metastasis, liver metastasis, surgery, and chemotherapy. We observed a higher C-index in both the training (.751) and validation cohorts (.736). The nomogram has higher area under the curve in predicting 6-, 12-, 18-, 24-, and 36-month survival probability of patients with C-SCLC. Meanwhile, the calibration curves also revealed high consistencies between the actual and predicted OS. DCA revealed that the nomogram could provide greater clinical net benefits to these patients. We found that the NRI for 6- and 12-month OS were .196 and .225, and the IDI for 6- and 12-month OS were .217 and .156 in the training group, suggesting that the nomogram can predict a more accurate survival probability. Similar results were also observed in the validation cohort.

CONCLUSION

We developed and verified a novel nomogram that can help clinicians recognize high-risk patients with C-SCLC and predict their OS.

Sites of Synchronous Distant Metastases, Prognosis, and Nomogram for Small Cell Lung Cancer Patients with Bone Metastasis: A Large Cohort Retrospective Study

Background

Small cell lung cancer (SCLC) is often associated with metastases at the time of diagnosis, and the bone is one of the most common sites. The primary aim of this study was to investigate the site of synchronous distant metastasis to other organs in SCLC patients with bone metastasis (BM) and develop a robust predictive prognostic model.

Methods

We retrospectively analyzed the data from patients diagnosed with SCLC with BM in the Surveillance, Epidemiology, and End Results database. Univariate and multivariate Cox analyses were used to identify independent prognostic factors. A prognostic nomogram was constructed and evaluated by calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). Then, according to the sites of metastasis and treatment modality, all patients were stratified into several subgroups. The relationship among sites of metastasis, treatment modality, and overall survival was then analyzed.

Results

A total of 6253 patients were included. Independent prognostic factors for SCLC with BM were age, sex, primary site, radiotherapy, chemotherapy, brain metastasis, liver metastasis, and marital status. Calibration, ROC curves, and DCA indicated the excellent performance of the prognostic nomogram. The liver is the most common organ for extraskeletal metastases, followed by the lung. Patients with only BM had the longest mean survival time (9.30 ± 0.31 months). In the subgroup analysis, chemotherapy was an independent prognostic factor for all subgroups. In contrast, radiotherapy showed a positive effect on the prognosis of patients in all subgroups except those with bone and brain metastases and those with bone, lung, and brain metastases.

Conclusions

The prognostic nomogram is expected to be an accurate and personalized tool for predicting the prognosis of SCLC patients with BM. Additionally, the determination of the sites of synchronous extraskeletal metastases and the associated prognosis helps in treatment selection.

Clinical outcomes of extensive-stage small cell lung cancer patients treated with thoracic radiotherapy at different times and fractionations

Objective

The purpose of this study was to assess whether thoracic radiotherapy (TRT) combined with chemotherapy (CHT) showed promising anti-tumour activity in extensive-stage small cell lung cancer (ES-SCLC), to explore practice patterns for the radiation time and dose/fractionation and to identify prognostic factors for patients who would benefit from CHT/TRT.

Methods

A total of 492 ES-SCLC patients were included from January 2010 to March 2019, 244 of whom received CHT/TRT. Propensity score matching was performed to minimize bias between the CHT/TRT and CHT-alone groups. Patients in the CHT/TRT group were categorized into four subgroups based on the number of induction CHT cycles. For effective dose fractionation calculations, we introduced the time-adjusted biological effective dose (tBED). Categorical variables were analysed with chi-square tests and Fisher’s exact tests. Kaplan–Meier curves were generated to estimate survival rates using the R-project. Multivariate prognostic analysis was performed with Cox proportional hazards models.

Results

Patients who received CHT/TRT experienced improved overall survival (OS) (18.1 vs 10.8 months), progression-free survival (PFS) (9.3 vs 6.0 months) and local recurrence-free survival (LRFS) (12.0 vs 6.6 months) before matching, with similar results after matching. In the CHT/TRT group, the median LRFS times for the groups based on the radiation time were 12.7, 12.0, 12.0, and 9.0 months, respectively. Early TRT had a tendency to prolong PFS (median 10.6 vs 9.8 vs 9.0 vs 7.7 months, respectively, p = 0.091) but not OS (median 17.6 vs 19.5 vs 17.2 vs 19.0 months, respectively, p = 0.622). Notably, patients who received TRT within 6 cycles of CHT experienced prolonged LRFS (p = 0.001). Regarding the radiation dose, patients in the high-dose group (tBED > 50 Gy) who achieved complete response and partial response (CR and PR) to systemic therapy had relatively short OS (median 27.1 vs 22.7, p = 0.026) and PFS (median 11.4 vs 11.2, p = 0.032), but the abovementioned results were not obtained after the exclusion of patients who received hyperfractionated radiotherapy (all p > 0.05).

Conclusion

CHT/TRT could improve survival for ES-SCLC patients. TRT performed within 6 cycles of CHT and hyperfractionated radiotherapy (45 Gy in 30 fractions) may be a feasible treatment scheme for ES-SCLC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-021-01773-x.

The landscape of small cell lung cancer metastases: Organ specificity and timing

BACKGROUND

Early metastasis is a hallmark of small cell lung cancer (SCLC). However, the mechanisms and resulting patterns of SCLC dissemination are unclear. Our aim was thus to investigate the organ specificity and timing of blood-borne metastases in a comprehensive large cohort of SCLC patients.

METHODS

In this retrospective non-interventional cross-sectional study of 1009 Caucasian SCLC patients, we investigated the correlation between the distinct locations of the primary tumor and metastatic sites.

RESULTS

The onset of bone (p < 0.001), brain (p < 0.001), and pericardial (p = 0.02) metastases were late events, whereas adrenal gland (p = 0.005) and liver (p < 0.001) metastases occurred earlier. No significant difference was found in the distribution of early versus late metastases when comparing central and peripheral primary tumors. Patients with bone metastases had a higher than expected likelihood of having liver metastases, while brain metastases tended to appear together with adrenal gland metastases. Pleural and both lung and pericardial metastases also tended to co-metastasize together more frequently than expected if metastatic events occurred independently. Notably, patients with central primary tumors had decreased median overall survival (OS) compared to those with peripheral tumors, although this tendency does not appear to be significant (p = 0.072).

CONCLUSION

Our results are suggestive for particular site- and sequence-specific metastasis patterns in human SCLC. SCLC bone metastases tend to appear together with liver metastases, while brain metastases occur together with adrenal gland metastases. Better understanding of metastasis distribution patterns might help to improve the diagnosis and therapeutic decision-making in SCLC patients.

Ageing as key factor for distant metastasis patterns and prognosis in patients with extensive-stage Small Cell Lung Cancer

Background: Small cell lung cancer (SCLC) represents about 13% of lung cancer cases, which is highly invasive and has a high mortality rate, with the 5-year overall survival (OS) rate being only 6.3%. Age at diagnosis of advanced SCLC is much older, but studies describing the ageing factor for distant metastasis patterns and prognosis of extensive-stage SCLC (ES-SCLC) are limited.

Methods: Using the Surveillance, Epidemiology, and End Results (SEER) registry, we identified 18,682 patients with ES-SCLC (9,089 women and 9,053 men) who had complete clinical information between 2008 and 2015. Patients were classified into three groups (older group: ≥80 yrs, middle-aged group: 60-79 yrs, and younger group: ≤59 yrs). The role of different age at diagnosis of ES-SCLC (especially older group) in metastasis patterns was investigated, and OS and cancer-specific survival (CSS) of different age groups of metastatic ES-SCLC was assessed.

Results: The most metastasis of ES-SCLC patients in the three groups was multiorgan metastases (MOM) metastasis (71.2%, 70.3% and 66.3%, respectively), the most single organ metastasis in the younger group was the lung (3.3%), the middle-aged group and the older group were the brain (3.5%, 3.1%, respectively). The analysis revealed that older patients were less likely to have MOM, but more likely to have all organs metastases than other two groups (p<0.001). Older group had the worst OS (p<0.001) and CSS (p<0.001). Furthermore, Radiotherapy and chemotherapy can improve survival (p<0.001), but the rate of radiotherapy and chemotherapy in older patients is lower than that in middle-aged and younger patients (50.4% vs 38.6% vs 20.7%, p<0.05). Compared with other two group, older group (odds ratios, ORs) for lung, all organ metastases, and MOM were 0.43 (95% CI 0.27-0.67), 1.77 (95% CI 1.55-2.03), 0.68 (95% CI 0.6-0.77), respectively.

Conclusion: The mortality risk is highest with MOM and all organs metastasis followed by brain, lung, bone and liver metastases in elderly ES-SCLC patients. These results will be helpful for pre-treatment evaluation regarding the prognosis of ES-SCLC patients.

S-1 Maintenance Therapy in Extensive Stage Small-Cell Lung Cancer-A Randomized Clinical Study

Small-cell lung cancer (SCLC) is a recalcitrant cancer for its dismal prognosis although extensive research had been done. Four to 6 cycles platinum-based chemotherapy is the mainstay treatment for the extensive-stage disease; but the role of maintenance treatment is not fully understood. This is a phase 2, open-label study. Patients with extensive-stage SCLC reaching an objective response or stable disease (SD) after induction chemotherapy were randomly assigned (1:1) with a minimization procedure. One group received oral S-1 and the other group received placebo as maintenance treatment until disease progression or unacceptable toxicities. The primary end point of this study was progression-free survival (PFS), and the secondary end points were overall survival (OS), response rates, and toxicities. This study was based on earlier work, the preliminary results was reported on 2019 ASCO annual meeting. A total of 89 patients were enrolled, of whom 45 received S-1 maintenance therapy and 44 received placebo. The median PFS and OS were 6.35 months and 10.82 months in the S-1 group, as compared to 5.98 months and 10.09 months in the placebo group. The PFS was 7.2 months and 5.3 months, and OS was 12.9 months and 10.9 months in patients with an objective response compared to in patients with SD after induction chemotherapy, respectively. S-1 maintenance therapy did not prolong PFS or OS in patients with extensive-stage SCLC; tumor regression rate was the prognostic factor of PFS or OS. Further research with novel agents in the maintenance setting is warranted.

Mining prognostic factors of extensive-stage small-cell lung cancer patients using nomogram model

This study is to establish the nomogram model and provide clinical therapy decision-making for extensive-stage small-cell lung cancer (ES-SCLC) patients with different metastatic sites using the Surveillance, Epidemiology, and End Results (SEER) Program.A total of 10,025 patients of ES-SCLC with metastasis from January 2010 to December 2016 were enrolled from the SEER database. All samples were randomly divided into a derivation cohort and a validation cohort, and the derivation cohort was divided into 6 groups by different metastatic sites: bone, liver, lung, brain, multiple organs, and other organs. Using Cox proportional hazards models to analyze candidate prognostic factors, screening out the independent prognostic factors to establish the nomogram. Compare the different models by Net reclassification improvement and integrated discrimination improvement. Concordance index (C-index) and the calibration curve were used to verify the prediction efficiency of the nomogram in the derivation cohort and validation cohort.In the derivation cohort, the median overall survival was 7 months. The overall survival rates at 6-month, 1-year, and 2-year were 55.07%, 24.61%, and 7.56%, respectively. The median survival time was 10, 8, 7, 9, 7, and 6 months for the 6 groups of different metastatic sites: other, bone, liver, lung, brain, and multiple organs, respectively. Age, sex, race, T, N, distant metastatic site, and chemotherapy were contained in the final nomogram prognostic model. The C-index was 0.6569777 in the derivation cohort and 0.8386301 in the validation cohort.The survival time of ES-SCLC patients with different metastatic sites was significantly different. The nomogram can effectively predict the prognosis of individuals and provide a basis for clinical decision-making.

Patterns of Extrathoracic Metastases in Different Histological Types of Lung Cancer

Lung cancer is the leading cause of cancer-related deaths mainly attributable to metastasis, especially extrathoracic metastasis. This large-cohort research is aimed to explore metastatic profiles in different histological types of lung cancer, as well as to assess clinicopathological and survival significance of diverse metastatic lesions. Lung cancer cases were extracted and enrolled from the Surveillance, Epidemiology, and End Results (SEER) database. χ²-tests were conducted to make comparisons of metastatic distribution among different histological types and odds ratios were calculated to analyze co-occurrence relationships between different metastatic lesions. Kaplan–Meier methods were performed to analyze survival outcomes according to different metastatic sites and Cox regression models were conducted to identify independent prognostic factors. In total, we included 159,241 lung cancer cases with detailed metastatic status and complete follow-up information. In order to understand their metastatic patterns, we elucidated the following points in this research: (1) Comparing the frequencies of different metastatic lesions in different histological types. The frequency of bone metastasis was highest in adenocarcinoma, squamous cell carcinoma, LCLC and NSCLC/NOS, while liver was the most common metastatic site in SCLC. (2) Elaborating the tendency of combined metastases. Bi-site metastases occurred more common than tri-site and tetra-site metastases. And several metastatic sites, such as bone and liver, intended to co-metastasize preferentially. (3) Clarifying the prognostic significance of single-site and bi-site metastases. All single-site metastases were independent prognostic factors and co-metastases ended up with even worse survival outcomes. Thus, our findings would be beneficial for research design and clinical practice.

Different distant metastasis patterns based on tumor size could be found in extensive-stage small cell lung cancer patients: a large, population-based SEER study

BACKGROUND

Small-cell lung cancer (SCLC) is a malignant cancer with the ability to metastasize quickly. The relationship between tumor size and the distant metastasis patterns of Extensive-Stage Small Cell Lung Cancer (ES-SCLC) has not been reported.

OBJECTIVES

The aim of this study was to determine the different distant metastasis patterns as they related to tumor size in ES-SCLC.

PATIENTS AND METHODS

We used Surveillance, Epidemiology, and End Results (SEER) population-based data collected from 2010 through 2013 to identify 11058 ES-SCLC patients with definite evidence of distant metastases. Multivariate logistic regression analysis was used to demonstrate the association between tumor size and distant metastasis patterns including bone, liver, brain, and lung metastases. Age, race, sex, and N stage were also selected in the logistic regression model.

RESULTS

Subtle differences in metastasis patterns were found among patients based on different tumor sizes. Patients with tumors 3-7 cm have a higher risk of bone metastasis compared with those that have tumors ≤3 cm (OR 1.165, 95% CI [1.055-1.287], P = 0.003) and patients with tumors ≥7 cm have a higher risk of lung metastasis (OR 1.183, 95% CI [1.039-1.347], P = 0.011). In addition, patients with tumors ≥7 cm had a lower risk of brain metastasis and liver metastasis than patients with tumors ≤3 cm (OR 0.799, 95% CI [0.709-0.901], P < 0.001; OR 0.747, 95% CI [0.672-0.830], P < 0.001). Interestingly, there was no correlation between a larger tumor and a higher risk of metastasis. However, the tumor metastasis pattern did have some correlation with age, gender, race and N-status.

CONCLUSION

The pattern of distant metastasis of ES-SCLC is related to the tumor size and the tumor size is indicative of the metastatic site. Larger tumor sizes did not correlate with a higher risk of distant metastasis, but the size is related to the pattern of distant metastasis. The study of different distant metastasis patterns based on tumor size and other clinical features (e.g., age, race, sex, and N stage) in ES-SCLC is clinically valuable.

Metastatic location of extensive stage small-cell lung cancer: implications for thoracic radiation

BACKGROUNDS

This study was designed to evaluate the role of thoracic radiotherapy (TRT) in a selected patient population with oligometastatic extensive stage small-cell lung cancer (ES-SCLC) without brain or liver involved. The underlying hypothesis was that TRT will improve outcomes in this favorable patient population.

METHODS

305 patients were included in an institutional review board (IRB)-approved study, of which 105 received TRT after chemotherapy (ChT) and 200 received ChT alone. The survival outcomes were compared between ChT+TRT group and ChT-alone group in patients with oligometastasis without brain or liver involved and patients with brain/liver/multimetastasis, respectively.

RESULTS

The 1-year, 2-year and 5-year overall survival (OS) for all patients were 60.3%, 23.9% and 1.6%, respectively. The addition of TRT significantly improved PFS in total patients than ChT alone (14.5 months vs. 10.1 months, p = 0.006), but the OS benefit was not significant (17.8 months vs. 16.5 months, p = 0.061). For patients with oligometastasis (n = 118), TRT offered significant progression free survival (PFS) (16.5 months vs. 9.1 months, p = 0.005) and OS (19.2 months vs. 15.6 months, p = 0.039) benefits. However, for patients with brain/liver/multimetastasis, the PFS and OS were not improved with TRT (p = 0.49, p = 0.811).

CONCLUSIONS

TRT provided significant PFS and OS benefits in patients with oligometastatic ES-SCLC without brain or liver involved. The consolidative TRT is a reasonable treatment option for this favorable patient population.

Association of specific metastatic organs with the prognosis and chemotherapeutic response in patients with advanced lung cancer

BACKGROUND

This study was performed to investigate the influence of specific metastatic organs on the prognosis and therapeutic effect in patients with advanced lung cancer.

METHODS

We retrospectively analyzed 400 patients with pathologically diagnosed advanced lung cancer to determine the association of the patients' metastatic status with their prognoses and responses to first-line therapy. Metastases within the chest cavity (pulmonary metastasis, pleural effusion, and pericardial effusion) were counted as one organ.

RESULTS

The numbers of metastatic organs in the patients were as follows: one (n=199 patients), two (n=99), three (n=61), and four or more (n=41). A multivariate analysis showed that liver and muscle metastases were independently associated with shorter overall survival (median of 207 and 120 days, respectively) and shorter progression-free survival (median of 125 and 53 days, respectively). Chest cavity, bone, brain, and lymph node metastases were not associated with survival. The presence of either muscle or skin metastasis was associated with a lower response rate to first-line therapy than was the absence of each metastasis (14.3% vs. 49.4% and 11.1% vs. 48.9% in patients with vs. without muscle or skin metastasis, respectively).

CONCLUSIONS

Muscle and liver metastases were associated with poor outcomes. Muscle and skin metastases were associated with a lower response rate to treatment. For patients with advanced lung cancer, oncologists should select treatment strategies considering the patients' metastatic statuses as well as other clinical characteristics.

Clinical associations and prognostic value of site-specific metastases in non-small cell lung cancer: A population-based study

The prognosis of non-small cell lung cancer (NSCLC) is poor, particularly for patients with metastatic disease. Numerous efforts have been made to improve the prognosis of these patients; however, only a small number of studies have explored the occurrence rate and prognostic value of different patterns of distant metastasis (DM) in NSCLC systematically. To investigate these, information from patients diagnosed with NSCLC between 2010 and 2014 was collected from the Surveillance, Epidemiology and End Results database. Survival rate comparisons were performed using Kaplan-Meier analysis and log-rank tests. A Cox proportional hazard model was established to determine factors associated with improved overall survival (OS) and cancer-specific survival (CSS). The present study revealed that the most common site of single metastasis occurrence was bone, and the least common was the liver for NSCLC. As for multi-site metastases, the most common two-site metastasis involved bone and lung, and the most common three-site metastasis involved bone, liver and lung. As for NSCLC subtypes, large cell carcinoma (LCC) exhibited more specific metastatic features. The most common single metastatic site was the brain for patients with LCC, and the most common two-site metastatic combination was bone and liver. Patients with isolated liver metastasis exhibited the worst OS and CSS among patients with single metastasis. Furthermore, for patients with multi-site metastases, metastases involving the liver were associated with the worst OS and CSS among various combinations. To the best of our knowledge, the present study is the first to investigate the occurrence rate and prognostic value of different metastatic patterns of site-specific DM for NSCLC using a large population-based dataset. The findings of the present study may have vital implications for classifying patients with advanced NSCLC, thus laying a foundation for individualized precise treatment.

Clinicopathological characteristics and prognostic factors of pulmonary large cell neuroendocrine carcinoma: A large population-based analysis

BACKGROUND

The study was conducted to compare the clinicopathological characteristics, survival outcomes, and metastatic patterns between pulmonary large cell neuroendocrine carcinoma (LCNEC) and other non-small cell lung cancer (ONSCLC), and to identify the prognostic factors of LCNEC.

METHODS

Data of patients diagnosed with LCNEC and ONSCLC from 2004 to 2014 were obtained from the Surveillance, Epidemiology, and End Results dataset. Pearson's chi-square tests were used to compare differences in clinicopathological characteristics. The Kaplan-Meier method was used for survival analysis. A propensity score was used for matching and a Cox proportional hazards model was used for multivariate and subgroup analyses.

RESULTS

A total of 2368 LCNEC cases and 231 672 ONSCLC cases were identified. LCNEC incidence increased slightly over time. Except for marital status, LCNEC patients had obviously different biological features to ONSCLC patients. Survival analysis showed that LCNEC had poorer outcomes than ONSCLC. Multivariate analysis revealed that female gender, black race, surgery, radiation, and chemotherapy were protective factors for LCNEC. Matched subgroup analysis further demonstrated that most subgroup factors favored ONSCLC, especially in early stage. Early-stage LCNEC patients had a higher risk of lung cancer-specific death than early-stage ONSCLC patients. Moreover, metastatic patterns were different between LCNEC and ONSCLC. LCNEC patients with isolated liver metastasis or combined invasion to other organs had poorer survival rates.

CONCLUSIONS

LCNEC has totally different clinicopathological characteristics and metastatic patterns to ONSCLC. LCNEC also has poorer survival outcomes, primarily because of isolated liver metastasis or combined invasion to other organs. Most subgroup factors are adverse factors for LCNEC.

Characteristics and survival difference of clinical tumor size 0 extensive-stage small cell lung cancer with different metastasis pattern

Background

Few studies were focused on the characteristics and survival prognosis of clinical tumor size 0 (cT0) extensive-stage small cell lung cancer (ES-SCLC) patients with distant metastasis.

Methods

The proper patients were screened based on the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2013. Chi-square test was used to analyze the characteristics of cT0 stage. The risk factors on cT0 stage were analyzed by Logistic regression model. The survival difference was compared by the Log rank test. The prognostic factors on survival were analyzed by univariate and multivariate Cox regression model.

Results

Only 1.2% of patients were diagnosed at cT0 stage. Clinical nodal status 0 (cN0) stage, no bone metastasis and no lung metastasis were all risk factors on cT0 stage. The patients at cT0 stage had better cancer specific survival (CSS) benefit (P=0.011). The cT0 stage was also an independent prognostic factor on CSS (P=0.018).

Conclusions

The patients at cT0 stage was a different subgroup compared with the patients at cT1-4 stage for ES-SCLC patients with distant metastasis.