Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung Cancer
This study aimed to create a risk score generated from CT-based radiomics signatures that could be used to predict overall survival in patients with non-small cell lung cancer (NSCLC). We retrospectively enrolled three sets of NSCLC patients (including 336, 84, and 157 patients for training, testing...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-07-01
|
| Series: | Cancers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-6694/13/14/3616 |
| _version_ | 1797527485903011840 |
|---|---|
| author | Viet-Huan Le Quang-Hien Kha Truong Nguyen Khanh Hung Nguyen Quoc Khanh Le |
| author_facet | Viet-Huan Le Quang-Hien Kha Truong Nguyen Khanh Hung Nguyen Quoc Khanh Le |
| author_sort | Viet-Huan Le |
| collection | DOAJ |
| description | This study aimed to create a risk score generated from CT-based radiomics signatures that could be used to predict overall survival in patients with non-small cell lung cancer (NSCLC). We retrospectively enrolled three sets of NSCLC patients (including 336, 84, and 157 patients for training, testing, and validation set, respectively). A total of 851 radiomics features for each patient from CT images were extracted for further analyses. The most important features (strongly linked with overall survival) were chosen by pairwise correlation analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression model, and univariate Cox proportional hazard regression. Multivariate Cox proportional hazard model survival analysis was used to create risk scores for each patient, and Kaplan–Meier was used to separate patients into two groups: high-risk and low-risk, respectively. ROC curve assessed the prediction ability of the risk score model for overall survival compared to clinical parameters. The risk score, which developed from ten radiomics signatures model, was found to be independent of age, gender, and stage for predicting overall survival in NSCLC patients (HR, 2.99; 95% CI, 2.27–3.93; <i>p</i> < 0.001) and overall survival prediction ability was 0.696 (95% CI, 0.635–0.758), 0.705 (95% CI, 0.649–0.762), 0.657 (95% CI, 0.589–0.726) (AUC) for 1, 3, and 5 years, respectively, in the training set. The risk score is more likely to have a better accuracy in predicting survival at 1, 3, and 5 years than clinical parameters, such as age 0.57 (95% CI, 0.499–0.64), 0.552 (95% CI, 0.489–0.616), 0.621 (95% CI, 0.544–0.689) (AUC); gender 0.554, 0.546, 0.566 (AUC); stage 0.527, 0.501, 0.459 (AUC), respectively, in 1, 3 and 5 years in the training set. In the training set, the Kaplan–Meier curve revealed that NSCLC patients in the high-risk group had a lower overall survival time than the low-risk group (<i>p</i> < 0.001). We also had similar results that were statistically significant in the testing and validation set. In conclusion, risk scores developed from ten radiomics signatures models have great potential to predict overall survival in NSCLC patients compared to the clinical parameters. This model was able to stratify NSCLC patients into high-risk and low-risk groups regarding the overall survival prediction. |
| first_indexed | 2024-03-10T09:43:28Z |
| format | Article |
| id | doaj.art-c8e2768a7ea14280a598eb9bb48d7c69 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-10T09:43:28Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-c8e2768a7ea14280a598eb9bb48d7c692023-11-22T03:26:19ZengMDPI AGCancers2072-66942021-07-011314361610.3390/cancers13143616Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung CancerViet-Huan Le0Quang-Hien Kha1Truong Nguyen Khanh Hung2Nguyen Quoc Khanh Le3International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, TaiwanInternational Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, TaiwanInternational Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, TaiwanInternational Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, TaiwanThis study aimed to create a risk score generated from CT-based radiomics signatures that could be used to predict overall survival in patients with non-small cell lung cancer (NSCLC). We retrospectively enrolled three sets of NSCLC patients (including 336, 84, and 157 patients for training, testing, and validation set, respectively). A total of 851 radiomics features for each patient from CT images were extracted for further analyses. The most important features (strongly linked with overall survival) were chosen by pairwise correlation analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression model, and univariate Cox proportional hazard regression. Multivariate Cox proportional hazard model survival analysis was used to create risk scores for each patient, and Kaplan–Meier was used to separate patients into two groups: high-risk and low-risk, respectively. ROC curve assessed the prediction ability of the risk score model for overall survival compared to clinical parameters. The risk score, which developed from ten radiomics signatures model, was found to be independent of age, gender, and stage for predicting overall survival in NSCLC patients (HR, 2.99; 95% CI, 2.27–3.93; <i>p</i> < 0.001) and overall survival prediction ability was 0.696 (95% CI, 0.635–0.758), 0.705 (95% CI, 0.649–0.762), 0.657 (95% CI, 0.589–0.726) (AUC) for 1, 3, and 5 years, respectively, in the training set. The risk score is more likely to have a better accuracy in predicting survival at 1, 3, and 5 years than clinical parameters, such as age 0.57 (95% CI, 0.499–0.64), 0.552 (95% CI, 0.489–0.616), 0.621 (95% CI, 0.544–0.689) (AUC); gender 0.554, 0.546, 0.566 (AUC); stage 0.527, 0.501, 0.459 (AUC), respectively, in 1, 3 and 5 years in the training set. In the training set, the Kaplan–Meier curve revealed that NSCLC patients in the high-risk group had a lower overall survival time than the low-risk group (<i>p</i> < 0.001). We also had similar results that were statistically significant in the testing and validation set. In conclusion, risk scores developed from ten radiomics signatures models have great potential to predict overall survival in NSCLC patients compared to the clinical parameters. This model was able to stratify NSCLC patients into high-risk and low-risk groups regarding the overall survival prediction.https://www.mdpi.com/2072-6694/13/14/3616non-small cell lung cancerradiomics radiologyoverall survivalprognostic biomarkersmultivariate analysis |
| spellingShingle | Viet-Huan Le Quang-Hien Kha Truong Nguyen Khanh Hung Nguyen Quoc Khanh Le Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung Cancer Cancers non-small cell lung cancer radiomics radiology overall survival prognostic biomarkers multivariate analysis |
| title | Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung Cancer |
| title_full | Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung Cancer |
| title_fullStr | Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung Cancer |
| title_full_unstemmed | Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung Cancer |
| title_short | Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung Cancer |
| title_sort | risk score generated from ct based radiomics signatures for overall survival prediction in non small cell lung cancer |
| topic | non-small cell lung cancer radiomics radiology overall survival prognostic biomarkers multivariate analysis |
| url | https://www.mdpi.com/2072-6694/13/14/3616 |
| work_keys_str_mv | AT viethuanle riskscoregeneratedfromctbasedradiomicssignaturesforoverallsurvivalpredictioninnonsmallcelllungcancer AT quanghienkha riskscoregeneratedfromctbasedradiomicssignaturesforoverallsurvivalpredictioninnonsmallcelllungcancer AT truongnguyenkhanhhung riskscoregeneratedfromctbasedradiomicssignaturesforoverallsurvivalpredictioninnonsmallcelllungcancer AT nguyenquockhanhle riskscoregeneratedfromctbasedradiomicssignaturesforoverallsurvivalpredictioninnonsmallcelllungcancer |