Monitoring of anatomical changes during adaptive brain radiotherapy in glioma patients

Purpose: We investigated the extent of changes in the anatomical position, shape and volume of lateral ventricles (LVs) and subventricular zones (SVZs). We included other critical organs at risk (OARs) to examine their contribution to the dose delivered to these regions. Additionally, the correlation between the SVZ radiation dose and clinical outcome was analyzed using the median SVZ dose as a cut-off value for both of the structures defined on the first planning CT and the data on the changed ipsi- and contralateral SVZs on the repeated CT during the course of irradiation. Methods: We examined changes in the ipsilateral/contralateral LV and SVZ, as well as in the relevant OARs. We evaluated the volumetric and dosimetric changes on both planning CT scans (primary CT1 and secondary CT2). The survival of the GBM patients was analyzed using the Kaplan–Meier method; the multivariate Cox regression was also performed. Results: LV and SVZ structures exhibited significant volumetric changes on CT2, resulting in an increase of dose coverage. At a cut-off point of 58 Gy, a significant correlation was detected between the iSVZ2 mean dose and OS (27.8 vs 15.6 months, p=0.048). In a multivariate analysis, glioblastoma multiforme (GBM) patients with a shorter time to postoperative chemoradiotherapy (<3.8 weeks), with good performance status (≥70%) and higher mean dose (≥58 Gy) to the iSVZ2 had significantly better overall survival (OS). We observed that the average of all investigated dose parameters to other OARs was lower at each volume dose level than on CT1 and replanning caused significant differences on most of them. Conclusions: Significant anatomical and dose distribution changes to the brain structures were observed, which have a relevant impact on the dose-effect relationship for GBM; therefore, involving the iSVZ in the target volume should be considered and adapted to the changes

Individually selected teletherapy technique for accelerated partial breast irradiation

Background: The aim of the study was to individualize accelerated partial breast irradiation based on optimal dose distribution, protect risk organ and predict most advantageous technique. Materials and methods: 138 breast cancer patients receiving postoperative APBI were enrolled. APBI plans were generated using 3D-conformal (3D-CRT), sliding window intensity-modulated radiotherapy (IMRT) and volumetric-modulated arc therapy (VMAT). In the case of superficial tumours, additional plans were developed by adding electron beam. To planning target volume (PTV) 37.5 Gy/10 fractions, 1 fraction/day was prescribed. A novel plan quality index (PQI) served as the basis for comparisons. Results: IMRT was the most advantageous technique regarding homogeneity. VMAT provided best conformity, 3D-CRT — the lowest lung and heart exposure. PQI was the best in 45 (32.61%) VMAT, 13 (9.42%) IMRT, 9 (6.52%) 3D-CRT plans. In 71 cases (51.45%) no difference was detected. In patients with large PTV, 3D-CRT was the most favourable. Additional electron beam improved PQI of 3D-CRT plans but had no meaningful effect on IMRT or VMAT. IMRT was superior to VMAT if the tumour was superficial (p &lt; 0.001), situated in the medial (p = 0.032) or upper quadrant (p = 0.046). Conclusions: In half of all cases, individually selected teletherapy techniques provide superior results over others; relevance of a certain technique may be predicted by volume and PTV localization

Deep-learning-based segmentation of organs-at-risk in the head for MR-assisted radiation therapy planning

Segmentation of organs-at-risk (OAR) in MR images has several clinical applications; including radiation therapy (RT) planning. This paper presents a deep-learning-based method to segment 15 structures in the head region. The proposed method first applies 2D U-Net models to each of the three planes (axial, coronal, sagittal) to roughly segment the structure. Then, the results of the 2D models are combined into a fused prediction to localize the 3D bounding box of the structure. Finally, a 3D U-Net is applied to the volume of the bounding box to determine the precise contour of the structure. The model was trained on a public dataset and evaluated on both public and private datasets that contain T2-weighted MR scans of the head-and-neck region. For all cases the contour of each structure was defined by operators trained by expert clinical delineators. The evaluation demonstrated that various structures can be accurately and efficiently localized and segmented using the presented framework. The contours generated by the proposed method were also qualitatively evaluated. The majority (92%) of the segmented OARs was rated as clinically useful for radiation therapy