Junming Jian1,2, Yong'ai Li3, Perry J Pickhardt4, Wei Xia2, Zhang He5, Rui Zhang2, Shuhui Zhao6, Xingyu Zhao1,2, Songqi Cai7, Jiayi Zhang2, Guofu Zhang8, Jingxuan Jiang9, Yan Zhang10, Keying Wang11, Guangwu Lin12, Feng Feng13, Xiaodong Wu2, Xin Gao14, Jinwei Qiang15. 1. University of Science and Technology of China, Hefei, 230026, Anhui, China. 2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, No.88 Keling Road, Suzhou New District, Suzhou, 215163, Jiangsu, China. 3. Department of Radiology, Jinshan Hospital, Fudan University, 1508 Longhang Road, Jinshan District, Shanghai, 201508, China. 4. Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, USA. 5. Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, 201508, China. 6. Department of Radiology, Xinhua Hospital, Medical College of Shanghai Jiao Tong University, Shanghai, 200092, China. 7. Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China. 8. Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, 200082, China. 9. Department of Radiology, Affiliated Hospital of Nantong University, Nantong, 226001, Jiangsu, China. 10. Department of Radiology, Guangdong Women and Children Hospital, Guangzhou, 511400, Guangdong, China. 11. Department of Radiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221000, Jiangsu, China. 12. Department of Radiology, Huadong Hospital, Fudan University, Shanghai, 200040, China. 13. Department of Radiology, Cancer Hospital, Nantong University, Nantong, 226361, Jiangsu, China. 14. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, No.88 Keling Road, Suzhou New District, Suzhou, 215163, Jiangsu, China. xingaosam@yahoo.com. 15. Department of Radiology, Jinshan Hospital, Fudan University, 1508 Longhang Road, Jinshan District, Shanghai, 201508, China. dr.jinweiqiang@163.com.
Abstract
OBJECTIVES: Epithelial ovarian cancers (EOC) can be divided into type I and type II according to etiology and prognosis. Accurate subtype differentiation can substantially impact patient management. In this study, we aimed to construct an MR image-based radiomics model to differentiate between type I and type II EOC. METHODS: In this multicenter retrospective study, a total of 294 EOC patients from January 2010 to February 2019 were enrolled. Quantitative MR imaging features were extracted from the following axial sequences: T2WI FS, DWI, ADC, and CE-T1WI. A combined model was constructed based on the combination of these four MR sequences. The diagnostic performance was evaluated by ROC-AUC. In addition, an occlusion test was carried out to identify the most critical region for EOC differentiation. RESULTS: The combined radiomics model exhibited superior diagnostic capability over all four single-parametric radiomics models, both in internal and external validation cohorts (AUC of 0.806 and 0.847, respectively). The occlusion test revealed that the most critical region for differential diagnosis was the border zone between the solid and cystic components, or the less compact areas of solid component on direct visual inspection. CONCLUSIONS: MR image-based radiomics modeling can differentiate between type I and type II EOC and identify the most critical region for differential diagnosis. KEY POINTS: • Combined radiomics models exhibited superior diagnostic capability over all four single-parametric radiomics models, both in internal and external validation cohorts (AUC of 0.834 and 0.847, respectively). • The occlusion test revealed that the most crucial region for differentiating type Ι and type ΙΙ EOC was the border zone between the solid and cystic components, or the less compact areas of solid component on direct visual inspection on T2WI FS. • The light-combined model (constructed by T2WI FS, DWI, and ADC sequences) can be used for patients who are not suitable for contrast agent use.
OBJECTIVES:Epithelial ovarian cancers (EOC) can be divided into type I and type II according to etiology and prognosis. Accurate subtype differentiation can substantially impact patient management. In this study, we aimed to construct an MR image-based radiomics model to differentiate between type I and type II EOC. METHODS: In this multicenter retrospective study, a total of 294 EOC patients from January 2010 to February 2019 were enrolled. Quantitative MR imaging features were extracted from the following axial sequences: T2WI FS, DWI, ADC, and CE-T1WI. A combined model was constructed based on the combination of these four MR sequences. The diagnostic performance was evaluated by ROC-AUC. In addition, an occlusion test was carried out to identify the most critical region for EOC differentiation. RESULTS: The combined radiomics model exhibited superior diagnostic capability over all four single-parametric radiomics models, both in internal and external validation cohorts (AUC of 0.806 and 0.847, respectively). The occlusion test revealed that the most critical region for differential diagnosis was the border zone between the solid and cystic components, or the less compact areas of solid component on direct visual inspection. CONCLUSIONS: MR image-based radiomics modeling can differentiate between type I and type II EOC and identify the most critical region for differential diagnosis. KEY POINTS: • Combined radiomics models exhibited superior diagnostic capability over all four single-parametric radiomics models, both in internal and external validation cohorts (AUC of 0.834 and 0.847, respectively). • The occlusion test revealed that the most crucial region for differentiating type Ι and type ΙΙ EOC was the border zone between the solid and cystic components, or the less compact areas of solid component on direct visual inspection on T2WI FS. • The light-combined model (constructed by T2WI FS, DWI, and ADC sequences) can be used for patients who are not suitable for contrast agent use.
Authors: Virendra Kumar; Yuhua Gu; Satrajit Basu; Anders Berglund; Steven A Eschrich; Matthew B Schabath; Kenneth Forster; Hugo J W L Aerts; Andre Dekker; David Fenstermacher; Dmitry B Goldgof; Lawrence O Hall; Philippe Lambin; Yoganand Balagurunathan; Robert A Gatenby; Robert J Gillies Journal: Magn Reson Imaging Date: 2012-08-13 Impact factor: 2.546
Authors: Evelyn Despierre; Betül T Yesilyurt; Sandrina Lambrechts; Nick Johnson; René Verheijen; Maria van der Burg; Antonio Casado; Gordon Rustin; Els Berns; Karin Leunen; Frédéric Amant; Philippe Moerman; Diether Lambrechts; Ignace Vergote Journal: Int J Gynecol Cancer Date: 2014-03 Impact factor: 3.437