![]() Regardless of the target, the challenges in creating such an algorithm are similar. Risk stratification could also mean predicting whether a distant metastasis will occur or how long a patient is likely to live. Researchers at Georgia State University have developed an algorithm to predict the risk of local recurrence of DCIS within ten years using digitized whole slide images. Many do not become invasive - but which ones will? There is a great deal of inter-observer variability amongst pathologists assessing such lesions. Take ductal carcinoma in situ (DCIS) as an example - a pre-invasive form of breast cancer. Prognostics refers to the likely outcome for a patient following the standard treatment. However, improving prognostic insights is an active area of research. ![]() Risk stratification can already be done using cancer staging, molecular features, or clinical variables. Algorithms must decipher large whole slide images without any prior knowledge of which regions of tissue or characteristics of its appearance are important. ![]() But biomarkers and outcomes are more complex. Simpler tasks rely upon pathologists’ annotations of specific features in the tissue. From detecting and classifying cells and tissue to predicting biomarkers and patient outcomes. Advances in AI applying deep learning to digital pathology images can stratify patients by risk.Ĭomputational pathology applications using artificial intelligence are becoming increasingly complex.
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