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Preoperative prediction of tumour deposits in rectal cancer by an artificial neural network-based US radiomics model.

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Title: Preoperative prediction of tumour deposits in rectal cancer by an artificial neural network-based US radiomics model.
Authors: Chen, Li-Da1 (AUTHOR), Li, Wei1 (AUTHOR), Xian, Meng-Fei2 (AUTHOR), Zheng, Xin1 (AUTHOR), Lin, Yuan3 (AUTHOR), Liu, Bao-Xian1 (AUTHOR), Lin, Man-Xia1 (AUTHOR), Li, Xin4 (AUTHOR), Zheng, Yan-Ling1 (AUTHOR), Xie, Xiao-Yan1 (AUTHOR), Lu, Ming-De1,5 (AUTHOR), Kuang, Ming1,5 (AUTHOR), Xu, Jian-Bo6 (AUTHOR) xjianb@mail.sysu.edu.cn, Wang, Wei1 (AUTHOR) wangw73@mail.sysu.edu.cn
Source: European Radiology. Apr2020, Vol. 30 Issue 4, p1969-1979. 11p. 2 Diagrams, 4 Charts, 2 Graphs.
Subjects: Rectal cancer, Endorectal ultrasonography, Artificial neural networks, Tumors
Abstract: Objective: To develop a machine learning-based ultrasound (US) radiomics model for predicting tumour deposits (TDs) preoperatively.Methods: From December 2015 to December 2017, 127 patients with rectal cancer were prospectively enrolled and divided into training and validation sets. Endorectal ultrasound (ERUS) and shear-wave elastography (SWE) examinations were conducted for each patient. A total of 4176 US radiomics features were extracted for each patient. After the reduction and selection of US radiomics features , a predictive model using an artificial neural network (ANN) was constructed in the training set. Furthermore, two models (one incorporating clinical information and one based on MRI radiomics) were developed. These models were validated by assessing their diagnostic performance and comparing the areas under the curve (AUCs) in the validation set.Results: The training and validation sets included 29 (33.3%) and 11 (27.5%) patients with TDs, respectively. A US radiomics ANN model was constructed. The model for predicting TDs showed an accuracy of 75.0% in the validation cohort. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and AUC were 72.7%, 75.9%, 53.3%, 88.0% and 0.743, respectively. For the model incorporating clinical information, the AUC improved to 0.795. Although the AUC of the US radiomics model was improved compared with that of the MRI radiomics model (0.916 vs. 0.872) in the 90 patients with both ultrasound and MRI data (which included both the training and validation sets), the difference was nonsignificant (p = 0.384).Conclusions: US radiomics may be a potential model to accurately predict TDs before therapy.Key Points: • We prospectively developed an artificial neural network model for predicting tumour deposits based on US radiomics that had an accuracy of 75.0%. • The area under the curve of the US radiomics model was improved than that of the MRI radiomics model (0.916 vs. 0.872), but the difference was not significant (p = 0.384). • The US radiomics-based model may potentially predict TDs accurately before therapy, but this model needs further validation with larger samples. [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: Preoperative prediction of tumour deposits in rectal cancer by an artificial neural network-based US radiomics model.
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  Data: <searchLink fieldCode="AR" term="%22Chen%2C+Li-Da%22">Chen, Li-Da</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Wei%22">Li, Wei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xian%2C+Meng-Fei%22">Xian, Meng-Fei</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zheng%2C+Xin%22">Zheng, Xin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lin%2C+Yuan%22">Lin, Yuan</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Bao-Xian%22">Liu, Bao-Xian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lin%2C+Man-Xia%22">Lin, Man-Xia</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Xin%22">Li, Xin</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zheng%2C+Yan-Ling%22">Zheng, Yan-Ling</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xie%2C+Xiao-Yan%22">Xie, Xiao-Yan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lu%2C+Ming-De%22">Lu, Ming-De</searchLink><relatesTo>1,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kuang%2C+Ming%22">Kuang, Ming</searchLink><relatesTo>1,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Jian-Bo%22">Xu, Jian-Bo</searchLink><relatesTo>6</relatesTo> (AUTHOR)<i> xjianb@mail.sysu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Wei%22">Wang, Wei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wangw73@mail.sysu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22European+Radiology%22">European Radiology</searchLink>. Apr2020, Vol. 30 Issue 4, p1969-1979. 11p. 2 Diagrams, 4 Charts, 2 Graphs.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Rectal+cancer%22">Rectal cancer</searchLink><br /><searchLink fieldCode="DE" term="%22Endorectal+ultrasonography%22">Endorectal ultrasonography</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Tumors%22">Tumors</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: <bold>Objective: </bold>To develop a machine learning-based ultrasound (US) radiomics model for predicting tumour deposits (TDs) preoperatively.<bold>Methods: </bold>From December 2015 to December 2017, 127 patients with rectal cancer were prospectively enrolled and divided into training and validation sets. Endorectal ultrasound (ERUS) and shear-wave elastography (SWE) examinations were conducted for each patient. A total of 4176 US radiomics features were extracted for each patient. After the reduction and selection of US radiomics features , a predictive model using an artificial neural network (ANN) was constructed in the training set. Furthermore, two models (one incorporating clinical information and one based on MRI radiomics) were developed. These models were validated by assessing their diagnostic performance and comparing the areas under the curve (AUCs) in the validation set.<bold>Results: </bold>The training and validation sets included 29 (33.3%) and 11 (27.5%) patients with TDs, respectively. A US radiomics ANN model was constructed. The model for predicting TDs showed an accuracy of 75.0% in the validation cohort. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and AUC were 72.7%, 75.9%, 53.3%, 88.0% and 0.743, respectively. For the model incorporating clinical information, the AUC improved to 0.795. Although the AUC of the US radiomics model was improved compared with that of the MRI radiomics model (0.916 vs. 0.872) in the 90 patients with both ultrasound and MRI data (which included both the training and validation sets), the difference was nonsignificant (p = 0.384).<bold>Conclusions: </bold>US radiomics may be a potential model to accurately predict TDs before therapy.<bold>Key Points: </bold>• We prospectively developed an artificial neural network model for predicting tumour deposits based on US radiomics that had an accuracy of 75.0%. • The area under the curve of the US radiomics model was improved than that of the MRI radiomics model (0.916 vs. 0.872), but the difference was not significant (p = 0.384). • The US radiomics-based model may potentially predict TDs accurately before therapy, but this model needs further validation with larger samples. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  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/s00330-019-06558-1
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        Text: English
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        PageCount: 11
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    Subjects:
      – SubjectFull: Rectal cancer
        Type: general
      – SubjectFull: Endorectal ultrasonography
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      – SubjectFull: Artificial neural networks
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      – SubjectFull: Tumors
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