Literature DB >> 30967418

Predictors of Long-Term Survival among High-Grade Serous Ovarian Cancer Patients.

Christina L Clarke1, Lawrence H Kushi2, Jessica Chubak3, Pamala A Pawloski4, Joanna E Bulkley5, Mara M Epstein6,7, Andrea N Burnett-Hartman8, Bethan Powell9, Celeste L Pearce10, Heather Spencer Feigelson8.   

Abstract

BACKGROUND: Relatively little is known about factors associated with long-term survival (LTS) following a diagnosis of ovarian cancer.
METHODS: We conducted a retrospective study of high-grade serous ovarian cancer (HGSOC) to explore predictors of LTS (defined as ≥7 years of survival) using electronic medical record data from a network of integrated health care systems. Multivariable logistic regression with forward selection was used to compare characteristics of women who survived ≥7 years after diagnosis (n = 148) to those who died within 7 years of diagnosis (n = 494).
RESULTS: Our final model included study site, age, stage at diagnosis, CA-125, comorbidity score, receipt of chemotherapy, BMI, and four separate comorbid conditions: weight loss, depression, hypothyroidism, and liver disease. Of these, only younger age, lower stage, and depression were statistically significantly associated with LTS.
CONCLUSIONS: We did not identify any new characteristics associated with HGSOC survival. IMPACT: Prognosis of ovarian cancer generally remains poor. Large, pooled studies of ovarian cancer are needed to identify characteristics that may improve survival. ©2019 American Association for Cancer Research.

Entities:  

Year:  2019        PMID: 30967418      PMCID: PMC6500478          DOI: 10.1158/1055-9965.EPI-18-1324

Source DB:  PubMed          Journal:  Cancer Epidemiol Biomarkers Prev        ISSN: 1055-9965            Impact factor:   4.254


  7 in total

1.  The Cancer Research Network: a platform for epidemiologic and health services research on cancer prevention, care, and outcomes in large, stable populations.

Authors:  Jessica Chubak; Rebecca Ziebell; Robert T Greenlee; Stacey Honda; Mark C Hornbrook; Mara Epstein; Larissa Nekhlyudov; Pamala A Pawloski; Debra P Ritzwoller; Nirupa R Ghai; Heather Spencer Feigelson; Heather A Clancy; V Paul Doria-Rose; Lawrence H Kushi
Journal:  Cancer Causes Control       Date:  2016-09-17       Impact factor: 2.506

2.  Comorbidity measures for use with administrative data.

Authors:  A Elixhauser; C Steiner; D R Harris; R M Coffey
Journal:  Med Care       Date:  1998-01       Impact factor: 2.983

Review 3.  Who are the long-term survivors of high grade serous ovarian cancer?

Authors:  Claire Hoppenot; Mark A Eckert; Samantha M Tienda; Ernst Lengyel
Journal:  Gynecol Oncol       Date:  2017-11-08       Impact factor: 5.482

4.  Characteristics of Long-Term Survivors of Epithelial Ovarian Cancer.

Authors:  Rosemary D Cress; Yingjia S Chen; Cyllene R Morris; Megan Petersen; Gary S Leiserowitz
Journal:  Obstet Gynecol       Date:  2015-09       Impact factor: 7.661

5.  Clinicopathologic characteristics associated with long-term survival in advanced epithelial ovarian cancer: an NRG Oncology/Gynecologic Oncology Group ancillary data study.

Authors:  C A Hamilton; A Miller; Y Casablanca; N S Horowitz; B Rungruang; T C Krivak; S D Richard; N Rodriguez; M J Birrer; F J Backes; M A Geller; M Quinn; M J Goodheart; D G Mutch; J J Kavanagh; G L Maxwell; M A Bookman
Journal:  Gynecol Oncol       Date:  2017-11-28       Impact factor: 5.482

6.  The HMO Research Network Virtual Data Warehouse: A Public Data Model to Support Collaboration.

Authors:  Tyler R Ross; Daniel Ng; Jeffrey S Brown; Roy Pardee; Mark C Hornbrook; Gene Hart; John F Steiner
Journal:  EGEMS (Wash DC)       Date:  2014-03-24

7.  History of Comorbidities and Survival of Ovarian Cancer Patients, Results from the Ovarian Cancer Association Consortium.

Authors:  Albina N Minlikeeva; Jo L Freudenheim; Kevin H Eng; Rikki A Cannioto; Grace Friel; J Brian Szender; Brahm Segal; Kunle Odunsi; Paul Mayor; Brenda Diergaarde; Emese Zsiros; Linda E Kelemen; Martin Köbel; Helen Steed; Anna deFazio; Susan J Jordan; Peter A Fasching; Matthias W Beckmann; Harvey A Risch; Mary Anne Rossing; Jennifer A Doherty; Jenny Chang-Claude; Marc T Goodman; Thilo Dörk; Robert Edwards; Francesmary Modugno; Roberta B Ness; Keitaro Matsuo; Mika Mizuno; Beth Y Karlan; Ellen L Goode; Susanne K Kjær; Estrid Høgdall; Joellen M Schildkraut; Kathryn L Terry; Daniel W Cramer; Elisa V Bandera; Lisa E Paddock; Lambertus A Kiemeney; Leon F A G Massuger; Rebecca Sutphen; Hoda Anton-Culver; Argyrios Ziogas; Usha Menon; Simon A Gayther; Susan J Ramus; Aleksandra Gentry-Maharaj; Celeste L Pearce; Anna H Wu; Jolanta Kupryjanczyk; Allan Jensen; Penelope M Webb; Kirsten B Moysich
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2017-09       Impact factor: 4.090

  7 in total
  5 in total

1.  Programmed Death-1 Receptor (PD-1) as a Potential Prognosis Biomarker for Ovarian Cancer Patients.

Authors:  Anna Pawłowska; Dorota Suszczyk; Rafał Tarkowski; Roman Paduch; Jan Kotarski; Iwona Wertel
Journal:  Cancer Manag Res       Date:  2020-10-07       Impact factor: 3.989

2.  Predictors of survival trajectories among women with epithelial ovarian cancer.

Authors:  Lauren C Peres; Sweta Sinha; Mary K Townsend; Brooke L Fridley; Beth Y Karlan; Susan K Lutgendorf; Eileen Shinn; Anil K Sood; Shelley S Tworoger
Journal:  Gynecol Oncol       Date:  2019-12-12       Impact factor: 5.482

3.  Long-Term Survival Among Histological Subtypes in Advanced Epithelial Ovarian Cancer: Population-Based Study Using the Surveillance, Epidemiology, and End Results Database.

Authors:  Shi-Ping Yang; Hui-Luan Su; Xiu-Bei Chen; Li Hua; Jian-Xian Chen; Min Hu; Jian Lei; San-Gang Wu; Juan Zhou
Journal:  JMIR Public Health Surveill       Date:  2021-11-17

4.  Ovarian cancer survival by stage, histotype, and pre-diagnostic lifestyle factors, in the prospective UK Million Women Study.

Authors:  Kezia Gaitskell; Carol Hermon; Isobel Barnes; Kirstin Pirie; Sarah Floud; Jane Green; Valerie Beral; Gillian K Reeves
Journal:  Cancer Epidemiol       Date:  2021-12-20       Impact factor: 2.984

5.  Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone.

Authors:  Anna Ray Laury; Sami Blom; Tuomas Ropponen; Anni Virtanen; Olli Mikael Carpén
Journal:  Sci Rep       Date:  2021-09-27       Impact factor: 4.379

  5 in total

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