Journal article
Predicting residual disease and extent of surgery after neoadjuvant chemotherapy for ovarian cancer: Does the MSKCC model from the primary setting apply?
Gynecologic oncology, Vol.212, pp.145-151
09/2026
DOI: 10.1016/j.ygyno.2026.08.011
PMID: 42612479
Abstract
Complete gross resection (CGR) a key determinant of survival in advanced ovarian cancer (AOC). Preoperative imaging is used to predict residual disease (RD) and surgical complexity in the primary setting, but its performance after neoadjuvant chemotherapy (NACT) remains unclear. We evaluated the ability of computed tomography (CT)-based models in predicting RD and surgical complexity at interval debulking surgery (IDS).
This multicentre retrospective cohort study included 246 patients with FIGO stage IIIC–IV AOC undergoing NACT-IDS between 2016 and 2021. The Memorial Sloan Kettering (MSK) CT-based predictive model for RD was applied and recalibrated. Pre- and post-NACT CT scans were independently reviewed by six radiologists for 18 predefined disease sites. Logistic regression identified radiologic and clinical predictors of RD and advanced surgical procedures.
CGR was achieved in 61% of patients; 35% had ≤1 cm RD, and 4% had >1 cm. The MSK model demonstrated limited discrimination for RD in the IDS setting (c-statistic 0.670). A revised model incorporating optimized age, CA-125 thresholds together with selected radiologic features improved discrimination (c-statistic 0.711). A model predicting the need for advanced surgical procedures achieved moderate performance (c-statistic 0.736). Subcapsular and perihepatic lesions were associated with diaphragm procedures; however absence on CT imaging did not preclude diaphragmatic stripping.
MSK algorithm demonstrated limited discrimination in predicting CGR during IDS. A revised model incorporating optimized age and CA-125 thresholds improved performance. While specific CT findings may help anticipate the need for targeted surgical techniques and multidisciplinary involvement, their absence does not preclude the need for such procedures.
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•CT-based model developed for RD prediction at PDS, performed poorly in the IDS setting.•Revised CT-based model modestly improved RD prediction at IDS (AUC 0.711).•CT-based model predicted need for advanced surgery with modest accuracy (AUC 0.736).•Specific CT findings correlated with upper abdominal procedures.•Inter-rater agreement was poor to fair for most CT variables.
Details
- Title: Subtitle
- Predicting residual disease and extent of surgery after neoadjuvant chemotherapy for ovarian cancer: Does the MSKCC model from the primary setting apply?
- Creators
- Chiara Ainio - Mayo Clinic in ArizonaGiuseppe Caruso - European Institute of OncologySarah Alessi - European Institute of OncologyAnnapaola Antonia Aiello - European Institute of OncologyGiuseppe Petralia - European Institute of OncologyMarina N. Rosanu - European Institute of OncologyLucia Ribero - European Institute of OncologyPamela I. Causa-Andrieu - Mayo Clinic in ArizonaCeylan Colak - Mayo Clinic in ArizonaAlan H. Stolpen - Mayo Clinic in ArizonaLuigi A. De Vitis - Mayo Clinic in ArizonaAmanika Kumar - Mayo Clinic in ArizonaCarrie L. Langstraat - Mayo Clinic in ArizonaMichaela E. McGree - Mayo Clinic in FloridaFrancesco Multinu - European Institute of OncologyStuart A. Ostby - Mayo Clinic in ArizonaGabriella Schivardi - European Institute of OncologyAmanda L. Tapia - Mayo Clinic in FloridaNicoletta Colombo - European Institute of OncologyGiovanni D. Aletti - European Institute of OncologyWilliam A. Cliby - Mayo Clinic
- Resource Type
- Journal article
- Publication Details
- Gynecologic oncology, Vol.212, pp.145-151
- DOI
- 10.1016/j.ygyno.2026.08.011
- PMID
- 42612479
- NLM abbreviation
- Gynecol Oncol
- ISSN
- 0090-8258
- eISSN
- 1095-6859
- Publisher
- Elsevier Inc
- Number of pages
- 7
- Grant note
- Virgil S. Counsellor MD Professorship in Surgery, Mayo Clinic
Funding for this study has been provided to Dr. Cliby by the Virgil S. Counsellor MD Professorship in Surgery, Mayo Clinic.
- Language
- English
- Date published
- 09/2026
- Academic Unit
- Radiology
- Record Identifier
- 9985219918202771
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