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  • Oxford University Press (OUP)  (2)
  • 2020-2024  (2)
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  • Oxford University Press (OUP)  (2)
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  • 2020-2024  (2)
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  • 1
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2024
    In:  JNCI: Journal of the National Cancer Institute ( 2024-02-23)
    In: JNCI: Journal of the National Cancer Institute, Oxford University Press (OUP), ( 2024-02-23)
    Abstract: Research on diet quality and ovarian cancer is limited. We examined the association between diet quality and ovarian cancer risk and survival in a large prospective cohort. Methods We utilized data from women in the prospective NIH-AARP Diet and Health Study enrolled from 1995-1996 and were 50-71 years old at baseline with follow-up through 12/31/2017. Participants completed a 124-item Food Frequency Questionnaire at baseline and diet quality was assessed via the Healthy Eating Index-2015 (HEI-2015), the alternate Mediterranean diet score (aMED), and the Dietary Approaches to Stop Hypertension score (DASH). Primary outcomes were first primary epithelial ovarian cancer diagnosis from cancer registry data, and among those diagnosed with ovarian cancer all-cause mortality. We used a semi-Markov multi-state model with Cox proportional hazards regression to account for semi-competing events. Results Among 150,643 participants with a median follow-up time of 20.5 years, 1,107 individuals were diagnosed with a first primary epithelial ovarian cancer. There was no evidence of an association between diet quality and ovarian cancer risk. Among those diagnosed with epithelial ovarian cancer, 893 deaths occurred with a median survival of 2.5 years. Better pre-diagnosis diet quality, according to the HEI-2015 (Quintile 5 vs Quintile 1 HR = 0.75 [0.60-0.93]) and aMED (Quintile 5 vs Quintile 1: HR = 0.68, [0.53-0.87] ) was associated with lower all-cause mortality. There was no evidence of an association between DASH and all-cause mortality. Conclusions Better pre-diagnosis diet quality was associated with lower all-cause mortality after ovarian cancer diagnosis, but was not associated with ovarian cancer risk.
    Type of Medium: Online Resource
    ISSN: 0027-8874 , 1460-2105
    RVK:
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2024
    detail.hit.zdb_id: 2992-0
    detail.hit.zdb_id: 1465951-7
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  • 2
    In: American Journal of Clinical Pathology, Oxford University Press (OUP), Vol. 156, No. 6 ( 2021-11-08), p. 1142-1148
    Abstract: Chronic myelogenous leukemia (CML) is a clonal stem cell disorder accounting for 15% of adult leukemias. We aimed to determine if machine learning models could predict CML using blood cell counts prior to diagnosis. Methods We identified patients with a diagnostic test for CML (BCR-ABL1) and at least 6 consecutive prior years of differential blood cell counts between 1999 and 2020 in the largest integrated health care system in the United States. Blood cell counts from different time periods prior to CML diagnostic testing were used to train, validate, and test machine learning models. Results The sample included 1,623 patients with BCR-ABL1 positivity rate 6.2%. The predictive ability of machine learning models improved when trained with blood cell counts closer to time of diagnosis: 2 to 5 years area under the curve (AUC), 0.59 to 0.67, 0.5 to 1 years AUC, 0.75 to 0.80, at diagnosis AUC, 0.87 to 0.92. Conclusions Blood cell counts collected up to 5 years prior to diagnostic workup of CML successfully predicted the BCR-ABL1 test result. These findings suggest a machine learning model trained with blood cell counts could lead to diagnosis of CML earlier in the disease course compared to usual medical care.
    Type of Medium: Online Resource
    ISSN: 0002-9173 , 1943-7722
    RVK:
    RVK:
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2039921-2
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