  Purpose To develop and demonstrate a novel tumor shell feature for predicting
distant failure in non-small cell lung cancer (NSCLC) and cervical cancer (CC)
patients. Patients and Methods The shell predictive model was constructed using
pretreatment positron emission tomography (PET) images from 48 NSCLC patients
received stereotactic body radiation therapy (SBRT) and 52 CC patients
underwent external beam radiation therapy and concurrent chemotherapy followed
with high-dose-rate intracavitary brachytherapy. A shell feature, consisting of
outer voxels around the tumor boundary, was extracted from a series of axial
PET slices. The hypothesis behind this feature is that non-invasive and
invasive tumors may have different morphologic patterns in the tumor periphery,
in turn reflecting the differences in radiological presentations in the PET
images. The utility of the shell was evaluated by the support vector machine
(SVM) classifier in comparison with intensity, geometry, gray level
co-occurrence matrix (GLCM) based texture, neighborhood gray tone difference
matrix (NGTDM) based texture, and a combination of these four features. The
results were assessed in terms of accuracy, sensitivity, specificity, and the
area under the receiver operating curve (AUC). Results For NSCLC, the AUC
achieved by the shell feature was 0.82 while the highest AUC achieved by the
other features was 0.76. Similarly, for CC, the AUC achieved by the shell
feature was 0.83 while the highest AUC achieved by the other features was 0.76.
Also, the difference in performance between shell and the other features was
significant (P < 0.005) in all cases. Conclusions We propose a boundary-based
shell feature that correlates with tumor metastasis. The shell feature showed
better predictive performance than all the other features for distant failure
prediction in both NSCLC and CC.
