Automated Small Kidney Cancer Detection in Non-Contrast Computed
  Tomography
William McGough Thomas Buddenkotte Stephan Ursprung Zeyu Gao Grant Stewart Mireia Crispin-Ortuzar
This study introduces an automated pipeline for renal cancer (RC) detection
in non-contrast computed tomography (NCCT). In the development of our pipeline,
we test three detections models: a shape model, a 2D-, and a 3D axial-sample
model. Training (n=1348) and testing (n=64) data were gathered from open
sources (KiTS23, Abdomen1k, CT-ORG) and Cambridge University Hospital (CUH).
Results from cross-validation and testing revealed that the 2D axial sample
model had the highest small ($\leq$40mm diameter) RC detection area under the
curve (AUC) of 0.804. Our pipeline achieves 61.9\% sensitivity and 92.7\%
specificity for small kidney cancers on unseen test data. Our results are much
more accurate than previous attempts to automatically detect small renal
cancers in NCCT, the most likely imaging modality for RC screening. This
pipeline offers a promising advance that may enable screening for kidney
cancers.