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Role of radiomics as a predictor of disease recurrence in ovarian cancer: a systematic review.

Ovarian cancer is associated with high cancer-related mortality rate attributed to late-stage diagnosis, limited treatment options, and frequent disease recurrence. As a result, careful patient selection is important especially in setting of radical surgery. Radiomics is an emerging field in medical...

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Published in:Abdominal Radiology Vol. 49; no. 10; pp. 3540 - 3548
Main Authors: O'Sullivan, Niall J., Temperley, Hugo C., Horan, Michelle T., Kamran, Waseem, Corr, Alison, O'Gorman, Catherine, Saadeh, Feras, Meaney, James M., Kelly, Michael E.
Format: Journal Article
Published: Springer Nature Oct2024
Online Access:View this record in EBSCOhost
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      dt: Oct2024
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s00261-024-04330-8
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        atl: Role of radiomics as a predictor of disease recurrence in ovarian cancer: a systematic review.
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          O'Sullivan, Niall J.
          Temperley, Hugo C.
          Horan, Michelle T.
          Kamran, Waseem
          Corr, Alison
          O'Gorman, Catherine
          Saadeh, Feras
          Meaney, James M.
          Kelly, Michael E.
        affil: https://ror.org/04c6bry31 Department of Radiology, St. James's Hospital, Dublin, Ireland
      sug:
      ab: Ovarian cancer is associated with high cancer-related mortality rate attributed to late-stage diagnosis, limited treatment options, and frequent disease recurrence. As a result, careful patient selection is important especially in setting of radical surgery. Radiomics is an emerging field in medical imaging, which may help provide vital prognostic evaluation and help patient selection for radical treatment strategies. This systematic review aims to assess the role of radiomics as a predictor of disease recurrence in ovarian cancer. A systematic search was conducted in Medline, EMBASE, and Web of Science databases. Studies meeting inclusion criteria investigating the use of radiomics to predict post-operative recurrence in ovarian cancer were included in our qualitative analysis. Study quality was assessed using the QUADAS-2 and Radiomics Quality Score tools. Six retrospective studies met the inclusion criteria, involving a total of 952 participants. Radiomic-based signatures demonstrated consistent performance in predicting disease recurrence, as evidenced by satisfactory area under the receiver operating characteristic curve values (AUC range 0.77–0.89). Radiomic-based signatures appear to good prognosticators of disease recurrence in ovarian cancer as estimated by AUC. The reviewed studies consistently reported the potential of radiomic features to enhance risk stratification and personalise treatment decisions in this complex cohort of patients. Further research is warranted to address limitations related to feature reliability, workflow heterogeneity, and the need for prospective validation studies.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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