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The Nottingham prognostic index (NPI) is used to determine prognosis following surgery for breast cancer. [1] [2] Its value is calculated using three pathological criteria: the size of the tumour; the number of involved lymph nodes; and the grade of the tumour. [1] It is calculated to select patients for adjuvant treatment.
The International Prognostic Index (IPI) is a clinical tool developed by oncologists to aid in predicting the prognosis of patients with aggressive non-Hodgkin's lymphoma. Previous to IPI's development, the primary consideration in assessing prognosis was the Ann Arbor stage alone, but this was increasingly found to be an inadequate means of ...
The result is a lifetime risk and a five-year risk based on factors that have been tied to a higher risk of breast cancer. For comparison, it also gives an average risk for U.S. women of the same ...
The site began in 1998 as a pen and paper questionnaire called the Harvard Cancer Risk Index. [2] In January 2000, The Harvard Cancer Risk Index developed into an online assessment and was renamed Your Cancer Risk, and offered assessments for four cancers: breast, colon, lung, and prostate. Six months later, eight additional cancers were added. [3]
The index was developed by Mary Charlson and colleagues in 1987, but the methodology has been adapted several times since then based on the findings of additional studies. [5] Many variations of the Charlson comorbidity index have been presented, including the Charlson/Deyo, Charlson/Romano, Charlson/Manitoba, and Charlson/D'Hoores comorbidity ...
A widely recognized method of estimating the risk of malignant ovarian cancer is the risk of malignancy index (RMI), calculated based on an initial workup. [28] [62] An RMI score of over 200 or 250 is generally felt to indicate high risk for ovarian cancer. [28] [31] The RMI is calculated as:
Continuous Individualized Risk Index (CIRI) (initialism pronounced /ˈsɪri/) is to a set of probabilistic risk models [1] utilizing Bayesian statistics for integrating diverse cancer biomarkers over time to produce a unified prediction of outcome risk, as originally described by Kurtz, Esfahani, et al. (2019) [2] [3] [4] from Ash Alizadeh's laboratory at Stanford.
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