  Spiculations are important predictors of lung cancer malignancy, which are
spikes on the surface of the pulmonary nodules. In this study, we proposed an
interpretable and parameter-free technique to quantify the spiculation using
area distortion metric obtained by the conformal (angle-preserving) spherical
parameterization. We exploit the insight that for an angle-preserved spherical
mapping of a given nodule, the corresponding negative area distortion precisely
characterizes the spiculations on that nodule. We introduced novel spiculation
scores based on the area distortion metric and spiculation measures. We also
semi-automatically segment lung nodule (for reproducibility) as well as vessel
and wall attachment to differentiate the real spiculations from lobulation and
attachment. A simple pathological malignancy prediction model is also
introduced. We used the publicly-available LIDC-IDRI dataset pathologists
(strong-label) and radiologists (weak-label) ratings to train and test
radiomics models containing this feature, and then externally validate the
models. We achieved AUC$=$0.80 and 0.76, respectively, with the models trained
on the 811 weakly-labeled LIDC datasets and tested on the 72 strongly-labeled
LIDC and 73 LUNGx datasets; the previous best model for LUNGx had AUC$=$0.68.
The number-of-spiculations feature was found to be highly correlated
(Spearman's rank correlation coefficient $\rho = 0.44$) with the radiologists'
spiculation score. We developed a reproducible and interpretable,
parameter-free technique for quantifying spiculations on nodules. The
spiculation quantification measures was then applied to the radiomics framework
for pathological malignancy prediction with reproducible semi-automatic
segmentation of nodule. Using our interpretable features (size, attachment,
spiculation, lobulation), we were able to achieve higher performance than
previous models.
