Objective: To investigate the expression of mage-a mRNA in lung cancer tissues of mice induced by coal tar pitch(CTP) fume and to discuss the above model for lung cancer immunotherapy with mage-a. Methods: Tumor tissue samples of lung cancer and paired non-tumor tissues were obtained from 8 lung cancer mice. Total RNA was extracted and cDNA was synthesized. Nested polymerase chain reaction amplification using mage-a specific primer was performed to detect the expression of mage-a. The 2 clones of mage-a mRNA positive PCR products were sequenced by DNAs sequencer (PE-377). Results: Of 29 mice in the experimental group, 8 were induced to lung cancer.Among which 5 (5/8) expressed mage-a mRNA. The expression of mage-a gene was not found in adjacent lung tissues. The DNA sequencing confirmed that the target gene fragments in 2 samples of PCR products were mage-a cDNA. Conclusion: The mage-a gene is highly expressed in lung cancer in mice induced by CTP fume, suggesting that CTP-induced lung cancer in mice may be an ideal animal model for lung cancer therapeutic experiment with MAGE-A.

Objective To evaluate the application of three deep learning algorithms in automatic segmentation of clinical target volumes (CTVs) in high-dose-rate brachytherapy after surgery for endometrial carcinoma. Methods A dataset comprising computed tomography scans from 306 post-surgery patients with endometrial carcinoma was divided into three subsets: 246 cases for training, 30 cases for validation, and 30 cases for testing. Three deep convolutional neural network models, 3D U-Net, 3D Res U-Net, and V-Net, were compared for CTV segmentation. Several commonly used quantitative metrics were employed, i.e., Dice similarity coefficient, Hausdorff distance, 95th percentile of Hausdorff distance, and Intersection over Union. Results During the testing phase, CTV segmentation with 3D U-Net, 3D Res U-Net, and V-Net showed a mean Dice similarity coefficient of 0.90 ± 0.07, 0.95 ± 0.06, and 0.95 ± 0.06, a mean Hausdorff distance of 2.51 ± 1.70, 0.96 ± 1.01, and 0.98 ± 0.95 mm, a mean 95th percentile of Hausdorff distance of 1.33 ± 1.02, 0.65 ± 0.91, and 0.40 ± 0.72 mm, and a mean Intersection over Union of 0.85 ± 0.11, 0.91 ± 0.09, and 0.92 ± 0.09, respectively. Segmentation based on V-Net was similarly to that performed by experienced radiation oncologists. The CTV segmentation time was < 3.2 s, which could save the work time of clinicians. Conclusion V-Net is better than other models in CTV segmentation as indicated by quantitative metrics and clinician assessment. Additionally, the method is highly consistent with the ground truth, reducing inter-doctor variability and treatment time.