A Radiomics-Clinical Model Predicts Overall Survival of Non-Small Cell Lung Cancer Patients Treated with Immunotherapy: A Multicenter Study
Background: Immune checkpoint inhibitors (ICIs) are a great breakthrough in cancer treatments and provide improved long-term survival in a subset of non-small cell lung cancer (NSCLC) patients. However, prognostic and predictive biomarkers of immunotherapy still remain an unmet clinical need. In thi...
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
|
| Series: | Cancers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-6694/15/15/3829 |
| _version_ | 1797587045364793344 |
|---|---|
| author | Sevinj Yolchuyeva Elena Giacomazzi Marion Tonneau Leyla Ebrahimpour Fabien C. Lamaze Michele Orain François Coulombe Julie Malo Wiam Belkaid Bertrand Routy Philippe Joubert Venkata S. K. Manem |
| author_facet | Sevinj Yolchuyeva Elena Giacomazzi Marion Tonneau Leyla Ebrahimpour Fabien C. Lamaze Michele Orain François Coulombe Julie Malo Wiam Belkaid Bertrand Routy Philippe Joubert Venkata S. K. Manem |
| author_sort | Sevinj Yolchuyeva |
| collection | DOAJ |
| description | Background: Immune checkpoint inhibitors (ICIs) are a great breakthrough in cancer treatments and provide improved long-term survival in a subset of non-small cell lung cancer (NSCLC) patients. However, prognostic and predictive biomarkers of immunotherapy still remain an unmet clinical need. In this work, we aim to leverage imaging data and clinical variables to develop survival risk models among advanced NSCLC patients treated with immunotherapy. Methods: This retrospective study includes a total of 385 patients from two institutions who were treated with ICIs. Radiomics features extracted from pretreatment CT scans were used to build predictive models. The objectives were to predict overall survival (OS) along with building a classifier for short- and long-term survival groups. We employed the XGBoost learning method to build radiomics and integrated clinical-radiomics predictive models. Feature selection and model building were developed and validated on a multicenter cohort. Results: We developed parsimonious models that were associated with OS and a classifier for short- and long-term survivor groups. The concordance indices (C-index) of the radiomics model were 0.61 and 0.57 to predict OS in the discovery and validation cohorts, respectively. While the area under the curve (AUC) values of the radiomic models for short- and long-term groups were found to be 0.65 and 0.58 in the discovery and validation cohorts. The accuracy of the combined radiomics-clinical model resulted in 0.63 and 0.62 to predict OS and in 0.77 and 0.62 to classify the survival groups in the discovery and validation cohorts, respectively. Conclusions: We developed and validated novel radiomics and integrated radiomics-clinical survival models among NSCLC patients treated with ICIs. This model has important translational implications, which can be used to identify a subset of patients who are not likely to benefit from immunotherapy. The developed imaging biomarkers may allow early prediction of low-group survivors, though additional validation of these radiomics models is warranted. |
| first_indexed | 2024-03-11T00:30:44Z |
| format | Article |
| id | doaj.art-c6c66178717343cc9f844c75b0df4b5a |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-11T00:30:44Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-c6c66178717343cc9f844c75b0df4b5a2023-11-18T22:42:00ZengMDPI AGCancers2072-66942023-07-011515382910.3390/cancers15153829A Radiomics-Clinical Model Predicts Overall Survival of Non-Small Cell Lung Cancer Patients Treated with Immunotherapy: A Multicenter StudySevinj Yolchuyeva0Elena Giacomazzi1Marion Tonneau2Leyla Ebrahimpour3Fabien C. Lamaze4Michele Orain5François Coulombe6Julie Malo7Wiam Belkaid8Bertrand Routy9Philippe Joubert10Venkata S. K. Manem11Department of Mathematics and Computer Science, Université du Québec à Trois Rivières, Trois-Rivières, QC G8Z 4M3, CanadaDepartment of Mathematics and Computer Science, Université du Québec à Trois Rivières, Trois-Rivières, QC G8Z 4M3, CanadaCentre de Recherche du Centre Hospitalier Universitaire de Montréal, Montréal, QC H2X 0A9, CanadaQuebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, CanadaQuebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, CanadaQuebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, CanadaQuebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, CanadaCentre de Recherche du Centre Hospitalier Universitaire de Montréal, Montréal, QC H2X 0A9, CanadaCentre de Recherche du Centre Hospitalier Universitaire de Montréal, Montréal, QC H2X 0A9, CanadaCentre de Recherche du Centre Hospitalier Universitaire de Montréal, Montréal, QC H2X 0A9, CanadaQuebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, CanadaDepartment of Mathematics and Computer Science, Université du Québec à Trois Rivières, Trois-Rivières, QC G8Z 4M3, CanadaBackground: Immune checkpoint inhibitors (ICIs) are a great breakthrough in cancer treatments and provide improved long-term survival in a subset of non-small cell lung cancer (NSCLC) patients. However, prognostic and predictive biomarkers of immunotherapy still remain an unmet clinical need. In this work, we aim to leverage imaging data and clinical variables to develop survival risk models among advanced NSCLC patients treated with immunotherapy. Methods: This retrospective study includes a total of 385 patients from two institutions who were treated with ICIs. Radiomics features extracted from pretreatment CT scans were used to build predictive models. The objectives were to predict overall survival (OS) along with building a classifier for short- and long-term survival groups. We employed the XGBoost learning method to build radiomics and integrated clinical-radiomics predictive models. Feature selection and model building were developed and validated on a multicenter cohort. Results: We developed parsimonious models that were associated with OS and a classifier for short- and long-term survivor groups. The concordance indices (C-index) of the radiomics model were 0.61 and 0.57 to predict OS in the discovery and validation cohorts, respectively. While the area under the curve (AUC) values of the radiomic models for short- and long-term groups were found to be 0.65 and 0.58 in the discovery and validation cohorts. The accuracy of the combined radiomics-clinical model resulted in 0.63 and 0.62 to predict OS and in 0.77 and 0.62 to classify the survival groups in the discovery and validation cohorts, respectively. Conclusions: We developed and validated novel radiomics and integrated radiomics-clinical survival models among NSCLC patients treated with ICIs. This model has important translational implications, which can be used to identify a subset of patients who are not likely to benefit from immunotherapy. The developed imaging biomarkers may allow early prediction of low-group survivors, though additional validation of these radiomics models is warranted.https://www.mdpi.com/2072-6694/15/15/3829immunotherapynon-small cell lung cancermachine learningoverall survivalradiomics |
| spellingShingle | Sevinj Yolchuyeva Elena Giacomazzi Marion Tonneau Leyla Ebrahimpour Fabien C. Lamaze Michele Orain François Coulombe Julie Malo Wiam Belkaid Bertrand Routy Philippe Joubert Venkata S. K. Manem A Radiomics-Clinical Model Predicts Overall Survival of Non-Small Cell Lung Cancer Patients Treated with Immunotherapy: A Multicenter Study Cancers immunotherapy non-small cell lung cancer machine learning overall survival radiomics |
| title | A Radiomics-Clinical Model Predicts Overall Survival of Non-Small Cell Lung Cancer Patients Treated with Immunotherapy: A Multicenter Study |
| title_full | A Radiomics-Clinical Model Predicts Overall Survival of Non-Small Cell Lung Cancer Patients Treated with Immunotherapy: A Multicenter Study |
| title_fullStr | A Radiomics-Clinical Model Predicts Overall Survival of Non-Small Cell Lung Cancer Patients Treated with Immunotherapy: A Multicenter Study |
| title_full_unstemmed | A Radiomics-Clinical Model Predicts Overall Survival of Non-Small Cell Lung Cancer Patients Treated with Immunotherapy: A Multicenter Study |
| title_short | A Radiomics-Clinical Model Predicts Overall Survival of Non-Small Cell Lung Cancer Patients Treated with Immunotherapy: A Multicenter Study |
| title_sort | radiomics clinical model predicts overall survival of non small cell lung cancer patients treated with immunotherapy a multicenter study |
| topic | immunotherapy non-small cell lung cancer machine learning overall survival radiomics |
| url | https://www.mdpi.com/2072-6694/15/15/3829 |
| work_keys_str_mv | AT sevinjyolchuyeva aradiomicsclinicalmodelpredictsoverallsurvivalofnonsmallcelllungcancerpatientstreatedwithimmunotherapyamulticenterstudy AT elenagiacomazzi aradiomicsclinicalmodelpredictsoverallsurvivalofnonsmallcelllungcancerpatientstreatedwithimmunotherapyamulticenterstudy AT mariontonneau aradiomicsclinicalmodelpredictsoverallsurvivalofnonsmallcelllungcancerpatientstreatedwithimmunotherapyamulticenterstudy AT leylaebrahimpour aradiomicsclinicalmodelpredictsoverallsurvivalofnonsmallcelllungcancerpatientstreatedwithimmunotherapyamulticenterstudy AT fabienclamaze aradiomicsclinicalmodelpredictsoverallsurvivalofnonsmallcelllungcancerpatientstreatedwithimmunotherapyamulticenterstudy AT micheleorain aradiomicsclinicalmodelpredictsoverallsurvivalofnonsmallcelllungcancerpatientstreatedwithimmunotherapyamulticenterstudy AT francoiscoulombe aradiomicsclinicalmodelpredictsoverallsurvivalofnonsmallcelllungcancerpatientstreatedwithimmunotherapyamulticenterstudy AT juliemalo aradiomicsclinicalmodelpredictsoverallsurvivalofnonsmallcelllungcancerpatientstreatedwithimmunotherapyamulticenterstudy AT wiambelkaid aradiomicsclinicalmodelpredictsoverallsurvivalofnonsmallcelllungcancerpatientstreatedwithimmunotherapyamulticenterstudy AT bertrandrouty aradiomicsclinicalmodelpredictsoverallsurvivalofnonsmallcelllungcancerpatientstreatedwithimmunotherapyamulticenterstudy AT philippejoubert aradiomicsclinicalmodelpredictsoverallsurvivalofnonsmallcelllungcancerpatientstreatedwithimmunotherapyamulticenterstudy AT venkataskmanem aradiomicsclinicalmodelpredictsoverallsurvivalofnonsmallcelllungcancerpatientstreatedwithimmunotherapyamulticenterstudy AT sevinjyolchuyeva radiomicsclinicalmodelpredictsoverallsurvivalofnonsmallcelllungcancerpatientstreatedwithimmunotherapyamulticenterstudy AT elenagiacomazzi radiomicsclinicalmodelpredictsoverallsurvivalofnonsmallcelllungcancerpatientstreatedwithimmunotherapyamulticenterstudy AT mariontonneau radiomicsclinicalmodelpredictsoverallsurvivalofnonsmallcelllungcancerpatientstreatedwithimmunotherapyamulticenterstudy AT leylaebrahimpour radiomicsclinicalmodelpredictsoverallsurvivalofnonsmallcelllungcancerpatientstreatedwithimmunotherapyamulticenterstudy AT fabienclamaze radiomicsclinicalmodelpredictsoverallsurvivalofnonsmallcelllungcancerpatientstreatedwithimmunotherapyamulticenterstudy AT micheleorain radiomicsclinicalmodelpredictsoverallsurvivalofnonsmallcelllungcancerpatientstreatedwithimmunotherapyamulticenterstudy AT francoiscoulombe radiomicsclinicalmodelpredictsoverallsurvivalofnonsmallcelllungcancerpatientstreatedwithimmunotherapyamulticenterstudy AT juliemalo radiomicsclinicalmodelpredictsoverallsurvivalofnonsmallcelllungcancerpatientstreatedwithimmunotherapyamulticenterstudy AT wiambelkaid radiomicsclinicalmodelpredictsoverallsurvivalofnonsmallcelllungcancerpatientstreatedwithimmunotherapyamulticenterstudy AT bertrandrouty radiomicsclinicalmodelpredictsoverallsurvivalofnonsmallcelllungcancerpatientstreatedwithimmunotherapyamulticenterstudy AT philippejoubert radiomicsclinicalmodelpredictsoverallsurvivalofnonsmallcelllungcancerpatientstreatedwithimmunotherapyamulticenterstudy AT venkataskmanem radiomicsclinicalmodelpredictsoverallsurvivalofnonsmallcelllungcancerpatientstreatedwithimmunotherapyamulticenterstudy |