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MR image-based radiomics to differentiate type Ι and type ΙΙ epithelial ovarian cancers.

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Title: MR image-based radiomics to differentiate type Ι and type ΙΙ epithelial ovarian cancers.
Authors: Jian, Junming1,2 (AUTHOR), Li, Yong'ai3 (AUTHOR), Pickhardt, Perry J.4 (AUTHOR), Xia, Wei2 (AUTHOR), He, Zhang5 (AUTHOR), Zhang, Rui2 (AUTHOR), Zhao, Shuhui6 (AUTHOR), Zhao, Xingyu1,2 (AUTHOR), Cai, Songqi7 (AUTHOR), Zhang, Jiayi2 (AUTHOR), Zhang, Guofu8 (AUTHOR), Jiang, Jingxuan9 (AUTHOR), Zhang, Yan10 (AUTHOR), Wang, Keying11 (AUTHOR), Lin, Guangwu12 (AUTHOR), Feng, Feng13 (AUTHOR), Wu, Xiaodong2 (AUTHOR), Gao, Xin2 (AUTHOR) xingaosam@yahoo.com, Qiang, Jinwei3 (AUTHOR) dr.jinweiqiang@163.com
Source: European Radiology. 2021, Vol. 31 Issue 1, p403-410. 8p. 1 Color Photograph, 1 Diagram, 2 Charts, 1 Graph.
Subjects: Ovarian epithelial cancer, Inspection & review, Magnetic resonance imaging, Differential diagnosis
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. [ABSTRACT FROM AUTHOR]
Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: MR image-based radiomics to differentiate type Ι and type ΙΙ epithelial ovarian cancers.
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  Data: <searchLink fieldCode="AR" term="%22Jian%2C+Junming%22">Jian, Junming</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Yong'ai%22">Li, Yong'ai</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pickhardt%2C+Perry+J%2E%22">Pickhardt, Perry J.</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xia%2C+Wei%22">Xia, Wei</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22He%2C+Zhang%22">He, Zhang</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Rui%22">Zhang, Rui</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Shuhui%22">Zhao, Shuhui</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Xingyu%22">Zhao, Xingyu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cai%2C+Songqi%22">Cai, Songqi</searchLink><relatesTo>7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Jiayi%22">Zhang, Jiayi</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Guofu%22">Zhang, Guofu</searchLink><relatesTo>8</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiang%2C+Jingxuan%22">Jiang, Jingxuan</searchLink><relatesTo>9</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yan%22">Zhang, Yan</searchLink><relatesTo>10</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Keying%22">Wang, Keying</searchLink><relatesTo>11</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lin%2C+Guangwu%22">Lin, Guangwu</searchLink><relatesTo>12</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Feng%2C+Feng%22">Feng, Feng</searchLink><relatesTo>13</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Xiaodong%22">Wu, Xiaodong</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gao%2C+Xin%22">Gao, Xin</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> xingaosam@yahoo.com</i><br /><searchLink fieldCode="AR" term="%22Qiang%2C+Jinwei%22">Qiang, Jinwei</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> dr.jinweiqiang@163.com</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22European+Radiology%22">European Radiology</searchLink>. 2021, Vol. 31 Issue 1, p403-410. 8p. 1 Color Photograph, 1 Diagram, 2 Charts, 1 Graph.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Ovarian+epithelial+cancer%22">Ovarian epithelial cancer</searchLink><br /><searchLink fieldCode="DE" term="%22Inspection+%26+review%22">Inspection & review</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging%22">Magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Differential+diagnosis%22">Differential diagnosis</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00330-020-07091-2
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