Comparison of the output of a deep learning segmentation model for locoregional breast cancer radiotherapy trained on 2 different datasets

Introduction: The development of deep learning (DL) models for auto-segmentation is increasing and more models become commercially available. Mostly, commercial models are trained on external data. To study the effect of using a model trained on external data, compared to the same model trained on in-house collected data, the performance of these two DL models was evaluated. Methods: The evaluation was performed using in-house collected data of 30 breast cancer patients. Quantitative analysis was performed using Dice similarity coefficient (DSC), surface DSC (sDSC) and 95th percentile of Hausdorff Distance (95% HD). These values were compared with previously reported inter-observer variations (IOV). Results: For a number of structures, statistically significant differences were found between the two models. For organs at risk, mean values for DSC ranged from 0.63 to 0.98 and 0.71 to 0.96 for the in-house and external model, respectively. For target volumes, mean DSC values of 0.57 to 0.94 and 0.33 to 0.92 were found. The difference of 95% HD values ranged 0.08 to 3.23 mm between the two models, except for CTVn4 with 9.95 mm. For the external model, both DSC and 95% HD are outside the range of IOV for CTVn4, whereas this is the case for the DSC found for the thyroid of the in-house model. Conclusions: Statistically significant differences were found between both models, which were mostly within published inter-observer variations, showing clinical usefulness of both models. Our findings could encourage discussion and revision of existing guidelines, to further decrease inter-observer, but also inter-institute variability

Evaluation of a clinically introduced deep learning model for radiotherapy treatment planning of breast cancer

Deep learning (DL) models are increasingly studied to automate the process of radiotherapy treatment planning. This study evaluates the clinical use of such a model for whole breast radiotherapy. Treatment plans were automatically generated, after which planners were allowed to manually adapt them. Plans were evaluated based on clinical goals and DVH parameters. Thirty-seven of 50plans did fulfill all clinical goals without adjustments. Thirteen of these 37 plans were still adjusted but did not improve mean heart or lung dose. These results leave room for improvement of both the DL model as well as education on clinically relevant adjustments

Artificial intelligence based treatment planning of radiotherapy for locally advanced breast cancer

Background and purpose: Treatment planning of radiotherapy for locally advanced breast cancer patients can be a time consuming process. Artificial intelligence based treatment planning could be used as a tool to speed up this process and maintain plan quality consistency. The purpose of this study was to create treatment plans for locally advanced breast cancer patients using a Convolutional Neural Network (CNN). Materials and methods: Data of 60 patients treated for left-sided breast cancer was used with a training, validation and test split of 36/12/12, respectively. The in-house built CNN model was a hierarchically densely connected U-net (HD U-net). The inputs for the HD U-net were 2D distance maps of the relevant regions of interest. Dose predictions, generated by the HD U-net, were used for a mimicking algorithm in order to create clinically deliverable plans. Results: Dose predictions were generated by the HD U-net and mimicked using a commercial treatment planning system. The predicted plans fulfilling all clinical goals while showing small (≤0.5 Gy) statistically significant differences (p < 0.05) in the doses compared to the manual plans. The mimicked plans show statistically significant differences in the average doses for the heart and lung of ≤0.5 Gy and a reduced D2% of all PTVs. In total, ten of the twelve mimicked plans were clinically acceptable. Conclusions: We created a CNN model which can generate clinically acceptable plans for left-sided locally advanced breast cancer patients. This model shows great potential to speed up the treatment planning process while maintaining consistent plan quality

Interobserver variability in the delineation of the primary lung cancer and lymph nodes on different four-dimensional computed tomography reconstructions

Item does not contain fulltextPURPOSE: The study compared interobserver variation in the delineation of the primary tumour (GTVp) and lymph nodes (GTVln) between three different 4DCT reconstruction types; Maximum Intensity Projection (MIP), Mid-Ventilation (Mid-V) and Mid-Position (Mid-P). MATERIAL AND METHODS: Seven radiation oncologists delineated the GTVp and GTVln on the MIP, Mid-V and Mid-P 4DCT image reconstructions of 10 lung cancer patients. The volumes, the mean standard deviation (SD) and distribution of SD (SD/area) over the median surface contour were compared for different tumour regions. RESULTS: The overall mean delineated volume on the MIP was significantly larger (p<0.001) than the Mid-V and Mid-P. For the GTVp the Mid-P had the lowest interobserver variation (SD=0.261cm), followed by Mid-V (SD=0.314cm) and MIP (SD=0.330cm) For GTVln the Mid-V had the lowest interobserver variation (SD=0.425cm) followed by the MIP (SD=0.477cm) and Mid-P (SD=0.543cm). The SD/area distribution showed a statistically significant difference between the MIP versus Mid-P and Mid-P versus Mid-V for both GTVp and GTVln (p<0.001), with outliers indicating interpretation differences for GTVp located close to the mediastinum and GTVln. CONCLUSION: The Mid-P reduced the interobserver variation for the GTVp. Delineation protocols must be improved to benefit from the improved image quality of Mid-P for the GTVln

Cardiovascular Disease Risk in a Large, Population-Based Cohort of Breast Cancer Survivors

Purpose: To conduct a large, population-based study on cardiovascular disease (CVD) in breast cancer (BC) survivors treated in 1989 or later. Methods and Materials: A large, population-based cohort comprising 70,230 surgically treated stage I to III BC patients diagnosed before age 75 years between 1989 and 2005 was linked with population-based registries for CVD. Cardiovascular disease risks were compared with the general population, and within the cohort using competing risk analyses. Results: Compared with the general Dutch population, BC patients had a slightly lower CVD mortality risk (standardized mortality ratio 0.92, 95% confidence interval [CI] 0.88-0.97). Only death due to valvular heart disease was more frequent (standardized mortality ratio 1.28, 95% CI 1.08-1.52). Left-sided radiation therapy after mastectomy increased the risk of any cardiovascular event compared with both surgery alone (subdistribution hazard ratio (sHR) 1.23, 95% CI 1.11-1.36) and right-sided radiation therapy (sHR 1.19, 95% CI 1.04-1.36). Radiation-associated risks were found for not only ischemic heart disease, but also for valvular heart disease and congestive heart failure (CHF). Risks were more pronounced in patients aged = 1997 (ie, anthracyline-based chemotherapy) increased the risk of CHF (sHR 1.35, 95% CI 1.00-1.83). Conclusion: Radiation therapy regimens used in BC treatment between 1989 and 2005 increased the risk of CVD, and anthracycline-based chemotherapy regimens increased the risk of CHF. (C) 2016 Elsevier Inc. All rights reserved

Clinical evaluation of a deep learning segmentation model including manual adjustments afterwards for locally advanced breast cancer

Introduction: Deep learning (DL) models are increasingly developed for auto-segmentation in radiotherapy. Qualitative analysis is of great importance for clinical implementation, next to quantitative. This study evaluates a DL segmentation model for left- and right-sided locally advanced breast cancer both quantitatively and qualitatively. Methods: For each side a DL model was trained, including primary breast CTV (CTVp), lymph node levels 1–4, heart, lungs, humeral head, thyroid and esophagus. For evaluation, both automatic segmentation, including correction of contours when needed, and manual delineation was performed and both processes were timed. Quantitative scoring with dice-similarity coefficient (DSC), 95% Hausdorff Distance (95%HD) and surface DSC (sDSC) was used to compare both the automatic (not-corrected) and corrected contours with the manual contours. Qualitative scoring was performed by five radiotherapy technologists and five radiation oncologists using a 3-point Likert scale. Results: Time reduction was achieved using auto-segmentation in 95% of the cases, including correction. The time reduction (mean ± std) was 42.4% ± 26.5% and 58.5% ± 19.1% for OARs and CTVs, respectively, corresponding to an absolute mean reduction (hh:mm:ss) of 00:08:51 and 00:25:38. Good quantitative results were achieved before correction, e.g. mean DSC for the right-sided CTVp was 0.92 ± 0.06, whereas correction statistically significantly improved this contour by only 0.02 ± 0.05, respectively. In 92% of the cases, auto-contours were scored as clinically acceptable, with or without corrections. Conclusions: A DL segmentation model was trained and was shown to be a time-efficient way to generate clinically acceptable contours for locally advanced breast cancer

Narrowing the difference in dose delivery for IOERT and IOBT for locally advanced and locally recurrent rectal cancer

Purpose: Intra-operative radiotherapy (IORT) has been used as a tool to provide a high-dose radiation boost to a limited volume of patients with fixed tumors with a likelihood of microscopically involved resection margins, in order to improve local control. Two main techniques to deliver IORT include high-dose-rate (HDR) brachytherapy, termed 'intra-operative brachytherapy' (IOBT), and electrons, termed 'intra-operative electron radiotherapy' (IOERT), both having very different dose distributions. A recent paper described an improved local recurrence-free survival favoring IOBT over IOERT for patients with locally advanced or recurrent rectal cancer and microscopically irradical resections. Although several factors may have contributed to this result, an important difference between the two techniques was the higher surface dose delivered by IOBT. This article described an adaptation of IOERT technique to achieve a comparable surface dose as dose delivered by IOBT. Material and methods: Two steps were taken to increase the surface dose for IOERT: 1. Introducing a bolus to achieve a maximum dose on the surface, and 2. Re-normalizing to allow for the same prescribed dose at reference depth. Conclusions: We describe and propose an adaptation of IOERT technique to increase surface dose, decreasing the differences between these two techniques, with the aim of further improving local control. In addition, an alternative method of dose prescription is suggested, to consider improved comparison with other techniques in the future