View in EDS HTML Full Text PDF Full Text

Predicting early stage lung cancer recurrence and survival from combined tumor motion amplitude and radiomics on free‐breathing 4D‐CT.

Saved in:
Bibliographic Details
Title: Predicting early stage lung cancer recurrence and survival from combined tumor motion amplitude and radiomics on free‐breathing 4D‐CT.
Authors: Ouraou, Emilie1 (AUTHOR), Tonneau, Marion2 (AUTHOR), Le, William T.1 (AUTHOR), Filion, Edith2 (AUTHOR), Campeau, Marie‐Pierre2 (AUTHOR), Vu, Toni2 (AUTHOR), Doucet, Robert2 (AUTHOR), Bahig, Houda2 (AUTHOR), Kadoury, Samuel1,2 (AUTHOR) samuel.kadoury@polymtl.ca
Source: Medical Physics. Mar2025, Vol. 52 Issue 3, p1926-1940. 15p.
Subjects: Lung cancer, Radiomics, Machine learning, Radiotherapy, Four-dimensional imaging, Survival analysis (Biometry), Cancer relapse, Cancer cell motility
Abstract: Background: Cancer control outcomes of lung cancer are hypothesized to be affected by several confounding factors, including tumor heterogeneity and patient history, which have been hypothesized to mitigate the dose delivery effectiveness when treated with radiation therapy. Providing an accurate predictive model to identify patients at risk would enable tailored follow‐up strategies during treatment. Purpose: Our goal is to demonstrate the added prognostic value of including tumor displacement amplitude in a predictive model that combines clinical features and computed tomography (CT) radiomics for 2‐year recurrence and survival in non‐small‐cell lung cancer (NSCLC) patients treated with curative‐intent stereotactic body radiation therapy. Methods: A cohort of 381 patients treated for primary lung cancer with radiotherapy was collected, each including a planning CT with a dosimetry plan, 4D‐CT, and clinical information. From this cohort, 101 patients (26.5%) experienced cancer progression (locoregional/distant metastasis) or death within 2 years of the end of treatment. Imaging data was analyzed for radiomics features from the tumor segmented image, as well as tumor motion amplitude measured on 4D‐CT. A random forest (RF) model was developed to predict the overall outcomes, which was compared to three other approaches — logistic regression, support vector machine, and convolutional neural networks. Results: A 6‐fold cross‐validation study yielded an area under the receiver operating characteristic curve of 72% for progression‐free survival when combining clinical data with radiomics features and tumor motion using a RF model (72% sensitivity and 81% specificity). The combined model showed significant improvement compared to standard clinical data. Model performances for loco‐regional recurrence and overall survival sub‐outcomes were established at 73% and 70%, respectively. No comparative methods reached statistical significance in any data configuration. Conclusions: Combined tumor respiratory motion and radiomics features from planning CT showed promising predictive value for 2‐year tumor control and survival, indicating the potential need for improving motion management strategies in future studies using machine learning‐based prognosis models. [ABSTRACT FROM AUTHOR]
Copyright of Medical Physics is the property of Wiley-Blackwell 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: 183916812
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Predicting early stage lung cancer recurrence and survival from combined tumor motion amplitude and radiomics on free‐breathing 4D‐CT.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Ouraou%2C+Emilie%22">Ouraou, Emilie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tonneau%2C+Marion%22">Tonneau, Marion</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Le%2C+William+T%2E%22">Le, William T.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Filion%2C+Edith%22">Filion, Edith</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Campeau%2C+Marie‐Pierre%22">Campeau, Marie‐Pierre</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Vu%2C+Toni%22">Vu, Toni</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Doucet%2C+Robert%22">Doucet, Robert</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bahig%2C+Houda%22">Bahig, Houda</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kadoury%2C+Samuel%22">Kadoury, Samuel</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> samuel.kadoury@polymtl.ca</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Mar2025, Vol. 52 Issue 3, p1926-1940. 15p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Lung+cancer%22">Lung cancer</searchLink><br /><searchLink fieldCode="DE" term="%22Radiomics%22">Radiomics</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Radiotherapy%22">Radiotherapy</searchLink><br /><searchLink fieldCode="DE" term="%22Four-dimensional+imaging%22">Four-dimensional imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Survival+analysis+%28Biometry%29%22">Survival analysis (Biometry)</searchLink><br /><searchLink fieldCode="DE" term="%22Cancer+relapse%22">Cancer relapse</searchLink><br /><searchLink fieldCode="DE" term="%22Cancer+cell+motility%22">Cancer cell motility</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Background: Cancer control outcomes of lung cancer are hypothesized to be affected by several confounding factors, including tumor heterogeneity and patient history, which have been hypothesized to mitigate the dose delivery effectiveness when treated with radiation therapy. Providing an accurate predictive model to identify patients at risk would enable tailored follow‐up strategies during treatment. Purpose: Our goal is to demonstrate the added prognostic value of including tumor displacement amplitude in a predictive model that combines clinical features and computed tomography (CT) radiomics for 2‐year recurrence and survival in non‐small‐cell lung cancer (NSCLC) patients treated with curative‐intent stereotactic body radiation therapy. Methods: A cohort of 381 patients treated for primary lung cancer with radiotherapy was collected, each including a planning CT with a dosimetry plan, 4D‐CT, and clinical information. From this cohort, 101 patients (26.5%) experienced cancer progression (locoregional/distant metastasis) or death within 2 years of the end of treatment. Imaging data was analyzed for radiomics features from the tumor segmented image, as well as tumor motion amplitude measured on 4D‐CT. A random forest (RF) model was developed to predict the overall outcomes, which was compared to three other approaches — logistic regression, support vector machine, and convolutional neural networks. Results: A 6‐fold cross‐validation study yielded an area under the receiver operating characteristic curve of 72% for progression‐free survival when combining clinical data with radiomics features and tumor motion using a RF model (72% sensitivity and 81% specificity). The combined model showed significant improvement compared to standard clinical data. Model performances for loco‐regional recurrence and overall survival sub‐outcomes were established at 73% and 70%, respectively. No comparative methods reached statistical significance in any data configuration. Conclusions: Combined tumor respiratory motion and radiomics features from planning CT showed promising predictive value for 2‐year tumor control and survival, indicating the potential need for improving motion management strategies in future studies using machine learning‐based prognosis models. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Medical Physics is the property of Wiley-Blackwell 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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=183916812
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1002/mp.17586
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 15
        StartPage: 1926
    Subjects:
      – SubjectFull: Lung cancer
        Type: general
      – SubjectFull: Radiomics
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Radiotherapy
        Type: general
      – SubjectFull: Four-dimensional imaging
        Type: general
      – SubjectFull: Survival analysis (Biometry)
        Type: general
      – SubjectFull: Cancer relapse
        Type: general
      – SubjectFull: Cancer cell motility
        Type: general
    Titles:
      – TitleFull: Predicting early stage lung cancer recurrence and survival from combined tumor motion amplitude and radiomics on free‐breathing 4D‐CT.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Ouraou, Emilie
      – PersonEntity:
          Name:
            NameFull: Tonneau, Marion
      – PersonEntity:
          Name:
            NameFull: Le, William T.
      – PersonEntity:
          Name:
            NameFull: Filion, Edith
      – PersonEntity:
          Name:
            NameFull: Campeau, Marie‐Pierre
      – PersonEntity:
          Name:
            NameFull: Vu, Toni
      – PersonEntity:
          Name:
            NameFull: Doucet, Robert
      – PersonEntity:
          Name:
            NameFull: Bahig, Houda
      – PersonEntity:
          Name:
            NameFull: Kadoury, Samuel
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 03
              Text: Mar2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 00942405
          Numbering:
            – Type: volume
              Value: 52
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: Medical Physics
              Type: main
ResultId 1