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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 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 ...
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 ...
Cancer slope factors (CSF) are used to estimate the risk of cancer associated with exposure to a carcinogenic or potentially carcinogenic substance. A slope factor is an upper bound, approximating a 95% confidence limit, on the increased cancer risk from a lifetime exposure to an agent by ingestion or inhalation.
For individual men, the discriminatory accuracy [5] for colon cancer was 0.71 and for pancreatic cancer was 0.72. These values exceed the performance of many other cancer risk prediction tools. [6] [7] The approach used to calculate cancer risks in Your Disease. Risk is also used to calculate the risks of the other diseases. [8]
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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