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Purpose To develop and demonstrate a novel tumor shell feature for predictingdistant failure in non-small cell lung cancer (NSCLC) and cervical cancer (CC)patients. Patients and Methods The shell predictive model was constructed usingpretreatment positron emission tomography (PET) images from 48 NSCLC patientsreceived stereotactic body radiation therapy (SBRT) and 52 CC patientsunderwent external beam radiation therapy and concurrent chemotherapy followedwith high-dose-rate intracavitary brachytherapy. A shell feature, consisting ofouter voxels around the tumor boundary, was extracted from a series of axialPET slices. The hypothesis behind this feature is that non-invasive andinvasive tumors may have different morphologic patterns in the tumor periphery,in turn reflecting the differences in radiological presentations in the PETimages. The utility of the shell was evaluated by the support vector machine(SVM) classifier in comparison with intensity, geometry, gray levelco-occurrence matrix (GLCM) based texture, neighborhood gray tone differencematrix (NGTDM) based texture, and a combination of these four features. Theresults were assessed in terms of accuracy, sensitivity, specificity, and thearea under the receiver operating curve (AUC). Results For NSCLC, the AUCachieved by the shell feature was 0.82 while the highest AUC achieved by theother features was 0.76. Similarly, for CC, the AUC achieved by the shellfeature 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 wassignificant (P < 0.005) in all cases. Conclusions We propose a boundary-basedshell feature that correlates with tumor metastasis. The shell feature showedbetter predictive performance than all the other features for distant failureprediction in both NSCLC and CC.

Shell feature: a new radiomics descriptor for predicting distant failure after radiotherapy in non-small cell lung cancer and cervix cancer