Evaluation of EGFR-TKIs and ICIs treatment stratification in non-small cell lung cancer using an encrypted multidimensional radiomics approach
Abstract Background Radiomics holds great potential for the noninvasive evaluation of EGFR-TKIs and ICIs responses, but data privacy and model robustness challenges limit its current efficacy and safety. This study aims to develop and validate an encrypted multidimensional radiomics approach to enha...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-01-01
|
| Series: | Cancer Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40644-025-00824-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832585424711712768 |
|---|---|
| author | Xingping Zhang Xingting Qiu Yue Zhang Qingwen Lai Yanchun Zhang Guijuan Zhang |
| author_facet | Xingping Zhang Xingting Qiu Yue Zhang Qingwen Lai Yanchun Zhang Guijuan Zhang |
| author_sort | Xingping Zhang |
| collection | DOAJ |
| description | Abstract Background Radiomics holds great potential for the noninvasive evaluation of EGFR-TKIs and ICIs responses, but data privacy and model robustness challenges limit its current efficacy and safety. This study aims to develop and validate an encrypted multidimensional radiomics approach to enhance the stratification and analysis of therapeutic responses. Materials and methods This multicenter study incorporated various data types from 506 NSCLC patients, which underwent preprocessing through anonymization methods and were securely encrypted using the AES-CBC algorithm. We developed one clinical model and three radiomics models based on clinical factors and radiomics scores (RadScore) of three distinct regions to evaluate treatment response. Additionally, an integrated radiomics-clinical model was created by combining clinical factors with RadScore. The study also explored the association between different EGFR mutations and PD-1/PD-L1 expression in radiomics biomarkers. Findings The radiomics-clinical model demonstrated high performance, with AUC values as follows: EGFR (0.884), 19Del (0.894), L858R (0.881), T790M (0.900), and PD-1/PD-L1 expression (0.893) in the test set. This model outperformed both clinical and single radiomics models. Decision curve analysis further supported its superior clinical utility. Additionally, our findings suggest that the efficacy of EGFR-TKIs and ICIs therapy may not depend on detecting a singular tumor feature or cell type. Conclusion The proposed method effectively balances the level of evidence with privacy protection, enhancing the study’s validity and security. Therefore, radiomics biomarkers are expected to complement molecular biology analyses and guide therapeutic strategies for EGFR-TKIs, ICIs, and their combinations. |
| format | Article |
| id | doaj-art-5dee82c8426d4fc08472440a9cc62ac7 |
| institution | Kabale University |
| issn | 1470-7330 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Cancer Imaging |
| spelling | doaj-art-5dee82c8426d4fc08472440a9cc62ac72025-01-26T12:50:36ZengBMCCancer Imaging1470-73302025-01-0125111410.1186/s40644-025-00824-wEvaluation of EGFR-TKIs and ICIs treatment stratification in non-small cell lung cancer using an encrypted multidimensional radiomics approachXingping Zhang0Xingting Qiu1Yue Zhang2Qingwen Lai3Yanchun Zhang4Guijuan Zhang5School of Medical Information Engineering, Gannan Medical UniversityDepartment of Radiology, First Affiliated Hospital of Gannan Medical UniversitySchool of Medical Information Engineering, Gannan Medical UniversityDepartment of Respiratory and Critical Care, First Affiliated Hospital of Gannan Medical UniversityInstitute for Sustainable Industries and Liveable Cities, Victoria UniversityDepartment of Respiratory and Critical Care, First Affiliated Hospital of Gannan Medical UniversityAbstract Background Radiomics holds great potential for the noninvasive evaluation of EGFR-TKIs and ICIs responses, but data privacy and model robustness challenges limit its current efficacy and safety. This study aims to develop and validate an encrypted multidimensional radiomics approach to enhance the stratification and analysis of therapeutic responses. Materials and methods This multicenter study incorporated various data types from 506 NSCLC patients, which underwent preprocessing through anonymization methods and were securely encrypted using the AES-CBC algorithm. We developed one clinical model and three radiomics models based on clinical factors and radiomics scores (RadScore) of three distinct regions to evaluate treatment response. Additionally, an integrated radiomics-clinical model was created by combining clinical factors with RadScore. The study also explored the association between different EGFR mutations and PD-1/PD-L1 expression in radiomics biomarkers. Findings The radiomics-clinical model demonstrated high performance, with AUC values as follows: EGFR (0.884), 19Del (0.894), L858R (0.881), T790M (0.900), and PD-1/PD-L1 expression (0.893) in the test set. This model outperformed both clinical and single radiomics models. Decision curve analysis further supported its superior clinical utility. Additionally, our findings suggest that the efficacy of EGFR-TKIs and ICIs therapy may not depend on detecting a singular tumor feature or cell type. Conclusion The proposed method effectively balances the level of evidence with privacy protection, enhancing the study’s validity and security. Therefore, radiomics biomarkers are expected to complement molecular biology analyses and guide therapeutic strategies for EGFR-TKIs, ICIs, and their combinations.https://doi.org/10.1186/s40644-025-00824-wRadiomics imaging biomarkersEGFR-TKIs therapyICIs therapyPrivacy protectionNon-small cell lung cancer |
| spellingShingle | Xingping Zhang Xingting Qiu Yue Zhang Qingwen Lai Yanchun Zhang Guijuan Zhang Evaluation of EGFR-TKIs and ICIs treatment stratification in non-small cell lung cancer using an encrypted multidimensional radiomics approach Cancer Imaging Radiomics imaging biomarkers EGFR-TKIs therapy ICIs therapy Privacy protection Non-small cell lung cancer |
| title | Evaluation of EGFR-TKIs and ICIs treatment stratification in non-small cell lung cancer using an encrypted multidimensional radiomics approach |
| title_full | Evaluation of EGFR-TKIs and ICIs treatment stratification in non-small cell lung cancer using an encrypted multidimensional radiomics approach |
| title_fullStr | Evaluation of EGFR-TKIs and ICIs treatment stratification in non-small cell lung cancer using an encrypted multidimensional radiomics approach |
| title_full_unstemmed | Evaluation of EGFR-TKIs and ICIs treatment stratification in non-small cell lung cancer using an encrypted multidimensional radiomics approach |
| title_short | Evaluation of EGFR-TKIs and ICIs treatment stratification in non-small cell lung cancer using an encrypted multidimensional radiomics approach |
| title_sort | evaluation of egfr tkis and icis treatment stratification in non small cell lung cancer using an encrypted multidimensional radiomics approach |
| topic | Radiomics imaging biomarkers EGFR-TKIs therapy ICIs therapy Privacy protection Non-small cell lung cancer |
| url | https://doi.org/10.1186/s40644-025-00824-w |
| work_keys_str_mv | AT xingpingzhang evaluationofegfrtkisandicistreatmentstratificationinnonsmallcelllungcancerusinganencryptedmultidimensionalradiomicsapproach AT xingtingqiu evaluationofegfrtkisandicistreatmentstratificationinnonsmallcelllungcancerusinganencryptedmultidimensionalradiomicsapproach AT yuezhang evaluationofegfrtkisandicistreatmentstratificationinnonsmallcelllungcancerusinganencryptedmultidimensionalradiomicsapproach AT qingwenlai evaluationofegfrtkisandicistreatmentstratificationinnonsmallcelllungcancerusinganencryptedmultidimensionalradiomicsapproach AT yanchunzhang evaluationofegfrtkisandicistreatmentstratificationinnonsmallcelllungcancerusinganencryptedmultidimensionalradiomicsapproach AT guijuanzhang evaluationofegfrtkisandicistreatmentstratificationinnonsmallcelllungcancerusinganencryptedmultidimensionalradiomicsapproach |