What's the False Positive Rate?
TP = ... flags as true and it is
FN = ... flags as false but it is true
TN = ... flags as false and it is false
To turn these into rates, you normalize them.
e.g. TPR = TP/P = TP/(TP + FN) = 1 - FNR
etc.
These are characteristics of a classification system
You will also hear sensitivity (TPR) vs. specificity (TNR) often, particularly in medical contexts. In other contexts you'll hear Type I (FP) vs. Type II (FN) error.
In most cases you a set of trade offs in your algorithm, and will need to pick a balance between sensitivity and specificity.
c.f. ROC: https://en.wikipedia.org/wiki/Receiver_operating_characteris...
For the malignancy prediction objective, the algorithm obtained an area under the receiver operating characteristic curve (AUC) of 0.91 (95% CI: 0.89, 0.93), with specificity of 77.3% (95% CI: 69.2%, 85.4%) at a sensitivity of 87%.
I haven't read the papers methods, but the data set size is small-ish for this sort of analysis.
AUC is 0.78
Sensitivity-Specificity Graph is here: http://images.rsna.org/index.html?doi=10.1148/radiol.2019182...