scholarly journals Using Nomogram and Machine Learning Models to Predict Non-Small Cell Lung Cancer Prognosis

2021 ◽  
Author(s):  
Haike Lei ◽  
Chun Liu ◽  
Zheng Xu ◽  
Na Hong ◽  
Xiaosheng Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Cox Regression ◽  
Small Cell ◽  
Seer Database ◽  
Learning Models ◽  
Small Cell Lung ◽  
Vital Status ◽  
Nsclc Patients ◽  
Machine Learning Models

Abstract BackgroundPatients with non-small cell lung cancer (NSCLC) often have a poor prognosis. Overall survival (OS) prediction through the early diagnosis of cancer has many benefits, such as allowing providers to design the best treatment plan for patients. In this study, we aimed to evaluate the prognostic factors in NSCLC patients, construct a nomogram, and develop machine learning models to predict the OS. We also conducted feature importance analysis to understand how relevant factors of NSCLC patients impact their OS.ResultsMultiple machine learning models were adopted in a retrospective cohort of patients from 2010 to 2015 in the Surveillance, Epidemiology, and End Results (SEER) database. Independent prognostic factors for NSCLC were determined using Cox proportional hazards regression analysis. We modeled OS and vital status as the outcomes and constructed and validated a nomogram to predict the OS of NSCLC. Furthermore, we applied logistic regression, random forest, XGBoost, decision tree, multilayer perceptron, and LightGBM to predict the patients’ vital status. We tested the prediction ability of the models and evaluated their performances using accuracy, sensitivity, specificity, precision, and the area under the receiver operating characteristic curve. A total of 34,567 patients selected from the SEER database that met our criteria were included in this study. The nomogram visualized the OS prediction results of the Cox regression model. Among the classifiers, XGBoost had the best prediction performance, with an area under the curve of 0.733.ConclusionsThe results demonstrated that machine learning-based classifier models are capable of predicting the outcomes of patients with NSCLC. And Cox regression model-based nomogram interpreted the results well and supports potential medical applications.

scholarly journals Machine learning for prediction of immunotherapy efficacy in non-small cell lung cancer from simple clinical and biological data

2021 ◽  
Author(s):  
Sébastien Benzekry ◽  
Mathieu Grangeon ◽  
Mélanie Karlsen ◽  
Maria Alexa ◽  
Isabella Bicalho-Frazeto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Random Forest ◽  
Predictive Value ◽  
Performance Status ◽  
Small Cell ◽  
Biological Data ◽  
Learning Models ◽  
Small Cell Lung ◽  
Machine Learning Models

ABSTRACTBackgroundImmune checkpoint inhibitors (ICIs) are now a therapeutic standard in advanced non-small cell lung cancer (NSCLC), but strong predictive markers for ICIs efficacy are still lacking. We evaluated machine learning models built on simple clinical and biological data to individually predict response to ICIs.MethodsPatients with metastatic NSCLC who received ICI in second line or later were included. We collected clinical and hematological data and studied the association of this data with disease control rate (DCR), progression free survival (PFS) and overall survival (OS). Multiple machine learning (ML) algorithms were assessed for their ability to predict response.ResultsOverall, 298 patients were enrolled. The overall response rate and DCR were 15.3 % and 53%, respectively. Median PFS and OS were 3.3 and 11.4 months, respectively. In multivariable analysis, DCR was significantly associated with performance status (PS) and hemoglobin level (OR 0.58, p<0.0001; OR 1.8, p<0.001). These variables were also associated with PFS and OS and ranked top in random forest-based feature importance. Neutrophils-to-lymphocytes ratio was also associated with DCR, PFS and OS. The best ML algorithm was a random forest. It could predict DCR with satisfactory efficacy based on these three variables. Ten-fold cross-validated performances were: accuracy 0.68 ± 0.04, sensitivity 0.58 ± 0.08; specificity 0.78 ± 0.06; positive predictive value 0.70 ± 0.08; negative predictive value 0.68 ± 0.06; AUC 0.74 ± 0.03.ConclusionCombination of simple clinical and biological data could accurately predict disease control rate at the individual level.Highlights-Machine learning applied to a large set of NSCLC patients could predict efficacy of immunotherapy with a 69% accuracy using simple routine data-Hemoglobin levels and performance status were the strongest predictors and significantly associated with DCR, PFS and OS-Neutrophils-to-lymphocyte ratio was also associated with outcome-Benchmark of 8 machine learning models

scholarly journals Serum miR-1228-3p and miR-181a-5p as Noninvasive Biomarkers for Non-Small Cell Lung Cancer Diagnosis and Prognosis

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Wei-Xiao Xue ◽  
Meng-Yu Zhang ◽  
Rui Li ◽  
Xiao Liu ◽  
Yun-Hong Yin ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Roc Analysis ◽  
Cox Regression ◽  
Small Cell ◽  
Healthy Controls ◽  
Small Cell Lung ◽  
Cox Regression Analysis ◽  
Diagnosis And Prognosis ◽  
Noninvasive Biomarkers ◽  
Nsclc Patients

Background. Lung cancer is the leading cause of cancer-related mortality worldwide, and non-small cell lung cancer (NSCLC) accounts for over 80% of all lung cancers. Serum microRNAs (miRNAs), due to their high stability, have the potential to become valuable noninvasive biomarkers. This present study was aimed to identify the serum miRNAs expression signatures for the diagnosis and prognosis of NSCLC using bioinformatics analysis. Methods. A total of 12 miRNAs profiling studies have been identified in Pubmed, Gene Expression Omnibus (GEO), and ArreyExpress databases. Differentially expressed miRNAs (DEmiRNAs) were analyzed according to GEO2R online tool and RRA method from R. Then, prediction of DEmiRNAs’ target genes from TargetScan, PicTar, miRDB, Tarbase, and miRanda database. Furthermore, we using reverse transcription– quantitative polymerase chain reaction (RT-qPCR) to evaluate the expression levels of DEmiRNAs in serum samples obtained from NSCLC patients and healthy controls. Subsequently, the clinical significance of the tested miRNAs was determined using receiver operating characteristic (ROC) analysis and Cox regression analysis. Results. A total of 27 DEmiRNAs were identified and 5 of them (miR-1228-3p, miR-1228-5p, miR-133a-3p, miR-1273f, miR-545-3p) were significantly up-regulated and 4 of them (miR-181a-5p, miR-266-5p, miR-361-5p, miR-130a-3p) were significantly down-regulated in NSCLC patients compared with healthy controls. RT-qPCR validated that miR-1228-3p (P =0.006) and miR-181a-5p (P =0.030) were significantly differentially expressed in the serum of NSCLC patients and healthy controls. ROC analysis on miR-1228-3p and miR-181a-5p revealed the area under the curve (AUC) of 0.685 (95% confidence interval [CI], 0.563–0.806; P =0.006) and 0.647 (95% CI, 0.506–0.758; P =0.049). ROC analysis on miR-1228-3p combined miR-181a-5p revealed the AUC of 0.711 (95% CI, 0.593–0.828; P =0.002). Multivariate Cox regression analysis demonstrated that the high serum miR-1228-3p level was an independent factor for the poor prognosis of NSCLC patients. Conclusions. Serum miR-1228-3p and miR-181a-5p are potential noninvasive biomarkers for the diagnosis and prognosis of NSCLC patients.

scholarly journals Marital Status Independently Predicts Survival in Patients with Small Cell Lung Cancer: A SEER Database Analysis

2021 ◽  
Author(s):  
Pei Luo ◽  
Yan Mao ◽  
Liping Yang ◽  
Chao Pan ◽  
Jun Guo
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Marital Status ◽  
Cell Lung Cancer ◽  
Cox Regression ◽  
Small Cell ◽  
Rank Test ◽  
Seer Database ◽  
Small Cell Lung ◽  
Kaplan Meier

Abstract Purpose This study will investigate the relationship between marital status and prognosis in small cell lung cancer patients. Methods Patients of SCLC was selected from the SEER database (1973-2013) and the patient sinformation. Kaplan-Meier analysis, log-rank test and Cox regression model were used for studying patientprognosis. Result 27069 SCLC patients eligible for inclusion were screened from the SEER database. Kaplan-meier test showed that the median OS values were 8, 7, 6 months in married, single and SDW patients, respectively. Conclusion This study shows that marital status is an independent prognostic factor for overall survival in SCLC patients. Married patients with small cell lung cancer have better prognosis than those who were divorced/separated, widowed and single.

scholarly journals Time Trend of Mediastinal Lymph Node Dissection in Stage IA Non-Small Cell Lung Cancer Patient Who Undergo Lobectomy: a Retrospective Study of Surveillance, Epidemiology, and End Results (SEER) database

2020 ◽  
Author(s):  
Liang Pan ◽  
Ran Mo ◽  
Lin hai Zhu ◽  
Wen feng Yu ◽  
Wang Lv ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Lymph Node ◽  
Time Trend ◽  
Mediastinal Lymph Node ◽  
Small Cell ◽  
Seer Database ◽  
Node Dissection ◽  
Small Cell Lung ◽  
Stage Ia ◽  
Nsclc Patients

Abstract Background: Although lobectomy with mediastinal lymph node dissection (MLND) is the first option for early-stage non-small cell lung cancer (NSCLC) patients, the time trends of MLND in stage IA NSCLC patients who undergo a lobectomy are not clear still.Methods: We included stage IA NSCLC patients who underwent lobectomy or lobectomy with MLND between 2003 and 2013 in the SEER database. The time trend of MLND was compared among patients who underwent a lobectomy.Results: For stage T1a patients, the lobectomy group and lobectomy with MLND group had no differences in postoperative overall survival (OS) (P=0.34) or lung-cancer specific survival (LCSS) (P=0.18) between 2003 and 2013. For stage T1b patients, the OS (P=0.01) and LCSS (P=0.01) were different between the lobectomy group and the lobectomy with MLND group in the period from 2003 to 2009; however, only OS (P=0.04), not LCSS (P=0.14), was different between the lobectomy group and the lobectomy with MLND group between 2009 and 2013. For T1c patients, the OS (P=0.01) and LCSS (P=0.02) were different between the two groups between 2003 and 2009 but not between 2009 and 2013 (P=0.60; P=0.39). From the Cox regression analysis, we found that the factors affecting OS/LCSS in T1b and T1c patients were age, sex, year of diagnosis, histology, and grade, in which year of diagnosis was the obvious factor (HR=0.79, CI=0.71-0.87; HR=0.73, CI=0.64-0.84).Conclusions: There was a time trend in prognosis differences between the lobectomy group and lobectomy with MLND group for T1b and T1c stage NSCLC patients.

scholarly journals Evaluation of Log Odds of Positive Lymph Nodes in Predicting the Survival of Neoadjuvant Therapy Patients with Non-Small Cell Lung Cancer After Surgery: A SEER Cohort-Based Study

2022 ◽  
Author(s):  
Qing Wang ◽  
Suyu Wang ◽  
Zhiyong Sun ◽  
Min Cao ◽  
Xiaojing Zhao
Keyword(s):  
Lung Cancer ◽  
Survival Analysis ◽  
Neoadjuvant Therapy ◽  
Cox Regression ◽  
Small Cell ◽  
Small Cell Lung ◽  
Log Odds ◽  
Nsclc Patients ◽  
N Staging ◽  
Positive Lymph Nodes

Abstract Background log odds of positive lymph nodes (LODDS) is a novel lymph node (LN) descriptor, demonstrating promising prognostic value in many tumors. However, there was limited information on LODDS in non-small cell lung cancer (NSCLC) patients, especially those receiving neoadjuvant therapy followed by lung surgery. Methods A total of 2,059 NSCLC patients who received neoadjuvant therapy and surgery were identified in the Surveillance, Epidemiology, and End Results (SEER) database. We used the X-tile software to calculate the cut-off value of LODDS. Kaplan-Meier survival analysis and receiver operating characteristics (ROC) curve were used to compare the predictive value of the American Joint Committee on Cancer (AJCC) N staging descriptor and LODDS. Univariate and multivariate Cox regression and inverse probability of treatment weighting (IPTW) analyses were conducted to construct the model predicting the prognosis. Results LODDS showed better differentiating ability in survival analysis than N staging descriptor (Log-rank test, P<0.0001 vs. P=0.031). The ROC curve demonstrated that the AUC of LODDS was significantly higher than the N staging descriptor in 1-year, 3-year, and 5-year survival analyses (All P<0.05). Univariate and multivariate Cox regression analysis showed that the LODDS was an independent risk factor for NSCLC patients receiving neoadjuvant therapy followed by surgery, both before and after IPTW (all P<0.001). A clinicopathological model with LODDS, age, gender, T, and radiotherapy could better predict the prognosis. Conclusions Compared with the AJCC N staging descriptor, LODDS exhibits better predictive ability for NSCLC patients receiving neoadjuvant therapy followed by surgery. A multivariate clinicopathological model with LODDS included demonstrates sound performance in predicting the prognosis.

scholarly journals A nomogram model to predict death rate among non-small cell lung cancer (NSCLC) patients with surgery in Surveillance, Epidemiology, and End Results (SEER) database

2020 ◽  
Author(s):  
Bo Jia ◽  
Qiwen Zheng ◽  
Jingjing Wang ◽  
Hongyan Sun ◽  
Jun Zhao ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Lymph Node ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Death Rate ◽  
Prognostic Model ◽  
Small Cell ◽  
Seer Database ◽  
Small Cell Lung ◽  
Nsclc Patients

Abstract Background This study aimed to establish a novel nomogram prognostic model to predict death probability for non-small cell lung cancer (NSCLC) patients who received surgery. Methods We collected data 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 mortality of NSCLC patients who received surgery. Results A total of 44,880 NSCLC patients who received surgery from 2004 to 2014 were included in this study. Gender, race, tumor anatomic sites, histologic subtype, tumor differentiation, clinical stage, tumor size, tumor extent, lymph node stage, examined lymph node, positive lymph node, type of surgery showed significant associations with lung cancer related death rate (P<0.001). Patients who received chemotherapy and radiotherapy had significant higher lung cancer related death rate but were associated with significant lower non-cancer related mortality (P<0.001). A nomogram model was established based on multivariate models of training data set. In the validation cohort, the unadjusted C-index was 0.73 (95% CI, 0.72-0.74), 0.71 (95% CI, 0.66-0.75) and 0.69 (95% CI, 0.68-0.70) for lung cancer related death, other cancer related death and non-cancer related death. Conclusions A prognostic nomogram model was constructed to predict death rate for NSCLC patients who received surgery. This novel prognostic model may be helpful for physicians to develop the most appropriate treatment strategies for resected NSCLC patients. Parts of these results were presented at the 2018 American Society of Clinical Oncology Annual Meeting (Abstract #8525)

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