  Spiculations/lobulations, sharp/curved spikes on the surface of lung nodules,
are good predictors of lung cancer malignancy and hence, are routinely assessed
and reported by radiologists as part of the standardized Lung-RADS clinical
scoring criteria. Given the 3D geometry of the nodule and 2D slice-by-slice
assessment by radiologists, manual spiculation/lobulation annotation is a
tedious task and thus no public datasets exist to date for probing the
importance of these clinically-reported features in the SOTA malignancy
prediction algorithms. As part of this paper, we release a large-scale
Clinically-Interpretable Radiomics Dataset, CIRDataset, containing 956
radiologist QA/QC'ed spiculation/lobulation annotations on segmented lung
nodules from two public datasets, LIDC-IDRI (N=883) and LUNGx (N=73). We also
present an end-to-end deep learning model based on multi-class Voxel2Mesh
extension to segment nodules (while preserving spikes), classify spikes
(sharp/spiculation and curved/lobulation), and perform malignancy prediction.
Previous methods have performed malignancy prediction for LIDC and LUNGx
datasets but without robust attribution to any clinically reported/actionable
features (due to known hyperparameter sensitivity issues with general
attribution schemes). With the release of this comprehensively-annotated
CIRDataset and end-to-end deep learning baseline, we hope that malignancy
prediction methods can validate their explanations, benchmark against our
baseline, and provide clinically-actionable insights. Dataset, code, pretrained
models, and docker containers are available at
https://github.com/nadeemlab/CIR.
