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Objective To investigate the clinical application value of 18F-FDG PET-CT simulation localization in radiotherapy of recurrent abdominal and pelvic tumors. Methods 18F-FDG PET-CT was used to simulate positioning 38 patients with abdominal and pelvic tumors who relapsed after treatment.Based on both CT images and 18F-FDG PRT-CT, we drew up a systemic treatment plan and outlined the radiotherapy target area, and then compared the differences between the two methods. Results In 38 patients, 21.1%(3/8) of patients were found to have distal metastases outside the pelvic and abdominal cavity, and changed the systemic treatment plan.The radiotherapy target was altered in 34(89.5%) patients.The mean value of GTVPET-CT was 118.14cm3and the mean value of GTVCT was 148.53cm3(P=0.044). Conclusion For patients with recurrent abdominal and pelvic tumors, 18F-FDG PET-CT simulation localization treatment improves tumor re-staging, changes the integrated therapy for some patients, and makes the target area of radiotherapy more accurate.

The shape and position of abdominal and pelvic organs change greatly during radiotherapy, so image-guided radiation therapy (IGRT) is urgently needed. The world's first integrated CT-linac platform, equipped with fan beam CT (FBCT), can provide a diagnostic-quality FBCT for achieve adaptive radiotherapy (ART). However, CT scans will bring the risk of excessive scanning radiation dose. Reducing the tube current of the FBCT system can reduce the scanning dose, but it will lead to serious noise and artifacts in the reconstructed images. In this study, we proposed a deep learning method, Content-Noise Cycle-Consistent Generative Adversarial Network (CNCycle-GAN), to improve the image quality and CT value accuracy of low-dose FBCT images to meet the requirements of adaptive radiotherapy. We selected 76 patients with abdominal and pelvic tumors who received radiation therapy. The patients received one low-dose CT scan and one normal-dose CT scan in IGRT mode during different fractions of radiotherapy. The normal dose CT images (NDCT) and low dose CT images (LDCT) of 70 patients were used for network training, and the remaining 6 patients were used to validate the performance of the network. The quality of low-dose CT images after network restoration (RCT) were evaluated in three aspects: image quality, automatic delineation performance and dose calculation accuracy. Taking NDCT images as a reference, RCT images reduced MAE from 34.34 ± 5.91 to 20.25 ± 4.27, PSNR increased from 34.08 ± 1.49 to 37.23 ± 2.63, and SSIM increased from 0.92 ± 0.08 to 0.94 ± 0.07. The P value is less than 0.01 of the above performance indicators indicated that the difference were statistically significant. The Dice similarity coefficients (DCS) between the automatic delineation results of organs at risk such as bladder, femoral heads, and rectum on RCT and the results of manual delineation by doctors both reached 0.98. In terms of dose calculation accuracy, compared with the automatic planning based on LDCT, the difference in dose distribution between the automatic planning based on RCT and the automatic planning based on NDCT were smaller. Therefore, based on the integrated CT-linac platform, combined with deep learning technology, it provides clinical feasibility for the realization of low-dose FBCT adaptive radiotherapy for abdominal and pelvic tumors. [ABSTRACT FROM AUTHOR]

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