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Updated: Feb 7, 2026

Author Spotlight: Advancing Personalized Medicine in Ovarian Cancer
Published on: February 23, 2024
A triage strategy in advanced ovarian cancer management based on multiple predictive models for R0 resection: a
Zheng Feng1,2, Hao Wen1,2, Zhaoxia Jiang2,3
1Department of Gynecological Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
Objective:
To present the surgical outcomes of advanced epithelial ovarian cancer (AEOC) since the implementation of a personalized approach and to validate multiple predictive models for R0 resection.
Methods:
Personalized strategies included: 1) Non-invasive model: preoperative clinico-radiological assessment according to Suidan criteria with a predictive score for all individuals. Patients with a score 0-2 were recommended for primary debulking surgery (PDS, group A), or otherwise were counseled on the choices of PDS, neoadjuvant chemotherapy (NAC, group B) or staging laparoscopy (S-LPS). 2) Minimally invasive model: S-LPS with a predictive index value (PIV) according to Fagotti. Individuals with a PIV <8 underwent PDS (group C) or otherwise received NAC (group D). Intraoperative assessment (with Eisenkop, peritoneal cancer index [PCI], and Aletti scores) and surgical results were prospectively collected.
Results:
Between September 2015 and August 2017, 161 pathologically confirmed epithelial ovarian cancer patients were included. A total of 52 (32.3%) patients had a predictive score of 0-2, and 109 (67.7%) patients had a score ≥3. Among these individuals, 41 (25.5%) patients received S-LPS. Finally, 110 (68.3%) patients underwent PDS (A+C), and 51 (31.7%) patients received NAC (B+D). The R0 resection rates in PDS and NAC patients were 56.4% and 60.8%, respectively. The area under the curve (AUC) of Suidan criteria was 0.548 for group (A+C). The AUC of Fagotti score was 0.702 for group C. The AUC of Eisenkop, PCI, and Aletti scores were 0.808, 0.797, and 0.524, respectively.
Conclusion:
The Suidan criteria were not effective in these AEOC patients. S-LPS was helpful in decision-making for PDS and should be endorsed in the future.
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