Radiomics based on 18F‐FDG PET/CT could differentiate breast carcinoma from breast lymphoma using machine‐learning approach: A preliminary study
Abstract Purpose Our study assessed the ability 18F‐fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) radiomics to differentiate breast carcinoma from breast lymphoma using machine‐learning approach. Methods Sixty‐five breast nodules from 44 patients diagnosed as b...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Cancer Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/cam4.2711 |
| _version_ | 1818944962431025152 |
|---|---|
| author | Xuejin Ou Jing Zhang Jian Wang Fuwen Pang Yongsheng Wang Xiawei Wei Xuelei Ma |
| author_facet | Xuejin Ou Jing Zhang Jian Wang Fuwen Pang Yongsheng Wang Xiawei Wei Xuelei Ma |
| author_sort | Xuejin Ou |
| collection | DOAJ |
| description | Abstract Purpose Our study assessed the ability 18F‐fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) radiomics to differentiate breast carcinoma from breast lymphoma using machine‐learning approach. Methods Sixty‐five breast nodules from 44 patients diagnosed as breast carcinoma or breast lymphoma were included. Standardized uptake value (SUV) and radiomic features from CT and PET images were extracted using local image features extraction software. Six discriminative models including PETa (based on clinical, SUV and radiomic features from PET images), PETb (SUV and radiomic features from PET images), PETc (radiomic features only from PET images), CTa (clinical and radiomic features from CT images), CTb (radiomic features only from CT images), and SUV model were generated using least absolute shrinkage and selection operator method and linear discriminant analysis. The areas under the receiver operating characteristic curve (AUCs), accuracy, sensitivity, and specificity were calculated to evaluate the discriminative ability of these models. Results PETa and CTa models showed the best ability to differentiation in training and validation group (AUCs of 0.867 and 0.806 for PETa model, AUCs of 0.891 and 0.759 for CTa model, respectively). Conclusion Models based on clinical, SUV, and radiomic features of 18F‐FDG PET/CT images could accurately discriminate breast carcinoma from breast lymphoma. |
| first_indexed | 2024-12-20T07:51:33Z |
| format | Article |
| id | doaj.art-62be8e56e9104c27b93968d0f1a7b351 |
| institution | Directory Open Access Journal |
| issn | 2045-7634 |
| language | English |
| last_indexed | 2024-12-20T07:51:33Z |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Cancer Medicine |
| spelling | doaj.art-62be8e56e9104c27b93968d0f1a7b3512022-12-21T19:47:49ZengWileyCancer Medicine2045-76342020-01-019249650610.1002/cam4.2711Radiomics based on 18F‐FDG PET/CT could differentiate breast carcinoma from breast lymphoma using machine‐learning approach: A preliminary studyXuejin Ou0Jing Zhang1Jian Wang2Fuwen Pang3Yongsheng Wang4Xiawei Wei5Xuelei Ma6Department of Biotherapy West China Hospital and State Key Laboratory of Biotherapy Sichuan University Chengdu PR ChinaState Key Laboratory of Biotherapy and Cancer Center West China Hospital Sichuan University Chengdu PR ChinaSchool of Computer Science Nanjing University of Science and Technology Nanjing PR ChinaDepartment of Nuclear Medicine West China Hospital Sichuan University Chengdu P.R. ChinaDepartment of Thoracic Oncology West China Hospital Sichuan University Chengdu PR ChinaLaboratory of Aging Research and Nanotoxicology State Key Laboratory of Biotherapy National Clinical Research Center for Geriatrics West China Hospital Sichuan University Chengdu Sichuan PR ChinaDepartment of Biotherapy West China Hospital and State Key Laboratory of Biotherapy Sichuan University Chengdu PR ChinaAbstract Purpose Our study assessed the ability 18F‐fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) radiomics to differentiate breast carcinoma from breast lymphoma using machine‐learning approach. Methods Sixty‐five breast nodules from 44 patients diagnosed as breast carcinoma or breast lymphoma were included. Standardized uptake value (SUV) and radiomic features from CT and PET images were extracted using local image features extraction software. Six discriminative models including PETa (based on clinical, SUV and radiomic features from PET images), PETb (SUV and radiomic features from PET images), PETc (radiomic features only from PET images), CTa (clinical and radiomic features from CT images), CTb (radiomic features only from CT images), and SUV model were generated using least absolute shrinkage and selection operator method and linear discriminant analysis. The areas under the receiver operating characteristic curve (AUCs), accuracy, sensitivity, and specificity were calculated to evaluate the discriminative ability of these models. Results PETa and CTa models showed the best ability to differentiation in training and validation group (AUCs of 0.867 and 0.806 for PETa model, AUCs of 0.891 and 0.759 for CTa model, respectively). Conclusion Models based on clinical, SUV, and radiomic features of 18F‐FDG PET/CT images could accurately discriminate breast carcinoma from breast lymphoma.https://doi.org/10.1002/cam4.2711breast lymphomadiagnosislinear discriminant analysismachine‐learningradiomic |
| spellingShingle | Xuejin Ou Jing Zhang Jian Wang Fuwen Pang Yongsheng Wang Xiawei Wei Xuelei Ma Radiomics based on 18F‐FDG PET/CT could differentiate breast carcinoma from breast lymphoma using machine‐learning approach: A preliminary study Cancer Medicine breast lymphoma diagnosis linear discriminant analysis machine‐learning radiomic |
| title | Radiomics based on 18F‐FDG PET/CT could differentiate breast carcinoma from breast lymphoma using machine‐learning approach: A preliminary study |
| title_full | Radiomics based on 18F‐FDG PET/CT could differentiate breast carcinoma from breast lymphoma using machine‐learning approach: A preliminary study |
| title_fullStr | Radiomics based on 18F‐FDG PET/CT could differentiate breast carcinoma from breast lymphoma using machine‐learning approach: A preliminary study |
| title_full_unstemmed | Radiomics based on 18F‐FDG PET/CT could differentiate breast carcinoma from breast lymphoma using machine‐learning approach: A preliminary study |
| title_short | Radiomics based on 18F‐FDG PET/CT could differentiate breast carcinoma from breast lymphoma using machine‐learning approach: A preliminary study |
| title_sort | radiomics based on 18f fdg pet ct could differentiate breast carcinoma from breast lymphoma using machine learning approach a preliminary study |
| topic | breast lymphoma diagnosis linear discriminant analysis machine‐learning radiomic |
| url | https://doi.org/10.1002/cam4.2711 |
| work_keys_str_mv | AT xuejinou radiomicsbasedon18ffdgpetctcoulddifferentiatebreastcarcinomafrombreastlymphomausingmachinelearningapproachapreliminarystudy AT jingzhang radiomicsbasedon18ffdgpetctcoulddifferentiatebreastcarcinomafrombreastlymphomausingmachinelearningapproachapreliminarystudy AT jianwang radiomicsbasedon18ffdgpetctcoulddifferentiatebreastcarcinomafrombreastlymphomausingmachinelearningapproachapreliminarystudy AT fuwenpang radiomicsbasedon18ffdgpetctcoulddifferentiatebreastcarcinomafrombreastlymphomausingmachinelearningapproachapreliminarystudy AT yongshengwang radiomicsbasedon18ffdgpetctcoulddifferentiatebreastcarcinomafrombreastlymphomausingmachinelearningapproachapreliminarystudy AT xiaweiwei radiomicsbasedon18ffdgpetctcoulddifferentiatebreastcarcinomafrombreastlymphomausingmachinelearningapproachapreliminarystudy AT xueleima radiomicsbasedon18ffdgpetctcoulddifferentiatebreastcarcinomafrombreastlymphomausingmachinelearningapproachapreliminarystudy |