Ad 1) and 2) I'm afraid I don't have a definitive answer for you here. My only advise is to check the examples, and to see what's used in literature. As to measuring the quality of the extraction, that really depends on what you want to measure.
Basically, all settings are 'correct' in the way that they describe you texture in a certain way. Still, some settings will lead to more robust values and some settings may produce values that will yield higher accuracy models.
As to measuring robustness, an ICC is usually used. For predictive power, you need to develop and validate your model.
ad 3) Not exactly. The output of PyRadiomics consists of all input columns + diagnostic features (starting with `diagnostic_`) + feature values (named as `<filter>_<class>_<name>`). `original_` prefix only indicates features extracted from non-filtered images.
Regards,
Joost
Op dinsdag 8 oktober 2019 12:59:25 UTC+2 schreef biobob: