Integrated prediction model of patient factors, resectability scores and surgical complexity to predict cytoreductive outcome and guide treatment plan in advanced ovarian cancer

Sabrina Piedimonte; Marcus Q Bernardini; Avrilynn Ding; Stephane Laframboise; Sarah E Ferguson; Genevieve Bouchard-Fortier; Paulina Cybulska; Lisa Avery; Taymaa May; Liat Hogen

doi:10.1016/j.ygyno.2022.06.026

Integrated prediction model of patient factors, resectability scores and surgical complexity to predict cytoreductive outcome and guide treatment plan in advanced ovarian cancer

Gynecol Oncol. 2022 Sep;166(3):453-459. doi: 10.1016/j.ygyno.2022.06.026. Epub 2022 Jul 9.

Affiliations

¹ Division of Gynecologic Oncology, University of Toronto, Toronto, Ontario, Canada.
² Division of Gynecologic Oncology, University of Toronto, Toronto, Ontario, Canada; Division of Gynecologic Oncology, Princess Margaret Cancer Center, Toronto, Ontario, Canada.
³ Department of Obstetrics and Gynecology, University of British Columbia, Canada.
⁴ Department of Biostatistics, University Health Network, Canada.
⁵ Division of Gynecologic Oncology, University of Toronto, Toronto, Ontario, Canada; Division of Gynecologic Oncology, Princess Margaret Cancer Center, Toronto, Ontario, Canada. Electronic address: liat.hogen@uhn.ca.

PMID: 35820987
DOI: 10.1016/j.ygyno.2022.06.026

Abstract

Objective: To report performance of an integrated predictive model (IPM) algorithm based on patient factors, surgical resectability and surgical complexity to predict outcome of primary cytoreductive surgery (PCS) and guide treatment plan in patients with advanced epithelial ovarian cancer (AEOC).

Methods: Patients with AEOC between October 2018 and October 2020 were enrolled into a dedicated AEOC program and decision for PCS or neoadjuvant chemotherapy (NACT) was based on multidisciplinary consensus. Data of unresectable stage IVb, patient factors (PF), surgical resectability scores (SRS) and surgical complexity scores (SCS) was prospectively documented. An integrated prediction model (IPM) was developed to predict outcome of optimal (RD < 1 cm) cytoreduction. Retrospective analysis was performed to assess the performance of the IPM. Cut-offs were selected using the Youden Index.

Results: Of 185 eligible patients, 81 underwent PCS and 104 were treated with NACT. Patients undergoing PCS had significantly lower median PF (0 vs 2, p < 0.01), SRS (2 vs 4, p < 0.01) and pre-operative SCS (6 vs 8.5, p = 0.01) compared to NACT. In patients undergoing PCS, 88% had optimal cytoreduction and 34.5% had grade 3-4 post-operative complications. A model triaging patients with unresectable Stage IVb, PF > 2, SRS > 5 and SCS > 9 to NACT had 85% sensitivity, 75% specificity and 85% accuracy for outcome of optimal cytoreduction. Our model would have improved triage of 3/10 sub-optimally cytoreduced patients to NACT. For outcome of no-gross residual disease (RD = 0 mm) using the same cut-offs sensitivity and specificity were 85% and 76% respectively.

Conclusion: The 4-step IPM algorithm had high sensitivity and specificity for optimal cytoreduction with acceptable morbidity without delay to adjuvant therapy. This algorithm may be used to triage patients to PCS or NACT once it is further validated.

Keywords: Neoadjuvant chemotherapy; Ovarian cancer; Prediction model; Primary cytoreductive surgery.

MeSH terms

Carcinoma, Ovarian Epithelial / drug therapy
Carcinoma, Ovarian Epithelial / surgery
Chemotherapy, Adjuvant
Cytoreduction Surgical Procedures*
Female
Humans
Neoadjuvant Therapy
Neoplasm Staging
Ovarian Neoplasms* / drug therapy
Ovarian Neoplasms* / surgery
Retrospective Studies