View in EDS HTML Full Text PDF Full Text

Risk stratification of gallbladder masses by machine learning-based ultrasound radiomics models: a prospective and multi-institutional study.

Saved in:
Bibliographic Details
Title: Risk stratification of gallbladder masses by machine learning-based ultrasound radiomics models: a prospective and multi-institutional study.
Authors: Wang, Li-Fan1 (AUTHOR), Wang, Qiao2,3 (AUTHOR), Mao, Feng4 (AUTHOR), Xu, Shi-Hao5 (AUTHOR), Sun, Li-Ping2,3 (AUTHOR), Wu, Ting-Fan6 (AUTHOR), Zhou, Bo-Yang1 (AUTHOR), Yin, Hao-Hao1 (AUTHOR), Shi, Hui2,3 (AUTHOR), Zhang, Ya-Qin2,3 (AUTHOR), Li, Xiao-Long1 (AUTHOR), Sun, Yi-Kang1 (AUTHOR), Lu, Dan1 (AUTHOR), Tang, Cong-Yu1 (AUTHOR), Yuan, Hai-Xia1,7 (AUTHOR) haixiayuan75@163.com, Zhao, Chong-Ke1 (AUTHOR) zhaochongke123@163.com, Xu, Hui-Xiong1 (AUTHOR) xu.huixiong@zs-hospital.sh.cn
Source: European Radiology. Dec2023, Vol. 33 Issue 12, p8899-8911. 13p.
Subjects: Radiomics, Machine learning, Gallbladder, Computer-assisted image analysis (Medicine), Feature extraction
Abstract: Objective: This study aimed to evaluate the diagnostic performance of machine learning (ML)–based ultrasound (US) radiomics models for risk stratification of gallbladder (GB) masses. Methods: We prospectively examined 640 pathologically confirmed GB masses obtained from 640 patients between August 2019 and October 2022 at four institutions. Radiomics features were extracted from grayscale US images and germane features were selected. Subsequently, 11 ML algorithms were separately used with the selected features to construct optimum US radiomics models for risk stratification of the GB masses. Furthermore, we compared the diagnostic performance of these models with the conventional US and contrast-enhanced US (CEUS) models. Results: The optimal XGBoost-based US radiomics model for discriminating neoplastic from non-neoplastic GB lesions showed higher diagnostic performance in terms of areas under the curves (AUCs) than the conventional US model (0.822–0.853 vs. 0.642–0.706, p < 0.05) and potentially decreased unnecessary cholecystectomy rate in a speculative comparison with performing cholecystectomy for lesions sized over 10 mm (2.7–13.8% vs. 53.6–64.9%, p < 0.05) in the validation and test sets. The AUCs of the XGBoost-based US radiomics model for discriminating carcinomas from benign GB lesions were higher than the conventional US model (0.904–0.979 vs. 0.706–0.766, p < 0.05). The XGBoost-US radiomics model performed better than the CEUS model in discriminating GB carcinomas (AUC: 0.995 vs. 0.902, p = 0.011). Conclusions: The proposed ML-based US radiomics models possess the potential capacity for risk stratification of GB masses and may reduce the unnecessary cholecystectomy rate and use of CEUS. Clinical relevance statement: The machine learning-based ultrasound radiomics models have potential for risk stratification of gallbladder masses and may potentially reduce unnecessary cholecystectomies. Key Points: • The XGBoost-based US radiomics models are useful for the risk stratification of GB masses. • The XGBoost-based US radiomics model is superior to the conventional US model for discriminating neoplastic from non-neoplastic GB lesions and may potentially decrease unnecessary cholecystectomy rate for lesions sized over 10 mm in comparison with the current consensus guideline. • The XGBoost-based US radiomics model could overmatch CEUS model in discriminating GB carcinomas from benign GB lesions. [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.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 173805947
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Risk stratification of gallbladder masses by machine learning-based ultrasound radiomics models: a prospective and multi-institutional study.
– Name: Author
  Label: Authors
  Group: Au
  Data: &lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Wang%2C+Li-Fan%22&quot;&gt;Wang, Li-Fan&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Wang%2C+Qiao%22&quot;&gt;Wang, Qiao&lt;/searchLink&gt;&lt;relatesTo&gt;2,3&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Mao%2C+Feng%22&quot;&gt;Mao, Feng&lt;/searchLink&gt;&lt;relatesTo&gt;4&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Xu%2C+Shi-Hao%22&quot;&gt;Xu, Shi-Hao&lt;/searchLink&gt;&lt;relatesTo&gt;5&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Sun%2C+Li-Ping%22&quot;&gt;Sun, Li-Ping&lt;/searchLink&gt;&lt;relatesTo&gt;2,3&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Wu%2C+Ting-Fan%22&quot;&gt;Wu, Ting-Fan&lt;/searchLink&gt;&lt;relatesTo&gt;6&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Zhou%2C+Bo-Yang%22&quot;&gt;Zhou, Bo-Yang&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Yin%2C+Hao-Hao%22&quot;&gt;Yin, Hao-Hao&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Shi%2C+Hui%22&quot;&gt;Shi, Hui&lt;/searchLink&gt;&lt;relatesTo&gt;2,3&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Zhang%2C+Ya-Qin%22&quot;&gt;Zhang, Ya-Qin&lt;/searchLink&gt;&lt;relatesTo&gt;2,3&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Li%2C+Xiao-Long%22&quot;&gt;Li, Xiao-Long&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Sun%2C+Yi-Kang%22&quot;&gt;Sun, Yi-Kang&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Lu%2C+Dan%22&quot;&gt;Lu, Dan&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Tang%2C+Cong-Yu%22&quot;&gt;Tang, Cong-Yu&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Yuan%2C+Hai-Xia%22&quot;&gt;Yuan, Hai-Xia&lt;/searchLink&gt;&lt;relatesTo&gt;1,7&lt;/relatesTo&gt; (AUTHOR)&lt;i&gt; haixiayuan75@163.com&lt;/i&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Zhao%2C+Chong-Ke%22&quot;&gt;Zhao, Chong-Ke&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt; (AUTHOR)&lt;i&gt; zhaochongke123@163.com&lt;/i&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Xu%2C+Hui-Xiong%22&quot;&gt;Xu, Hui-Xiong&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt; (AUTHOR)&lt;i&gt; xu.huixiong@zs-hospital.sh.cn&lt;/i&gt;
– Name: TitleSource
  Label: Source
  Group: Src
  Data: &lt;searchLink fieldCode=&quot;JN&quot; term=&quot;%22European+Radiology%22&quot;&gt;European Radiology&lt;/searchLink&gt;. Dec2023, Vol. 33 Issue 12, p8899-8911. 13p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: &lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Radiomics%22&quot;&gt;Radiomics&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Machine+learning%22&quot;&gt;Machine learning&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Gallbladder%22&quot;&gt;Gallbladder&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Computer-assisted+image+analysis+%28Medicine%29%22&quot;&gt;Computer-assisted image analysis (Medicine)&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Feature+extraction%22&quot;&gt;Feature extraction&lt;/searchLink&gt;
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Objective: This study aimed to evaluate the diagnostic performance of machine learning (ML)–based ultrasound (US) radiomics models for risk stratification of gallbladder (GB) masses. Methods: We prospectively examined 640 pathologically confirmed GB masses obtained from 640 patients between August 2019 and October 2022 at four institutions. Radiomics features were extracted from grayscale US images and germane features were selected. Subsequently, 11 ML algorithms were separately used with the selected features to construct optimum US radiomics models for risk stratification of the GB masses. Furthermore, we compared the diagnostic performance of these models with the conventional US and contrast-enhanced US (CEUS) models. Results: The optimal XGBoost-based US radiomics model for discriminating neoplastic from non-neoplastic GB lesions showed higher diagnostic performance in terms of areas under the curves (AUCs) than the conventional US model (0.822–0.853 vs. 0.642–0.706, p &lt; 0.05) and potentially decreased unnecessary cholecystectomy rate in a speculative comparison with performing cholecystectomy for lesions sized over 10 mm (2.7–13.8% vs. 53.6–64.9%, p &lt; 0.05) in the validation and test sets. The AUCs of the XGBoost-based US radiomics model for discriminating carcinomas from benign GB lesions were higher than the conventional US model (0.904–0.979 vs. 0.706–0.766, p &lt; 0.05). The XGBoost-US radiomics model performed better than the CEUS model in discriminating GB carcinomas (AUC: 0.995 vs. 0.902, p = 0.011). Conclusions: The proposed ML-based US radiomics models possess the potential capacity for risk stratification of GB masses and may reduce the unnecessary cholecystectomy rate and use of CEUS. Clinical relevance statement: The machine learning-based ultrasound radiomics models have potential for risk stratification of gallbladder masses and may potentially reduce unnecessary cholecystectomies. Key Points: • The XGBoost-based US radiomics models are useful for the risk stratification of GB masses. • The XGBoost-based US radiomics model is superior to the conventional US model for discriminating neoplastic from non-neoplastic GB lesions and may potentially decrease unnecessary cholecystectomy rate for lesions sized over 10 mm in comparison with the current consensus guideline. • The XGBoost-based US radiomics model could overmatch CEUS model in discriminating GB carcinomas from benign GB lesions. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: &lt;i&gt;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&#39;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.&lt;/i&gt; (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=173805947
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s00330-023-09891-8
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
        StartPage: 8899
    Subjects:
      – SubjectFull: Radiomics
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Gallbladder
        Type: general
      – SubjectFull: Computer-assisted image analysis (Medicine)
        Type: general
      – SubjectFull: Feature extraction
        Type: general
    Titles:
      – TitleFull: Risk stratification of gallbladder masses by machine learning-based ultrasound radiomics models: a prospective and multi-institutional study.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Wang, Li-Fan
      – PersonEntity:
          Name:
            NameFull: Wang, Qiao
      – PersonEntity:
          Name:
            NameFull: Mao, Feng
      – PersonEntity:
          Name:
            NameFull: Xu, Shi-Hao
      – PersonEntity:
          Name:
            NameFull: Sun, Li-Ping
      – PersonEntity:
          Name:
            NameFull: Wu, Ting-Fan
      – PersonEntity:
          Name:
            NameFull: Zhou, Bo-Yang
      – PersonEntity:
          Name:
            NameFull: Yin, Hao-Hao
      – PersonEntity:
          Name:
            NameFull: Shi, Hui
      – PersonEntity:
          Name:
            NameFull: Zhang, Ya-Qin
      – PersonEntity:
          Name:
            NameFull: Li, Xiao-Long
      – PersonEntity:
          Name:
            NameFull: Sun, Yi-Kang
      – PersonEntity:
          Name:
            NameFull: Lu, Dan
      – PersonEntity:
          Name:
            NameFull: Tang, Cong-Yu
      – PersonEntity:
          Name:
            NameFull: Yuan, Hai-Xia
      – PersonEntity:
          Name:
            NameFull: Zhao, Chong-Ke
      – PersonEntity:
          Name:
            NameFull: Xu, Hui-Xiong
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 12
              Text: Dec2023
              Type: published
              Y: 2023
          Identifiers:
            – Type: issn-print
              Value: 09387994
          Numbering:
            – Type: volume
              Value: 33
            – Type: issue
              Value: 12
          Titles:
            – TitleFull: European Radiology
              Type: main
ResultId 1