Virtual biopsy for non-invasive identification of follicular lymphoma histologic transformation using radiomics-based imaging biomarker from PET/CT
Abstract Background This study aimed to construct a radiomics-based imaging biomarker for the non-invasive identification of transformed follicular lymphoma (t-FL) using PET/CT images. Methods A total of 784 follicular lymphoma (FL), diffuse large B-cell lymphoma, and t-FL patients from 5 independen...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-01-01
|
| Series: | BMC Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12916-025-03893-7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832571601001906176 |
|---|---|
| author | Chong Jiang Chunjun Qian Qiuhui Jiang Hang Zhou Zekun Jiang Yue Teng Bing Xu Xin Li Chongyang Ding Rong Tian |
| author_facet | Chong Jiang Chunjun Qian Qiuhui Jiang Hang Zhou Zekun Jiang Yue Teng Bing Xu Xin Li Chongyang Ding Rong Tian |
| author_sort | Chong Jiang |
| collection | DOAJ |
| description | Abstract Background This study aimed to construct a radiomics-based imaging biomarker for the non-invasive identification of transformed follicular lymphoma (t-FL) using PET/CT images. Methods A total of 784 follicular lymphoma (FL), diffuse large B-cell lymphoma, and t-FL patients from 5 independent medical centers were included. The unsupervised EMFusion method was applied to fuse PET and CT images. Deep-based radiomic features were extracted from the fusion images using a deep learning model (ResNet18). These features, along with handcrafted radiomics, were utilized to construct a radiomic signature (R-signature) using automatic machine learning in the training and internal validation cohort. The R-signature was then tested for its predictive ability in the t-FL test cohort. Subsequently, this R-signature was combined with clinical parameters and SUVmax to develop a t-FL scoring system. Results The R-signature demonstrated high accuracy, with mean AUC values as 0.994 in the training cohort and 0.976 in the internal validation cohort. In the t-FL test cohort, the R-signature achieved an AUC of 0.749, with an accuracy of 75.2%, sensitivity of 68.0%, and specificity of 77.5%. Furthermore, the t-FL scoring system, incorporating the R-signature along with clinical parameters (age, LDH, and ECOG PS) and SUVmax, achieved an AUC of 0.820, facilitating the stratification of patients into low, medium, and high transformation risk groups. Conclusions This study offers a promising approach for identifying t-FL non-invasively by radiomics analysis on PET/CT images. The developed t-FL scoring system provides a valuable tool for clinical decision-making, potentially improving patient management and outcomes. |
| format | Article |
| id | doaj-art-93b444a7b4224174b6f61580e1c3e2a3 |
| institution | Kabale University |
| issn | 1741-7015 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medicine |
| spelling | doaj-art-93b444a7b4224174b6f61580e1c3e2a32025-02-02T12:28:09ZengBMCBMC Medicine1741-70152025-01-0123111410.1186/s12916-025-03893-7Virtual biopsy for non-invasive identification of follicular lymphoma histologic transformation using radiomics-based imaging biomarker from PET/CTChong Jiang0Chunjun Qian1Qiuhui Jiang2Hang Zhou3Zekun Jiang4Yue Teng5Bing Xu6Xin Li7Chongyang Ding8Rong Tian9Department of Nuclear Medicine, West China Hospital, Sichuan University, Guoxue AlleySchool of Electrical and Information Engineering, Changzhou Institute of TechnologyDepartment of Hematology, School of Medicine, The First Affiliated Hospital of Xiamen University and Institute of Hematology, Xiamen UniversityDepartment of Nuclear Medicine, Qilu Hospital of Shandong UniversityBiomedical Big Data Center, West China Hospital, Sichuan UniversityDepartment of Nuclear Medicine, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical SchoolDepartment of Hematology, School of Medicine, The First Affiliated Hospital of Xiamen University and Institute of Hematology, Xiamen UniversityDepartment of Nuclear Medicine, Qilu Hospital of Shandong UniversityDepartment of Nuclear Medicine, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province HospitalDepartment of Nuclear Medicine, West China Hospital, Sichuan University, Guoxue AlleyAbstract Background This study aimed to construct a radiomics-based imaging biomarker for the non-invasive identification of transformed follicular lymphoma (t-FL) using PET/CT images. Methods A total of 784 follicular lymphoma (FL), diffuse large B-cell lymphoma, and t-FL patients from 5 independent medical centers were included. The unsupervised EMFusion method was applied to fuse PET and CT images. Deep-based radiomic features were extracted from the fusion images using a deep learning model (ResNet18). These features, along with handcrafted radiomics, were utilized to construct a radiomic signature (R-signature) using automatic machine learning in the training and internal validation cohort. The R-signature was then tested for its predictive ability in the t-FL test cohort. Subsequently, this R-signature was combined with clinical parameters and SUVmax to develop a t-FL scoring system. Results The R-signature demonstrated high accuracy, with mean AUC values as 0.994 in the training cohort and 0.976 in the internal validation cohort. In the t-FL test cohort, the R-signature achieved an AUC of 0.749, with an accuracy of 75.2%, sensitivity of 68.0%, and specificity of 77.5%. Furthermore, the t-FL scoring system, incorporating the R-signature along with clinical parameters (age, LDH, and ECOG PS) and SUVmax, achieved an AUC of 0.820, facilitating the stratification of patients into low, medium, and high transformation risk groups. Conclusions This study offers a promising approach for identifying t-FL non-invasively by radiomics analysis on PET/CT images. The developed t-FL scoring system provides a valuable tool for clinical decision-making, potentially improving patient management and outcomes.https://doi.org/10.1186/s12916-025-03893-7Follicular lymphomaHistologic transformationScoring systemRadiomicsPET/CT |
| spellingShingle | Chong Jiang Chunjun Qian Qiuhui Jiang Hang Zhou Zekun Jiang Yue Teng Bing Xu Xin Li Chongyang Ding Rong Tian Virtual biopsy for non-invasive identification of follicular lymphoma histologic transformation using radiomics-based imaging biomarker from PET/CT BMC Medicine Follicular lymphoma Histologic transformation Scoring system Radiomics PET/CT |
| title | Virtual biopsy for non-invasive identification of follicular lymphoma histologic transformation using radiomics-based imaging biomarker from PET/CT |
| title_full | Virtual biopsy for non-invasive identification of follicular lymphoma histologic transformation using radiomics-based imaging biomarker from PET/CT |
| title_fullStr | Virtual biopsy for non-invasive identification of follicular lymphoma histologic transformation using radiomics-based imaging biomarker from PET/CT |
| title_full_unstemmed | Virtual biopsy for non-invasive identification of follicular lymphoma histologic transformation using radiomics-based imaging biomarker from PET/CT |
| title_short | Virtual biopsy for non-invasive identification of follicular lymphoma histologic transformation using radiomics-based imaging biomarker from PET/CT |
| title_sort | virtual biopsy for non invasive identification of follicular lymphoma histologic transformation using radiomics based imaging biomarker from pet ct |
| topic | Follicular lymphoma Histologic transformation Scoring system Radiomics PET/CT |
| url | https://doi.org/10.1186/s12916-025-03893-7 |
| work_keys_str_mv | AT chongjiang virtualbiopsyfornoninvasiveidentificationoffollicularlymphomahistologictransformationusingradiomicsbasedimagingbiomarkerfrompetct AT chunjunqian virtualbiopsyfornoninvasiveidentificationoffollicularlymphomahistologictransformationusingradiomicsbasedimagingbiomarkerfrompetct AT qiuhuijiang virtualbiopsyfornoninvasiveidentificationoffollicularlymphomahistologictransformationusingradiomicsbasedimagingbiomarkerfrompetct AT hangzhou virtualbiopsyfornoninvasiveidentificationoffollicularlymphomahistologictransformationusingradiomicsbasedimagingbiomarkerfrompetct AT zekunjiang virtualbiopsyfornoninvasiveidentificationoffollicularlymphomahistologictransformationusingradiomicsbasedimagingbiomarkerfrompetct AT yueteng virtualbiopsyfornoninvasiveidentificationoffollicularlymphomahistologictransformationusingradiomicsbasedimagingbiomarkerfrompetct AT bingxu virtualbiopsyfornoninvasiveidentificationoffollicularlymphomahistologictransformationusingradiomicsbasedimagingbiomarkerfrompetct AT xinli virtualbiopsyfornoninvasiveidentificationoffollicularlymphomahistologictransformationusingradiomicsbasedimagingbiomarkerfrompetct AT chongyangding virtualbiopsyfornoninvasiveidentificationoffollicularlymphomahistologictransformationusingradiomicsbasedimagingbiomarkerfrompetct AT rongtian virtualbiopsyfornoninvasiveidentificationoffollicularlymphomahistologictransformationusingradiomicsbasedimagingbiomarkerfrompetct |