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High-grade prostatic intraepithelial neoplasia (HGPIN) is an abnormality of prostatic glands and believed to precede the development of prostate adenocarcinoma (the most common form of prostate cancer). [1] [2] It may be referred to simply as prostatic intraepithelial neoplasia (PIN).
However, high magnification (right image) shows the key feature of prominent nucleoli (visible at 200x magnification to make the diagnosis of "high-grade"), as well as other typical features of HGPIN. Reference for features: - Margaret Sanders, M.B.B.Ch., Murali Varma, M.B.B.S.. High grade prostatic intraepithelial neoplasia (HGPIN).
Two aspects of the patient's state may be reported. The first aspect is the patient's current state, which may be reported as "good" or "serious," for instance. Second, the patient's short-term prognosis may be reported. Examples include that the patient is improving or getting worse.
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 ...
Source: Social Security Administration. The projected 2025 COLA for Social Security is 2.5%, according to an emailed September 11 TSCL press release, resulting in another drop.
On a subsequent biopsy, given the diagnosis of ASAP, the chance of finding prostate adenocarcinoma is approximately 40%; this is higher than if there is high-grade prostatic intraepithelial neoplasia (HGPIN). [2]
Prognosis (Greek: πρόγνωσις "fore-knowing, foreseeing"; pl.: prognoses) is a medical term for predicting the likelihood or expected development of a disease, including whether the signs and symptoms will improve or worsen (and how quickly) or remain stable over time; expectations of quality of life, such as the ability to carry out daily activities; the potential for complications and ...
Data-driven prognostics usually use pattern recognition and machine learning techniques to detect changes in system states. [3] The classical data-driven methods for nonlinear system prediction include the use of stochastic models such as the autoregressive (AR) model, the threshold AR model, the bilinear model, the projection pursuit, the multivariate adaptive regression splines, and the ...