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BackgroundOmitting the placement of clips inside tumour bed during breast cancer surgery poses a challenge for delineation of lumpectomy cavity clinical target volume (CTVLC). We aimed to quantify inter-observer variation and accuracy for CT- and MRI-based segmentation of CTVLC in patients without clips.Patients and methodsCT- and MRI-simulator images of 12 breast cancer patients, treated by breast conserving surgery and radiotherapy, were included in this study. Five radiation oncologists recorded the cavity visualization score (CVS) and delineated CTVLC on both modalities. Expert-consensus (EC) contours were delineated by a senior radiation oncologist, respecting opinions of all observers. Inter-observer volumetric variation and generalized conformity index (CIgen) were calculated. Deviations from EC contour were quantified by the accuracy index (AI) and inter-delineation distances (IDD).ResultsMean CVS was 3.88 +/− 0.99 and 3.05 +/− 1.07 for MRI and CT, respectively (p = 0.001). Mean volumes of CTVLC were similar: 154 +/− 26 cm3 on CT and 152 +/− 19 cm3 on MRI. Mean CIgen and AI were superior for MRI when compared with CT (CIgen: 0.74 +/− 0.07 vs. 0.67 +/− 0.12, p = 0.007; AI: 0.81 +/− 0.04 vs. 0.76 +/− 0.07; p = 0.004). CIgen and AI increased with increasing CVS. Mean IDD was 3 mm +/− 1.5 mm and 3.6 mm +/− 2.3 mm for MRI and CT, respectively (p = 0.017).ConclusionsWhen compared with CT, MRI improved visualization of post-lumpectomy changes, reduced interobserver variation and improved the accuracy of CTVLC contouring in patients without clips in the tumour bed. Further studies with bigger sample sizes are needed to confirm our findings.

BackgroundOptimal applicator insertion is a precondition for the success of cervix cancer brachytherapy (BT). We aimed to assess feasibility and efficacy of MRI-assisted pre-planning, based on applicator insertion in para-cervical anaesthesia (PCA).Patients and methods.Five days prior to BT, the pre-planning procedure was performed in 18 cervix cancer patients: tandem-ring applicator was inserted under PCA, pelvic MRI obtained and applicator removed. Procedure tolerability was assessed. High risk clinical target volume (HR CTV) and organs at risk were delineated on the pre-planning MRI, virtual needles placed at optimal positions, and dose planning performed. At BT, insertion was carried out in subarachnoidal anaesthesia according to pre-planned geometry. Pre-planned and actual treatment parameters were compared.ResultsPre-planning procedure was well tolerated. Median difference between the pre-planned and actual needle insertion depth and position were 2 (0–10) mm and 4 (0–30) degrees, respectively. The differences between the pre-planned and actual geometric and dosimetric parameters were statistically non-significant. All actual needles were positioned inside the HR CTV and outside the organs at risk (OAR).ConclusionsOur pre-planning approach is well tolerated and effective. Pre-planned geometry and dose distribution can be reproduced at BT.

Purpose Magnetic resonance (MR) imaging is the gold standard in image‐guided brachytherapy (IGBT) due to its superior soft‐tissue contrast for target and organs‐at‐risk (OARs) delineation. Accurate and fast segmentation of MR images are very important for high‐quality IGBT treatment planning. The purpose of this work is to implement and evaluate deep learning (DL) models for the automatic segmentation of targets and OARs in MR image‐based high‐dose‐rate (HDR) brachytherapy for cervical cancer. Methods A 2D DL model using residual neural network architecture (ResNet50) was developed to contour the targets (gross tumor volume (GTV), high‐risk clinical target volume (HR CTV), and intermediate‐risk clinical target volume (IR CTV)) and OARs (bladder, rectum, sigmoid, and small intestine) automatically on axial MR slices of HDR brachytherapy patients. Furthermore, two additional 2D DL models using sagittal and coronal images were also developed. A 2.5D model was generated by combining the outputs from axial, sagittal, and coronal DL models. Similarly, a 2D and 2.5D DL models were also generated for the inception residual neural network (InceptionResNetv2 (InRN)) architecture. The geometric (Dice similarity coefficient (DSCs) and 95th percentile of Hausdorff distance (HD)) and dosimetric accuracy of 2D (axial only) and 2.5D (axial + sagittal + coronal) DL model generated contours were calculated and compared. Results The mean (range) DSCs of ResNet50 across all contours were 0.674 (0.05–0.96) and 0.715 (0.26–0.96) for the 2D and 2.5D models, respectively. For InRN, these were 0.676 (0.11–0.96) and 0.723 (0.35–0.97) for the 2D and 2.5D models, respectively. The mean HD of ResNet50 across all contours was 15.6 mm (1.8–69 mm) and 12.1 mm (1.7–44 mm) for the 2D and 2.5D models, respectively. The similar results for InRN were 15.4 mm (2–68 mm) and 10.3 mm (2.7–39 mm) for the 2D and 2.5D models, respectively. The dosimetric parameters (D90) of GTV and HR CTV for manually contoured plans matched better with the 2.5D model (p > 0.6) and the results from the 2D model were slightly lower (p < 0.08). On the other hand, the IR CTV doses (D90) for all of the models were slightly lower (2D: ‐1.3 to ‐1.5 Gy and 2.5D: ‐0.5 to ‐0.6 Gy) and the differences were statistically significant for the 2D model (2D: p < 0.000002 and 2.5D: p > 0.06). In case of OARs, the 2.5D model segmentations resulted in closer dosimetry than 2D models (2D: p = 0.07–0.91 and 2.5D: p = 0.16–1.0). Conclusions The 2.5D DL models outperformed their respective 2D models for the automatic contouring of targets and OARs in MR image‐based HDR brachytherapy for cervical cancer. The InceptionResNetv2 model performed slightly better than ResNet50.

BackgroundDuring radiotherapy of left-sided breast cancer, parts of the heart are irradiated, which may lead to late toxicity. We report on the experience of single institution with cardiac-sparing radiotherapy using voluntary deep inspiration breath hold (V-DIBH) and compare its dosimetric outcome with free breathing (FB) technique.Patients and methodsLeft-sided breast cancer patients, treated at our department with postoperative radiotherapy of breast/chest wall +/– regional lymph nodes between May 2015 and January 2017, were considered for inclusion. FB-computed tomography (CT) was obtained and dose-planning performed. Cases with cardiac V25Gy ≥ 5% or risk factors for heart disease were coached for V-DIBH. Compliant patients were included. They underwent additional CT in V-DIBH for planning, followed by V-DIBH radiotherapy. Dose volume histogram parameters for heart, lung and optimized planning target volume (OPTV) were compared between FB and BH. Treatment setup shifts and systematic and random errors for V-DIBH technique were compared with FB historic control.ResultsSixty-three patients were considered for V-DIBH. Nine (14.3%) were non-compliant at coaching, leaving 54 cases for analysis. When compared with FB, V-DIBH resulted in a significant reduction of mean cardiac dose from 6.1 +/– 2.5 to 3.2 +/– 1.4 Gy (p < 0.001), maximum cardiac dose from 51.1 +/– 1.4 to 48.5 +/– 6.8 Gy (p = 0.005) and cardiac V25Gy from 8.5 +/– 4.2 to 3.2 +/– 2.5% (p < 0.001). Heart volumes receiving low (10–20 Gy) and high (30–50 Gy) doses were also significantly reduced. Mean dose to the left anterior coronary artery was 23.0 (+/– 6.7) Gy and 14.8 (+/– 7.6) Gy on FB and V-DIBH, respectively (p < 0.001). Differences between FB- and V-DIBH-derived mean lung dose (11.3 +/– 3.2 vs. 10.6 +/– 2.6 Gy), lung V20Gy (20.5 +/– 7 vs. 19.5 +/– 5.1 Gy) and V95% for the OPTV (95.6 +/– 4.1 vs. 95.2 +/– 6.3%) were non-significant. V-DIBH-derived mean shifts for initial patient setup were ≤ 2.7 mm. Random and systematic errors were ≤ 2.1 mm. These results did not differ significantly from historic FB controls.ConclusionsWhen compared with FB, V-DIBH demonstrated high setup accuracy and enabled significant reduction of cardiac doses without compromising the target volume coverage. Differences in lung doses were non-significant.

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