Prognostic Model
Prognostic Model - Related Videos
07:13
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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Cited by 1 •
2025
This study evaluates prognostic systems for colorectal signet-ring cell carcinoma patients using machine learning models and competing risk analyses. It identifies log odds of positive lymph nodes as a superior predictor compared to pN staging, demonstrating strong predictive performance and aiding clinical decision-making through robust survival prediction tools.
06:03
Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis
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Cited by 41 •
2020
Breast cancer related lymphedema is frequent in breast cancer survivors, but there are not widely employed guidelines for its diagnosis and quantification. Here, we introduce a reliable and cost-effective protocol to define, quantify, and compare upper limb volume in breast cancer patients.
08:08
Cell-based Assay Protocol for the Prognostic Prediction of Idiopathic Scoliosis Using Cellular Dielectric Spectroscopy
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Cited by 8 •
2013
A structured protocol is presented for a cell-based assay as a functional test to predict the prognosis of idiopathic scoliosis using cellular dielectric spectroscopy (CDS). The assay can be performed with fresh or frozen peripheral blood mononuclear cells (PBMCs) and the procedure is completed within 4 days.
12:31
In Vivo Modeling of the Morbid Human Genome using Danio rerio
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Cited by 66 •
2013
Here, we present a systematic approach for developing physiologically relevant, sensitive and specific in vivo assays for interpreting variation in human pathology. Transient genetic manipulation via microinjection of WT and mutant human mRNA and morpholino (MO) antisense oligonucleotides harness the tractability of the developing zebrafish embryo to rapidly assay pathogenic mutations, especially, but not exclusively, in the context of human developmental disorders.
05:16
Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
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Cited by 1 •
2025
This protocol presents R codes for performing restricted cubic spline multivariable Cox regression analysis to determine the cutoff value of the phase angle for prognosis in acute heart failure as well as codes for calculating statistics that measure the discriminatory ability of each model and evaluate its goodness of fit.
FAQs
The calculation links measured predictors with relationships observed in outcome data. Genetic variants, clinical characteristics, and molecular biomarkers can contribute jointly, allowing the model to estimate an individual’s risk or expected disease trajectory rather than relying on one measurement alone. This integrated approach helps researchers examine how different sources of biological and clinical information relate to progression, treatment response, or survival.
Inherited variation may provide information about genetic factors present across an individual’s biology, whereas tumor-associated variation can reflect characteristics of the disease itself. A model can examine either type in relation to progression, treatment response, or survival. Distinguishing these sources of variation helps frame whether the prediction concerns broader inherited risk or features associated with the tumor and its course.
These assessments address different properties of a prediction. Accuracy concerns how well estimated outcomes correspond to observed outcomes, while calibration examines whether predicted risk levels align appropriately with what occurs. Clinical relevance asks whether the information is meaningful for research or patient stratification. Considering all three prevents a model from being judged by predictive performance alone.
Validation in an appropriate population tests whether relationships learned from observed data remain useful for the people or groups being studied. Differences between the development population and the intended population can affect prediction quality and interpretation. This step helps determine whether estimates are sufficiently reliable, calibrated, and relevant for applications involving disease progression, treatment response, or survival.
A typical workflow identifies relevant predictors, including genetic variants, clinical characteristics, or molecular biomarkers, and relates them to observed outcomes. The resulting calculations generate risk estimates or expected trajectories for individuals or populations. Researchers then validate the model in appropriate populations and assess accuracy, calibration, and clinical relevance before using its results for stratification or study planning.
In genetics, these models can organize individuals according to predicted disease course, treatment response, or survival. That stratification can support comparisons among risk groups and help structure research studies. Predictions also contribute to personalized research by connecting inherited or tumor-associated variation with expected outcomes. Their usefulness depends on validation showing that the estimates are interpretable and relevant in the intended setting.