1 Leiomyosarcoma: The exploration of prognostic factors 2 for risk using a stratification model60 Introduction 61 Leiomyosarcoma is one of the most common forms of soft tissue sarcoma, accounting 5-10% of all 62 soft tissue sarcomas1-3. It can occur in a variety of different sites in the ...
We have developed a radiomic machine learning model for malaria risk stratification in asymptomatic children aged 5 to 15 years in endemic areas of sub-Saharan Africa, using photos of easily accessible peripheral tissue (the inner eyelid) and overcoming the limitations of camera resolution. The use ...
Recent evidence showed that CMR as a noninvasive imaging tool plays an important role in the risk stratification of patients with suspected myocarditis. To modify recommendations regarding sport behavior in physically active individuals with myocarditis, more evidence, based on large multicenter registries...
To modify these risk factors and make them actionable, Pengetnze pointed out that a risk stratification approach must combine risk profiles effectively for each patient. “Our strategy is always to build a model that takes into consideration different risk factors from different walks of life...
Therefore, predictive risk stratification models play an important role in clinical decision making. Determining whether a given predictive model is suitable for clinical use usually involves evaluating the model’s performance on large patient datasets using standard statistical measures of success (e.g....
3,4 The USPSTF reviewed evidence from randomized trials, observational studies, and diagnostic accuracy studies of risk stratification models in women without preexisting breast cancer or DCIS. Studies that included women with pathogenic BRCA1/2 genetic mutations were included in the review criteria; ...
The last decade has seen the development and refinement of predictive risk stratification models in the management of prostate cancer (CaP) (Parekh et al, 2006). These tools are validated for clinical decision-making and facilitating informed patient consent to treatment. These tools aim to apply ...
According to the risk stratification labelling described in the statistical analysis section, there were two low-risk clusters: the first low-risk cluster (n = 2424, 62.15% of the sample) and the second low-risk cluster (n = 351, 9% of the sample). The third cluster (n =...
On the basis of these results, the overall C-Lung-RADS pipeline for the four-category risk stratification of nodules was developed, which integrated the classification tree in phase 1, the DCNN model in phase 2 and the gradient-boosting regression (GBR) model in phase 2+, executed ...
The genes identified through variable reduction were combined with the PERSEVERE-based mortality probability/mortality risk to derive a risk stratification model for 28-day mortality using Classification and Regression Tree methodology (n=307). The derived tree, PERSEVERE-XP, was then tested in a ...