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Validation analysis of the novel imaging-based prognostic radiomic signature in patients undergoing primary surgery for advanced high-grade serous ovarian cancer (HGSOC).


ABSTRACT:

Background

Predictive models based on radiomics features are novel, highly promising approaches for gynaecological oncology. Here, we wish to assess the prognostic value of the newly discovered Radiomic Prognostic Vector (RPV) in an independent cohort of high-grade serous ovarian cancer (HGSOC) patients, treated within a Centre of Excellence, thus avoiding any bias in treatment quality.

Methods

RPV was calculated using standardised algorithms following segmentation of routine preoperative imaging of patients (n = 323) who underwent upfront debulking surgery (01/2011-07/2018). RPV was correlated with operability, survival and adjusted for well-established prognostic factors (age, postoperative residual disease, stage), and compared to previous validation models.

Results

The distribution of low, medium and high RPV scores was 54.2% (n = 175), 33.4% (n = 108) and 12.4% (n = 40) across the cohort, respectively. High RPV scores independently associated with significantly worse progression-free survival (PFS) (HR = 1.69; 95% CI:1.06-2.71; P = 0.038), even after adjusting for stage, age, performance status and residual disease. Moreover, lower RPV was significantly associated with total macroscopic tumour clearance (OR = 2.02; 95% CI:1.56-2.62; P = 0.00647).

Conclusions

RPV was validated to independently identify those HGSOC patients who will not be operated tumour-free in an optimal setting, and those who will relapse early despite complete tumour clearance upfront. Further prospective, multicentre trials with a translational aspect are warranted for the incorporation of this radiomics approach into clinical routine.

SUBMITTER: Fotopoulou C 

PROVIDER: S-EPMC8979975 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Publications

Validation analysis of the novel imaging-based prognostic radiomic signature in patients undergoing primary surgery for advanced high-grade serous ovarian cancer (HGSOC).

Fotopoulou Christina C   Rockall Andrea A   Lu Haonan H   Lee Philippa P   Avesani Giacomo G   Russo Luca L   Petta Federica F   Ataseven Beyhan B   Waltering Kai-Uwe KU   Koch Jens Albrecht JA   Crum William R WR   Cunnea Paula P   Heitz Florian F   Harter Philipp P   Aboagye Eric O EO   du Bois Andreas A   Prader Sonia S  

British journal of cancer 20211218 7


<h4>Background</h4>Predictive models based on radiomics features are novel, highly promising approaches for gynaecological oncology. Here, we wish to assess the prognostic value of the newly discovered Radiomic Prognostic Vector (RPV) in an independent cohort of high-grade serous ovarian cancer (HGSOC) patients, treated within a Centre of Excellence, thus avoiding any bias in treatment quality.<h4>Methods</h4>RPV was calculated using standardised algorithms following segmentation of routine preo  ...[more]

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