Organs at Risk

BackgroundIn radiotherapy inaccuracy in organ at risk (OAR) delineation can impact treatment plan optimisation and treatment plan evaluation. Brouwer et al. showed significant interobserver variability (IOV) in OAR delineation in head and neck cancer (HNC) and published international consensus guidelines (ICG) for OAR delineation in 2015. The aim of our study was to evaluate IOV in the presence of these guidelines.MethodsHNC radiation oncologists (RO) from each Belgian radiotherapy centre were invited to complete a survey and submit contours for 5 HNC cases. Reference contours (OARref) were obtained by a clinically validated artificial intelligence-tool trained using ICG. Dice similarity coefficients (DSC), mean surface distance (MSD) and 95% Hausdorff distances (HD95) were used for comparison.ResultsFourteen of twenty-two RO (64%) completed the survey and submitted delineations. Thirteen (93%) confirmed the use of delineation guidelines, of which six (43%) used the ICG. The OARs whose delineations agreed best with the OARref were mandible [median DSC 0.9, range (0.8-0.9); median MSD 1.1 mm, range (0.8-8.3), median HD95 3.4 mm, range (1.5-38.7)], brainstem [median DSC 0.9 (0.6-0.9); median MSD 1.5 mm (1.1-4.0), median HD95 4.0 mm (2.3-15.0)], submandibular glands [median DSC 0.8 (0.5-0.9); median MSD 1.2 mm (0.9-2.5), median HD95 3.1 mm (1.8-12.2)] and parotids [median DSC 0.9 (0.6-0.9); median MSD 1.9 mm (1.2-4.2), median HD95 5.1 mm (3.1-19.2)]. Oral cavity, cochleas, PCMs, supraglottic larynx and glottic area showed more variation. RO who used the consensus guidelines showed significantly less IOV (p = 0.008).ConclusionsAlthough ICG for delineation of OARs in HNC exist, they are only implemented by about half of RO participating in this study, which partly explains the delineation variability. However, this study highlights that guidelines alone do not suffice to eliminate IOV and that more effort needs to be done to accomplish further treatment standardisation, for example with artificial intelligence.

BackgroundIn breast cancer patients receiving radiotherapy (RT), accurate target delineation and reduction of radiation doses to the nearby normal organs is important. However, manual clinical target volume (CTV) and organs-at-risk (OARs) segmentation for treatment planning increases physicians' workload and inter-physician variability considerably. In this study, we evaluated the potential benefits of deep learning-based auto-segmented contours by comparing them to manually delineated contours for breast cancer patients.MethodsCTVs for bilateral breasts, regional lymph nodes, and OARs (including the heart, lungs, esophagus, spinal cord, and thyroid) were manually delineated on planning computed tomography scans of 111 breast cancer patients who received breast-conserving surgery. Subsequently, a two-stage convolutional neural network algorithm was used. Quantitative metrics, including the Dice similarity coefficient (DSC) and 95% Hausdorff distance, and qualitative scoring by two panels from 10 institutions were used for analysis. Inter-observer variability and delineation time were assessed; furthermore, dose-volume histograms and dosimetric parameters were also analyzed using another set of patient data.ResultsThe correlation between the auto-segmented and manual contours was acceptable for OARs, with a mean DSC higher than 0.80 for all OARs. In addition, the CTVs showed favorable results, with mean DSCs higher than 0.70 for all breast and regional lymph node CTVs. Furthermore, qualitative subjective scoring showed that the results were acceptable for all CTVs and OARs, with a median score of at least 8 (possible range: 0-10) for (1) the differences between manual and auto-segmented contours and (2) the extent to which auto-segmentation would assist physicians in clinical practice. The differences in dosimetric parameters between the auto-segmented and manual contours were minimal.ConclusionsThe feasibility of deep learning-based auto-segmentation in breast RT planning was demonstrated. Although deep learning-based auto-segmentation cannot be a substitute for radiation oncologists, it is a useful tool with excellent potential in assisting radiation oncologists in the future. Trial registration Retrospectively registered.

BackgroundThe addition of regional nodal radiation (RNI) to whole breast irradiation for high risk breast cancer improves metastases free survival and new data suggests it contributes additional benefit to overall survival. Deep inspiration breath hold (DIBH) has been shown to reduce cardiac and pulmonary dose in the context of left-sided disease treated with or without RNI, yet few studies have investigated its utility for right-breast cancer. This study investigates the potential advantages of DIBH in local and locoregional radiotherapy for right-sided breast cancer.MethodsFree-breathing (FB) and DIBH computed tomography datasets were obtained from twenty patients who previously underwent radiotherapy for left-sided breast cancer. Ten patients were retrospectively planned for whole right breast only irradiation and ten patients were planned for irradiation to the whole breast plus ipsilateral supra-clavicular (SC) nodes, with and without irradiation of the ipsilateral internal mammary nodes (IMN). Dose-volume metrics for the clinical target volume, lungs, heart, left anterior descending artery, right coronary artery (RCA) and liver were recorded. Differences between FB and DIBH plans were analysed using Wilcoxon signed-rank tests, with P ResultsDIBH increased the average total lung volume compared to FB in both breast only and breast plus RNI cohorts (P = 0.001). For the breast only group, there was no significant improvement in any ipsilateral lung dose-volume metric between FB and DIBH. However, for the breast plus RNI group, there was an improvement in ipsilateral lung mean dose (18.9 ± 3.2 Gy to 15.9 ± 2.3 Gy, P = 0.002) and V20Gy (45.3 ± 13.3% to 32.9 ± 9.4%, P = 0.002). In addition, DIBH significantly reduced the maximum dose to the RCA for RNI (11.6 ± 7.2 Gy to 5.6 ± 2.9 Gy, P = 0.03). Significant reductions in the liver V20Gy and maximum dose were observed in all cohorts during DIBH compared to FB.ConclusionsDIBH is a promising approach for right-breast radiotherapy with considerable sparing of normal tissue, particularly when the ipsilateral IMNs are also irradiated.

BackgroundAccurate delineation of organs at risk (OARs) is crucial yet time-consuming in the radiotherapy treatment planning workflow. Modern artificial intelligence (AI) technologies had made automation of OAR contouring feasible. This report details a single institution's experience in evaluating two commercial auto-contouring software tools and making well-informed decisions about their clinical adoption.MethodsA cohort of 36 patients previously treated at our institution were selected for the software performance assessment. Fifty-eight OAR structures from seven disease sites were automatically segmented with each tool. Five radiation oncologists with different specialties qualitatively scored the automatic OAR contours' clinical usability by a 4-level scale (0-3), termed as quality score (QS), representing from "0: not usable" to "3: directly usable for a clinic." Additionally, quantitative comparison with clinically approved contours using Dice similarity coefficient (DSC) and the 95% Hausdorff distance (HD95) was performed in complement to QS from physicians.ResultSoftware A achieved an average QS of 2.17 ± 0.69, comparable to Software B's average QS of 2.17 ± 0.72. Software B performed better with more OARs (42 vs. 37) that required minor or no modification than Software A. Major modifications were needed for 13 out of 58 automated contours from both tools. Both DSC and HD95 scores for the two tools were comparable to each other, with DSC: 0.67 ± 0.23 versus 0.66 ± 0.21 and HD95: 13.07 ± 15.84 versus 15.55 ± 18.45 for Software A and Software B, respectively. Correlation coefficients between the physician score and the quantitative metrics suggested that the contouring results from Software A aligned more closely with the physician's evaluations.ConclusionBased on our study, either software tool could produce clinically acceptable contours for about 65% of the OAR structures. However, further refinement is necessary for several challenging OARs to improve model performance.