Automatic Segmentation Using Deep Learning for Online Dose Optimization During Adaptive Radiotherapy of Cervical Cancer

Abstract

A phase II clinical trial was designed to propose daily online dose optimization to eliminate need for ITV expansions in order to reduce toxicity events of EBRT for cervical cancer. This study investigates deep learning models for automatic segmentation. The cervix-uterus, vagina, bladder, rectum, sigmoid, femoral heads, kidneys, spinal cord, and bowel bag were delineated on 223 CT scans from 157 patients. The data were divided into 149 training, 36 validation and 38 test CTs. Two models were investigated: the multiclass 2D DeepLabV3+ and the 2-steps 3D U-net of RayStation. The 2D model was trained with a window optimizer of 3 channels, triangular cyclic learning rate, no dropout and no augmentation. The 3D model was trained with batch normalization, data augmentation (translation, rotation and deformation) and no dropout. Both models were trained with the Adam optimizer. Three IMRT plans were generated on the planning CT of 20 unique cases from the test set using the manual, 2D or 3D model contours following the EMBRACE II constraints with reduced PTVT/N margins of 5/3 mm. Both models were compared with manual contours on the validation and test sets using the Dice coefficient (DC). Using manual contours as reference, clinical dose indices were extracted on the manual, 2D and 3D model optimized doses for comparison. The mean (min – max) of the average DC of all organs on the validation and test sets were 0.86 (0.61 – 0.93) and 0.85 (0.67 – 0.93) for the 2D model and 0.84 (0.70 – 0.93) and 0.82 (0.65 – 0.92) for the 3D model. The sigmoid and vagina were the most challenging organs with a DC range of 0.61 – 0.72 for both models on the validation and test sets. The 2D model provided better DC for the spinal and bowel on all cases (p< 0.001). The 3D model provided a significantly better DC for the sigmoid on the validation set (p = 0.001), otherwise no difference was found. Removing the spinal and bowel from analysis, the 3D model provided a superior DC for 60% of all cases. The mean (min – max) DC of the CTVT on the 20 test cases were 0.89 (0.75 – 0.93) and 0.88 (0.69 – 0.95) for the 2D and 3D models. Using the manual CTVT on the 2D and 3D model optimized doses, 90% and 80% passed the V42.75Gy>98% (p = 0.07). The remaining 10% and 20% cases had a mean V42.75Gy of 95.6% for 2D and 93.7% for 3D model. The V42.75Gy decrease of the 3D model was significantly correlated with the DC of the CTVT and PTVT (R2 = 0.56 and R2 = 0.5 p<0.001). A decrease of 0.1 in DC was equivalent to a decrease of 3.1% and 5.5% of the V42.75Gy for the CTVT and PTVT. For all organs, 91%, 80%, and 80% of clinical constraints were met for the manual, 2D and 3D model optimized doses. The 2D model showed better robustness while the 3D model provided better representation of complex organ shape. Deep learning model-based segmentation is promising for implementing reduced margin online dose optimization during EBRT of cervical cancer.