Nomogram prediction for the survival of the patients with small cell lung cancer

Validity of nomograms for predicting cancer risk in oral leukoplakia and oral lichen planus

OBJECTIVES

This study assessed the validity of nomograms for predicting malignant transformation (MT) among patients with oral leukoplakia (OL) and oral lichen planus (OLP).

MATERIALS AND METHODS

Two nomograms were identified following a systematic search. Variables to interrogate both nomograms were obtained for a retrospective OL/OLP cohort. Then, the nomograms were applied to estimate MT probabilities twice and their average was used to calculate the discriminatory performance, calibration, and potential net benefit of the models. Subgroup analyses were performed for patients with OL, OLP, and oral epithelial dysplasia.

RESULTS

Predicted probabilities were mostly significantly higher among OL/OLP patients who developed MT compared to those who did not (p = <0.001-0.034). AUC values and Brier scores of the nomograms were 0.644-0.844 and 0.040-0.088 among OL patients and 0.580-0.743 and 0.008-0.072 among OLP patients. Decision curve analysis suggested that the nomograms had some net benefit for risk stratification. However, the models did not best binary dysplasia grading in discriminatory validity and net benefit among patients with OL and oral epithelial dysplasia.

CONCLUSION

Nomograms for predicting MT may have satisfactory validity among patients with OL than OLP, but they do not outperform binary dysplasia grading in risk stratification of OL.

Incidence and risk factors of pulmonary complications after lung cancer surgery: A systematic review and meta-analysis

Postoperative pulmonary complications (PPCs) are associated with high mortality rates after lung cancer surgery. Although some studies have discussed the different risk factors for PPCs, the relationship between these factors and their impact on PPCs remains unclear. Hence, this study aimed to systematically summarize the incidence and determine the risk factors for PPCs. We conducted a systematic search of five English and four Chinese databases from their inception to April 1, 2023. A total of 34 articles (8 cohort studies and 26 case-control studies) (n = 31696, 5833 with PPCs) were included in the analysis. The primary outcome was the incidence of PPC. The secondary outcome was the odds ratio (OR) of PPCs based on the identified risk factors calculated by RevMan 5.4. A narrative descriptive summary of the study results was presented when pooling the results or conducting a meta-analysis was not possible. The pooled incidence of PPCs was 18.4 %. This meta-analysis demonstrated that TNM staging (OR 4.29, 95 % CI 2.59-7.13), chronic obstructive pulmonary disease (COPD) (OR 2.47, 95 % CI 1.80-3.40), smoking history (OR 2.37, 95 % CI 1.33-4.21), poor compliance with respiratory rehabilitation (OR 1.64, 95 % CI 1.17-2.30), male sex (OR 1.62, 95 % CI 1.28-2.04), diabetes (OR 1.56, 95 % CI 1.07-2.27), intraoperative bleeding volume (OR 1.44, 95 % CI 1.02-2.04), Eastern Cooperative Oncology Group score (ECOG) > 1 (OR 1.37, 95 % CI 1.04-1.80), history of chemotherapy and/or radiotherapy (OR 1.32, 95 % CI 1.03-1.70), older age (OR 1.18, 95 % CI 1.11-1.24), and duration of surgery (OR 1.07, 95 % CI 1.04-1.10) were significantly associated with a higher risk of PPCs. In contrast, the peak expiratory flow rate (PEF) (OR 0.99, 95 % CI 0.98-0.99) was a protective factor. Clinicians should implement targeted and effective interventions to prevent the occurrence of PPCs.

Nomograms Predict PFS and OS for SCLC Patients After Standardized Treatment: A Real-World Study

Purpose

This study aims to investigate the process of small cell lung cancer (SCLC) patients from achieving optimal efficacy to experiencing disease progression until death. It examines the predictive value of the treatment response on progression free survival (PFS) and overall survival (OS) of SCLC patients.

Patients and Methods

We conducted a retrospective analysis on 136 SCLC patients diagnosed from 1992 to 2018. Important prognostic factors were identified to construct nomogram models. The predictive performance of the models was evaluated using the receiver operating characteristic curves and calibration curves. Survival differences between groups were compared using Kaplan-Meier survival curves. Subsequently, an independent cohort consisting of 106 SCLC patients diagnosed from 2014 to 2021 was used for validation.

Results

We constructed two nomograms to predict first-line PFS (PFS1) and OS of SCLC. The area under the receiver operating characteristic curves for the PFS1 nomogram predicting PFS at 3-, 6-, and 12-months were 0.919 (95% CI: 0.867-0.970), 0.908 (95% CI: 0.860-0.956) and 0.878 (95% CI: 0.798-0.958), and for the OS nomogram predicting OS at 6-, 12-, and 24-months were 0.814 (95% CI: 0.736-0.892), 0.819 (95% CI: 0.749-0.889) and 0.809 (95% CI: 0.678-0.941), indicating those two models with a high discriminative ability. The calibration curves demonstrated the models had a high degree of consistency between predicted and observed values. According to the risk scores, patients were divided into high-risk and low-risk groups, showing a significant difference in survival rate. And these findings were validated in another independent validation cohort.

Conclusion

Based on the patients' treatment response after standardized treatment, we developed and validated two nomogram models to predict PFS1 and OS of SCLC. The models demonstrated good accuracy, reliability and clinical applicability by validating in an independent cohort.

Prognostic Model for High-Grade Neuroendocrine Carcinoma of the Lung Incorporating Genomic Profiling and Poly (ADP-ribose) Polymerase-1 Expression

PURPOSE

High-grade neuroendocrine carcinoma (HGNEC) of the lung is an aggressive cancer with a complex biology. We aimed to explore the prognostic value of genetic aberrations and poly(ADP-ribose) polymerase-1 (PARP1) expression in HGNEC and to establish a novel prognostic model.

MATERIALS AND METHODS

We retrospectively enrolled 191 patients with histologically confirmed HGNEC of the lung. Tumor tissues were analyzed using PARP1 immunohistochemistry (IHC; N = 191) and comprehensive cancer panel sequencing (n = 102). Clinical and genetic data were used to develop an integrated Cox hazards model.

RESULTS

Strong PARP1 IHC expression (intensity 3) was observed in 153 of 191 (80.1%) patients, and the mean PARP1 H-score was 285 (range, 5-300). To develop an integrated Cox hazard model, our data set included information from 357 gene mutations and 19 clinical profiles. When the targeted mutation profiles were combined with clinical profiles, 12 genes (ATRX, CCND2, EXT2, FGFR2, FOXO1, IL21R, MAF, TGM7, TNFAIP3, TP53, TSHR, and DDR2) were identified as prognostic factors for survival. The integrated Cox hazard model, which combines mutation profiles with a baseline model, outperformed the baseline model (incremental area under the curve 0.84 v 0.78; P = 8.79e-12). The integrated model stratified patients into high- and low-risk groups with significantly different disease-free and overall survival (integrated model: hazard ratio, 7.14 [95% CI, 4.07 to 12.54]; P < .01; baseline model: 4.38 [2.56 to 7.51]; P < .01).

CONCLUSION

We introduced a new prognostic model for HGNEC that combines genetic and clinical data. The integrated Cox hazard model outperformed the baseline model in predicting the survival of patients with HGNEC.

Construction, validation, and visualization of a web-based nomogram to predict overall survival in small-cell lung cancer patients with brain metastasis

INTRODUCTION

Brain metastasis (BM) is an aggressive complication with an extremely poor prognosis in patients with small-cell lung cancer (SCLC). A well-constructed prognostic model could help in providing timely survival consultation or optimizing treatments.

METHODS

We analyzed clinical data from SCLC patients between 2000 and 2018 based on the Surveillance, Epidemiology, and End Results (SEER) database. We identified significant prognostic factors and integrated them using a multivariable Cox regression approach. Internal validation of the model was performed through a bootstrap resampling procedure. Model performance was evaluated based on the area under the curve (AUC) and calibration curve.

RESULTS

A total of 2,454 SCLC patients' clinical data was collected from the database. It was determined that seven clinical parameters were associated with prognosis in SCLC patients with BM. A satisfactory level of discrimination was achieved by the predictive model, with 6-, 12-, and 18-month AUC values of 0.726, 0.707, and 0.737 in the training cohort; and 0.759, 0.742, and 0.744 in the validation cohort. As measured by survival rate probabilities, the calibration curve agreed well with actual observations. Furthermore, prognostic scores were found to significantly alter the survival curves of different risk groups. We then deployed the prognostic model onto a website server so that users can access it easily.

CONCLUSIONS

In this study, a nomogram and a web-based predictor were developed to predict overall survival in SCLC patients with BM. It may assist physicians in making informed clinical decisions and determining the best treatment plan for each patient.

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.

Construction and validation of a nomogram to predict the overall survival of small cell lung cancer: a multicenter retrospective study in Shandong province, China

BACKGROUND

Patients diagnosed with small cell lung cancer (SCLC) typically experience a poor prognosis, and it is essential to predict overall survival (OS) and stratify patients based on distinct prognostic risks.

METHODS

Totally 2309 SCLC patients from the hospitals in 15 cities of Shandong from 2010 - 2014 were included in this multicenter, population-based retrospective study. The data of SCLC patients during 2010-2013 and in 2014 SCLC were used for model development and validation, respectively. OS served as the primary outcome. Univariate and multivariate Cox regression were applied to identify the independent prognostic factors of SCLC, and a prognostic model was developed based on these factors. The discrimination and calibration of this model were assessed by the time-dependent C-index, time-dependent receiver operator characteristic curves (ROC), and calibration curves. Additionally, Decision Curve Analysis (DCA) curves, Net Reclassification Improvement (NRI), and Integrated Discriminant Improvement (IDI) were used to assess the enhanced clinical utility and predictive accuracy of the model compared to TNM staging systems.

RESULTS

Multivariate analysis showed that region (Southern/Eastern, hazard ratio [HR] = 1.305 [1.046 - 1.629]; Western/Eastern, HR = 0.727 [0.617 - 0.856]; Northern/Eastern, HR = 0.927 [0.800 - 1.074]), sex (female/male, HR = 0.838 [0.737 - 0.952]), age (46-60/≤45, HR = 1.401 [1.104 - 1.778]; 61-75/≤45, HR = 1.500 [1.182 - 1.902]; >75/≤45, HR = 1.869 [1.382 - 2.523]), TNM stage (II/I, HR = 1.119[0.800 - 1.565]; III/I, HR = 1.478 [1.100 - 1.985]; IV/I, HR = 1.986 [1.477 - 2.670], surgery (yes/no, HR = 0.677 [0.521 - 0.881]), chemotherapy (yes/no, HR = 0.708 [0.616 - 0.813]), and radiotherapy (yes/no, HR = 0.802 [0.702 - 0.917]) were independent prognostic factors of SCLC patients and were included in the nomogram. The time-dependent AUCs of this model in the training set were 0.699, 0.683, and 0.683 for predicting 1-, 3-, and 5-year OS, and 0.698, 0.698, and 0.639 in the validation set, respectively. The predicted calibration curves aligned with the ideal curves, and the DCA curves, the IDI, and the NRI collectively demonstrated that the prognostic model had a superior net benefit than the TNM staging system.

CONCLUSION

The nomogram using SCLC patients in Shandong surpassed the TNM staging system in survival prediction accuracy and enabled the stratification of patients with distinct prognostic risks based on nomogram scores.

Identification of a visualized web-based nomogram for overall survival prediction in patients with limited stage small cell lung cancer

Small-cell lung cancer (SCLC) is an aggressive lung cancer subtype with an extremely poor prognosis. The 5-year survival rate for limited-stage (LS)-SCLC cancer is 10-13%, while the rate for extensive-stage SCLC cancer is only 1-2%. Given the crucial role of the tumor stage in the disease course, a well-constructed prognostic model is warranted for patients with LS-SCLC. The LS-SCLC patients' clinical data extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2018 were reviewed. A multivariable Cox regression approach was utilized to identify and integrate significant prognostic factors. Bootstrap resampling was used to validate the model internally. The Area Under Curve (AUC) and calibration curve evaluated the model's performance. A total of 5463 LS-SCLC patients' clinical data was collected from the database. Eight clinical parameters were identified as significant prognostic factors for LS-SCLC patients' OS. The predictive model achieved satisfactory discrimination capacity, with 1-, 2-, and 3-year AUC values of 0.91, 0.88, and 0.87 in the training cohort; and 0.87, 0.87, and 0.85 in the validation cohort. The calibration curve showed a good agreement with actual observations in survival rate probability. Further, substantial differences between survival curves of the different risk groups stratified by prognostic scores were observed. The nomogram was then deployed into a website server for ease of access. This study developed a nomogram and a web-based predictor for predicting the overall survival of patients with LS-SCLC, which may help physicians make personalized clinical decisions and treatment strategies.

Construction and validation of a nomogram based on the log odds of positive lymph nodes to predict cancer-specific survival in patients with small cell lung cancer after surgery

Background

The lymph node ratio (LNR) is useful for predicting survival in patients with small cell lung cancer (SCLC). The present study compared the effectiveness of the N stage, number of positive LNs (NPLNs), LNR, and log odds of positive LNs (LODDS) to predict cancer-specific survival (CSS) in patients with SCLC.

Materials and methods

674 patients were screened using the Surveillance Epidemiology and End Results database. The Kaplan-Meier survival and receiver operating characteristic (ROC) curves were performed to address optimal estimation of the N stage, NPLNs, LNR, and LODDS to predict CSS. The optimal LN status group was incorporated into a nomogram to estimate CSS in SCLC patients. The ROC curve, decision curve analysis, and calibration plots were utilized to test the discriminatory ability and accuracy of this nomogram.

Results

The LODDS model showed the highest accuracy compared to the N stage, NPLNs, and LNR in predicting CSS for SCLC patients. LODDS, age, sex, tumor size, and radiotherapy status were included in the nomogram. The results of calibration plots provided evidences of nice consistency. The ROC and DCA plots suggested a better discriminatory ability and clinical applicability of this nomogram than the 8th TNM and SEER staging systems.

Conclusions

LODDS demonstrated a better predictive power than other LN schemes in SCLC patients after surgery. A novel LODDS-incorporating nomogram was built to predict CSS in SCLC patients after surgery, proving to be more precise than the 8th TNM and SEER staging.

Comparation between novel online models and the AJCC 8th TNM staging system in predicting cancer-specific and overall survival of small cell lung cancer

Background

Most of previous studies on predictive models for patients with small cell lung cancer (SCLC) were single institutional studies or showed relatively low Harrell concordance index (C-index) values. To build an optimal nomogram, we collected clinicopathological characteristics of SCLC patients from Surveillance, Epidemiology, and End Results (SEER) database.

Methods

24,055 samples with SCLC from 2010 to 2016 in the SEER database were analyzed. The samples were grouped into derivation cohort (n=20,075) and external validation cohort (n=3,980) based on America's different geographic regions. Cox regression analyses were used to construct nomograms predicting cancer-specific survival (CSS) and overall survival (OS) using derivation cohort. The nomograms were internally validated by bootstrapping technique and externally validated by calibration plots. C-index was computed to compare the accuracy and discrimination power of our nomograms with the 8th of version AJCC TNM staging system and nomograms built in previous studies. Decision curve analysis (DCA) was applied to explore whether the nomograms had better clinical efficiency than the 8th version of AJCC TNM staging system.

Results

Age, sex, race, marital status, primary site, differentiation, T classification, N classification, M classification, surgical type, lymph node ratio, radiotherapy, and chemotherapy were chosen as predictors of CSS and OS for SCLC by stepwise multivariable regression and were put into the nomograms. Internal and external validations confirmed the nomograms were accurate in prediction. C-indexes of the nomograms were relatively satisfactory in derivation cohort (CSS: 0.761, OS: 0.761) and external validation cohort (CSS: 0.764, OS: 0.764). The accuracy of the nomograms was superior to that of nomograms built in previous studies. DCA showed the nomograms conferred better clinical efficiency than 8th version of TNM staging system.

Conclusions

We developed practical nomograms for CSS (https://guowei2020.shinyapps.io/DynNom-CSS-SCLC/) and OS (https://drboidedwater.shinyapps.io/DynNom-OS-SCLC/) prediction of SCLC patients which may facilitate clinicians in individualized therapeutics.

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.

Accuracy of papillary thyroid cancer prognostic nomograms: a systematic review

Objective

Current staging and risk-stratification systems for predicting survival or recurrence of patients with differentiated thyroid carcinoma may be ineffective at predicting outcomes in individual patients. In recent years, nomograms have been proposed as an alternative to conventional systems for predicting personalized clinical outcomes. We conducted a systematic review to evaluate the predictive performance of available nomograms for thyroid cancer patients.

Design and methods

PROSPERO registration (CRD42022327028). A systematic search was conducted without time and language restrictions. PICOT questions: population, patients with papillary thyroid cancer; comparator prognostic factor, single-arm studies; outcomes, overall survival, disease-free survival, cancer-specific survival, recurrence, central lymph node metastases, or lateral lymph node metastases; timing, all periods; setting, hospital setting. Risk of bias was assessed through PROBAST tool.

Results

Eighteen studies with a total of 20 prognostic models were included in the systematic review (90,969 papillary thyroid carcinoma patients). Fourteen models were at high risk of bias and four were at unclear risk of bias. The greatest concerns arose in the analysis domain. The accuracy of nomograms for overall survival was assessed in only one study and appeared limited (0.77, 95% CI: 0.75-0.79). The accuracy of nomograms for disease-free survival ranged from 0.65 (95% CI: 0.55-0.75) to 0.92 (95% CI: 0.91-0.95). The C-index for predicting lateral lymph node metastasis ranged from 0.72 to 0.92 (95% CI: 0.86-0.97). For central lymph node metastasis, the C-index of externally validated studies ranged from 0.706 (95% CI: 0.685-0.727) to 0.923 (95% CI: 0.893-0.946).

Conclusions

Our work highlights the extremely high heterogeneity among nomograms and the critical lack of external validation studies that limit the applicability of nomograms in clinical practice. Further studies ideally using commonly adopted risk factors as the backbone to develop nomograms are required.

Significance statement

Nomograms may be appropriate tools to plan treatments and predict personalized clinical outcomes in patients with papillary thyroid cancer. However, the nomograms developed to date are very heterogeneous, and their results seem to be closely related to the specific samples studied to generate the same nomograms. The lack of rigorous external validation procedures and the use of risk factors that sometimes appear to be far from those commonly used in clinical practice, as well as the great heterogeneity of the risk factors considered, limit the ability of nomograms to predict patient outcomes and thus their current introduction in clinical practice.

Platelet-lymphocyte ratio is a prognostic marker in small cell lung cancer-A systemic review and meta-analysis

Aim

The aim of this study was to evaluate the relationship between platelet-lymphocyte ratio (PLR) and prognosis in small cell lung cancer (SCLC) patients.

Method

A comprehensive search was carried out to collect related studies. Two independent investigators extracted the data of hazard ratio (HR) and 95% confidence interval (CI) for overall survival (OS) or progression-free survival (PFS). A random-effect model was applied to analyze the effect of different PLR levels on OS and PFS in SCLC patients. Moreover, subgroup analysis was conducted to seek out the source of heterogeneity.

Results

A total of 26 articles containing 5,592 SCLC patients were included for this meta-analysis. SCLC patients with a high PLR level had a shorter OS compared with patients with a low PLR level, in both univariate (HR = 1.56, 95% CI 1.28-1.90, p < 0.0001) and multivariate (HR = 1.31, 95% CI 1.08-1.59, p = 0.007) models. SCLC patients with a high PLR level had a shorter PFS compared with patients with a low PLR level, in the univariate model (HR = 1.71, 95% CI 1.35-2.16, p < 0.0001), but not in the multivariate model (HR = 1.17, 95% CI 0.95-1.45, p = 0.14). Subgroup analysis showed that a high level of PLR shortened OS in some subgroups, including the Asian subgroup, the younger subgroup, the mixed-stage subgroup, the chemotherapy-dominant subgroup, the high-cutoff-point subgroup, and the retrospective subgroup. PLR level did not affect OS in other subgroups.

Conclusion

PLR was a good predictor for prognosis of SCLC patients, especially in patients received chemotherapy dominant treatments and predicting OS.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022383069.

Interpretable deep learning survival predictive tool for small cell lung cancer

Background

Small cell lung cancer (SCLC) is an aggressive and almost universally lethal neoplasm. There is no accurate predictive method for its prognosis. Artificial intelligence deep learning may bring new hope.

Methods

By searching the Surveillance, Epidemiology, and End Results database (SEER), 21,093 patients' clinical data were eventually included. Data were then divided into two groups (train dataset/test dataset). The train dataset (diagnosed in 2010-2014, N = 17,296) was utilized to conduct a deep learning survival model, validated by itself and the test dataset (diagnosed in 2015, N = 3,797) in parallel. According to clinical experience, age, sex, tumor site, T, N, M stage (7th American Joint Committee on Cancer TNM stage), tumor size, surgery, chemotherapy, radiotherapy, and history of malignancy were chosen as predictive clinical features. The C-index was the main indicator to evaluate model performance.

Results

The predictive model had a 0.7181 C-index (95% confidence intervals, CIs, 0.7174-0.7187) in the train dataset and a 0.7208 C-index (95% CIs, 0.7202-0.7215) in the test dataset. These indicated that it had a reliable predictive value on OS for SCLC, so it was then packaged as a Windows software which is free for doctors, researchers, and patients to use.

Conclusion

The interpretable deep learning survival predictive tool for small cell lung cancer developed by this study had a reliable predictive value on their overall survival. More biomarkers may help improve the prognostic predictive performance of small cell lung cancer.

A novel nomogram predicting cancer-specific survival in small cell lung cancer patients with brain metastasis

Background

Brain metastasis (BM) is one of the most common metastatic sites in patients with small cell lung cancer (SCLC), and the prognosis remains very poor. This study aimed to establish a novel nomogram for predicting the cancer-specific survival (CSS) in SCLC patients with BM.

Methods

SCLC patients with BM from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015 were retrospectively collected. Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors, which were further used to construct the prognostic nomogram. The discrimination and calibration of nomogram were evaluated by concordance index (C-index), receiver operating characteristic (ROC) curve, the area under ROC curve (AUC) and calibration plot. Decision curve analysis (DCA) was used to assess the clinical usefulness. Kaplan-Meier survival curve was applied to analyze the survival outcome.

Results

A total of 2,462 patients were enrolled in this study, and randomly assigned into training cohort (n=1,723) and validation cohort (n=739). Age, N stage, surgery, radiation, chemotherapy, bone metastasis, liver metastasis and lung metastasis were identified as independent prognostic factors of CSS. The C-indexes of nomogram was 0.683 [95% confidence interval (CI): 0.667-0.699] in the training cohort, and 0.659 (95% CI: 0.634-0.684) in the validation cohort. The AUC values of 6-, 9- and 12-month CSS were 0.723, 0.742 and 0.737 respectively in the training cohort, while 0.715, 0.737 and 0.739 in the validation cohort. The ROC, calibration and DCA curves showed good discrimination, calibration and clinical applicability of this nomogram in predicting prognosis. Moreover, patients in high-risk group had a worse survival outcome than patients in medium-risk and low-risk groups.

Conclusions

A novel nomogram was constructed and validated for predicting individual prognosis in SCLC patients with BM. This nomogram could help clinicians make effective treatment strategies for 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.

Develop and validate nomogram to predict cancer-specific survival for patients with testicular yolk sac tumors

Purpose

Testicular yolk sac tumor (TYST) is a rare malignant germ cell tumor that mainly occurs in young men. Due to the low incidence of yolk sac tumors, there is a lack of prospective cohort studies with large samples. We aimed to develop a nomogram to predict cancer-specific survival (CSS) in patients with TYST.

Materials and methods

Patient information was downloaded from the Surveillance, Epidemiology and End Results (SEER) database. We enrolled all patients with TYST from 2000 to 2018, and all patients were randomly divided into a training set and a validation set. Univariate and multivariate Cox proportional hazards regression models were used to identify independent risk factors for patients. We constructed a nomogram based on the multivariate Cox regression model to predict 1-, 3-, and 5-year CSS in patients with TYST. We used a series of validation methods to test the accuracy and reliability of the model, including the concordance index (C-index), calibration curve and the area under the receiver operating characteristic curve (AUC).

Results

619 patients with TYST were enrolled in the study. Univariate and multivariate Cox regression analysis showed that age, T stage, M stage and chemotherapy were independent risk factors for CSS. A nomogram was constructed to predict the patient's CSS. The C-index of the training set and the validation set were 0.901 (95%CI: 0.859-0.847) and 0.855 (95%CI: 0.865-0.845), respectively, indicating that the model had excellent discrimination. The AUC showed the same results. The calibration curve also indicated that the model had good accuracy.

Conclusions

In this study, we constructed the nomogram for the first time to predict the CSS of patients with TYST, which has good accuracy and reliability and can help doctors and patients make clinical decisions.

Health behavior changes and mortality among South Korean cancer survivors

Considering the rapid growth in the number of cancer survivors, the successful management of their health behaviors requires further attention. However, there are lack of information about cancer survivors’ health behaviors and the risk of mortality using Korean cohort data. This study aimed to examine the effects of health behavior changes on mortality among cancer survivors and to develop a validated nomogram. This cohort study was conducted using claims data. Data from adult cancer survivors from the National Health Insurance Service–National Sample Cohort, conducted between 2002 and 2015, were included. Individuals who were alive for five years after their cancer diagnosis were defined as cancer survivors. Cox proportional-hazards regression was used to estimate the target associations. Discrimination (Harrell’s C-index) and calibration (Hosmer–Lemeshow test) were employed to validate the nomogram. Data from 9300 cancer survivors were used for analysis. Compared to non-smokers, those who started or quit smoking had a higher risk of all-cause mortality. Those who were physically inactive had a higher risk of all-cause mortality than those who were continuously active. In the nomogram, the C-index value was 0.79 in the training data and 0.81 in the testing data. Hosmer–Lemeshow test was not significant, demonstrating a good fit. We found that individuals with unhealthy behaviors had a higher risk of mortality, thereby highlighting the importance of managing health behaviors among cancer survivors. The development of a validated nomogram may provide useful insights regarding official policies and existing practices in healthcare systems, which would benefit cancer survivors. Our study could provide the evidence to inform the priority of guideline for managing the health behavior among cancer survivors.

Development and validation of a prognostic nomogram for early stage non-small cell lung cancer: a study based on the SEER database and a Chinese cohort

Objective

This study aimed to construct a nomogram to effectively predict the overall survival (OS) of patients with early-stage non-small-cell lung cancer (NSCLC).

Methods

For the training and internal validation cohorts, a total of 26,941 patients with stage I and II NSCLC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. A nomogram was constructed based on the risk factors affecting prognosis using a Cox proportional hazards regression model. And 505 patients were recruited from Jiaxing First Hospital for external validation. The discrimination and calibration of the nomogram were evaluated by C-index and calibration curves.

Results

A Nomogram was created after identifying independent prognostic factors using univariate and multifactorial factor analysis. The C-index of this nomogram was 0.726 (95% CI, 0.718–0.735) and 0.721 (95% CI, 0.709–0.734) in the training cohort and the internal validation cohort, respectively, and 0.758 (95% CI, 0.691–0.825) in the external validation cohort, which indicates that the model has good discrimination. Calibration curves for 1-, 3-, and 5-year OS probabilities showed good agreement between predicted and actual survival. In addition, DCA analysis showed that the net benefit of the new model was significantly higher than that of the TNM staging system.

Conclusion

We developed and validated a survival prediction model for patients with non-small cell lung cancer in the early stages. This new nomogram is superior to the traditional TNM staging system and can guide clinicians to make the best clinical decisions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-10067-8.

Clinical characteristics and cancer-specific survival analysis of double primary cancer patients with lung cancer as the first primary cancer

The objective of this study is to explore the prognostic factors of double primary cancer patients with lung cancer as the first primary cancer (FPC). The Surveillance, Epidemiology, and End Results (SEER) database is a database established by the National Institutes of Research for cancer registration purposes, which collects relatively complete demographic characteristics and clinical data for assessing the epidemiological characteristics of cancer worldwide. Clinical data on patients with a clear histopathological diagnosis of double primary with lung cancer as the FPC were identified and collected from the SEER database from 2010 to 2015. Survival curves were plotted by Kaplan-Meier survival analysis. Independent prognostic factors of patients were analyzed by COX proportional risk model. Clinical data were collected from a total of 9306 patients, including 6516 patients in the modeling group and 2790 patients in the validation group. When we retrieved that the FPC was lung cancer, we found that the most common site of the second primary cancer was located in the respiratory system (54.0%). In addition, the most common site of first primary lung cancer in patients with double primary cancer was the right upper lobe (33.3%). A total of 14 independent prognostic factors were included, and the constructed survival nomogram had high accuracy and clinical applicability. The nomogram established in this study can help to raise awareness of clinical workers and the importance of such diseases, and guide the treatment and follow-up strategies.

Prediction of lung malignancy progression and survival with machine learning based on pre-treatment FDG-PET/CT

Background

Pre-treatment FDG-PET/CT scans were analyzed with machine learning to predict progression of lung malignancies and overall survival (OS).

Methods

A retrospective review across three institutions identified patients with a pre-procedure FDG-PET/CT and an associated malignancy diagnosis. Lesions were manually and automatically segmented, and convolutional neural networks (CNNs) were trained using FDG-PET/CT inputs to predict malignancy progression. Performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Image features were extracted from CNNs and by radiomics feature extraction, and random survival forests (RSF) were constructed to predict OS. Concordance index (C-index) and integrated brier score (IBS) were used to evaluate OS prediction.

Findings

1168 nodules (n=965 patients) were identified. 792 nodules had progression and 376 were progression-free. The most common malignancies were adenocarcinoma (n=740) and squamous cell carcinoma (n=179). For progression risk, the PET+CT ensemble model with manual segmentation (accuracy=0.790, AUC=0.876) performed similarly to the CT only (accuracy=0.723, AUC=0.888) and better compared to the PET only (accuracy=0.664, AUC=0.669) models. For OS prediction with deep learning features, the PET+CT+clinical RSF ensemble model (C-index=0.737) performed similarly to the CT only (C-index=0.730) and better than the PET only (C-index=0.595), and clinical only (C-index=0.595) models. RSF models constructed with radiomics features had comparable performance to those with CNN features.

Interpretation

CNNs trained using pre-treatment FDG-PET/CT and extracted performed well in predicting lung malignancy progression and OS. OS prediction performance with CNN features was comparable to a radiomics approach. The prognostic models could inform treatment options and improve patient care.

Funding

NIH NHLBI training grant (5T35HL094308-12, John Sollee).

Lung cancer survival prediction and biomarker identification with an ensemble machine learning analysis of tumor core biopsy metabolomic data

INTRODUCTION

While prediction of short versus long term survival from lung cancer is clinically relevant in the context of patient management and therapy selection, it has proven difficult to identify reliable biomarkers of survival. Metabolomic markers from tumor core biopsies have been shown to reflect cancer metabolic dysregulation and hold prognostic value.

OBJECTIVES

Implement and validate a novel ensemble machine learning approach to evaluate survival based on metabolomic biomarkers from tumor core biopsies.

METHODS

Data were obtained from tumor core biopsies evaluated with high-resolution 2DLC-MS/MS. Unlike biofluid samples, analysis of tumor tissue is expected to accurately reflect the cancer metabolism and its impact on patient survival. A comprehensive suite of machine learning algorithms were trained as base learners and then combined into a stacked-ensemble meta-learner for predicting "short" versus "long" survival on an external validation cohort. An ensemble method of feature selection was employed to find a reliable set of biomarkers with potential clinical utility.

RESULTS

Overall survival (OS) is predicted in external validation cohort with AUROC_TEST of 0.881 with support vector machine meta learner model, while progression-free survival (PFS) is predicted with AUROC_TEST of 0.833 with boosted logistic regression meta learner model, outperforming a nomogram using covariate data (staging, age, sex, treatment vs. non-treatment) as predictors. Increased relative abundance of guanine, choline, and creatine corresponded with shorter OS, while increased leucine and tryptophan corresponded with shorter PFS. In patients that expired, N6,N6,N6-Trimethyl-L-lysine, L-pyrogluatmic acid, and benzoic acid were increased while cystine, methionine sulfoxide and histamine were decreased. In patients with progression, itaconic acid, pyruvate, and malonic acid were increased.

CONCLUSION

This study demonstrates the feasibility of an ensemble machine learning approach to accurately predict patient survival from tumor core biopsy metabolomic data.

Development and Validation of a Nomogram for Predicting Overall Survival in Patients with Second Primary Small Cell Lung Cancer After Non-Small Cell Lung Cancer: A SEER-Based Study

Background

Non-small cell lung cancer (NSCLC) survivors are at an increased risk of developing second primary malignancies, such as small cell lung cancer. This paper sought to establish a prognostic nomogram to assess overall survival (OS) in patients with second primary small cell lung cancer (SPSCLC) after NSCLC.

Methods

420 patients who developed SPSCLC after NSCLC were randomly split into the training and validation groups. A nomogram was established by stepwise regression. Area under the curve (AUC) and calibration plots were applied to assess the prognostic performance of the nomogram. Concordance index (C-index), integrated discrimination improvement (IDI), net reclassification index (NRI) and decision curve analysis (DCA) were performed to compare the nomogram with the American Joint Committee on Cancer (AJCC) 8th staging system. Survival risk classification was constructed based on the nomogram.

Results

Five variables were chosen to construct the nomogram. The AUC showed that it had a satisfactory discrimination ability. All calibration plots displayed good concordance between nomogram and observation. The C-index, IDI, NRI and DCA showed the nomogram was superior to the AJCC 8th staging system. The Kaplan-Meier curves suggested huge differences in prognosis among the three risk groups.

Conclusion

This study build a nomogram and risk stratification system for predicting probabilities of OS in patients with SPSCLC after NSCLC, which can help clinicians in individualized survival assessment and treatment decisions.

Prognostic Nomogram for Overall Survival in Small Cell Lung Cancer Patients Treated with Chemotherapy: A SEER-Based Retrospective Cohort Study

INTRODUCTION

Small cell lung cancer (SCLC) is known for its rapid clinical progression and poor prognosis. In this study, we sought to establish a prognostic nomogram among SCLC patients who received chemotherapy.

METHODS

We obtained 4971 SCLC patients' clinical information from the Surveillance, Epidemiology, and End Results (SEER) database for the period between 2004 and 2015. Patients were divided into training and validation sets. Two nomograms were established based on limited stage (LS) and extensive stage (ES) SCLC patients to predict 1-, 2-, and 3-year overall survival (OS) incorporating superior parameters from multivariate Cox regression. Receiver-operating characteristic curves (ROCs) were applied to assess the discrimination ability of the nomogram while the calibration plots were applied to verify the model. Kaplan-Meier method was applied to find survival curves. Decision curve analysis (DCA) was applied to compare OS between the nomograms and 7th American Joint Committee on Cancer (AJCC) tumor node metastasis (TNM) staging system.

RESULTS

Four and six clinical parameters were identified as significant prognostic factors for LS-SCLC and ES-SCLC patient's OS, respectively. The ROC curves indicated satisfactory discrimination capacity of the nomogram, with 1-, 2-, and 3-year area under curve (AUC) values of 0.89, 0.81, and 0.79 in LS-SCLC patients and 0.71, 0.66, and 0.66 in ES-SCLC patients, respectively. Calibration curves indicated that the nomogram showed good agreement with actual observations in survival rate probability. The survival curves among the LS-SCLC and ES-SCLC cohorts were consistent with the high-risk group having a worse prognosis than the low-risk group. Moreover, ROC and DCA curves showed our nomograms had more benefits than the 7th AJCC-TNM staging system.

CONCLUSIONS

We established two nomograms that can present individual predictions of OS among LS-SCLC and ES-SCLC patients who received chemotherapy. These proposed nomograms may aid clinicians in treatment strategy and design of clinical trials.

Clinical characteristics and prognostic model for extensive-stage small cell lung cancer: A retrospective study over an 8-year period

Background

Small cell lung cancer (SCLC) is a highly aggressive neuroendocrine tumor with a short replication time and a rapid growth rate. Prognostic factors for SCLC in clinical practice are scarce. Retrospective analysis of 8‐year extensive‐stage SCLC data from the Department Respiratory and Intensive Care Unit, Peking Union Medical College Hospital (Beijing, China) was performed to develop a risk prediction model that can facilitate the identification of extensive‐stage SCLC with differing prognosis in clinical practice.

Methods

A retrospective analysis of data from patients with extensive‐stage SCLC at a single‐center from January 2013 to January 2021, including age, sex, ECOG physical score, immunohistochemistry (CgA, Syn, CD56, TTF1, and Ki67), staging, treatment regimen, laboratory examinations, and survival period, was performed. Clinical variables with potential prognostic significance were screened by univariate Cox analysis. Next, multifactor Cox risk prediction regression analysis was performed to establish an extensive‐stage SCLC risk prognostic model. Survival curves and ROC curves for high and low risk groups were plotted according to risk scores. Nomogram and calibration curves were developed to assess the accuracy of the risk prediction model.

Results

This study included 300 patients who were diagnosed with extensive‐stage SCLC at our center from January 2013 to January 2021. The most common first presentation was respiratory symptoms, especially cough (162, 54%). The most common extra‐thoracic metastatic organs were bone (36.3%), liver (24.7%), brain (15.7%), and adrenal glands (15.7%). A total of 99% of patients received first‐line systemic therapy, with 86.3% of patients treated with platinum‐etoposide and 10.7% of patients treated with immune checkpoint inhibitor combined with platinum‐etoposide backbone. First‐line progression‐free survival was up to 198 days, and the median OS was 439 days. After Cox regression screening and backward stepwise selection, “time from initial therapy to relapse or progression (PFS1), liver metastases, adrenal metastases, M stage and first‐line treatment pattern” were retained to establish a prognostic model with an AUC value of 0.763. The prognostic model was shown as a nomogram with good agreement between predicted and observed outcomes.

Conclusions

The first‐line treatment of SCLC patients admitted to our hospital in the past 8 years was relatively standardized, and the progression‐free survival and OS were slightly longer than those reported in the literature. We developed a prognostic risk score model for extensive‐stage SCLC to calculate individual survival in clinical practice.

A new nomogram and risk classification system for predicting survival in small cell lung cancer patients diagnosed with brain metastasis: a large population-based study

BACKGROUND

The prognosis of patients with small cell lung cancer (SCLC) is poor, most of them are in the extensive stage at the time of diagnosis, and are prone to brain metastasis. In this study, we established a nomogram combined with some clinical parameters to predict the survival of SCLC patients with brain metastasis.

METHODS

The 3522 eligible patients selected from the SEER database between 2010 and 2015 were randomly divided into training cohort and validation cohort. Univariate and multivariate Cox regression analysis were used to evaluate the ability of each parameter to predict OS. The regression coefficients obtained in multivariate analysis were visualized in the form of nomogram, thus a new nomogram and risk classification system were established. The calibration curves were used to verify the model. And ROC curves were used to evaluate the discrimination ability of the newly constructed nomogram. Survival curves were made by Kaplan-Meier method and compared by Log rank test.

RESULTS

Univariate and multivariate analysis showed that age, race, sex, T stage, N stage and marital status were independent prognostic factors and were included in the predictive model. The calibration curves showed that the predicted value of the 1- and 3-year survival rate by the nomogram was in good agreement with the actual observed value of the 1- and 3-year survival rate. And, the ROC curves implied the good discrimination ability of the predictive model. In addition, the results showed that in the total cohort, training cohort, and validation cohort, the prognosis of the low-risk group was better than that of the high-risk group.

CONCLUSIONS

We established a nomogram and a corresponding risk classification system to predict OS in SCLC patients with brain metastasis. This model could help clinicians make clinical decisions and stratify treatment for patients.

Survival and pretreatment prognostic factors for extensive-stage small cell lung cancer: A comprehensive analysis of 358 patients

BACKGROUND

Extensive-stage small cell lung cancer (ES-SCLC) is deemed as a fatal malignancy with a poor prognosis. Although immunotherapy has gradually played an important role in the treatment of ES-SCLC since 2018, ES-SCLC treatment data and patient outcome before 2018, when chemotherapy served as a fundamental therapeutic strategy, is still meaningful as a summary of the situation regarding previous medical treatment and is a baseline for comparative data. In addition, the prognostic factors of ES-SCLC have failed to reach a consensus until now. Therefore, this study aimed to evaluate survival and identify the prognostic factors in an ES-SCLC population.

METHODS

We retrospectively collected the detailed medical records of 358 patients with ES-SCLC from January 1, 2011 to December 31, 2018 in a Chinese top-level cancer hospital. The prognostic factors were evaluated by Cox univariate and multivariate analysis.

RESULTS

The median overall survival (OS) of ES-SCLC patients (N = 358) was 14.0 months, the one- and two-year OS rates were 56.2% and 21.7%, respectively. Moreover, we identified two demographic characters (age ≥ 70, smoking index ≥ 400), one tumor burden factor (bone multimetastasis), two tumor biomarkers (cyfra211, CA125) and two laboratory indexes (decreased Na, PLR < 76) as independent prognostic factors for OS in this patient population. Progression-free survival (PFS) data of 238 patients was obtained for further analysis, and the median PFS was 6.2 months, and six-month and one-year PFS rates were 51.7% and 14.3%, respectively. Elevated cyfra211, decreased Hb and Na were identified as independent prognostic factors for PFS.

CONCLUSIONS

This study provides real-world evidence of the survival and prognosis of ES-SCLC patients which will enable better evaluation and clinical decision-making in the future.

Development and Validation of a Nomogram Prognostic Model for Resected Limited-Stage Small Cell Lung Cancer Patients

Background

In this study, we developed and validated nomograms for predicting the survival in surgically resected limited-stage small cell lung cancer (SCLC) patients.

Methods

The SCLC patients extracted from the Surveillance, Epidemiology, and End Results database between 2000 and 2014 were reviewed. Significant prognostic factors were identified and integrated to develop the nomogram using multivariable Cox regression. The model was then validated internally by bootstrap resampling, and externally using an independent SCLC cohort diagnosed between 2000 and 2015 at our institution. The prognostic performance was measured by the concordance index (C-index) and calibration curve.

Results

A total of 1006 resected limited-stage SCLC patients were included in the training cohort. Overall, 444 cases from our institution constituted the validation cohort. Seven prognostic factors were identified and entered into the nomogram construction. The C-indexes of this model in the training cohort were 0.723, 0.722, and 0.746 for predicting 1-, 3-, and 5-year overall survival (OS), respectively, and 0.816, 0.710, and 0.693, respectively, in the validation cohort. The calibration curve showed optimal agreement between nomogram-predicted survival and actual observed survival. Additionally, significant distinctions in survival curves between different risk groups stratified by prognostic scores were also observed. The proposed nomogram was then deployed into a website server for convenient application.

Conclusions

We developed and validated novel nomograms for individual prediction of survival for resected limited-stage SCLC patients. These models perform better than the previously widely used staging system and may offer clinicians instructions for strategy making and the design of clinical trials.

Supplementary Information

The online version of this article (10.1245/s10434-020-09552-w) contains supplementary material, which is available to authorized users.

Pre-radiotherapy lymphocyte count and platelet-to-lymphocyte ratio may improve survival prediction beyond clinical factors in limited stage small cell lung cancer: model development and validation

Background

Few small sample size studies have reported lymphocyte count was prognostic for survival in small-cell lung cancer (SCLC). This study aimed to validate this finding, to build prediction model for overall survival (OS) and to study whether novel models that combine lymphocyte-related variables can predict OS more accurately than a conventional model using clinical factors alone in a large cohort of limited-stage SCLC patients.

Methods

This study enrolled 544 limited-stage SCLC patients receiving definitive chemo-radiation with pre-radiotherapy lymphocyte-related variables including absolute lymphocyte count (ALC), platelet-to-lymphocyte ratio (P/L ratio), neutrophil-to-lymphocyte ratio (N/L ratio), and lymphocyte-to-monocyte ratio (L/M ratio). The primary endpoint was OS. These patients were randomly divided into a training dataset (n=274) and a validation dataset (n=270). Multivariate survival models were built in the training dataset, and the performance of these models were further tested in the validation dataset using the concordance index (C-index).

Results

The median follow-up time was 36 months for all patients. In the training dataset, univariate analysis showed that ALC (P=0.020) and P/L ratio (P=0.023) were significantly correlated with OS, while L/M ratio (P=0.091) and N/L ratio (P=0.436) were not. Multivariate modeling demonstrated the significance of ALC (P=0.063) and P/L ratio (P=0.003), and the improvement for OS prediction in combined models with the addition of ALC (C-index =0.693) or P/L ratio (C-index =0.688) over the conventional model (C-index =0.679). The validation dataset analysis confirmed a modest improvement of C-index with the addition of ALC or P/L ratio. All these models showed reasonable discriminations and calibrations.

Conclusions

This study validated the significant value of pre-radiotherapy ALC and P/L ratio on OS in limited-stage SCLC. The combined model with ALC or P/L ratio showed additional OS prediction values than the conventional model with clinical factors alone.

Prognostic value of pretreatment smoking status for small cell lung cancer: A meta-analysis

Background

Although tobacco exposure remains the most important risk factor of tumorigenesis of small cell lung cancer (SCLC), its prognostic value has failed to reach a consensus until now. Accordingly, we conducted a meta‐analysis to investigate the prognostic value of pretreatment smoking status (smokers vs. never‐smokers) in SCLC.

Methods

The four databases PubMed, Medline, Embase, and Cochrane library were searched to identify the relevant literature from the inception dates to 24 June 2020. The primary outcome was overall survival (OS), and the secondary endpoint was progression‐free survival (PFS). The hazard ratios (HRs) with 95% confidence intervals (CIs) were extracted to assess the relationship between pretreatment smoking status and patient survival. Sensitivity analysis was performed to assess the stability of the pooled results. Begg's funnel plot and Egger's test were applied to detect the publication bias. All statistical analyses were performed using RevMan V.5.3 and STATA version 15.0 software.

Results

A total of 27 studies involving 12 047 patients with SCLC (9137 smokers and 2910 never‐smokers) were included in this meta‐analysis. The results showed that smoking history was closely related to poorer survival outcome (OS: HR = 1.17, 95% CI: 1.12–1.23, P < 0.00001; I² = 0%; PFS: HR = 1.20, 95% CI: 1.06–1.35, P = 0.004; I² = 0%).

Conclusions

Smoking history should be considered as an independent poor prognostic factor for patients with SCLC. More large‐scale prospective studies are warranted to testify the prognostic value of pretreatment smoking status.

Neutrophil-to-lymphocyte ratio can predict outcome in extensive-stage small cell lung cancer

Background

The neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR) were analyzed in various carcinomas and their potential prognostic significance was determined. The objective of present study was to determine the correlation between these parameters and the survival of patients with small cell lung cancer (SCLC), since very few studies have been published on this type of carcinoma.

Patients and methods

One hundred and forty patients diagnosed with SCLC at University Hospital Center Zagreb, between 2012 and 2016 were retrospectively analyzed. Extensive-stage disease (ED) was verified in 80 patients and limited-stage disease (LD) in 60 patients. We analyzed the potential prognostic significance of various laboratory parameters, including NLR, PLR, and LMR, measured before the start of treatment.

Results

Disease extension, response to therapy, chest irradiation and prophylactic cranial irradiation (PCI), as well as hemoglobin, monocyte count, C-reactive protein (CRP), and lactate dehydrogenase (LDH) showed a prognostic significance in all patients. When we analyzed the patients separately, depending on the disease extension, we found that only skin metastases as well as LDH and NLR values, regardless of the cut-off value, had a prognostic significance in ED. Meanwhile, the ECOG performance status, chest irradiation, PCI, and hemoglobin and creatinine values had a prognostic significance in LD.

Conclusions

NLR calculated before the start of the treatment had a prognostic significance for ED, while PLR and LMR had no prognostic significance in any of the analyzed groups of patients.

Development and validation of a prognostic model of resectable small-cell lung cancer: a large population-based cohort study and external validation

Background

Survival outcomes of patients with resected SCLC differ widely. The aim of our study was to build a model for individualized risk assessment and accurate prediction of overall survival (OS) in resectable SCLC patients.

Methods

We collected 1052 patients with resected SCLC from the Surveillance, Epidemiology, and End Results (SEER) database. Independent prognostic factors were selected by COX regression analyses, based on which a nomogram was constructed by R code. External validation were performed in 114 patients from Shandong Provincial Hospital. We conducted comparison between the new model and the AJCC staging system. Kaplan–Meier survival analyses were applied to test the application of the risk stratification system.

Results

Sex, age, T stage, N stage, LNR, surgery and chemotherapy were identified to be independent predictors of OS, according which a nomogram was built. Concordance index (C-index) of the training cohort were 0.721, 0.708, 0.726 for 1-, 3- and 5-year OS, respectively. And that in the validation cohort were 0.819, 0.656, 0.708, respectively. Calibration curves also showed great prediction accuracy. In comparison with 8th AJCC staging system, improved net benefits in decision curve analyses (DCA) and evaluated integrated discrimination improvement (IDI) were obtained. The risk stratification system can significantly distinguish the ones with different survival risk. We implemented the nomogram in a user-friendly webserver.

Conclusions

We built a novel nomogram and risk stratification system integrating clinicopathological characteristics and surgical procedure for resectable SCLC. The model showed superior prediction ability for resectable SCLC.

Prognostic value of neuron-specific enolase for small cell lung cancer: a systematic review and meta-analysis

Background

Neuron-specific enolase (NSE) has become a widely used and easily attainable laboratory assay of small cell lung cancer (SCLC). However, the prognostic value of NSE for SCLC patients remains controversial. The aim of the study was to evaluate the correlation between elevated serum NSE before therapy and survival of SCLC patients.

Methods

We performed a systematic review and meta-analysis. A systematic literature search was conducted in PubMed, Embase, and the Cochrane Central Register from the inception dates to December 2019. Eligible articles were included according to inclusion and exclusion criteria; then, data extraction and quality assessment were performed. The primary outcome was overall survival (OS), and the secondary endpoint was progression-free survival (PFS).

Results

We identified 18 studies comprising 2981 patients. Pooled results revealed that elevated NSE was associated with worse OS (HR = 1.78, 95% CI 1.55–2.06, p < 0.001) and PFS (HR = 1.50, 95% CI 1.16–1.93, p = 0.002). In subgroup analysis, elevated NSE did not predict worse OS in patients who received only chemotherapy (HR 1.22, 95% CI 0.96–1.55, p = 0.10) or part of whom received surgical resection before chemotherapy and radiotherapy (HR = 2.16, 95% CI 0.82–5.69, p = 0.12).

Conclusion

Elevated serum NSE before any therapy of SCLC patients may be a negative prognostic factor for OS and PFS. The prognostic value of NSE for OS was particularly observed in patients treated by standard management.

The association between plasma fibrinogen levels and lung cancer: a meta-analysis

Background

Published studies have presented an inconsistent association between plasma fibrinogen level and poor prognosis or clinicopathological characteristics in lung cancer.

Methods

In the absence of significant quality difference, combined hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) were calculated according to overall survival (OS), progression-free survival (PFS) and disease-free survival (DFS). Risk ratio (RR), odds ratio (OR) and standardized mean difference (SMD) with CIs were pooled to appraise the effect of plasma fibrinogen on clinicopathological characteristics. Furthermore, we directly combined the P values to estimate the association of plasma fibrinogen and tumor size. We adjusted the publication bias using trim-and fill method.

Results

Twenty studies with 6,494 patients were contained in meta-analysis. The pooled data indicated that elevated fibrinogen level associated with poor prognosis in lung cancer. Typically, the pooled HRs were 1.44 (95% CI, 1.34-1.55), 1.49 (95% CI, 1.24-1.80) and 1.69 (95% CI, 1.31-2.17) for OS, PFS and DFS of lung cancer, respectively. In addition, the combined ORs were 1.50 (95% CI, 1.23-1.84) and 2.01 (95% CI, 1.66-2.44) for lymph node metastasis and III-IV stage; and the combined RR was 2.15 (95% CI, 1.11-4.15) for disease control rate (DCR). Moreover, patients with distant metastasis or III-IV stage had significantly higher plasma fibrinogen level (SMD: 0.20, 95% CI, 0.04-0.36; SMD: 0.31, 95% CI, 0.18-0.44, respectively).

Conclusions

The summary results indicated that plasma fibrinogen was a marker of prognosis and clinicopathological characteristics in lung cancer.

Neutrophil-lymphocyte ratio is prognostic in early stage resected small-cell lung cancer

Background

For selected early stage small cell lung cancer (SCLC), curative intent surgery is often performed. Previous studies, predominantly from East Asia, reported that high neutrophil to lymphocyte ratio (NLR), and platelet–lymphocyte ratio (PLR) correlate with poor prognosis in several types of tumors including SCLC. Our aim was to investigate the prognostic value of NLR and PLR in Caucasian patients with resected SCLC, as potential tool to select patients for multimodal treatment including surgery.

Methods

Consecutive patients evaluated at three centers between 2000 and 2013 with histologically confirmed and surgically resected SCLC were retrospectively analyzed. NLR and PLR at diagnosis was used to categorize patients into “high” and “low” groups based on receiver operating curve analysis. Univariate and multivariate analyses were used to evaluate the impact of clinical and pathological characteristics on outcome.

Results

There were a total of 189 patients with a median age of 58 years, and the majority had stage I or II disease. We found a significant correlation between NLR and tumor stage (p = 0.007) and age (p = 0.038). Low NLR (LNLR) was associated with significantly longer overall survival, while PLR had no prognostic impact. There were significant associations between NLR and PLR but not with gender, vascular involvement, tumor necrosis, peritumoral inflammation, or tumor grade.

Conclusion

Pre-operative LNLR may be a favorable prognostic factor in stage I–II SCLCs. PLR is not prognostic in this population. LNLR is easy to assess and can be integrated into routine clinical practice. Further prospective studies are needed to confirm these observations.

Nomogram to predict cause-specific mortality in extensive-stage small cell lung cancer: A competing risk analysis

Background

Small‐cell lung cancer (SCLC) is one of the most aggressive types of lung cancer. The prognosis for SCLC patients depends on many factors. The intent of this study was to construct a nomogram model to predict mortality for extensive‐stage SCLC.

Methods

Original data was collected from the Surveillance, Epidemiology, and End Results (SEER) database of the National Cancer Institute in the United States. A nomogram prognostic model was constructed to predict death probability for extensive‐stage SCLC.

Results

A total of 16 554 extensive‐stage SCLC patients from 2004 to 2014 in the SEER database were included in this study. Gender, race, age, TNM staging (including tumor extent, nodal status, and metastasis), and treatment (surgery, chemotherapy, and radiotherapy) were identified as independent predictors for lung cancer‐specific death for extensive‐stage SCLC patients. A nomogram model was constructed based on multivariate models for lung cancer related death and other cause related death. Performance of the two models was validated by calibration and discrimination, with C‐index values of 0.714 and 0.638, respectively.

Conclusion

A prognostic nomogram model was established to predict death probability for extensive‐stage SCLC. This validated prognostic model may be beneficial for treatment strategy choice and survival prediction.

Cause-specific death assessment of patients with stage I small-cell lung cancer: a competing risk analysis

Aim: Stage I small-cell lung cancer (SCLC) is a potentially curable disease that needs timely and multidisciplinary management. The aim of this study was to evaluate the probability of cause-specific mortality for patients with stage I SCLC. Material & methods: We identified patients in the SEER database and constructed a proportional subdistribution hazard model to evaluate cancer-specific mortality. A nomogram was built based on Fine and Gray competing risk regression model. Results: A total of 864 stage I SCLC patients were identified. The 5-year cumulative incidence of SCLC-specific mortality was 56.2%, while that for other causes of death was 17.3%. The c-index for the prognostic prediction model was 0.66. Besides, the nomogram was well calibrated. Conclusion: Our nomogram might serve as a reference for clinicians when evaluating the prognosis of stage I SCLC.

A novel approach to determine two optimal cut-points of a continuous predictor with a U-shaped relationship to hazard ratio in survival data: simulation and application

BACKGROUND

In clinical and epidemiological researches, continuous predictors are often discretized into categorical variables for classification of patients. When the relationship between a continuous predictor and log relative hazards is U-shaped in survival data, there is a lack of a satisfying solution to find optimal cut-points to discretize the continuous predictor. In this study, we propose a novel approach named optimal equal-HR method to discretize a continuous variable that has a U-shaped relationship with log relative hazards in survival data.

METHODS

The main idea of the optimal equal-HR method is to find two optimal cut-points that have equal log relative hazard values and result in Cox models with minimum AIC value. An R package 'CutpointsOEHR' has been developed for easy implementation of the optimal equal-HR method. A Monte Carlo simulation study was carried out to investigate the performance of the optimal equal-HR method. In the simulation process, different censoring proportions, baseline hazard functions and asymmetry levels of U-shaped relationships were chosen. To compare the optimal equal-HR method with other common approaches, the predictive performance of Cox models with variables discretized by different cut-points was assessed.

RESULTS

Simulation results showed that in asymmetric U-shape scenarios the optimal equal-HR method had better performance than the median split method, the upper and lower quantiles method, and the minimum p-value method regarding discrimination ability and overall performance of Cox models. The optimal equal-HR method was applied to a real dataset of small cell lung cancer. The real data example demonstrated that the optimal equal-HR method could provide clinical meaningful cut-points and had good predictive performance in Cox models.

CONCLUSIONS

In general, the optimal equal-HR method is recommended to discretize a continuous predictor with right-censored outcomes if the predictor has an asymmetric U-shaped relationship with log relative hazards based on Cox regression models.

Development and Validation of a Nomogram Prognostic Model for SCLC Patients

INTRODUCTION

SCLC accounts for almost 15% of lung cancer cases in the United States. Nomogram prognostic models could greatly facilitate risk stratification and treatment planning, as well as more refined enrollment criteria for clinical trials. We developed and validated a new nomogram prognostic model for SCLC patients using a large SCLC patient cohort from the National Cancer Database (NCDB).

METHODS

Clinical data for 24,680 SCLC patients diagnosed from 2004 to 2011 were used to develop the nomogram prognostic model. The model was then validated using an independent cohort of 9700 SCLC patients diagnosed from 2012 to 2013. The prognostic performance was evaluated using p value, concordance index and integrated area under the (time-dependent receiver operating characteristic) curve (AUC).

RESULTS

The following variables were contained in the final prognostic model: age, sex, race, ethnicity, Charlson/Deyo score, TNM stage (assigned according to the American Joint Committee on Cancer [AJCC] eighth edition), treatment type (combination of surgery, radiation therapy, and chemotherapy), and laterality. The model was validated in an independent testing group with a concordance index of 0.722 ± 0.004 and an integrated area under the curve of 0.79. The nomogram model has a significantly higher prognostic accuracy than previously developed models, including the AJCC eighth edition TNM-staging system. We implemented the proposed nomogram and four previously published nomograms in an online webserver.

CONCLUSIONS

We developed a nomogram prognostic model for SCLC patients, and validated the model using an independent patient cohort. The nomogram performs better than earlier models, including models using AJCC staging.

Nomograms to Predict Individual Prognosis of Patients with Primary Small Cell Carcinoma of the Bladder

Objectives: To develop reliable nomograms to estimate individualized overall survival (OS) and cancer specific survival (CSS) for patients with primary small cell carcinoma of the bladder (SCCB) and compare the predictive value with the AJCC stages.

Patients and Methods: 582 eligible SCCB patients identified in the Surveillance, Epidemiology, and End Results (SEER) dataset were randomly divided into training (n=482) and validation (n=100) cohorts. Akaike information criterion was used to select the clinically important variables in multivariate Cox models when establishing nomograms. The performance of nomograms was bootstrapped validated internally and externally using the concordance index (C-index) with 95% confidence interval (95% CI) and calibration curves and was compared with that of the AJCC stages using C-index, Kaplan-Meier curves and decision curve analysis (DCA).

Results: Two nomograms shared common indicators including age, tumor size, T stage, lymph node ratio, metastases, chemotherapy, radiation and radical cystectomy, while marriage and gender were only incorporated in the OS nomogram. The C-indices of nomograms for OS and CSS were 0.736 (95%CI 0.711-0.761) and 0.731(95%CI 0.704-0.758), respectively, indicating considerable predictive accuracy. Calibration curves showed consistency between the nomograms and the actual observation. The results remained reproducible when nomograms were applied to the validation cohort. Additionally, comparisons between C-indices, Kaplan-Meier curves and DCA proved that the nomograms obtained obvious superiority over the AJCC stages with wide practical threshold probabilities.

Conclusions: We proposed the first two nomograms for individualized prediction of OS and CSS in SCCB patients with satisfactory predictive accuracy, good robustness and wide applicability.

Surgical Management and Prognostic Prediction of Adenocarcinoma of Jejunum and Ileum

We conducted a retrospective study based on the Surveillance, Epidemiology, and End Results Program (SEER) database to establish a novel nomogram prognostic prediction system and to estimate the association between overall survival and prognostic factors, as well as to explore surgical treatment strategies for adenocarcinoma of the jejunum and ileum. A total of 883 patients from the SEER database were included in this study. Eight potential prognostic factors were included in a nomogram model and discriminatory power and accuracy were examined using the Harrell's C-index and Akaike Information Criterion (AIC) index. In comparison with the AJCC TNM staging system, the nomogram prediction system was more accurate and homogeneous (Harrell's C-index, 0.731 vs. 0.667; AIC index, 4852.9 vs. 4913.723). For surgical management, resection of more than 12 local lymph nodes could improve the likelihood of survival. This study demonstrates that our nomogram model is more accurate and homogeneous than the traditional AJCC TNM staging system, and proper surgical strategies for mesenteric lymphadenectomy improve overall survival.