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Nomogram development and validation to predict hepatocellular carcinoma tumor behavior by preoperative gadoxetic acid-enhanced MRI.

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Title: Nomogram development and validation to predict hepatocellular carcinoma tumor behavior by preoperative gadoxetic acid-enhanced MRI.
Authors: Tang, Mimi1 (AUTHOR), Zhou, Qian2 (AUTHOR), Huang, Mengqi1 (AUTHOR), Sun, Kaiyu3 (AUTHOR), Wu, Tingfan4 (AUTHOR), Li, Xin4 (AUTHOR), Liao, Bing5 (AUTHOR), Chen, Lili5 (AUTHOR), Liao, Junbin6 (AUTHOR), Peng, Sui2,7,8 (AUTHOR), Chen, Shuling9 (AUTHOR) chenshling@mail.sysu.edu.cn, Feng, Shi-Ting1 (AUTHOR) fengsht@mail.sysu.edu.cn
Source: European Radiology. Nov2021, Vol. 31 Issue 11, p8615-8627. 13p. 1 Color Photograph, 2 Black and White Photographs, 1 Diagram, 5 Charts, 1 Graph.
Subjects: Hepatocellular carcinoma, Medical personnel, Magnetic resonance imaging, Nomography (Mathematics), Logistic regression analysis, Alanine aminotransferase
Abstract: Objectives: Pretreatment evaluation of tumor biology and microenvironment is important to predict prognosis and plan treatment. We aimed to develop nomograms based on gadoxetic acid-enhanced MRI to predict microvascular invasion (MVI), tumor differentiation, and immunoscore. Methods: This retrospective study included 273 patients with HCC who underwent preoperative gadoxetic acid-enhanced MRI. Patients were assigned to two groups: training (N = 191) and validation (N = 82). Univariable and multivariable logistic regression analyses were performed to investigate clinical variables and MRI features' associations with MVI, tumor differentiation, and immunoscore. Nomograms were developed based on features associated with these three histopathological features in the training cohort, then validated, and evaluated. Results: Predictors of MVI included tumor size, rim enhancement, capsule, percent decrease in T1 images (T1D%), standard deviation of apparent diffusion coefficient, and alanine aminotransferase levels, while capsule, peritumoral enhancement, mean relaxation time on the hepatobiliary phase (T1E), and alpha-fetoprotein levels predicted tumor differentiation. Predictors of immunoscore included the radiologic score constructed by tumor number, intratumoral vessel, margin, capsule, rim enhancement, T1D%, relaxation time on plain scan (T1P), and alpha-fetoprotein and alanine aminotransferase levels. Three nomograms achieved good concordance indexes in predicting MVI (0.754, 0.746), tumor differentiation (0.758, 0.699), and immunoscore (0.737, 0.726) in the training and validation cohorts, respectively. Conclusion: MRI-based nomograms effectively predict tumor behaviors in HCC and may assist clinicians in prognosis prediction and pretreatment decisions. Key Points: • This study developed and validated three nomograms based on gadoxetic acid-enhanced MRI to predict MVI, tumor differentiation, and immunoscore in patients with HCC. • The pretreatment prediction of tumor microenvironment may be useful to guide accurate prognosis and planning of surgical and immunological therapies for individual patients with HCC. [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: Nomogram development and validation to predict hepatocellular carcinoma tumor behavior by preoperative gadoxetic acid-enhanced MRI.
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  Data: <searchLink fieldCode="AR" term="%22Tang%2C+Mimi%22">Tang, Mimi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Qian%22">Zhou, Qian</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+Mengqi%22">Huang, Mengqi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+Kaiyu%22">Sun, Kaiyu</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Tingfan%22">Wu, Tingfan</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Xin%22">Li, Xin</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liao%2C+Bing%22">Liao, Bing</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Lili%22">Chen, Lili</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liao%2C+Junbin%22">Liao, Junbin</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Peng%2C+Sui%22">Peng, Sui</searchLink><relatesTo>2,7,8</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Shuling%22">Chen, Shuling</searchLink><relatesTo>9</relatesTo> (AUTHOR)<i> chenshling@mail.sysu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Feng%2C+Shi-Ting%22">Feng, Shi-Ting</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> fengsht@mail.sysu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22European+Radiology%22">European Radiology</searchLink>. Nov2021, Vol. 31 Issue 11, p8615-8627. 13p. 1 Color Photograph, 2 Black and White Photographs, 1 Diagram, 5 Charts, 1 Graph.
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  Data: <searchLink fieldCode="DE" term="%22Hepatocellular+carcinoma%22">Hepatocellular carcinoma</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+personnel%22">Medical personnel</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging%22">Magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Nomography+%28Mathematics%29%22">Nomography (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Logistic+regression+analysis%22">Logistic regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Alanine+aminotransferase%22">Alanine aminotransferase</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Objectives: Pretreatment evaluation of tumor biology and microenvironment is important to predict prognosis and plan treatment. We aimed to develop nomograms based on gadoxetic acid-enhanced MRI to predict microvascular invasion (MVI), tumor differentiation, and immunoscore. Methods: This retrospective study included 273 patients with HCC who underwent preoperative gadoxetic acid-enhanced MRI. Patients were assigned to two groups: training (N = 191) and validation (N = 82). Univariable and multivariable logistic regression analyses were performed to investigate clinical variables and MRI features' associations with MVI, tumor differentiation, and immunoscore. Nomograms were developed based on features associated with these three histopathological features in the training cohort, then validated, and evaluated. Results: Predictors of MVI included tumor size, rim enhancement, capsule, percent decrease in T1 images (T1D%), standard deviation of apparent diffusion coefficient, and alanine aminotransferase levels, while capsule, peritumoral enhancement, mean relaxation time on the hepatobiliary phase (T1E), and alpha-fetoprotein levels predicted tumor differentiation. Predictors of immunoscore included the radiologic score constructed by tumor number, intratumoral vessel, margin, capsule, rim enhancement, T1D%, relaxation time on plain scan (T1P), and alpha-fetoprotein and alanine aminotransferase levels. Three nomograms achieved good concordance indexes in predicting MVI (0.754, 0.746), tumor differentiation (0.758, 0.699), and immunoscore (0.737, 0.726) in the training and validation cohorts, respectively. Conclusion: MRI-based nomograms effectively predict tumor behaviors in HCC and may assist clinicians in prognosis prediction and pretreatment decisions. Key Points: • This study developed and validated three nomograms based on gadoxetic acid-enhanced MRI to predict MVI, tumor differentiation, and immunoscore in patients with HCC. • The pretreatment prediction of tumor microenvironment may be useful to guide accurate prognosis and planning of surgical and immunological therapies for individual patients with HCC. [ABSTRACT FROM AUTHOR]
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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/s00330-021-07941-7
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      – SubjectFull: Hepatocellular carcinoma
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