Literature DB >> 25913130

Development of a prediction model for residual disease in newly diagnosed advanced ovarian cancer.

Jo Marie Tran Janco1, Gretchen Glaser2, Bohyun Kim3, Michaela E McGree4, Amy L Weaver4, William A Cliby1, Sean C Dowdy1, Jamie N Bakkum-Gamez5.   

Abstract

OBJECTIVES: To construct a tool, using computed tomography (CT) imaging and preoperative clinical variables, to estimate successful primary cytoreduction for advanced epithelial ovarian cancer (EOC).
METHODS: Women who underwent primary cytoreductive surgery for stage IIIC/IV EOC at Mayo Clinic between 1/2/2003 and 12/30/2011 and had preoperative CT images of the abdomen and pelvis within 90days prior to their surgery available for review were included. CT images were reviewed for large-volume ascites, diffuse peritoneal thickening (DPT), omental cake, lymphadenopathy (LP), and spleen or liver involvement. Preoperative factors included age, body mass index (BMI), Eastern Cooperative Oncology Group performance status (ECOG PS), American Society of Anesthesiologists (ASA) score, albumin, CA-125, and thrombocytosis. Two prediction models were developed to estimate the probability of (i) complete and (ii) suboptimal cytoreduction (residual disease (RD) >1cm) using multivariable logistic analysis with backward and stepwise variable selection methods. Internal validation was assessed using bootstrap resampling to derive an optimism-corrected estimate of the c-index.
RESULTS: 279 patients met inclusion criteria: 143 had complete cytoreduction, 26 had suboptimal cytoreduction (RD>1cm), and 110 had measurable RD ≤1cm. On multivariable analysis, age, absence of ascites, omental cake, and DPT on CT imaging independently predicted complete cytoreduction (c-index=0.748). Conversely, predictors of suboptimal cytoreduction were ECOG PS, DPT, and LP on preoperative CT imaging (c-index=0.685).
CONCLUSIONS: The generated models serve as preoperative evaluation tools that may improve counseling and selection for primary surgery, but need to be externally validated.
Copyright © 2015 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Cytoreduction; Ovarian cancer; Preoperative prediction

Mesh:

Year:  2015        PMID: 25913130     DOI: 10.1016/j.ygyno.2015.04.013

Source DB:  PubMed          Journal:  Gynecol Oncol        ISSN: 0090-8258            Impact factor:   5.482


  10 in total

1.  The Influence of "Omental Cake" Presence on the Completeness of Cytoreduction in Advanced-stage Ovarian Cancer.

Authors:  Nicolae Bacalbasa; Camelia Diaconu; Laura Iliescu; Cornel Savu; Ovidiu Gabriel Bratu; Ciprian Bolca; Dragos Cretoiu; Alexandru Filipescu; Simona Dima; Cristian Balalau; Irina Balescu
Journal:  In Vivo       Date:  2020 Jul-Aug       Impact factor: 2.155

2.  Predictive modeling for determination of microscopic residual disease at primary cytoreduction: An NRG Oncology/Gynecologic Oncology Group 182 Study.

Authors:  Neil S Horowitz; G Larry Maxwell; Austin Miller; Chad A Hamilton; Bunja Rungruang; Noah Rodriguez; Scott D Richard; Thomas C Krivak; Jeffrey M Fowler; David G Mutch; Linda Van Le; Roger B Lee; Peter Argenta; David Bender; Krishnansu S Tewari; David Gershenson; James J Java; Michael A Bookman
Journal:  Gynecol Oncol       Date:  2017-11-23       Impact factor: 5.482

3.  Impact of ascites volume on clinical outcomes in ovarian cancer: A cohort study.

Authors:  J Brian Szender; Tiffany Emmons; Sarah Belliotti; Danielle Dickson; Aalia Khan; Kayla Morrell; A N M Nazmul Khan; Kelly L Singel; Paul C Mayor; Kirsten B Moysich; Kunle Odunsi; Brahm H Segal; Kevin H Eng
Journal:  Gynecol Oncol       Date:  2017-06-16       Impact factor: 5.482

Review 4.  Prediction of optimal debulking surgery in ovarian cancer.

Authors:  Yong Jung Song
Journal:  Gland Surg       Date:  2021-03

5.  An Orthotopic Murine Model of Peritoneal Carcinomatosis of Ovarian Origin for Intraoperative PDT.

Authors:  Thierry Michy; Claire Bernard; Jean-Luc Coll; Véronique Josserand
Journal:  Methods Mol Biol       Date:  2022

6.  Model for Prediction of Optimal Debulking of Epithelial Ovarian Cancer

Authors:  Maliheh Arab; Farzaneh Jamdar; Maryam Sadat Hosseini; Robabe Ghodssi- Ghasemabadi; Farah Farzaneh; Tahereh Ashrafganjoei
Journal:  Asian Pac J Cancer Prev       Date:  2018-05-26

7.  Predictive significance of preoperative CT findings for suboptimal cytoreduction in advanced ovarian cancer: a meta-analysis.

Authors:  Ting Wen Yi Hu; Dan Nie; Jin Hai Gou; Zheng Yu Li
Journal:  Cancer Manag Res       Date:  2018-07-16       Impact factor: 3.989

8.  A Prediction Model for Optimal Primary Debulking Surgery Based on Preoperative Computed Tomography Scans and Clinical Factors in Patients With Advanced Ovarian Cancer: A Multicenter Retrospective Cohort Study.

Authors:  Yu Gu; Meng Qin; Ying Jin; Jing Zuo; Ning Li; Ce Bian; Yu Zhang; Rong Li; Yu-Mei Wu; Chun-Yan Wang; Ke-Qiang Zhang; Ying Yue; Ling-Ying Wu; Ling-Ya Pan
Journal:  Front Oncol       Date:  2021-01-07       Impact factor: 6.244

9.  Preoperative Predictors of Optimal Tumor Resectability in Patients With Epithelial Ovarian Cancer.

Authors:  Kehinde S Okunade; Adaiah P Soibi-Harry; Benedetto Osunwusi; Ephraim Ohazurike; Sarah O John-Olabode; Adeyemi Okunowo; Garba Rimi; Omolola Salako; Muisi Adenekan; Rose Anorlu
Journal:  Cureus       Date:  2022-01-19

10.  High-Density Lipoprotein Cholesterol, Blood Urea Nitrogen, and Serum Creatinine Can Predict Severe Acute Pancreatitis.

Authors:  Wandong Hong; Suhan Lin; Maddalena Zippi; Wujun Geng; Simon Stock; Vincent Zimmer; Chunfang Xu; Mengtao Zhou
Journal:  Biomed Res Int       Date:  2017-08-22       Impact factor: 3.411

  10 in total

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