A nomogram to predict the survival of stage IIIA-N2 non-small cell lung cancer after surgery

Development and Validation of a Nomogram Model to Predict Obstructive Sleep Apnea

Objectives: Polysomnography was class I test for who was suspected of obstructive sleep apnea (OSA) which would cost lots of time and money. This study aimed to develop a nomogram model mainly based on oxygen and blood routine indicators to predict OSA. Methods: We retrospectively analyzed 685 patients with suspected OSA at our hospital. Multivariate analysis was used to construct a nomogram. The performance of the nomogram was assessed using calibration and discrimination. Results: The multivariate analysis identified age, gender, body mass index, mean pulse oxygen saturation, percent nighttime with oxygen saturation less than 90%, red blood cell, hematocrit, and red blood cell distribution width SD as significant factors (P < .05). A nomogram was created for the prediction of OSA using these clinical parameters and was internally validated using a bootstrapping method. Our nomogram model showed good discrimination and calibration in terms of predicting OSA, and had a C-index of 0.935 [95% confidence interval (CI), 0.917-0.954] according to the internal validation. Discrimination and calibration in the validation group were also good (C-index, 0.957; 95% CI, 0.930-0.984). Conclusion: The newly developed nomogram can effectively help physicians make better clinical decisions, which may save a lot of time and costs.

Development of a nomogram to predict the prognosis of patients with secondary bone tumors in the intensive care unit: a retrospective analysis based on the MIMIC IV database

PURPOSE

The present study aimed to develop a nomogram to predict the prognosis of patients with secondary bone tumors in the intensive care unit to facilitate risk stratification and treatment planning.

METHODS

We used the MIMIC IV 2.0 (the Medical Information Mart for Intensive Care IV) to retrieve patients with secondary bone tumors as a study cohort. To evaluate the predictive ability of each characteristic on patient mortality, stepwise Cox regression was used to screen variables, and the selected variables were included in the final Cox proportional hazard model. Finally, the performance of the model was tested using the decision curve, calibration curve, and receiver operating characteristic (ROC) curve.

RESULTS

A total of 1028 patients were enrolled after excluding cases with missing information. In the training cohort, albumin, APSIII (Acute Physiology Score III), chemotherapy, lactate, chloride, hepatic metastases, respiratory failure, SAPSII (Simplified Acute Physiology Score II), and total protein were identified as independent risk factors for patient death and then incorporated into the final model. The model showed good and robust prediction performance.

CONCLUSION

We developed a nomogram prognostic model for patients with secondary bone tumors in the intensive care unit, which provides effective survival prediction information.

Adverse independent prognostic effect of initial lung cancer on female patients with second primary breast cancer: a propensity score-matched study based on the SEER database

OBJECTIVE

To investigate the prognostic impact of initial lung cancer (LC) on second primary breast cancer after LC (LC-BC) and further develop a nomogram for predicting the survival of patients.

METHODS

All patients diagnosed with LC-BC and first primary BC (BC-1) during 2000-2017 were collected from Surveillance, Epidemiology, and End Results database. Pathological features, treatment strategies and survival outcomes were compared between LC-BC and BC-1 before and after propensity score matching (PSM). Cox regression analysis was performed to identify the prognostic factors associated with LC in patients with LC-BC. Additionally, least absolute shrinkage and selection operator regression analysis was used to select clinical characteristics for nomogram construction, which were subsequently evaluated using the concordance index (C-index), calibration curve and decision curve analysis (DCA).

RESULTS

827 429 patients with BC-1 and 1445 patients with LC-BC were included in the analysis. Before and after PSM, patients with BC-1 had a better prognosis than individuals with LC-BC in terms of both overall survival (OS) and breast cancer-specific survival (BCSS). Furthermore, characteristics such as more regional lymph node dissection, earlier stage and the lack of chemotherapy and radiation for LC were found to have a stronger predictive influence on LC-BC. The C-index values (OS, 0.748; BCSS, 0.818), calibration curves and DCA consistently demonstrated excellent predictive accuracy of the nomogram.

CONCLUSION

In conclusion, patients with LC-BC have a poorer prognosis than those with BC-1, and LC traits can assist clinicians estimate survival of patients with LC-BC more accurately.

Application of Novel Transcription Factor Machine Learning Model and Targeted Drug Combination Therapy Strategy in Triple Negative Breast Cancer

Transcription factors (TFs) have been shown to play a key role in the occurrence and development of tumors, including triple-negative breast cancer (TNBC), with a worse prognosis. Machine learning is widely used for establishing prediction models and screening key tumor drivers. Current studies lack TF integration in TNBC, so targeted research on TF prognostic models and targeted drugs is beneficial to improve clinical translational application. The purpose of this study was to use the Least Absolute Shrinkage and Selection Operator to build a prognostic TFs model after cohort normalization based on housekeeping gene expression levels. Potential targeted drugs were then screened on the basis of molecular docking, and a multi-drug combination strategy was used for both in vivo and in vitro experimental studies. The machine learning model of TFs built by E2F8, FOXM1, and MYBL2 has broad applicability, with an AUC value of up to 0.877 at one year. As a high-risk clinical factor, its abnormal disorder may lead to upregulation of the activity of pathways related to cell proliferation. This model can also be used to predict the adverse effects of immunotherapy in patients with TNBC. Molecular docking was used to screen three drugs that target TFs: Trichostatin A (TSA), Doxorubicin (DOX), and Calcitriol. In vitro and in vivo experiments showed that TSA + DOX was able to effectively reduce DOX dosage, and TSA + DOX + Calcitriol may be able to effectively reduce the toxic side effects of DOX on the heart. In conclusion, the machine learning model based on three TFs provides new biomarkers for clinical and prognostic diagnosis of TNBC, and the combination targeted drug strategy offers a novel research perspective for TNBC treatment.

Glutamine and amino acid metabolism as a prognostic signature and therapeutic target in endometrial cancer

INTRODUCTION

Endometrial cancer (EC) is the most common female reproductive system cancer in developed countries with growing incidence and associated mortality, which may be due to the growing prevalence of obesity. Metabolism reprogramming including glucose, amino acid, and lipid remodeling is a hallmark of tumors. Glutamine metabolism has been reported to participate in tumor proliferation and development. This study aimed to develop a glutamine metabolism-related prognostic model for EC and explore potential targets for cancer treatment.

METHOD

Transcriptomic data and survival outcome of EC were retrieved from The Cancer Genome Atlas (TCGA). Differentially expressed genes related to glutamine metabolism were recognized and utilized to build a prognostic model by univariate and multivariate Cox regressions. The model was confirmed in the training, testing, and the entire cohort. A nomogram combing prognostic model and clinicopathologic features was established and tested. Moreover, we explored the effect of a key metabolic enzyme, PHGDH, on the biological behavior of EC cell lines and xenograft model.

RESULTS

Five glutamine metabolism-related genes, including PHGDH, OTC, ASRGL1, ASNS, and NR1H4, were involved in prognostic model construction. Kaplan-Meier curve suggested that patients recognized as high risk underwent inferior outcomes. The receiver operating characteristic (ROC) curve showed the model was sufficient to predict survival. Enrichment analysis recognized DNA replication and repair dysfunction in high-risk patients whereas immune relevance analysis revealed low immune scores in the high-risk group. Finally, a nomogram integrating the prognostic model and clinical factors was created and verified. Further, knockdown of PHGDH showed cell growth inhibition, increasing apoptosis, and reduced migration. Promisingly, NCT-503, a PHGDH inhibitor, significantly repressed tumor growth in vivo (p = 0.0002).

CONCLUSION

Our work established and validated a glutamine metabolism-related prognostic model that favorably evaluates the prognosis of EC patients. DNA replication and repair may be the crucial point that linked glutamine metabolism, amino acid metabolism, and EC progression. High-risk patients stratified by the model may not be sufficient for immune therapy. PHGDH might be a crucial target that links serine metabolism, glutamine metabolism as well as EC progression.

Nomogram predicting overall survival of stage IIIB non-small-cell lung cancer patients based on the SEER database

PURPOSE

We aimed to evaluate the prognostic value of stage IIIB non-small-cell (NSCLC) lung cancer patients and to construct a nomogram to effectively predict their overall survival (OS).

METHODS

In total, 4323 patients with stage IIIB NSCLC diagnosed between 1975 and 2018 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Multiple prognostic factors were combined to construct a nomogram for predicting OS of patients with stage IIIB NSCLC. The discrimination and calibration of the nomogram were evaluated by C-indexes and calibration curves. The nomogram was evaluated for predictive ability using receiver operating characteristic (ROC) curves, decision curve analysis curve (DCA), and clinical impact curve (CIC).

RESULTS

The nomogram was built on data of 4323 patients with stage IIIB NSCLC and consisted of the following prognostic factors: age, race, sex, primary labeled, pathology, T stage, whether to receive surgery, whether to receive radiotherapy, and whether to receive chemotherapy. The C-index in the training and validation sets for the nomogram was 0.672 (95% CI: 0.661-0.683) and 0.675 (95% CI: 0.656-0.694), respectively. According to scores of the nomogram, patients in the complete set, validation set, and training set were classified into two risk groups, low risk and high risk.

CONCLUSIONS

We developed the first validated nomogram to estimate the OS of patients with stage IIIB NSCLC. The nomogram was based on nine prognostic factors and provided an individualized risk estimate of 3-year and 5-year OS survival in patients with stage IIIB NSCLC.

A Nomogram Based on Atelectasis/Obstructive Pneumonitis Could Predict the Metastasis of Lymph Nodes and Postoperative Survival of Pathological N0 Classification in Non-small Cell Lung Cancer Patients

The eighth TNM staging system proposal classifies lung cancer with partial or complete atelectasis/obstructive pneumonia into the T2 category. We aimed to develop nomograms to predict the possibility of lymph node metastasis (LNM) and the prognosis for NSCLC based on atelectasis and obstructive pneumonitis.

METHODS

NSCLC patients over 20 years old diagnosed between 2004 and 2015 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. The nomograms were based on risk factors that were identified by Logistic regression. The area under the receiver operating characteristic (ROC) curve (AUC) was performed to confirm the predictive values of our nomograms. Cox proportional hazards analysis and Kaplan-Meier survival analysis were also used in this study.

RESULTS

A total of 470,283 patients were enrolled. Atelectasis/obstructive pneumonitis, age, gender, race, histologic types, grade, and tumor size were defined as independent predictive factors; then, these seven factors were integrated to establish nomograms of LNM. The AUC is 0.70 (95% CI: 0.694-0.704). Moreover, the Cox proportional hazards analysis and Kaplan-Meier survival analysis showed that the scores derived from the nomograms were significantly correlated with the survival of pathological N0 classification.

CONCLUSION

Nomograms based on atelectasis/obstructive pneumonitis were developed and validated to predict LNM and the postoperative prognosis of NSCLC.

A user-friendly nomogram for predicting radioiodine refractory differentiated thyroid cancer

BACKGROUND

The diagnosis of radioiodine refractory differentiated thyroid cancer (RAIR-DTC) is primarily based on clinical evolution and iodine uptake over the lesions, which is still time-consuming, thus urging a predictive model for timely RAIR-DTC informing. The aim of this study was to develop a nomogram model for RAIR prediction among DTC patients with distant metastases (DM).

METHODS

Data were extracted from the treatment and follow-up databases of Peking Union Medical College Hospital between 2010 and 2021. A total of 124 patients were included and divided into RAIR (n=71) and non-RAIR (n=53) according to 2015 ATA guidelines. All patients underwent total thyroidectomy followed by at least two courses of RAI treatment. Serological markers and various clinical, pathological, genetic status, and imaging factors were integrated into this study. The pre-treatment stimulated Tg and pre- and post-treatment suppressed Tg at the first and second course RAI treatment were defined as s-Tg1, s-Tg2, sup-Tg1, and sup-Tg2, respectively. Δs-Tg denoted s-Tg1/s-Tg2, and Δs-TSH denoted s-TSH1/s-TSH2. Multivariate logistic regression and correlation analysis were utilized to determine the independent predictors of RAIR. The performance of the nomogram was assessed by internal validation and receiver operating characteristic (ROC) curve, and benefit in clinical decision-making was assessed using decision curve.

RESULTS

In univariate logistic regression, nine possible risk factors were related to RAIR. Correlation analysis showed four of the above factors associated with RAIR. Through multivariate logistic regression, Δs-Tg/Δs-TSH<1.50 and age upon diagnosis were obtained to develop a convenient nomogram model for predicting RAIR. The model was internally validated and had good predictive efficacy with an AUC of 0.830, specificity of 0.830, and sensitivity of 0.755. The decision curve also showed that if the model is used for clinical decision-making when the probability threshold is between 0.23 and 0.97, the net benefit of patients is markedly higher than that of the TreatAll and TreatNone control groups.By using 1.50 as a cut-off ofΔs-Tg/Δs-TSH, differing biochemical progression among the generally so-called RAIR can be further stratified as meaningfully rapidly or slowly progressive patients (P=0.012).

CONCLUSIONS

A convenient user-friendly nomogram model was developed with good predictive efficacy for RAIR. The progression of RAIR can be further stratified as rapidly or slowly progressive by using 1.50 as a cut-off value of Δs-Tg/Δs-TSH.

A Simple-to-Use Nomogram for Predicting Postoperative Early Death Risk in Elderly Patients with Spinal Tumors: A Population-Based Study

Background

For elderly patients with primary spinal tumors, surgery is the best option for many elderly patients, in addition to palliative care. However, due to the unique physical function of elderly patients, the short-term prognosis is often unpredictable. It is therefore essential to develop a novel nomogram as a clinical aid to predict the risk of early death for elderly patients with primary spinal tumors who undergo surgery.

Materials and Methods

In this study, clinical data were obtained from 651 patients through the SEER database, and they were retrospectively analyzed. Logistic regression analyses were used for risk-factor screening. Predictive modeling was performed through the R language. The prediction models were calibrated as well as evaluated for accuracy in the validation cohort. The receiver operating characteristic (ROC) curve and the decision curve analysis (DCA) were used to evaluate the functionality of the nomogram.

Results

We identified four separate risk factors for constructing nomograms. The area under the receiver operating characteristic curve (training set 0.815, validation set 0.815) shows that the nomogram has good discrimination ability. The decision curve analysis demonstrates the clinical use of this nomogram. The calibration curve indicates that this nomogram has high accuracy. At the same time, we have also developed a web version of the online nomogram for clinical practitioners to apply.

Conclusions

We have successfully developed a nomogram that can accurately predict the risk of early death of elderly patients with primary spinal tumors undergoing surgery, which can provide a reference for clinicians.

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.

PKIB involved in the metastasis and survival of osteosarcoma

Osteosarcoma is frequently metastasized at the time of diagnosis in patients. However, the underlying mechanism of osteosarcoma metastasis remains poorly understood. In this study, we evaluated DNA methylation profiles combined with gene expression profiles of 21 patients with metastatic osteosarcoma and 64 patients with non-metastatic osteosarcoma from TARGET database and identified PKIB and AIM2 as hub genes related to the metastasis of osteosarcoma. To verify the effects of PKIB on migration and invasion of osteosarcoma, we performed wound-healing assay and transwell assay. The results showed that PKIB significantly inhibited the migration and invasion of osteosarcoma cells, and the Western blot experiments showed that the protein level of E-cad was upregulated and of VIM was downregulated in 143-B cell recombinant expression PKIB. These results indicate that PKIB inhibit the metastasis of osteosarcoma. CCK-8 assay results showed that PKIB promote the proliferation of osteosarcoma. In addition, the Western blot results showed that the phosphorylation level of Akt was upregulated in 143-B cells overexpressing PKIB, indicating that PKIB promotes the proliferation of osteosarcoma probably through signaling pathway that Akt involved in. These results give us clues that PKIB was a potential target for osteosarcoma therapy. Furthermore, combined clinical profiles analysis showed that the expression of AIM2- and PKIB- related risk scores was significantly related to the overall survival of patients with osteosarcoma. Thus, we constructed a nomogram based on AIM2 and PKIB expression–related risk scores for osteosarcoma prognostic assessment to predict the 1-, 2-, 3-, and 5-year overall survival rate of patients with metastatic osteosarcoma, assisting clinicians in the diagnosis and treatment of metastatic osteosarcoma.

Risk factors for Drug-resistant Epilepsy (DRE) and a nomogram model to predict DRE development in post-traumatic epilepsy patients

Objectives

To identify factors affecting the development of drug‐resistant epilepsy (DRE), and establish a reliable nomogram to predict DRE development in post‐traumatic epilepsy (PTE) patients.

Methods

This study conducted a retrospective clinical analysis in patients with PTE who visited the Epilepsy Center, Beijing Tiantan Hospital from January 2013 to December 2018. All participants were followed up for at least 3 years, and the development of DRE was assessed. Data from January 2013 to December 2017 were used as development dataset for model building. Those independent predictors of DRE were included in the final multivariable logistic regression, and a derived nomogram was built. Data from January 2018 to December 2018 were used as validation dataset for internal validation.

Results

Complete clinical information was available for 2830 PTE patients (development dataset: 2023; validation dataset: 807), of which 21.06% (n = 596) developed DRE. Among all parameters of interest including gender, age at PTE, family history, severity of traumatic brain injury (TBI), single or multiple injuries, lesion location, post‐TBI treatments, acute seizures, PTE latency, seizure type, status epilepticus (SE), and electroencephalogram (EEG) findings, four predictors showed independent effect on DRE, they were age at PTE, seizure type, SE, and EEG findings. A model incorporating these four variables was created, and a nomogram to calculate the probability of DRE using the coefficients of the model was developed. The C‐index of the predictive model and the validation was 0.662 and 0.690, respectively. The goodness‐of‐fit test indicated good calibration for model development and validation (p = 0.272, 0.572).

Conclusions

The proposed nomogram achieved significant potential for clinical utility in the prediction of DRE among PTE patients. The risk of DRE for individual PTE patients can be estimated by using this nomogram, and identified high‐risk patients might benefit from non‐pharmacological therapies at an early stage.

A decision support framework for postoperative radiotherapy in patients with pathological N2 non-small cell lung cancer

INTRODUCTION

Postoperative radiotherapy (PORT) plays a highly controversial role in pathological N2 (pN2) non-small cell lung cancer (NSCLC) disease. Recent studies reveal that not all patients can benefit from PORT. Further research is needed to identify predictors of PORT.

METHODS

A total of 1044 pathologic stage T1-3N2M0 NSCLC patients were analyzed. Risk factors of distant metastasis were identified by the log-rank tests and the multivariable Cox models. We integrated risk factors of distant metastasis and our previously published loco-regional recurrence (LRR) related prognostic index into a decision support framework (DSF) to predict the outcomes of PORT. An independent cohort was used to validate the DSF.

RESULTS

We defined patients with more than two of three identified LRR-related features (heavy cigarette smoking history, clinical N2 status, and more than four positive lymph nodes) as a high LRR risk group. We found the high-intermediate-risk histological type (with micropapillary and/or solid components) was associated with a higher risk of distant metastasis (HR=1.207, 95% CI 1.062 to 1.371, P=0.0129), but not LRR. We built the DSF by combining these two types of features. Patients were stratified into four groups by using the DSF. PORT significantly improved OS only in the subgroup without high-risk histological features (without micropapillary or solid components) and with a high risk for LRR (three-year OS: 66.7% in the PORT group vs. 50.2% in the non-PORT group; P=0.023).

CONCLUSIONS

A particular pN2 subgroup with a high risk of LRR and without micropapillary or solid components could benefit from PORT.

Automated whole-slide images assessment of immune infiltration in resected non-small-cell lung cancer: towards better risk-stratification

Background

High immune infiltration is associated with favourable prognosis in patients with non-small-cell lung cancer (NSCLC), but an automated workflow for characterizing immune infiltration, with high validity and reliability, remains to be developed.

Methods

We performed a multicentre retrospective study of patients with completely resected NSCLC. We developed an image analysis workflow for automatically evaluating the density of CD3⁺ and CD8⁺ T-cells in the tumour regions on immunohistochemistry (IHC)-stained whole-slide images (WSIs), and proposed an immune scoring system “I-score” based on the automated assessed cell density.

Results

A discovery cohort (n = 145) and a validation cohort (n = 180) were used to assess the prognostic value of the I-score for disease-free survival (DFS). The I-score (two-category) was an independent prognostic factor after adjusting for other clinicopathologic factors. Compared with a low I-score (two-category), a high I-score was associated with significantly superior DFS in the discovery cohort (adjusted hazard ratio [HR], 0.54; 95% confidence interval [CI] 0.33–0.86; P = 0.010) and validation cohort (adjusted HR, 0.57; 95% CI 0.36–0.92; P = 0.022). The I-score improved the prognostic stratification when integrating it into the Cox proportional hazard regression models with other risk factors (discovery cohort, C-index 0.742 vs. 0.728; validation cohort, C-index 0.695 vs. 0.685).

Conclusion

This automated workflow and immune scoring system would advance the clinical application of immune microenvironment evaluation and support the clinical decision making for patients with resected NSCLC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03458-9.

Somatic mutations combined with clinical features can predict the postoperative prognosis of stage IIIA lung adenocarcinoma

Background

Prognostic factors for stage IIIA lung adenocarcinoma (LUAD) are unclear. The current main treatment for stage IIIA LUAD is still controversial. Some Clinicians advocate synchronous chemoradiotherapy as the main treatment for stage IIIA LUAD. In contrast, some clinicians argue that there are still certain patients with stage IIIA LUAD who have a better postoperative prognosis. This study aimed to analyze preoperative factors as well as the association between somatic mutations and prognosis in stage IIIA LUAD [including overall survival (OS) time and the risk of postoperative recurrence].

Methods

This study retrospectively reviewed the data of patients with stage IIIA LUAD who underwent radical resection of lung cancer in the thoracic surgery department of Tianjin Chest Hospital from January 01, 2011 to September 30, 2016. All patients involved in the study provided written informed consent. The associations between OS and DFS and the clinical characteristics as well as somatic mutations of patients were analyzed separately. The Kaplan-Meier method was used for univariate analysis, and survival curves were drawn. Multivariate analysis was performed by the Cox regression model.

Results

For univariate analysis, the prognostic factors of OS were the level of preoperative CYFRA21-1, the number of metastatic lymph node stations (NMLS), maximum tumor diameter, EGFR (epidermal growth factor receptor) classical base mutations, and the number of copies of POLE (polymerase epsilon) mutation (NCPM). Preoperative total protein level, preoperative CYFRA21-1 level, the number of metastatic lymph nodes (NMLN), maximum tumor diameter, the number of mutated genes (NMG) in tumor samples, TP53 mutations, and the number of copies of POLE mutation (NCPM) were associated with disease-free survival (DFS). The multivariate analysis showed that the preoperative CYFRA21-1 level, the number of metastatic lymph node stations (NMLS), and EGFR typical base mutations were independent prognostic factors of OS. The number of mutated genes (NMG), EGFR classical base mutations, preoperative NSE level, maximum tumor diameter, and the number of metastatic lymph node stations (NMLS) were independent prognostic factors for DFS.

Conclusions

The preoperative level of tumor markers, the number of metastatic lymph node stations, and EGFR typical base mutations are important factors for the prognosis of patients with resectable stage IIIA LUAD.

Nomogram based on prognostic nutrition index and Chest CT imaging signs predicts lymph node metastasis in NSCLC patients

OBJECTIVE

To establish and validate a model capable of predicting lymph node metastasis (LNM) of non-small cell lung cancer (NSCLC) patients.

METHODS

Preoperative clinical and CT imaging data on patients with NSCLC undergoing surgery were retrospectively analyzed. A model was developed using a training cohort of 290 patients. The univariate analysis followed by dichotomous logistic regression was performed to estimate different risk factors of lymph node metastasis, and a nomogram was constructed. Using another testing cohort of 120 patients, the performance of the nomogram was validated using several evaluation methods and indices and evaluated including via the area under the curve (AUC), calibration curve, Hosmer-Lemeshow test and decision curve analysis (DCA).

RESULTS

CT-based imaging signs were important independent risk factors for lymph node metastasis in NSCLC patients. The possible risk factors also included four other independent risk factors through dichotomous logistic regression, i.e., age, SIRI, PNI and CEA, which were filtered and included in the nomogram. Nomogram yields AUC values of 0.828 [95% confidence interval (CI): 0.778-0.877] in the training cohort and 0.816 (95% CI: 0.737-0.895) in the validation cohort, respectively. The calibration curves showed high agreement in both the training and validation cohorts. At the threshold probability of 0-0.8, the nomogram increases the net outcomes compared to the treat-none and treat-all lines in the decision curve.

CONCLUSIONS

The nomogram based on the PNI and CT images signs holds promise as a novel and accurate tool for predicting the LNM in NSCLC patients and guiding intraoperative lymph node dissection.

Tumor size as a predictor for prognosis of patients with surgical IIIA-N2 non-small cell lung cancer after surgery

Background

The 8^th edition of the American Joint Committee on Cancer staging system for lung cancer made major revisions to T staging, especially the size division of stage II/III patients. However, the value of tumor size in the postoperative prognosis of IIIA–N2 non-small cell lung cancer (NSCLC) is seldom mentioned, and survival data of such patients should be re-evaluated according to the 8^th edition staging system.

Methods

Patients with IIIA-N2 NSCLC after surgery were identified in the Surveillance, Epidemiology, and End Results database (n=4,128). All patients were stratified according to tumor size, 5-year overall survival (OS) was then compared. Cox regression analysis was used to determine the value of size to discriminate patients with prognostic differences and establish a predictive nomogram system. Patients with IIIA-N2 NSCLC from our own institute (n=583) were used to validate the results.

Results

The prognosis of patients with tumor sizes of 0–2, 2–4 and 4–5 cm differed greatly from each other in the training cohort, with 5-year OS rates of 53.7%, 43.9% and 36.9% respectively (P<0.001), in the validation cohort, the rates were 54.1%, 38.4% and 33.8% respectively. Tumor size >2 cm was considered an independent risk factor compared to the ≤2 cm group in the Cox regression analysis: 2–4 cm (HR =1.25, 1.12–1.39; P<0.001), 4–5 cm (HR =1.51, 1.32–1.39; P<0.001), the validation cohort showed the same trend. The concordance index of the training set was 0.634 (0.622–0.646), while that of the validation set was 0.716 (0.686–0.746). The calibration plot showed optimal consistency between the nomogram predicted survival and observed survival.

Conclusions

Tumors with different sizes showed significant postoperative survival differences among patients with IIIA-N2 NSCLC. Tumor size should be considered when making surgery decisions in such patients, with tumor size ≤2 cm showing considerably better prognosis.

The risk and prognosis of secondary primary malignancy in lung cancer: a population-based study

Aim: To assess and predict risk and prognosis of lung cancer (LC) patients with second primary malignancy (SPM). Methods: LC patients diagnosed from 1992 to 2016 were obtained through the Surveillance, Epidemiology, and End Results database. Standardized incidence ratios were calculated to evaluate SPM risk. Cox regression and competing risk models were applied to assess the factors associated with overall survival, SPM development and LC-specific survival. Nomograms were built to predict SPM probability and overall survival. Results & conclusion: LC patients remain at higher risk of SPM even though the incidence declines. Patients with SPM have a better prognosis than patients without SPM. The consistency indexes for nomograms of SPM probability and overall survival are 0.605 (95% CI: 0.598-0.611) and 0.644 (95% CI: 0.638-0.650), respectively.

Construction of a Nomogram for Predicting Survival in Elderly Patients With Lung Adenocarcinoma: A Retrospective Cohort Study

Elderly patients with non-small-cell lung cancer (NSCLC) exhibit worse reactions to anticancer treatments. Adenocarcinoma (AC) is the predominant histologic subtype of NSCLC, is diverse and heterogeneous, and shows different outcomes and responses to treatment. The aim of this study was to establish a nomogram that includes the important prognostic factors based on the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. We collected 53,694 patients of older than 60 who have been diagnosed with lung AC from the SEER database. Univariate and multivariate Cox regression analyses were used to screen the independent prognostic factors, which were used to construct a nomogram for predicting survival rates in elderly AC patients. The nomogram was evaluated using the concordance index (C-index), calibration curves, net reclassification index (NRI), integrated discrimination improvement (IDI), and decision-curve analysis (DCA). Elderly AC patients were randomly divided into a training cohort and validation cohort. The nomogram model included the following 11 prognostic factors: age, sex, race, marital status, tumor site, histologic grade, American Joint Committee for Cancer (AJCC) stage, surgery status, radiotherapy status, chemotherapy status, and insurance type. The C-indexes of the training and validation cohorts for cancer-specific survival (CSS) (0.832 and 0.832, respectively) based on the nomogram model were higher than those of the AJCC model (0.777 and 0.774, respectively). The CSS discrimination performance as indicated by the AUC was better in the nomogram model than the AJCC model at 1, 3, and 5 years in both the training cohort (0.888 vs. 0.833, 0.887 vs. 0.837, and 0.876 vs. 0.830, respectively) and the validation cohort (0.890 vs. 0.832, 0.883 vs. 0.834, and 0.880 vs. 0.831, respectively). The predicted CSS probabilities showed optimal agreement with the actual observations in nomogram calibration plots. The NRI, IDI, and DCA for the 1-, 3-, and 5-year follow-up examinations verified the clinical usability and practical decision-making effects of the new model. We have developed a reliable nomogram for determining the prognosis of elderly AC patients, which demonstrated excellent discrimination and clinical usability and more accurate prognosis predictions. The nomogram may improve clinical decision-making and prognosis predictions for elderly AC patients.

Nomogram for Predicting the Relationship between the Extent of Visceral Pleural Invasion and Survival in Non-Small-Cell Lung Cancer

Objective

Although visceral pleural invasion (VPI) has already been incorporated into the TNM staging system, few studies have been conducted to evaluate the prognostic value of the extent of VPI for the survival of non-small-cell lung cancer (NSCLC) patients. Thus, we utilized the Surveillance, Epidemiology, and End Results (SEER) database to assess the correlation between the extent of VPI and survival in NSCLC.

Methods

We identified and incorporated the extent of VPI to build a prognostic nomogram in this study. Patients in the SEER database diagnosed with NSCLC (n = 87,045) from 2010 to 2015 were further analyzed and randomly assigned into either the training group (n = 60,933) or validation group (n = 26,112). Clinical variables were calculated by means of multivariate Cox regressions and incorporated into the predictive model. Subsequently, the accuracy and discrimination of nomogram were further assessed through the concordance index (C-index), calibration curves, and Kaplan–Meier curves.

Results

Multivariate analysis demonstrated that the extent of visceral pleural invasion was an independent and unfavorable prognostic factor. The C-indexes of the training and validation groups were 0.772 (95% CI: 0.770–0.774) and 0.769 (95% CI: 0.765–0.773), respectively, which revealed that the nomogram had sufficient credibility and stable predictive accuracy. The calibration curve displayed consistency between the actual and predictive values in both training and validation groups.

Conclusion

The prognostic nomogram with the extent of VPI could offer an accurate risk evaluation for patients with NSCLC. Independent external validation of this research should be conducted in the future.

Prognostic index for estimating the survival benefit of postoperative radiotherapy in pathologic N2 non-small cell lung cancer: A real-world validation study

OBJECTIVES

This study aimed to evaluate the effect of postoperative radiotherapy (PORT) in patients with resected pathologic N2 (pN2) non-small cell lung cancer (NSCLC) with different locoregional recurrence (LRR) risks.

MATERIALS AND METHODS

The primary cohort and validation cohort were retrieved from two independent medical centres. Data for all consecutive patients with completely resected pathologic stage T1-3N2M0 NSCLC were analysed. Patients without PORT in the primary cohort were identified as a training set. Significant prognostic factors for LRR were identified by the Fine-Gray model to develop a prognostic index (PI) in the training set.

RESULTS

The primary cohort consisted of 357 patients who met the eligibility criteria (training set, 287 patients without PORT). The external validation cohort consisted of 1044 patients who met the eligibility criteria (validation set, 711 patients without PORT). Heavy cigarette smoking history, clinical N2 status (cN2), and the number of positive lymph nodes >4 were identified as independent risk factors. The PI was computed as follows: PI＝0.8*smoking history＋0.5*cN2＋0.7*the number of involved lymph nodes (reference level was assigned the value 1 and risk level the value 2). In the low-risk group (PI score< = 3), PORT showed a trend towards decreased LRR rates but not significantly improved overall survival (OS). In the high-risk group (PI score>3), PORT significantly reduced the risk of LRR and improved OS.

CONCLUSIONS

We constructed and validated a PI to predict individually the effect of PORT in patients with completely resected pN2 NSCLC. Patients with a higher PI score can benefit from PORT in terms of OS.

A nomogram for predicting overall survival in patients with resected non-small cell lung cancer treated with chemotherapy

Background

Chemotherapy is a common treatment for patients with resected non-small cell lung cancer (NSCLC). However, there are few models for predicting the survival outcomes of these patients. Here, we developed a clinical nomogram for predicting overall survival (OS) in this cohort.

Methods

A total of 16,661 patients with resected NSCLC treated with chemotherapy were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. We identified prognostic factors and integrated them into a nomogram. The model was subjected to bootstrap internal validation using the SEER database and external validation using a database in China and the National Cancer Database (NCDB). The model’s predictive accuracy and discriminative ability were tested by calibration and concordance index (C-index).

Results

Age, sex, number of dissected lymph nodes, extent of surgery, N stage, T stage, and grade were independent factors for OS and were integrated into the model. The calibration curves for probability of 1-, 3-, and 5-year OS showed excellent agreement between the predicted and actual survivals. The C-index of the nomogram was higher than that of the Tumor-Node-Metastasis staging system for predicting OS (training cohort, 0.62 vs. 0.58; China cohort, 0.68 vs. 0.63; NCDB cohort, 0.59 vs. 0.57).

Conclusions

We developed a nomogram that can present individual prediction of OS for patients with resected NSCLC who are undergoing chemotherapy. This practical prognostic tool may help clinicians in treatment planning.

Patient prognostic scores and association with survival improvement offered by postoperative radiotherapy for resected IIIA/N2 non-small cell lung cancer: A population-based study

BACKGROUND

Currently, there is no consensus on the role of postoperative adjuvant radiotherapy (PORT) for resected stage IIIA/N2 non-small cell lung cancer (NSCLC). Our study sought to determine which patients may be able to benefit from PORT, based on a patient prognostic score.

METHODS

A retrospective cohort study was conducted to identify patients diagnosed with IIIA/N2 NSCLC between 1988 and 2016 in the SEER database. Eligible patients were divided into the following two groups: PORT group and non-PORT group. We classified patient prognostic scores as an ordinal factor and stratified patients based on prognostic scores. A Cox proportional hazards model with propensity score weighting was performed to evaluate cancer-specific mortality (CSM) between the two groups.

RESULTS

We identified 7060 eligible patients with IIIA/N2 NSCLC, 2833 (40.1%) in the PORT group and 4227 (59.9%) in the non-PORT group. Overall, the 10-year CSM rate in the weighted cohorts was 70.4% in the PORT group, 72.0% in the non-PORT group, and patients who received PORT had a lower CSM rate (p = 0.001). Compared with the non-PORT group, significant survival improvements in the PORT group were observed in patients with higher age, grade, T stage and lymph node ratio (LNR), and without chemotherapy. The improved survival of patients receiving PORT was significantly correlated with patient prognostic scores (p < 0.001).

CONCLUSIONS

In our population-based study, the prognostic score was associated with the survival improvement offered by PORT in IIIA/N2 NSCLC, suggesting that prognostic scores and clinicopathological characteristics may be helpful in proper candidate selection for PORT.

A nomogram for predicting the risk of lymph node metastasis in T1-2 non-small-cell lung cancer based on PET/CT and clinical characteristics

Background

Accurately predicting the risk level for a lymph node metastasis is critical in the treatment of non-small cell lung cancer (NSCLC). This study aimed to construct a novel nomogram to identify patients with a risk of lymph node metastasis in T1–2 NSCLC based on positron emission tomography/computed tomography (PET/CT) and clinical characteristics.

Methods

From January 2011 to November 2017, the records of 318 consecutive patients who had undergone PET/CT examination within 30 days before surgical resection for clinical T1–2 NSCLC were retrospectively reviewed. A nomogram to predict the risk of lymph node metastasis was constructed. The model was confirmed using bootstrap resampling, and an independent validation cohort contained 156 patients from June 2017 to February 2020 at another institution.

Results

Six factors [age, tumor location, histology, the lymph node maximum standardized uptake value (SUVmax), the tumor SUVmax and the carcinoembryonic antigen (CEA) value] were identified and entered into the nomogram. The nomogram developed based on the analysis showed robust discrimination, with an area under the receiver operating characteristic curve of 0.858 in the primary cohort and 0.749 in the validation cohort. The calibration curve for the probability of lymph node metastasis showed excellent concordance between the predicted and actual results. Decision curve analysis suggested that the nomogram was clinically useful.

Conclusions

We set up and validated a novel and effective nomogram that can predict the risk of lymph node metastasis for individual patients with T1–2 NSCLC. This model may help clinicians to make treatment recommendations for individuals.

Prognostic nomogram for surgery of lung cancer in HIV-infected patients

Background

This study aimed to establish an effective prognostic nomogram for surgery of lung cancer in HIV-infected patients.

Methods

The nomogram is based on a retrospective study of 51 patients who underwent lung cancer surgery at the Shanghai Public Health Clinical Center from July 2012 to November 2019. The predictive accuracy and discriminative ability of the nomogram were determined by concordance index (C-index) and calibration curve analysis. Internal validity was assessed using bootstrapping validation.

Results

Predictors contained in the prognostic nomogram included age, CD4+ cell count, surgery method, and pathological stage. The model displayed good discrimination with a C-index of 0.755 (95% CI: 0.715-0.795) and good calibration. A high C-index value of 0.844 was reached after internal validation.

Conclusions

The proposed nomogram may result in more-accurate prognostic predictions for surgery of lung cancer in HIV-infected patients.

Development and validation of a VHL-associated immune prognostic signature for clear cell renal cell carcinoma

Background

VHL mutation is the most common mutation in clear cell renal cell carcinoma (ccRCC). Here, we developed and validated an immune-related signature to predict the prognosis of ccRCC with VHL mutations.

Methods

VHL mutation status and RNA expression were analysed in the TCGA datasets and our cohort. LASSO Cox analysis was performed to develop an immune-related signature. Candidate genes for the immune-related signature were differentially expressed between VHLwt and VHLmut ccRCC patients.

Results

VHL mutations resulted in the downregulation of the immune response in ccRCC. To develop an immune-related signature, LASSO Cox analysis was constructed by immune-related genes that were differentially expressed between VHLwt (WHL wild type) and VHLmut (VHL mutation) ccRCC patients. The signature was developed and validated in the TCGA and our own cohort to classify patients into groups based on having a low or high risk of poor survival. Functional enrichment analysis showed that the immune-related pathway represented the major function and pathway. In addition, patients in the high-risk group had a positive correlation with low fractions of CD4 + T cells and dendritic cells and presented a lower expression of CTLA-4 and PD-1 than the low-risk group.

Conclusion

In this study, we proposed a novel immune-related signature, which is a feasible biomarker for predicting the overall survival in VHLmut patients with ccRCC.

A prognostic model for elderly patients with squamous non-small cell lung cancer: a population-based study

Background

Squamous cell carcinoma (SCC) is a main pathological type of non-small cell lung cancer. It is common among elderly patients with poor prognosis. We aimed to establish an accurate nomogram to predict survival for elderly patients (≥ 60 years old) with SCC based on the Surveillance, Epidemiology, and End Results (SEER) database.

Methods

The gerontal patients diagnosed with SCC from 2010 to 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. The independent prognostic factors were identified using multivariate Cox proportional hazards regression analysis, which were utilized to conduct a nomogram for predicting survival. The novel nomogram was evaluated by Concordance index (C-index), calibration curves, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA).

Results

32,474 elderly SCC patients were included in the analysis, who were randomly assigned to training cohort (n = 22,732) and validation cohort (n = 9742). The following factors were contained in the final prognostic model: age, sex, race, marital status, tumor site, AJCC stage, surgery, radiation and chemotherapy. Compared to AJCC stage, the novel nomogram exhibited better performance: C-index (training group: 0.789 vs. 0.730, validation group: 0.791 vs. 0.733), the areas under the receiver operating characteristic curve of the training set (1-year AUC: 0.846 vs. 0.791, 3-year AUC: 0.860 vs. 0.801, 5-year AUC: 0.859 vs. 0.794) and the validation set (1-year AUC: 0.846 vs. 0.793, 3-year AUC: 0.863 vs. 0.806, 5-year AUC: 0.866 vs. 0.801), and the 1-, 3- and 5-year calibration plots. Additionally, the NRI and IDI and 1-, 3- and 5-year DCA curves all confirmed that the nomogram was a great prognosis tool.

Conclusions

We constructed a novel nomogram that could be practical and helpful for precise evaluation of elderly SCC patient prognosis, thus helping clinicians in determining the appropriate therapy strategies for individual SCC patients.

A clinical nomogram and heat map for assessing survival in patients with stage I non-small cell lung cancer after complete resection

Background:

Assessing the prognosis of patients with early-stage non-small cell lung cancer (NSCLC) has become a major clinical issue. This study aimed to devise an effective clinical nomogram and heat map for assessing the survival of patients with stage I NSCLC receiving complete resection.

Methods:

Nomograms were established based on a retrospective study of 654 patients with stage I NSCLC who underwent radical resection at Sun Yat-Sen University Cancer Center between January 2009 and December 2014. The concordance index (C-index) and calibration curve were used to measure the accuracy and discriminative ability of the final nomogram. Heat maps were constructed with prognostic factors and survival probabilities. Survival curves were depicted using the Kaplan–Meier method, and the log-rank test was used to determine significance. Patients were classified into low- and high-risk subgroups using recursive partitioning analysis based on nomogram scores.

Results:

In univariate and multivariate analyses, the independent factors for overall survival (OS) and disease-free survival (DFS) were age, sex, tumor size, and visceral pleural invasion, which were all selected in the nomogram. The C-indices of the nomogram for predicting OS and DFS were 0.694 [95% confidence interval (CI) 0.651–0.737] and 0.653 (95% CI 0.61–0.696), respectively. The calibration curves for OS and DFS probabilities showed a good agreement between the nomogram prediction and actual observation. A heat map was generated using the above independent factors for OS and DFS. High-risk patients had shorter OS [hazard ratio (HR) = 3.535, 95% CI 2.444–5.113, p < 0.001] and DFS (HR = 2.607, 95% CI 1.922–3.537, p < 0.001) than low-risk patients.

Conclusion:

We established a prognostic nomogram and heat map that can be useful for evaluating survival in patients with stage I NSCLC after complete resection. The tools resulted in more accurate prediction and may guide clinicians in making treatment decisions.

Predict overall survival of spinal conventional chordoma: Development and assessment of a new predictive nomogram

OBJECTIVE

To predict the 5-year overall survival (OS) rate in patients with conventional chordoma of the spine PATIENTS AND METHODS: The Surveillance, Epidemiology, and End Results (SEER) Registry was used to identify patients with conventional chordoma of the spine from 1994 to 2013. The entire cohort(n = 294) was randomly divided into training (n = 147) and validation (n = 147) cohorts to construct a nomogram. We used the univariate Log-rank test and multivariate Cox model to examine the independent prognostic factors associated with OS. These prognostic factors were integrated to construct a nomogram through R studio. The predictive and validating capacity of the nomogram was calculated by Harrell's concordance index (C-index) and calibration curves.

RESULTS

A total of 294 patients were identified with conventional chordoma of the spine. The patients' age at diagnosis, tumor size, EOD (extent of disease), and treatment were independent prognostic factors and associated with OS. These prognostic factors were incorporated to construct a nomogram. The concordance index for the nomogram was 0.771 and 0.732 in the training cohort and validation cohort, respectively. Internal and external calibration curves for 5-year OS showed excellent matching between nomogram prediction and observed outcomes.

CONCLUSIONS

The findings of this study provide population-based estimates of patients with conventional chordoma of the spine. Using this nomogram, surgeons can classify patients into different risk groups and achieve individualized treatment.

Prognostic factors for overall survival of stage III non-small cell lung cancer patients on computed tomography: A systematic review and meta-analysis

INTRODUCTION

Prognosis prediction is central in treatment decision making and quality of life for non-small cell lung cancer (NSCLC) patients. However, conventional computed tomography (CT) related prognostic factors may not apply to the challenging stage III NSCLC group. The aim of this systematic review was therefore to identify and evaluate CT-related prognostic factors for overall survival (OS) of stage III NSCLC.

METHODS

The Medline, Embase, and Cochrane electronic databases were searched. After study selection, risk of bias was estimated for the included studies. Meta-analysis of univariate results was performed when sufficient data were available.

RESULTS

1595 of the 11,996 retrieved records were selected for full text review, leading to inclusion of 65 studies that reported data of 144,513 stage III NSCLC patients andcompromising 26 unique CT-related prognostic factors. Relevance and validity varied substantially, few studies had low relevance and validity. Only four studies evaluated the added value of new prognostic factors compared with recognized clinical factors. Included studies suggested gross tumor volume (meta-analysis: HR = 1.22, 95%CI: 1.05-1.42), tumor diameter, nodal volume, and pleural effusion, are prognostic in patients treated with chemoradiation. Clinical T-stage and location (right/left) were likely not prognostic within stage III NSCLC. Inconclusive are several radiomic features, tumor volume, atelectasis, location (pulmonary lobes, central/peripheral), interstitial lung abnormalities, great vessel invasion, pit-fall sign, and cavitation.

CONCLUSIONS

Tumor-size and nodal size-related factors are prognostic for OS in stage III NSCLC. Future studies should carefully report study characteristics and contrast factors with guideline recognized factors to improve evidence evaluation and validation.

A Novel Nomogram including AJCC Stages Could Better Predict Survival for NSCLC Patients Who Underwent Surgery: A Large Population-Based Study

Objective

In this study, we aimed to establish a novel nomogram model which was better than the current American Joint Committee on Cancer (AJCC) stage to predict survival for non-small-cell lung cancer (NSCLC) patients who underwent surgery. Patients and Methods. 19617 patients with initially diagnosed NSCLC were screened from Surveillance Epidemiology and End Results (SEER) database between 2010 and 2015. These patients were randomly divided into two groups including the training cohort and the validation cohort. The Cox proportional hazard model was used to analyze the influence of different variables on overall survival (OS). Then, using R software version 3.4.3, we constructed a nomogram and a risk classification system combined with some clinical parameters. We visualized the regression equation by nomogram after obtaining the regression coefficient in multivariate analysis. The concordance index (C-index) and calibration curve were used to perform the validation of nomogram. Receiver operating characteristic (ROC) curves were used to evaluate the clinical utility of the nomogram.

Results

Univariate and multivariate analyses demonstrated that seven factors including age, sex, stage, histology, surgery, and positive lymph nodes (all, P < 0.001) were independent predictors of OS. Among them, stage (C-index = 0.615), positive lymph nodes (C-index = 0.574), histology (C-index = 0.566), age (C-index = 0.563), and sex (C-index = 0.562) had a relatively strong ability to predict the OS. Based on these factors, we established and validated the predictive model by nomogram. The calibration curves showed good consistency between the actual OS and predicted OS. And the decision curves showed great clinical usefulness of the nomogram. Then, we built a risk classification system and divided NSCLC patients into two groups including high-risk group and low-risk group. The Kaplan-Meier curves revealed that OS in the two groups was accurately differentiated in the training cohort (P < 0.001). And then, we validated this result in the validation cohort which also showed that patients in the high-risk group had worse survival than those in the low-risk group.

Conclusion

The results proved that the nomogram model had better performance to predict survival for NSCLC patients who underwent surgery than AJCC stage. These tools may be helpful for clinicians to evaluate prognostic indicators of patients undergoing operation.

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

Background

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

Methods

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

Results

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

Conclusions

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

Development and External Validation of a Nomogram for Predicting Survival in Patients With Stage IA Non-small Cell Lung Cancer ≤2 cm Undergoing Sublobectomy

Background: Postoperative prognosis of early stage non-small cell lung cancer (NSCLC) undergoing sublobectomy is heterogeneous. Therefore, we sought to construct a novel survival prediction model for stage IA NSCLC ≤2 cm undergoing sublobectomy.

Methods: Based on the data from the Surveillance, Epidemiology, and End Results (SEER) program, we successfully determined and incorporated independent prognostic markers to construct the nomogram. Internal validation of the constructed nomogram was conducted through 1,000 bootstrap resamples. The constructed nomogram was further subjected to external validation with an independent cohort of patients from two Chinese institutions. The performance of the survival prediction model was assessed by concordance index, calibration plots, and risk subgroup classification.

Results: A total of 3,238 patients from SEER registries (development cohort), as well as 769 patients from two Chinese institutions (validation cohort) was included. Gender, age, size, histologic type, grade, and examined lymph nodes count were identified as significant prognostic parameters. A novel nomogram was developed and externally validated. Concordance index of constructed nomogram was significantly better than that of the current TNM staging system. Calibration plots demonstrated an optimal consistency between the nomogram predicted and actual observed probability of survival. Survival curves of different risk subgroups within respective TNM stage demonstrated significant distinctions.

Conclusion: We developed and externally validated a survival prediction model for patients with stage IA NSCLC ≤2 cm undergoing sublobectomy. This novel nomogram outperforms the conventional TNM staging system and could help clinicians in postoperative surveillance and future clinical trial design.

Survival nomogram for patients with initially diagnosed metastatic non-small-cell lung cancer: a SEER-based study

Aim: Prognosis of patients with metastatic non-small-cell lung cancer differ widely. Methods: All patients were randomly divided into training or validation cohort. Cox-regression analyses were conducted to select independent predictors. We built a nomogram by R code and evaluated the accuracy and the reliability of the model using C-index, calibration curves and decision curve analyses. We made a risk classification system based on the nomogram. Results: In the validation cohort, C-index was 0.729 and 0.738 for 1- and 2-year overall survival. Calibration plots and decision curve analyses presented great prognostic accuracy and clinical applicability. Its prognostic accuracy preceded the American Joint Committee on Cancer staging with evaluated integrated discrimination improvement. Conclusion: The model can be a practical tool in treatment decision and individual counseling.

Incidence, prognostic factors, and a nomogram of lung cancer with bone metastasis at initial diagnosis: a population-based study

BACKGROUND

Bone is one of the common metastatic sites of lung cancer, and its prognosis is not optimistic. We performed a study to evaluate the incidence, survival, and prognostic factors of lung cancer with bone metastasis (LCBM) at initial diagnosis, and to develop a nomogram to predict its outcomes.

METHODS

We conducted a retrospective study choosing 13,541 patients with LCBM from the Surveillance, Epidemiology, and End Results (SEER) 18 registry database. An X-tile analysis provided the optimal age cutoff point. The incidence, overall survival, and prognosis of bone metastasis were evaluated according to the patient information, characteristics of the tumor, and therapy. We also used multivariable Cox regression to estimate mortality hazard ratios (HRs) among patients with LCBM, while a visual nomogram was established to judge the prognosis.

RESULTS

The incidence of disease increased with age, but survival rates show the opposite trend. The median survival time was about 4 months. In addition, although the differences for patient race is not significant (P=0.445), White patients are prone to have bone metastases from lung cancer according to the incidence analysis. The difference for laterality is also not significant (P=0.534), while the factors of age, gender, the total number of sites, histological types, grade, tumor size, and treatment are significantly related to the outcome of patients with LCBM. Furthermore, our nomogram could predict the probability of surviving to the median survival time of the population with a c-index of 0.72.

CONCLUSIONS

Age, characteristics of the tumor, and therapy should be considered for prediction of prognosis for patients with lung cancer bone metastasis. Putatively, the younger patients and the patients with chemotherapy and surgery may indicate improved survival.

A Prediction Rule for Overall Survival in Non-Small-Cell Lung Cancer Patients with a Pathological Tumor Size Less Than 30 mm

We sought to develop and validate a clinical nomogram model for predicting overall survival (OS) in non-small-cell lung cancer (NSCLC) patients with resected tumors that were 30 mm or smaller, using clinical data and molecular marker findings. We retrospectively analyzed 786 NSCLC patients with a pathological tumor size less than 30 mm who underwent surgery between 2007 and 2017 at our institution. We identified and integrated significant prognostic factors to build the nomogram model using the training set, which was subjected to the internal data validation. The prognostic performance was calibrated and evaluated by the concordance index (C-index) and risk group stratification. Multivariable analysis identified the pathological tumor size, lymph node metastasis, and Ki-67 expression as independent prognostic factors, which were entered into the nomogram model. The nomogram-predicted probabilities of OS at 1 year, 3 years, and 5 years posttreatment represented optimal concordance with the actual observations. Harrell's C-index of the constructed nomogram with the training set was 0.856 (95% CI: 0.804-0.908), whereas TNM staging was 0.814 (95% CI: 0.742-0.886, P = 5.280221e − 13). Survival analysis demonstrated that NSCLC subgroups showed significant differences in the training and validation sets (P < 0.001). A nomogram model was established for predicting survival in NSCLC patients with a pathological tumor size less than 30 mm, which would be further validated using demographic and clinicopathological data. In the future, this prognostic model may assist clinicians during treatment planning and clinical studies.

Effect of lymph node examined count on accurate staging and survival of resected esophageal cancer

Background

We examined the association between numbers of lymph nodes examined (LNEs) and accurate staging and survival to determine the optimal LNE count during esophagectomy using data from the Surveillance, Epidemiology, and End Results (SEER) cancer registry and the Department of Thoracic Surgery of a single institution (SI).

Methods

A total of 7356 EC patients met our inclusion criteria from the SEER database and 1275 patients from SI. We applied multivariate models to investigate the relationship between the LNE count and LN metastasis and cancer‐specific survival (CSS). Odds ratios (ORs) and hazard ratios (HRs) generated by the multivariate models were fitted with Locally Weighted Scatterplot Smoothing, and the structural breakpoints were determined by the Chow test.

Results

Higher numbers of LNEs were linked to a higher proportion of LN metastasis and better CSS in both cohorts. Cut‐point analysis determined a threshold of LNEs of 12 for adenocarcinoma and 14 for esophageal squamous cell cancer (ESCC) considering accurate staging, and 15 for adenocarcinoma and 14 for ESCC considering OS. The cut‐points for CSS were examined in the SEER database and validated in the divided cohort from SI (all P < 0.05).

Conclusion

A greater number of LNEs are significantly associated with more accurate N staging and better survival in EC patients. We recommend 15 and 14 as the threshold LNE counts for adenocarcinoma and ESCC patients, respectively.

Serotonin and YAP/VGLL4 Balance Correlated with Progression and Poor Prognosis of Hepatocellular Carcinoma

YAP-TEAD complex plays an important role in tumorigenesis. 5-HT is proved to upregulate YAP expression by our previous study and VGLL4 is found to compete with YAP for binding to TEAD in several of cancers. Here, we investigated whether 5-HT could affect progression and prognosis of hepatocellular carcinoma (HCC) patients and regulate YAP/VGLL4 balance. We found that 5-HT and YAP/VGLL4 ratio were higher in HCC patients and closely related with progression and poor prognosis. Furthermore, 5-HT level, YAP/VGLL4 ratio and tumor size were proved as independent risk factors of HCC patients in our study. Based on the independent risk factors, nomogram was established to exactly predict prognosis of HCC patients. Additionally, the study revealed that a higher total point of the nomogram was closely correlated with poorer prognosis. As a result, 5-HT might contribute to the progression and poor prognosis of hepatocellular carcinoma via regulating YAP/VGLL4 balance. Therefore, the established nomogram based on the independent risk factors may become an important part of HCC prediction system and YAP/VGLL4 balance may be a potential therapeutic target in future.