Evaluation of short-term gastrointestinal motion and its impact on dosimetric parameters in stereotactic body radiation therapy for pancreatic cancer.

Background: The aim of this study is to quantify the short-term motion of the gastrointestinal tract (GI-tract) and its impact on dosimetric parameters in stereotactic body radiation therapy (SBRT) for pancreatic cancer. Methods: The analyzed patients were eleven pancreatic cancer patients treated with SBRT or proton beam therapy. To ensure a fair analysis, the simulation SBRT plan was generated on the planning CT in all patients with the dose prescription of 40 Gy in 5 fractions. The GI-tract motion (stomach, duodenum, small and large intestine) was evaluated using three CT images scanned at spontaneous expiration. After fiducial-based rigid image registration, the contours in each CT image were generated and transferred to the planning CT, then the organ motion was evaluated. Planning at risk volumes (PRV) of each GI-tract were generated by adding 5 mm margins, and the volume receiving at least 33 Gy (V33) < 0.5 cm3 was evaluated as the dose constraint. Results: The median interval between the first and last CT scans was 736 s (interquartile range, IQR:624-986). To compensate for the GI-tract motion based on the planning CT, the necessary median margin was 8.0 mm (IQR: 8.0-10.0) for the duodenum and 14.0 mm (12.0-16.0) for the small intestine. Compared to the planned V33 with the worst case, the median V33 in the PRV of the duodenum significantly increased from 0.20 cm3 (IQR: 0.02-0.26) to 0.33 cm3 (0.10-0.59) at Wilcoxon signed-rank test (p = 0.031). Conclusion: The short-term motions of the GI-tract lead to high dose differences.

Predicting the daily gastrointestinal doses of stereotactic body radiation therapy for pancreatic cancer based on the shortest distance between the tumor and the gastrointestinal tract using daily computed tomography images.

Objectives: We aimed to investigate whether daily computed tomography (CT) images could predict the daily gastroduodenal, small intestine, and large intestine doses of stereotactic body radiation therapy (SBRT) for pancreatic cancer based on the shortest distance between the gross tumor volume (GTV) and gastrointestinal (GI) tract. Methods: Twelve patients with pancreatic cancer received SBRT of 40 Gy in five fractions. We recalculated the reference clinical SBRT plan (PLANref) using daily CT images and calculated the shortest distance from the GTV to each GI tract. The maximum dose delivered to 0.5 cc (D0.5cc) was evaluated for each planning at-risk volume of the GI tract. Spearman's correlation test was used to determine the association between the daily change in the shortest distance (Δshortest distance) and the ratio of ΔD0.5cc dose to D0.5cc dose in PLANref (ΔD0.5cc/PLANref) for quantitative analysis. Results: The median shortest distance in PLANref was 0 mm in the gastroduodenum (interquartile range, 0-2.7), 16.7 mm in the small intestine (10.0-23.7), and 16.7 mm in the large intestine (8.3-28.1 mm). The D0.5cc of PLANref in the gastroduodenum was >30 Gy in all patients, with 10 (83.3%) having the highest dose. A significant association was found between the Δshortest distance and ΔD0.5cc/ PLANref in the small or large intestine (p < 0.001) but not in the gastroduodenum (p = 0.404). Conclusions: The gastroduodenum had a higher D0.5cc and predicting the daily dose was difficult. Daily dose calculations of the GI tract are recommended for safe SBRT. Advances in knowledge: This study aimed to predict the daily doses in SBRT for pancreatic cancer from the shortest distance between the GTV and the gastrointestinal tract.Daily changes in the shortest distance can predict the daily dose to the small or large intestines, but not to the gastroduodenum.

Prediction of liver Dmean for proton beam therapy using deep learning and contour-based data augmentation.

The prediction of liver Dmean with 3-dimensional radiation treatment planning (3DRTP) is time consuming in the selection of proton beam therapy (PBT), and deep learning prediction generally requires large and tumor-specific databases. We developed a simple dose prediction tool (SDP) using deep learning and a novel contour-based data augmentation (CDA) approach and assessed its usability. We trained the SDP to predict the liver Dmean immediately. Five and two computed tomography (CT) data sets of actual patients with liver cancer were used for the training and validation. Data augmentation was performed by artificially embedding 199 contours of virtual clinical target volume (CTV) into CT images for each patient. The data sets of the CTVs and OARs are labeled with liver Dmean for six different treatment plans using two-dimensional calculations assuming all tissue densities as 1.0. The test of the validated model was performed using 10 unlabeled CT data sets of actual patients. Contouring only of the liver and CTV was required as input. The mean relative error (MRE), the mean percentage error (MPE) and regression coefficient between the planned and predicted Dmean was 0.1637, 6.6%, and 0.9455, respectively. The mean time required for the inference of liver Dmean of the six different treatment plans for a patient was 4.47±0.13 seconds. We conclude that the SDP is cost-effective and usable for gross estimation of liver Dmean in the clinic although the accuracy should be improved further if we need the accuracy of liver Dmean to be compatible with 3DRTP.