Two-dimensional and three-dimensional T2 weighted imaging-based radiomic signatures for the preoperative discrimination of ovarian borderline tumors and malignant tumors
Abstract Background Ovarian cancer is the most women malignancy in the whole world. It is difficult to differentiate ovarian cancers from ovarian borderline tumors because of some similar imaging findings.Radiomics study may help clinicians to make a proper diagnosis before invasive surgery. Purpose...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2022-02-01
|
| Series: | Journal of Ovarian Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13048-022-00943-z |
| _version_ | 1797971488427474944 |
|---|---|
| author | Xuefen Liu Tianping Wang Guofu Zhang Keqin Hua Hua Jiang Shaofeng Duan Jun Jin He Zhang |
| author_facet | Xuefen Liu Tianping Wang Guofu Zhang Keqin Hua Hua Jiang Shaofeng Duan Jun Jin He Zhang |
| author_sort | Xuefen Liu |
| collection | DOAJ |
| description | Abstract Background Ovarian cancer is the most women malignancy in the whole world. It is difficult to differentiate ovarian cancers from ovarian borderline tumors because of some similar imaging findings.Radiomics study may help clinicians to make a proper diagnosis before invasive surgery. Purpose To evaluate the ability of T2-weighted imaging (T2WI)-based radiomics to discriminate ovarian borderline tumors (BOTs) from malignancies based on two-dimensional (2D) and three-dimensional (3D) lesion segmentation methods. Methods A total of 95 patients with pathologically proven ovarian BOTs and 101 patients with malignancies were retrospectively included in this study. We evaluated the diagnostic performance of the signatures derived from T2WI-based radiomics in their ability to differentiate between BOTs and malignancies and compared the performance differences in the 2D and 3D segmentation models. The least absolute shrinkage and selection operator method (Lasso) was used for radiomics feature selection and machine learning processing. Results The radiomics score between BOTs and malignancies in four types of selected T2WI-based radiomics models differed significantly at the statistical level (p < 0.0001). For the classification between BOTs and malignant masses, the 2D and 3D coronal T2WI-based radiomics models yielded accuracy values of 0.79 and 0.83 in the testing group, respectively; the 2D and 3D sagittal fat-suppressed (fs) T2WI-based radiomics models yielded an accuracy of 0.78 and 0.99, respectively. Conclusions Our results suggest that T2WI-based radiomic features were highly correlated with ovarian tumor subtype classification. 3D-sagittal MRI radiomics features may help clinicians differentiate ovarian BOTs from malignancies with high ACC. |
| first_indexed | 2024-04-11T03:33:26Z |
| format | Article |
| id | doaj.art-6a5f914b7957495e92455acb30ecd94b |
| institution | Directory Open Access Journal |
| issn | 1757-2215 |
| language | English |
| last_indexed | 2024-04-11T03:33:26Z |
| publishDate | 2022-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Ovarian Research |
| spelling | doaj.art-6a5f914b7957495e92455acb30ecd94b2023-01-02T05:50:47ZengBMCJournal of Ovarian Research1757-22152022-02-011511910.1186/s13048-022-00943-zTwo-dimensional and three-dimensional T2 weighted imaging-based radiomic signatures for the preoperative discrimination of ovarian borderline tumors and malignant tumorsXuefen Liu0Tianping Wang1Guofu Zhang2Keqin Hua3Hua Jiang4Shaofeng Duan5Jun Jin6He Zhang7Department of Radiology, Obstetrics and Gynecology Hospital, Fudan UniversityDepartment of Radiology, Obstetrics and Gynecology Hospital, Fudan UniversityDepartment of Radiology, Obstetrics and Gynecology Hospital, Fudan UniversityDepartment of Gynecology, Obstetrics and Gynecology Hospital, Fudan UniversityDepartment of Gynecology, Obstetrics and Gynecology Hospital, Fudan UniversityGE HealthcareDepartment of Pathology, Obstetrics and Gynecology Hospital, Fudan UniversityDepartment of Radiology, Obstetrics and Gynecology Hospital, Fudan UniversityAbstract Background Ovarian cancer is the most women malignancy in the whole world. It is difficult to differentiate ovarian cancers from ovarian borderline tumors because of some similar imaging findings.Radiomics study may help clinicians to make a proper diagnosis before invasive surgery. Purpose To evaluate the ability of T2-weighted imaging (T2WI)-based radiomics to discriminate ovarian borderline tumors (BOTs) from malignancies based on two-dimensional (2D) and three-dimensional (3D) lesion segmentation methods. Methods A total of 95 patients with pathologically proven ovarian BOTs and 101 patients with malignancies were retrospectively included in this study. We evaluated the diagnostic performance of the signatures derived from T2WI-based radiomics in their ability to differentiate between BOTs and malignancies and compared the performance differences in the 2D and 3D segmentation models. The least absolute shrinkage and selection operator method (Lasso) was used for radiomics feature selection and machine learning processing. Results The radiomics score between BOTs and malignancies in four types of selected T2WI-based radiomics models differed significantly at the statistical level (p < 0.0001). For the classification between BOTs and malignant masses, the 2D and 3D coronal T2WI-based radiomics models yielded accuracy values of 0.79 and 0.83 in the testing group, respectively; the 2D and 3D sagittal fat-suppressed (fs) T2WI-based radiomics models yielded an accuracy of 0.78 and 0.99, respectively. Conclusions Our results suggest that T2WI-based radiomic features were highly correlated with ovarian tumor subtype classification. 3D-sagittal MRI radiomics features may help clinicians differentiate ovarian BOTs from malignancies with high ACC.https://doi.org/10.1186/s13048-022-00943-zOvarian neoplasmMagnetic resonance imagingComputer-Assisted DiagnosisRadiomics |
| spellingShingle | Xuefen Liu Tianping Wang Guofu Zhang Keqin Hua Hua Jiang Shaofeng Duan Jun Jin He Zhang Two-dimensional and three-dimensional T2 weighted imaging-based radiomic signatures for the preoperative discrimination of ovarian borderline tumors and malignant tumors Journal of Ovarian Research Ovarian neoplasm Magnetic resonance imaging Computer-Assisted Diagnosis Radiomics |
| title | Two-dimensional and three-dimensional T2 weighted imaging-based radiomic signatures for the preoperative discrimination of ovarian borderline tumors and malignant tumors |
| title_full | Two-dimensional and three-dimensional T2 weighted imaging-based radiomic signatures for the preoperative discrimination of ovarian borderline tumors and malignant tumors |
| title_fullStr | Two-dimensional and three-dimensional T2 weighted imaging-based radiomic signatures for the preoperative discrimination of ovarian borderline tumors and malignant tumors |
| title_full_unstemmed | Two-dimensional and three-dimensional T2 weighted imaging-based radiomic signatures for the preoperative discrimination of ovarian borderline tumors and malignant tumors |
| title_short | Two-dimensional and three-dimensional T2 weighted imaging-based radiomic signatures for the preoperative discrimination of ovarian borderline tumors and malignant tumors |
| title_sort | two dimensional and three dimensional t2 weighted imaging based radiomic signatures for the preoperative discrimination of ovarian borderline tumors and malignant tumors |
| topic | Ovarian neoplasm Magnetic resonance imaging Computer-Assisted Diagnosis Radiomics |
| url | https://doi.org/10.1186/s13048-022-00943-z |
| work_keys_str_mv | AT xuefenliu twodimensionalandthreedimensionalt2weightedimagingbasedradiomicsignaturesforthepreoperativediscriminationofovarianborderlinetumorsandmalignanttumors AT tianpingwang twodimensionalandthreedimensionalt2weightedimagingbasedradiomicsignaturesforthepreoperativediscriminationofovarianborderlinetumorsandmalignanttumors AT guofuzhang twodimensionalandthreedimensionalt2weightedimagingbasedradiomicsignaturesforthepreoperativediscriminationofovarianborderlinetumorsandmalignanttumors AT keqinhua twodimensionalandthreedimensionalt2weightedimagingbasedradiomicsignaturesforthepreoperativediscriminationofovarianborderlinetumorsandmalignanttumors AT huajiang twodimensionalandthreedimensionalt2weightedimagingbasedradiomicsignaturesforthepreoperativediscriminationofovarianborderlinetumorsandmalignanttumors AT shaofengduan twodimensionalandthreedimensionalt2weightedimagingbasedradiomicsignaturesforthepreoperativediscriminationofovarianborderlinetumorsandmalignanttumors AT junjin twodimensionalandthreedimensionalt2weightedimagingbasedradiomicsignaturesforthepreoperativediscriminationofovarianborderlinetumorsandmalignanttumors AT hezhang twodimensionalandthreedimensionalt2weightedimagingbasedradiomicsignaturesforthepreoperativediscriminationofovarianborderlinetumorsandmalignanttumors |