Potential Defects and Improvements of Equivalent Uniform Dose Prediction Model Based on the Analysis of Radiation-Induced Brain Injury

PurposeTo study the impact of dose distribution on volume-effect parameter and predictive ability of equivalent uniform dose (EUD) model, and to explore the improvements.Methods and MaterialsThe brains of 103 nasopharyngeal carcinoma patients treated with IMRT were segmented according to dose distribution (brain and left/right half-brain for similar distributions but different sizes; VD with different D for different distributions). Predictive ability of EUDVD (EUD of VD) for radiation-induced brain injury was assessed by receiver operating characteristics curve (ROC) and area under the curve (AUC). The optimal volume-effect parameter a of EUD was selected when AUC was maximal (mAUC). Correlations between mAUC, a and D were analyzed by Pearson correlation analysis. Both mAUC and a in brain and half-brain were compared by using paired samples t-tests. The optimal DV and VD points were selected for a simple comparison.ResultsThe mAUC of brain/half-brain EUD was 0.819/0.821 and the optimal a value was 21.5/22. When D increased, mAUC of EUDVD increased, while a decreased. The mAUC reached the maximum value when D was 50–55 Gy, and a was always 1 when D ≥55 Gy. The difference of mAUC/a between brain and half-brain was not significant. If a was in range of 1 to 22, AUC of brain/half-brain EUDV55 Gy (0.857–0.830/0.845–0.830) was always larger than that of brain/half-brain EUD (0.681–0.819/0.691–0.821). The AUCs of optimal dose/volume points were 0.801 (brain D2.5 cc), 0.823 (brain V70 Gy), 0.818 (half-brain D1 cc), and 0.827 (half-brain V69 Gy), respectively. Mean dose (equal to EUDVD with a = 1) of high-dose volume (V50 Gy–V60 Gy) was superior to traditional EUD and dose/volume points.ConclusionVolume-effect parameter of EUD is variable and related to dose distribution. EUD with large low-dose volume may not be better than simple dose/volume points. Critical-dose-volume EUD could improve the predictive ability and has an invariant volume-effect parameter. Mean dose may be the case in which critical-dose-volume EUD has the best predictive ability.

Predicting dose-volume histogram of organ-at-risk using spatial geometric-encoding network for esophageal treatment planning

Dose-volume histogram (DVH) is an important tool to evaluate the radiation treatment plan quality, which could be predicted based on the distance-volume spatial relationship between planning target volumes (PTV) and organs-at-risks (OARs). However, the prediction accuracy is still limited due to the complicated calculation process and the omission of detailed spatial geometric features. In this paper, we propose a spatial geometric-encoding network (SGEN) to incorporate 3D spatial information with an efficient 2D convolutional neural networks (CNN) for accurate prediction of DVH for esophageal radiation treatments. 3D computed tomography (CT) scans, 3D PTV scans and 3D distance images are used as the multi-view input of the proposed model. The dilation convolution based Multi-scale concurrent Spatial and Channel Squeeze & Excitation (msc-SE) structure in the proposed model not only can maintain comprehensive spatial information with less computation cost, but also can extract the features of organs at different scales effectively. Five-fold cross-validation on 200 intensity-modulated radiation therapy (IMRT) esophageal radiation treatment plans were used in this paper. The mean absolute error (MAE) of DVH focusing on the left lung can achieve 2.73 ± 2.36, while the MAE was 7.73 ± 3.81 using traditional machine learning prediction model. In addition, extensive ablation studies have been conducted and the quantitative results demonstrate the effectiveness of different components in the proposed method.

Degree of reduction in normal liver complication probability from free-breathing to breath-hold liver SBRT: A dose-escalation strategy using radiation dose-volume effect

Introduction: This study aimed to analyze the degree of reduction in normal liver complication probability (NTCP) from free-breathing (FB) to breath-hold (BH) liver SBRT. The effect of the radiation dose-volume on the mean liver dose (MLD) was also analyzed due to dose prescription, normal liver volume (NLV), and PTV. Materials and Methods: Thirty-three stereotactic body radiation therapy (SBRT) cases of hepatocellular carcinoma were selected, retrospectively. For FB, the treatments were planned on average intensity projection scan (CTavg), and patient-specific internal target volume (ITV) margins were applied. To simulate the BH treatment, computed tomography (CT) scan correspond to the 40% - 50% of the respiratory cycle (CT40%-50%) was chosen, and an appropriate intrafraction margin of 2 mm, 1.5 mm, and 1.5 mm were given in craniocaudal (CC), superior-inferior (SI), and lateral direction to generate the final iGTV. As per RTOG 1112, all organs at risk (OAR’s) were considered during the optimization of treatment plans. NTCP was calculated using LKB fractionated model. Multivariate regression analysis was performed to see the effect of EQD2Gy, NLV, and PTV on MLD2Gy. Results: A significant dosimetric difference was observed in the normal liver (liver-ITV/iGTV). A reduction of 1.7% in NTCP was observed from FB to BH technique. The leverage of dose escalation is more in BH because MLD2Gy corresponds to 5%, 10%, 20%, and 50% NTCP was 0.099 Gy, 0.41 Gy, 1.21 Gy, and 3.432 Gy more in BH as compared to FB technique. In MVRA, the major factor which was attributed to a change in MLD2Gy is EQD2Gy. Conclusion: From FB to BH technique, a significant reduction in NTCP was observed. The dose prescription is a major factor attributed to the change in MLD2Gy. Advances in knowledge: If feasible, prefer BH treatment either for tumor dose escalation or for the reduction in NTCP.

CT-on-Rails versus in-Room CBCT for Online Daily Adaptive Proton Therapy of Head-and-Neck Cancers

Purpose: To compare the efficacy of CT-on-rails versus in-room CBCT for daily adaptive proton therapy. Methods: We analyzed a cohort of ten head-and-neck patients with daily CBCT and corresponding virtual CT images. The necessity of moving the patient after a CT scan is the most significant difference in the adaptation workflow, leading to an increased treatment execution uncertainty σ. It is a combination of the isocenter-matching σi and random patient movements induced by the couch motion σm. The former is assumed to never exceed 1 mm. For the latter, we studied three different scenarios with σm = 1, 2, and 3 mm. Accordingly, to mimic the adaptation workflow with CT-on-rails, we introduced random offsets after Monte-Carlo-based adaptation but before delivery of the adapted plan. Results: There were no significant differences in accumulated dose-volume histograms and dose distributions for σm = 1 and 2 mm. Offsets with σm = 3 mm resulted in underdosage to CTV and hot spots of considerable volume. Conclusion: Since σm typically does not exceed 2 mm for in-room CT, there is no clinically significant dosimetric difference between the two modalities for online adaptive therapy of head-and-neck patients. Therefore, in-room CT-on-rails can be considered a good alternative to CBCT for adaptive proton therapy.

The Therapeutic Value of Volumetric-Modulated Arc Therapy In Advanced Non-Small Cell Lung Cancer Primary Tumors: A Multicenter Retrospective Analysis Based On Propensity Score Matching And Comparison With Intensity-Modulated Radiotherapy

Objective:.To explore the feasibility of volumetric-modulated arc therapy (VMAT) instead of intensity-modulated radiotherapy (IMRT) for primary tumors of advanced non–small-cell lung cancer (A-NSCLC). Methods:.We used propensity score matching (PSM) and multicenter retrospective analysis to study the efficacy and toxicity of VMAT technology in radiotherapy for primary tumors of A-NSCLC.We used the chi-squared test to analyze the response rate(RR), local control (LC), acute radiation injury, and dose-volume parameters; the Kaplan–Meier and log-rank tests were used to determine local-regional progression-free survival (LRPFS) and overall survival (OS). Results: LRPFS was significantly prolonged in stage III patients treated with IMRT before PSM (P< .05) and cases of grade 1 or 2 acute radiation esophagitis (RE) or radiation pneumonia(RP) in the IMRT than the VMAT(P< .05), but cases of grade 3 or 4 RE and RP were not significantly different between the groups (P> .05). Before PSM, there was no significant difference in the LRPFS and OS rates of the whole study group and the RR, LC, and stage IV subgroups, respectively (P> .05). After PSM, there was no significant difference in the RR, LC, LRPFS, OS, RP, or RE of patients treated with VMAT and IMRT (P> .05), normal whole-lung (V5, V20, MLD), heart (V30, V40, MHD), and equal dose-volume parameters were significantly lower in the VMAT group (P< .05).Conclusion:.Radiation therapy of A-NSCLC primary tumors using VMAT can achieve a similar efficacy to that of IMRT but with significantly lower low-dose volume parameters of normal tissues and organs; this is true especially in stage N2 cases, where the reduction in radiation damage may be more favorable.

Biological dosiomic features for the prediction of radiation pneumonitis in esophageal cancer patients

Objective The purpose of this study was to develop a model using dose volume histogram (DVH) and dosiomic features to predict the risk of radiation pneumonitis (RP) in the treatment of esophageal cancer with radiation therapy and to compare the performance of DVH and dosiomic features after adjustment for the effect of fractionation by correcting the dose to the equivalent dose in 2 Gy (EQD2). Materials and methods DVH features and dosiomic features were extracted from the 3D dose distribution of 101 esophageal cancer patients. The features were extracted with and without correction to EQD2. A predictive model was trained to predict RP grade ≥ 1 by logistic regression with L1 norm regularization. The models were then evaluated by the areas under the receiver operating characteristic curves (AUCs). Result The AUCs of both DVH-based models with and without correction of the dose to EQD2 were 0.66 and 0.66, respectively. Both dosiomic-based models with correction of the dose to EQD2 (AUC = 0.70) and without correction of the dose to EQD2 (AUC = 0.71) showed significant improvement in performance when compared to both DVH-based models. There were no significant differences in the performance of the model by correcting the dose to EQD2. Conclusion Dosiomic features can improve the performance of the predictive model for RP compared with that obtained with the DVH-based model.