Prognostication of life expectancy is of utmost importance to patients, families, and oncologists, particularly in the advanced cancer setting. Accurate prognostication is essential for oncologists in formulating their recommendations. Patients’ predicted survival can have a profound impact on important decisions, such as palliative care and hospice referral, initiation of specific medications, and avoidance of aggressive therapies. As cancer patients progress over the course of their illness, knowing what to expect can also provide them with a sense of control and facilitate the process of advance care planning. 1 Clinicians have been consistently found to overestimate survival. 2 Therefore, prognostication tools are needed to enhance the accuracy of survival predictions. Traditional prognosis for cancer patients is primarily based on tumor-related factors, specifically disease burden and aggressiveness as indicated by various clinical, imaging, laboratory, pathologic, and molecular features. Prognostic models consisting of various combinations of these factors have been validated to provide highly accurate estimates of long-term survival for specific cancer types, allowing oncologists to provide risk-adapted treatment plans and to inform patients of their general prognosis. However, they tend to be less precise when it comes to predicting short-term outcomes in advanced states of disease, limiting their utility in day-to-day clinical decision making for patients with progressive cancer. To obtain a more accurate estimate of short-term prognosis, newer models have incorporated various patient-related prognostic factors, such as performance status, comorbidities, and physiologic parameters. While a number of prognostic models are now available for advanced cancer patients 3,4 and far advanced cancer patients, 5,6 there are significant barriers preventing their use in routine practice. These include limited applicability due to complexity of the model, restricted generalizability secondary to poor patient selection for the derivation cohort, and overall limited accuracy.
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