Development and verification of radiomics framework for computed tomography image segmentation

BACKGROUND: Radiomics has been considered an imaging marker for capturing quantitative image information (QII). The introduction of radiomics to image segmentation is desirable but challenging. PURPOSE: This study aims to develop and validate a radiomics‐based framework for image segmentation (RFIS). METHODS: RFIS is designed using features extracted from volume (svfeatures) created by sliding window (swvolume). The 53 svfeatures are extracted from 11 phantom series. Outliers in the svfeature datasets are detected by isolation forest (iForest) and specified as the mean value. The percentage coefficient of variation (%COV) is calculated to evaluate the reproducibility of svfeatures. RFIS is constructed and applied to the gross target volume (GTV) segmentation from the peritumoral region (GTV with a 10 mm margin) to assess its feasibility. The 127 lung cancer images are enrolled. The test–retest method, correlation matrix, and Mann–Whitney U test (p < 0.05) are used to select non‐redundant svfeatures of statistical significance from the reproducible svfeatures. The synthetic minority over‐sampling technique is utilized to balance the minority group in the training sets. The support vector machine is employed for RFIS construction, which is tuned in the training set using 10‐fold stratified cross‐validation and then evaluated in the test sets. The swvolumes with the consistent classification results are grouped and merged. Mode filtering is performed to remove very small subvolumes and create relatively large regions of completely uniform character. In addition, RFIS performance is evaluated by the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, specificity, and Dice similarity coefficient (DSC). RESULTS: 30249 phantom and 145008 patient image swvolumes were analyzed. Forty‐nine (92.45% of 53) svfeatures represented excellent reproducibility(%COV<15). Forty‐five features (91.84% of 49) included five categories that passed test‐retest analysis. Thirteen svfeatures (28.89% of 45) svfeatures were selected for RFIS construction. RFIS showed an average (95% confidence interval) sensitivity of 0.848 (95% CI:0.844–0.883), a specificity of 0.821 (95% CI: 0.818–0.825), an accuracy of 83.48% (95% CI: 83.27%–83.70%), and an AUC of 0.906 (95% CI: 0.904–0.908) with cross‐validation. The sensitivity, specificity, accuracy, and AUC were equal to 0.762 (95% CI: 0.754–0.770), 0.840 (95% CI: 0.837–0.844), 82.29% (95% CI: 81.90%–82.60%), and 0.877 (95% CI: 0.873–0.881) in the test set, respectively. GTV was segmented by grouping and merging swvolume with identical classification results. The mean DSC after mode filtering was 0.707 ± 0.093 in the training sets and 0.688 ± 0.072 in the test sets. CONCLUSION: Reproducible svfeatures can capture the differences in QII among swvolumes. RFIS can be applied to swvolume classification, which achieves image segmentation by grouping and merging the swvolume with similar QII.