Fully automated volumetric modulated arc therapy technique for radiation therapy of locally advanced breast cancer.

BACKGROUND: This study aimed to evaluate an a-priori multicriteria plan optimization algorithm (mCycle) for locally advanced breast cancer radiation therapy (RT) by comparing automatically generated VMAT (Volumetric Modulated Arc Therapy) plans (AP-VMAT) with manual clinical Helical Tomotherapy (HT) plans. METHODS: The study included 25 patients who received postoperative RT using HT. The patient cohort had diverse target selections, including both left and right breast/chest wall (CW) and III-IV node, with or without internal mammary node (IMN) and Simultaneous Integrated Boost (SIB). The Planning Target Volume (PTV) was obtained by applying a 5 mm isotropic expansion to the CTV (Clinical Target Volume), with a 5 mm clip from the skin. Comparisons of dosimetric parameters and delivery/planning times were conducted. Dosimetric verification of the AP-VMAT plans was performed. RESULTS: The study showed statistically significant improvements in AP-VMAT plans compared to HT for OARs (Organs At Risk) mean dose, except for the heart and ipsilateral lung. No significant differences in V95% were observed for PTV breast/CW and PTV III-IV, while increased coverage (higher V95%) was seen for PTV IMN in AP-VMAT plans. HT plans exhibited smaller values of PTV V105% for breast/CW and III-IV, with no differences in PTV IMN and boost. HT had an average (± standard deviation) delivery time of (17 ± 8) minutes, while AP-VMAT took (3 ± 1) minutes. The average γ passing rate for AP-VMAT plans was 97%±1%. Planning times reduced from an average of 6 h for HT to about 2 min for AP-VMAT. CONCLUSIONS: Comparing AP-VMAT plans with clinical HT plans showed similar or improved quality. The implementation of mCycle demonstrated successful automation of the planning process for VMAT treatment of locally advanced breast cancer, significantly reducing workload.

Validation of a commercial deformable image registration for surface-guided radiotherapy using an ad hoc-developed deformable phantom.

PURPOSE: The use of optical surface systems (OSSs) for patient setup verification in external radiation therapy is increasing. To manage potential deformations in a patient's anatomy, a novel deformable image registration (DIR) tool has been applied in a commercial OSS. In this study we investigate the accuracy of the DIR as compared to rigid image registration (RR). METHODS AND MATERIALS: The positioning accuracy of the DIR and RR implemented in the OSS was investigated using an ad hoc-developed anthropomorphic deformable phantom, named Mary. The phantom consists of 33 slices of expanded polystyrene slabs shaped thus to simulate part of a female body. Anatomical details, simulating the ribs and spinal cord, together with 10 inner targets at different depths are included in thorax and abdominal parts. Mary is capable of realistic body movements and deformations, such as head and arm rotations, body torsion and moderate breast/abdomen swelling. The accuracy of DIR and RR was investigated for four internal targets after deliberately deforming the phantom nine times. Breast and abdomen enlargements and torsions around x, y, and z axes were applied. For reference purposes, rigid displacements (where Mary's anatomy was kept intact) were included. The phantom was positioned on the linac couch under the OSS guidance and for each target and displacement a CBCT was acquired. The accuracy of DIR and RR was assessed evaluating the difference in means of absolute values between CBCT and the OSS registration parameters (lateral, longitudinal, vertical, rot, pitch, and roll), using both a reference surface extracted from CT (CTr) or acquired with the OSS (OSSr). A comparison of the four different combinations, DIR + OSSr, DIR + CTr, RR + OSSr, and RR + CTr, was carried out to evaluate the position accuracy for the various combinations. Finally, the positioning accuracy of the different target positions using only OSSr was investigated for the DIR. A paired sample Wilcoxon signed-rank test (P < 0.05) and a two-tailed Mann-Whitney test (P < 0.05) were carried out. RESULTS: The DIR in combination with OSSr showed significantly (P < 0.05) improved positioning accuracy in the lateral and longitudinal directions and in pitch, compared to RR, when deformations were applied to Mary. The positioning accuracy improved from 1.9 ± 1.5 mm, 1.1 ± 0.8 mm to 1.1 ± 1.2 mm, 0.6 ± 0.5 mm in lateral and longitudinal directions, respectively, and from 0.8 ± 0.6° to 0.4 ± 0.4° in pitch, using DIR compared to RR. Both the DIR and RR showed a similar positioning accuracy when rigid displacements of Mary were applied. For DIR, the OSSr generally showed improved calculation accuracy compared to CTr. Independent of the reference image used, the target position influenced the registration accuracy, and hence, one target could not be evaluated using RR due to its inability to calculate the correct position. CONCLUSIONS: Improved positioning accuracy was observed for DIR with respect to RR when deformations of Mary's anatomy were applied. For both DIR and RR, improved positioning accuracy was observed using OSSr as compared to CTr. The position of the target inside the phantom influenced the positioning accuracy for DIR.

Comprehensive Analysis of Radiomic Datasets by RadAR.

Quantitative analysis of biomedical images, referred to as radiomics, is emerging as a promising approach to facilitate clinical decisions and improve patient stratification. The typical radiomic workflow includes image acquisition, segmentation, feature extraction, and analysis of high-dimensional datasets. While procedures for primary radiomic analyses have been established in recent years, processing the resulting radiomic datasets remains a challenge due to the lack of specific tools for doing so. Here we present RadAR (Radiomics Analysis with R), a new software to perform comprehensive analysis of radiomic features. RadAR allows users to process radiomic datasets in their entirety, from data import to feature processing and visualization, and implements multiple statistical methods for analysis of these data. We used RadAR to analyze the radiomic profiles of more than 850 patients with cancer from publicly available datasets and showed that it was able to recapitulate expected results. These results demonstrate RadAR as a reliable and valuable tool for the radiomics community. SIGNIFICANCE: A new computational tool performs comprehensive analysis of high-dimensional radiomic datasets, recapitulating expected results in the analysis of radiomic profiles of >850 patients with cancer from independent datasets.