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Original research
Validation of the Integrated Prediction Model algorithm for outcome of cytoreduction in advanced ovarian cancer
  1. Sabrina Piedimonte1,
  2. Marcus Q Bernardini2,
  3. Avrilynn Ding3,
  4. Stephane Laframboise4,
  5. Sarah Elizabeth Ferguson4,
  6. Genevieve Bouchard-Fortier5,
  7. Lisa Avery6,
  8. Taymaa May7 and
  9. Liat Hogen4
  1. 1Division of Gynecologic Oncology, University of Toronto, Toronto, Ontario, Canada
  2. 2Princess Margaret Hospital Cancer Centre, Toronto, Ontario, Canada
  3. 3Department of Obstetrics and Gynecology, University of British Columbia, Vancouver, British Columbia, Canada
  4. 4Department of Gynecologic Oncology, Princess Margaret Hospital Cancer Centre, Toronto, Ontario, Canada
  5. 5Princess Margaret Cancer Centre, Toronto, Ontario, Canada
  6. 6Department of Biostatistics, University of Toronto, Toronto, Ontario, Canada
  7. 7Department of Gynecologic Oncology, University Health Network, Toronto, Ontario, Canada
  1. Correspondence to Dr Liat Hogen, Department of Gynecologic Oncology, Princess Margaret Hospital Cancer Centre, Toronto, Ontario, Canada; liat.hogen{at}uhn.ca

Abstract

Background We previously developed the Integrated Prediction Model using a 4-step algorithm of unresectable stage IVB, patient factors, surgical resectability, and surgical complexity to predict outcome of <1 cm cytoreduction in advanced epithelial ovarian cancer, and triaged patients to neoadjuvant chemotherapy or primary cytoreductive surgery.

Objective To validate the Integrated Prediction Model on a retrospective cohort of patients.

Methods A retrospective cohort study of 107 patients with advanced ovarian cancer treated between January 2017 and September 2018 was carried out. The above mentioned 4-step algorithm determined cut-off points using the Youden Index. This validation study reports sensitivity, specificity, negative and positive predictive value on an external cohort.

Results Among 107 patients, 61 had primary surgery and 46 had neoadjuvant chemotherapy. Compared with primary surgery, patients treated with neoadjuvant chemotherapy were significantly older (63.5 vs 61, p=0.037), more likely to have stage IV disease (52% vs 18%, p<0.001), Eastern Cooperative Oncology Group (ECOG) score >1 (30% vs 11%, 0.045), lower pre-operative albumin (37 vs 40, p<0.001), and higher CA-125 (970 vs 227.5, p<0.001). They also had higher patient factors (2 vs 0, p=0.013), surgical resectability (4 vs 1, p<0.001), and anticipated surgical complexity (8 vs 5, p<0.001). There was no significant difference in outcome of cytoreduction (<1 cm residual disease: 85% for primary surgery vs 87% interval surgery, p=0.12)

In this validation cohort, triaging patients with patient factors ≤2, surgical resectability score ≤5, and surgical complexity score ≤9 to primary surgery had a sensitivity of 91% for optimal cytoreduction <1 cm and a specificity of 81%. The positive predictive value, negative predictive value, and accuracy were 83%, 90%, and 86%, respectively. Application of the Integrated Prediction Model would have prevented five patients from receiving suboptimal cytoreduction and triaged them to neoadjuvant chemotherapy.

Conclusions We validated the proposal that a triage algorithm integrating patient factors, surgical complexity, and surgical resectability in advanced ovarian cancer had high sensitivity and specificity to predict optimal cytoreduction <1 cm.

  • surgery
  • ovarian neoplasms

Data availability statement

Data are available upon reasonable request.

http://dx.doi.org/10.1136/ijgc-2022-004202

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    Data availability statement

    Data are available upon reasonable request.

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    Footnotes

    • Presented at This study was presented as a poster presentation at the American Society of Clinical Oncology meeting in Chicago, USA June 37, 2022.

    • Correction notice This article has been corrected since it was first published. The corresponding author has been updated to Dr Liat Hogen.

    • Contributors SP: conceptualization, data curation, data interpretation, manuscript writing and editing; MQB: conceptualization, manuscript editing and revision; AD: data collection; SEF, SL, GB-F, and TM: manuscript editing and revision; LA: formal analysis; LH: funding senior author, conceptualization, data interpretation, manuscript writing and editing, guarantor.

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.