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Our findings demonstrate the potential impact of different segmentation methods for prognostic models using standardised implementations of radiomic features within clinical practice.
However, when using these methods for prognostic studies in prospective cohorts, it is important to note some cautions and limitations.
A multimodal approach combining several methods for prognostic evaluation, including neurological examination, electrophysiological studies and NSE measurements, should be used.
Genomics also has the potential to substitute several classical analyses since the correlation between gene expression data and classical analytical methods for prognostic breast cancer markers (estrogen and progesterone receptor and KI67 expression) is excellent (U. Pfeffer, unpublished observations).
We aimed to analyse and compare commonly employed single-gene and gene-set methods for prognostic classification alongside the more recent network-based approaches involving integration of PPI networks with gene expression information [ 8, 11].
Similar(55)
We presented a new supervised prediction method for prognostic applications.
This method could provide a cost-effective method for prognostic subclassification of luminal/HR+ BC in routine clinical practice.
The method described could provide a cost-effective method for prognostic subclassification of luminal/hormone receptor-positive breast cancer in routine clinical practice.
However several authors have highlighted the limitations of this method for prognostic models, [ 21, 24] particularly relating to the appropriate modelling of the baseline hazards function.
Further research focused on explants and imaging in a prospective study may clarify the robustness of this method for prognostic use in bDMARD treatment.
Moreover, considerable debate surrounds the best methods for disclosing prognostic information as patients vary in their preferences for the framing and presentation of information.
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