Development and comprehensive evaluation of a national DBCG consensus-based auto-segmentation model for lymph node levels in breast cancer radiotherapy.

BACKGROUND AND PURPOSE: This study aimed at training and validating a multi-institutional deep learning (DL) auto segmentation model for nodal clinical target volume (CTVn) in high-risk breast cancer (BC) patients with both training and validation dataset created with multi-institutional participation, with the overall aim of national clinical implementation in Denmark. MATERIALS AND METHODS: A gold standard (GS) dataset and a high-quality training dataset were created by 21 BC delineation experts from all radiotherapy centres in Denmark. The delineations were created according to ESTRO consensus delineation guidelines. Four models were trained: One per laterality and extension of CTVn internal mammary nodes. The DL models were tested quantitatively in their own test-set and in relation to interobserver variation (IOV) in the GS dataset with geometrical metrics, such as the Dice Similarity Coefficient (DSC). A blinded qualitative evaluation was conducted with a national board, presented to both DL and manual delineations. RESULTS: A median DSC > 0.7 was found for all, except the CTVn interpectoral node in one of the models. In the qualitative evaluation 'no corrections needed' were acquired for 297 (36 %) in the DL structures and 286 (34 %) for manual delineations. A higher rate of 'major corrections' and 'easier to start from scratch' was found in the manual delineations. The models performed within the IOV of an expert group, with two exceptions. CONCLUSION: DL models were developed on a national consensus cohort and performed on par with the IOV between BC experts and had a comparable or higher clinical acceptance than expert manual delineations.

Heart and Lung Dose as Predictors of Overall Survival in Patients With Locally Advanced Lung Cancer. A National Multicenter Study.

Introduction: It is an ongoing debate how much lung and heart irradiation impact overall survival (OS) after definitive radiotherapy for lung cancer. This study uses a large national cohort of patients with locally advanced NSCLC to investigate the association between OS and irradiation of lung and heart. Methods: Treatment plans were acquired from six Danish radiotherapy centers, and patient characteristics were obtained from national registries. A hybrid segmentation tool automatically delineated the heart and substructures. Dose-volume histograms for all structures were extracted and analyzed using principal component analyses (PCAs). Parameter selection for a multivariable Cox model for OS prediction was performed using cross-validation based on bootstrapping. Results: The population consisted of 644 patients with a median survival of 26 months (95% confidence interval [CI]: 24-29). The cross-validation selected two PCA variables to be included in the multivariable model. PCA1 represented irradiation of the heart and affected OS negatively (hazard ratio, 1.14; 95% CI: 1.04-1.26). PCA2 characterized the left-right balance (right atrium and left ventricle) irradiation, showing better survival for tumors near the right side (hazard ratio, 0.92; 95% CI: 0.84-1.00). Besides the two PCA variables, the multivariable model included age, sex, body-mass index, performance status, tumor dose, and tumor volume. Conclusions: Besides the classic noncardiac risk factors, lung and heart doses had a negative impact on survival, while it is suggested that the left side of the heart is a more radiation dose-sensitive region. The data indicate that overall heart irradiation should be reduced to improve the OS if possible.

Shared decision making with breast cancer patients - does it work? Results of the cluster-randomized, multicenter DBCG RT SDM trial.

BACKGROUND AND PURPOSE: Shared decision making (SDM) is a patient engaging process advocated especially for preference-sensitive decisions, such as adjuvant treatment after breast cancer. An increasing call for patient engagement in decision making highlights the need for a systematic SDM approach. The objective of this trial was to investigate whether the Decision Helper (DH), an in-consultation patient decision aid, increases patient engagement in decisions regarding adjuvant whole breast irradiation. MATERIAL AND METHODS: Oncologists at four radiotherapy units were randomized to practice SDM using the DH versus usual practice. Patient candidates for adjuvant whole breast irradiation after breast conserving surgery for node-negative breast cancer were eligible. The primary endpoint was patient-reported engagement in the decision process assessed with the Shared Decision Making Questionnaire (SDM-Q-9) (range 0-100, 4 points difference considered clinical relevant). Other endpoints included oncologist-reported patient engagement, decisional conflict, fear of cancer recurrence, and decision regret after 6 months. RESULTS: Of the 674 included patients, 635 (94.2%) completed the SDM-Q-9. Patients in the intervention group reported higher level of engagement (median 80; IQR 68.9 to 94.4) than the control group (71.1; IQR 55.6 to 82.2; p < 0.0001). Oncologist-reported patient engagement was higher in the invention group (93.3; IQR 82.2 to 100) compared to control group (73.3; IQR 60.0 to 84.4) (p < 0.0001). CONCLUSION: Patient engagement in medical decision making was significantly improved with the use of an in-consultation patient decision aid compared to standard. The DH on adjuvant whole breast irradiation is now recommended as standard of care in the Danish guideline.

Radiation therapy in older patients with cancer.

The general population is aging, which expectedly will lead to a future increase in older patients with cancer. This review summarises the recent advances in radiotherapy. Technological advances have led radiotherapy to be an efficient and well-tolerated treatment option in older patient with cancer. Studies show no difference in toxicity and disease control rates compared with the ones in younger patients with cancer. MR-guided radiotherapy, proton therapy, and integration of artificial intelligence in treatment planning represent the latest advances in the field of radiotherapy and hold potential to further improve the treatment of older patients with cancer.

An open source auto-segmentation algorithm for delineating heart and substructures - Development and validation within a multicenter lung cancer cohort.

BACKGROUND AND PURPOSE: Irradiation of the heart in thoracic cancers raises toxicity concerns. For accurate dose estimation, automated heart and substructure segmentation is potentially useful. In this study, a hybrid automatic segmentation is developed. The accuracy of delineation and dose predictions were evaluated, testing the method's potential within heart toxicity studies. MATERIALS AND METHODS: The hybrid segmentation method delineated the heart, four chambers, three large vessels, and the coronary arteries. The method consisted of a nnU-net heart segmentation and partly atlas- and model-based segmentation of the substructures. The nnU-net training and atlas segmentation was based on lung cancer patients and was validated against a national consensus dataset of 12 patients with breast cancer. The accuracy of dose predictions between manual and auto-segmented heart and substructures was evaluated by transferring the dose distribution of 240 previously treated lung cancer patients to the consensus data set. RESULTS: The hybrid auto-segmentation method performed well with a heart dice similarity coefficient (DSC) of 0.95, with no statistically significant difference between the automatic and manual delineations. The DSC for the chambers varied from 0.78-0.86 for the automatic segmentation and was comparable with the inter-observer variability. Most importantly, the automatic segmentation was as precise as the clinical experts in predicting the dose distribution to the heart and all substructures. CONCLUSION: The hybrid segmentation method performed well in delineating the heart and substructures. The prediction of dose by the automatic segmentation was aligned with the manual delineations, enabling measurement of heart and substructure dose in large cohorts. The delineation algorithm will be available for download.

An open-source nnU-net algorithm for automatic segmentation of MRI scans in the male pelvis for adaptive radiotherapy.

Background: Adaptive MRI-guided radiotherapy (MRIgRT) requires accurate and efficient segmentation of organs and targets on MRI scans. Manual segmentation is time-consuming and variable, while deformable image registration (DIR)-based contour propagation may not account for large anatomical changes. Therefore, we developed and evaluated an automatic segmentation method using the nnU-net framework. Methods: The network was trained on 38 patients (76 scans) with localized prostate cancer and tested on 30 patients (60 scans) with localized prostate, metastatic prostate, or bladder cancer treated at a 1.5 T MRI-linac at our institution. The performance of the network was compared with the current clinical workflow based on DIR. The segmentation accuracy was evaluated using the Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff distance (HD) metrics. Results: The trained network successfully segmented all 600 structures in the test set. High similarity was obtained for most structures, with 90% of the contours having a DSC above 0.9 and 86% having an MSD below 1 mm. The largest discrepancies were found in the sigmoid and colon structures. Stratified analysis on cancer type showed that the best performance was seen in the same type of patients that the model was trained on (localized prostate). Especially in patients with bladder cancer, the performance was lower for the bladder and the surrounding organs. A complete automatic delineation workflow took approximately 1 minute. Compared with contour transfer based on the clinically used DIR algorithm, the nnU-net performed statistically better across all organs, with the most significant gain in using the nnU-net seen for organs subject to more considerable volumetric changes due to variation in the filling of the rectum, bladder, bowel, and sigmoid. Conclusion: We successfully trained and tested a network for automatically segmenting organs and targets for MRIgRT in the male pelvis region. Good test results were seen for the trained nnU-net, with test results outperforming the current clinical practice using DIR-based contour propagation at the 1.5 T MRI-linac. The trained network is sufficiently fast and accurate for clinical use in an online setting for MRIgRT. The model is provided as open-source.

A national repository of complete radiotherapy plans: design, Results, and experiences.

BACKGROUND: Previously, many radiotherapy (RT) trials were based on a few selected dose measures. Many research questions, however, rely on access to the complete dose information. To support such access, a national RT plan database was created. The system focuses on data security, ease of use, and re-use of data. This article reports on the development and structure, and the functionality and experience of this national database. METHODS AND MATERIALS: A system based on the DICOM-RT standard, DcmCollab, was implemented with direct connections to all Danish RT centres. Data is segregated into any number of collaboration projects. User access to the system is provided through a web interface. The database has a finely defined access permission model to support legal requirements. RESULTS: Currently, data for more than 14,000 patients have been submitted to the system, and more than 50 research projects are registered. The system is used for data collection, trial quality assurance, and audit data set generation.Users reported that the process of submitting data, waiting for it to be processed, and then manually attaching it to a project was resource intensive. This was accommodated with the introduction of triggering features, eliminating much of the need for users to manage data manually. Many other features, including structure name mapping, RT plan viewer, and the Audit Tool were developed based on user input. CONCLUSION: The DcmCollab system has provided an efficient means to collect and access complete datasets for multi-centre RT research. This stands in contrast with previous methods of collecting RT data in multi-centre settings, where only singular data points were manually reported. To accommodate the evolving legal environment, DcmCollab has been defined as a 'data processor', meaning that it is a tool for other research projects to use rather than a research project in and of itself.

Development of a national deep learning-based auto-segmentation model for the heart on clinical delineations from the DBCG RT nation cohort.

BACKGROUND: This study aimed at investigating the feasibility of developing a deep learning-based auto-segmentation model for the heart trained on clinical delineations. MATERIAL AND METHODS: This study included two different datasets. The first dataset contained clinical heart delineations from the DBCG RT Nation study (1,561 patients). The second dataset was smaller (114 patients), but with corrected heart delineations. Before training the model on the clinical delineations an outlier-detection was performed, to remove cases with gross deviations from the delineation guideline. No outlier detection was performed for the dataset with corrected heart delineations. Both models were trained with a 3D full resolution nnUNet. The models were evaluated with the dice similarity coefficient (DSC), 95% Hausdorff distance (HD95) and Mean Surface Distance (MSD). The difference between the models were tested with the Mann-Whitney U-test. The balance of dataset quantity versus quality was investigated, by stepwise reducing the cohort size for the model trained on clinical delineations. RESULTS: During the outlier-detection 137 patients were excluded from the clinical cohort due to non-compliance with delineation guidelines. The model trained on the curated clinical cohort performed with a median DSC of 0.96 (IQR 0.94-0.96), median HD95 of 4.00 mm (IQR 3.00 mm-6.00 mm) and a median MSD of 1.49 mm (IQR 1.12 mm-2.02 mm). The model trained on the dedicated and corrected cohort performed with a median DSC of 0.95 (IQR 0.93-0.96), median HD95 of 5.65 mm (IQR 3.37 mm-8.62 mm) and median MSD of 1.63 mm (IQR 1.35 mm-2.11 mm). The difference between the two models were found non-significant for all metrics (p > 0.05). Reduction of cohort size showed no significant difference for all metrics (p > 0.05). However, with the smallest cohort size, a few outlier structures were found. CONCLUSIONS: This study demonstrated a deep learning-based auto-segmentation model trained on curated clinical delineations which performs on par with a model trained on dedicated delineations, making it easier to develop multi-institutional auto-segmentation models.

End-to-end framework for automated collection of large multicentre radiotherapy datasets demonstrated in a Danish Breast Cancer Group cohort.

Large Digital Imaging and Communications in Medicine (DICOM) datasets are key to support research and the development of machine learning technology in radiotherapy (RT). However, the tools for multi-centre data collection, curation and standardisation are not readily available. Automated batch DICOM export solutions were demonstrated for a multicentre setup. A Python solution, Collaborative DICOM analysis for RT (CORDIAL-RT) was developed for curation, standardisation, and analysis of the collected data. The setup was demonstrated in the DBCG RT-Nation study, where 86% (n = 7748) of treatments in the inclusion period were collected and quality assured, supporting the applicability of the end-to-end framework.

Consistency in contouring of organs at risk by artificial intelligence vs oncologists in head and neck cancer patients.

BACKGROUND: In the Danish Head and Neck Cancer Group (DAHANCA) 35 trial, patients are selected for proton treatment based on simulated reductions of Normal Tissue Complication Probability (NTCP) for proton compared to photon treatment at the referring departments. After inclusion in the trial, immobilization, scanning, contouring and planning are repeated at the national proton centre. The new contours could result in reduced expected NTCP gain of the proton plan, resulting in a loss of validity in the selection process. The present study evaluates if contour consistency can be improved by having access to AI (Artificial Intelligence) based contours. MATERIALS AND METHODS: The 63 patients in the DAHANCA 35 pilot trial had a CT from the local DAHANCA centre and one from the proton centre. A nationally validated convolutional neural network, based on nnU-Net, was used to contour OARs on both scans for each patient. Using deformable image registration, local AI and oncologist contours were transferred to the proton centre scans for comparison. Consistency was calculated with the Dice Similarity Coefficient (DSC) and Mean Surface Distance (MSD), comparing contours from AI to AI and oncologist to oncologist, respectively. Two NTCP models were applied to calculate NTCP for xerostomia and dysphagia. RESULTS: The AI contours showed significantly better consistency than the contours by oncologists. The median and interquartile range of DSC was 0.85 [0.78 - 0.90] and 0.68 [0.51 - 0.80] for AI and oncologist contours, respectively. The median and interquartile range of MSD was 0.9 mm [0.7 - 1.1] mm and 1.9 mm [1.5 - 2.6] mm for AI and oncologist contours, respectively. There was no significant difference in ΔNTCP. CONCLUSIONS: The study showed that OAR contours made by the AI algorithm were more consistent than those made by oncologists. No significant impact on the ΔNTCP calculations could be discerned.

Impact of abdominal compression on intra-fractional motion and delivered dose in magnetic resonance image-guided adaptive radiation ablation of adrenal gland metastases.

PURPOSE: The current study investigated the impact of abdominal compression on motion and the delivered dose during non-gated, magnetic resonance image (MRI)-guided radiation ablation of adrenal gland metastases. METHODS: Thirty-one patients with adrenal gland metastases treated to 45-60 Gy in 3-8 fractions on a 1.5 T MRI-linac were included in the study. The patients were breathing freely (n = 14) or with motion restricted by using an abdominal compression belt (n = 17). The time-resolved position of the target in online 2D cine MR images acquired during treatment was assessed and used to estimate the dose delivered to the GTV and abutting luminal organs at risk (OAR). RESULTS: The median (range) 3D root-mean-square target position error was significantly higher in patients treated without a compression belt [2.9 (1.9-5.6) mm] compared to patients using the belt [2.1 (1.2-3.5) mm] (P < 0.01). The median (range) GTV V95% was significantly reduced from planned 98.6 (65.9-100) % to delivered 96.5 (64.5-99.9) % due to motion (P < 0.01). Most prominent dose reductions were found in patients showing either large target drift or respiration motion and were mainly treated without abdominal compression. Motion did not lead to an increased number of constraint violations for luminal OAR. CONCLUSIONS: Acceptable target coverage and dose to OAR was observed in the vast majority of patients despite intra-fractional motion during adaptive MRI-guided radiation ablation. The use of abdominal compression significantly reduced the target position error and prevented the most prominent target coverage degradations and is, therefore, recommended as motion management at MRI-linacs.

Proton therapy for early breast cancer patients in the DBCG proton trial: planning, adaptation, and clinical experience from the first 43 patients.

BACKGROUND: The Danish Breast Cancer Group (DBCG) Proton Trial randomizes breast cancer patients selected on high mean heart dose (MHD) or high lung dose (V20Gy/V17Gy) in the photon plan between photon and proton therapy. This study presents the proton plans and adaptation strategy for the first 43 breast cancer patients treated with protons in Denmark. MATERIAL AND METHODS: Forty-four proton plans (one patient with bilateral cancer) were included; 2 local and 42 loco-regional including internal mammary nodes (IMN). Nineteen patients had a mastectomy and 25 a lumpectomy. The prescribed dose was either 50 Gy in 25 fractions (n = 30) or 40 Gy in 15 fractions (n = 14) wherefrom five received simultaneous integrated boost to the tumor bed. Using 2-3 en face proton fields, single-field optimization, robust optimization and a 5 cm range shifter ensured robustness towards breathing motion, setup- and range uncertainties. An anatomical evaluation was performed by evaluating the dose after adding/removing 3 mm and 5 mm tissue to/from the body-outline and used to define treatment tolerances for anatomical changes. RESULTS: The nominal and robust criteria were met for all patients except two. The median MHD was 1.5 Gy (0.5-3.4 Gy, 50 Gy) and 1.1 Gy (0.0-1.5 Gy, 40 Gy). The anatomical evaluations showed how 5 mm shrinkage approximately doubled the MHD while 5 mm swelling reduced target coverage of the IMN below constraints. Ensuring 3-5 mm robustness toward swelling was prioritized but not always achieved by robust optimization alone emphasizing the need for a distal margin. Twenty-eight patients received plan adaptation, eight patients received two, and one received five. CONCLUSION: This proton planning strategy ensured robust treatment plans within a pre-defined level of acceptable anatomical changes that fulfilled the planning criteria for most of the patients and ensured low MHD.

A national study on the inter-observer variability in the delineation of organs at risk in the brain.

BACKGROUND: The Danish Neuro Oncology Group (DNOG) has established national consensus guidelines for the delineation of organs at risk (OAR) structures based on published literature. This study was conducted to finalise these guidelines and evaluate the inter-observer variability of the delineated OAR structures by expert observers. MATERIAL AND METHODS: The DNOG delineation guidelines were formed by participants from all Danish centres that treat brain tumours with radiotherapy. In a two-day workshop, guidelines were discussed and finalised based on a pilot study. Following this, the ten participants contoured the following OARs on T1-weighted gadolinium enhanced MRI from 13 patients with brain tumours: optic tracts, optic nerves, chiasm, spinal cord, brainstem, pituitary gland and hippocampus. The metrics used for comparison were the Dice similarity coefficient (Dice), mean surface distance (MSD) and others. RESULTS: A total of 968 contours were delineated across the 13 patients. On average eight (range six to nine) individual contour sets were made per patient. Good agreement was found across all structures with a median MSD below 1 mm for most structures, with the chiasm performing the best with a median MSD of 0.45 mm. The Dice was as expected highly volume dependent, the brainstem (the largest structure) had the highest Dice value with a median of 0.89 whereas smaller volumes such as the chiasm had a Dice of 0.71. CONCLUSION: Except for the caudal definition of the spinal cord, the variances observed in the contours of OARs in the brain were generally low and consistent. Surface mapping revealed sub-regions of higher variance for some organs. The data set is being prepared as a validation data set for auto-segmentation algorithms for use within the Danish Comprehensive Cancer Centre - Radiotherapy and potential collaborators.

Automatic treatment planning of VMAT for left-sided breast cancer with lymph nodes.

BACKGROUND: The standard in Denmark for treating breast cancer patients receiving loco-regional irradiation is tangential 3D Conformal RadioTherapy (3DCRT), treated in deep inspiration breath-hold (DIBH). Treating with Volumetric Modulated Arc Therapy (VMAT) may reduce the treatment time, which is particularly important for DIBH treatments. The VMAT should be performed without increased dose to the heart, lung, and contralateral breast. This study compares VMAT and 3DCRT for left-sided breast cancer patients with intramammary lymph node involvement. MATERIAL AND METHODS: Twenty left-sided breast cancer patients were included. VMAT and tangential plans were created for all patients, with a prescription dose of 50 Gy. The tangential plans used 6 MV and for larger breast combined with 18 MV. The VMAT plans utilised two 6 MV fields in a butterfly configuration. Dose planning was done in Pinnacle3 16.0 using the Auto-Planning module for the VMAT plans. Comparison of the plans was based on: mean doses, metrics provided by DBCG guidelines, dose-volume histograms and required number of breath-holds for treatment delivery in DIBH. RESULTS: For most OAR, the doses were similar for VMAT and 3DCRT. The target coverage was comparable, with VMAT having a statistically significant improved dose homogeneity of the target volumes. Less than half the number of breath-hold was required for VMAT compared to 3DCRT. Mean gamma pass rates (3 mm and 3%) from ArcCHECK of the VMAT plans was 98.4% (range 96.6-99.8%). CONCLUSION: Automatic VMAT planning of left-sided breast cancer patients with lymph node involvement can produce dose distributions comparable to those of tangential 3DCRT, while reducing the number of breath-holds in DIBH by more than a factor of two. The reduction in breath-holds is beneficial for patient comfort and reduces the risk of intra-fraction patient motion.

Selection criteria for early breast cancer patients in the DBCG proton trial - The randomised phase III trial strategy.

BACKGROUND AND PURPOSE: Adjuvant radiotherapy of internal mammary nodes (IMN) improves survival in high-risk early breast cancer patients but inevitably leads to more dose to heart and lung. Target coverage is often compromised to meet heart/lung dose constraints. We estimate heart and lung dose when target coverage is not compromised in consecutive patients. These estimates are used to guide the choice of selection criteria for the randomised Danish Breast Cancer Group (DBCG) Proton Trial. MATERIALS AND METHODS: 179 breast cancer patients already treated with loco-regional IMN radiotherapy from 18 European departments were included. If the clinically delivered treatment plan did not comply with defined target coverage requirements, the plan was modified retrospectively until sufficient coverage was reached. The choice of selection criteria was based on the estimated number of eligible patients for different heart and lung dose thresholds in combination with proton therapy capacity limitations and dose-response relationships for heart and lung. RESULTS: Median mean heart dose was 3.0 Gy (range, 1.1-8.2 Gy) for left-sided and 1.4 Gy (0.4-11.5 Gy) for right-sided treatment plans. Median V17Gy/V20Gy (hypofractionated/normofractionated plans) for ipsilateral lung was 31% (9-57%). The DBCG Radiotherapy Committee chose mean heart dose ≥ 4 Gy and/or lung V17Gy/V20Gy ≥ 37% as thresholds for inclusion in the randomised trial. Using these thresholds, we estimate that 22% of patients requiring loco-regional IMN radiotherapy will be eligible for the trial. CONCLUSION: The patient selection criteria for the DBCG Proton Trial are mean heart dose ≥ 4 Gy and/or lung V17Gy/V20Gy ≥ 37%.

Analysis of cardiac substructure dose in a large, multi-centre danish breast cancer cohort (the DBCG HYPO trial): Trends and predictive modelling.

BACKGROUND AND PURPOSE: Radiotherapy for breast cancer can increase the risks of heart disease. Patient-specific risk assessment may be improved with the inclusion of doses to cardiac substructures. The purpose of this work was to use automatic segmentation to evaluate substructure doses and develop predictive models for these based on the dose to the whole heart. MATERIAL AND METHODS: Automatic segmentation was used to delineate cardiac substructures in a Danish breast cancer trial (DBCG HYPO) dataset comprising over 1500 Danish women treated between 2009 and 2014. Trends in contouring practices and cardiac doses over time were investigated, and models to predict substructure doses from whole heart dose parameters were fit to the data. RESULTS: Manual contouring consistency improved over the study period when compared with automatic segmentation; systematic differences between automatically and manually defined heart volume decreased from 106 cm3 to 12.0 cm3. Doses to the heart and cardiac substructures also decreased. Mean whole heart doses for left-sided treatments in 2009 and 2014 were 1.94±1.19 Gy and 1.29±0.69 Gy (average ± SD), respectively. Prediction of mean substructure doses is accurate, with R2 scores in the range 0.45-0.95 (average 0.77), depending on the particular structure. CONCLUSION: This study reports heart and cardiac substructure doses in a large breast cancer cohort. Predictive models generated in this work can be used to estimate mean cardiac substructure doses for datasets where patient imaging and dose distributions are not available, provided the tangential field techniques are consistent with those used in the trial.

Delineation of whole heart and substructures in thoracic radiation therapy: National guidelines and contouring atlas by the Danish Multidisciplinary Cancer Groups.

BACKGROUND AND PURPOSE: This study presents Danish consensus guidelines for delineation of the heart and cardiac substructures across relevant Danish Multidisciplinary Cancer Groups. MATERIAL AND METHODS: Consensus guidelines for the heart and cardiac substructures were reached among 15 observers representing the radiotherapy (RT) committees of four Danish Multidisciplinary Cancer Groups. The guidelines were validated on CT scans of 12 patients, each with five independent contour sets. The Sørensen-Dice similarity coefficient (DSC), the distance between the centers of the arteries and the mean surface distance were used to evaluate the inter-observer variation. RESULTS: National guidelines for contouring the heart and cardiac substructures were achieved. The median DSC was 0.78-0.96 for the heart and the four cardiac chambers. For the four substructures of the left ventricle, the median DSC was 0.35-0.57. The coronary arteries were contoured in ten segments, with the best agreement for the left anterior descending coronary artery segments, with a median distance between the arteries ranging from 2.4-4.4 mm. The median variation was 3.7-12.8 mm for the right coronary artery segments and 3.7-6.2 mm for the left circumflex coronary artery segments, with the most pronounced inter-observer variation in the distal segment for all three coronary arteries. CONCLUSION: National guidelines for contouring the heart and cardiac substructures were developed across relevant Danish Multidisciplinary Cancer Groups, where RT dose to the heart is of concern. The inter-observer contour overlap was best for the heart and chambers and decreased for smaller structures.

Hypothyroidism and the risk of breast cancer recurrence and all-cause mortality - a Danish population-based study.

BACKGROUND: Hypothyroidism may occur as a late effect of breast cancer-directed treatment, particularly after radiotherapy, but little is known whether hypothyroidism affects the prognosis after breast cancer. We investigated the association between hypothyroidism and breast cancer recurrence, and all-cause mortality. METHODS: In this population-based cohort study, we used national medical registries to identify all Danish women 35 years or older diagnosed with stage I-III, operable breast cancer between 1996 and 2009. Hypothyroidism was defined as hospital diagnoses ascertained via diagnostic codes, or as prescriptions for levothyroxine. Two analytic models were used: (i) hypothyroidism present at the time of the breast cancer diagnosis (prevalent) and (ii) hypothyroidism diagnosed during follow-up as a time-varying exposure lagged by 1 year (incident). Breast cancer recurrence was defined as any local, regional, or distant recurrence or contralateral breast cancer. All-cause mortality included death from any cause in any setting. We used Cox regression models accounting for competing risks to compute adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) of breast cancer recurrence and all-cause mortality. RESULTS: The study cohort included 35,463 women with breast cancer with 212,641 person-years of follow-up. At diagnosis, 1272 women had hypothyroidism and 859 women developed hypothyroidism during follow-up. In total, 5810 patients developed recurrent breast cancer. Neither prevalent nor incident hypothyroidism was associated with breast cancer recurrence (adjusted HRprevalent 1.01, 95% CI 0.87-1.19; adjusted HRincident 0.93, 95% CI 0.75-1.16, respectively). Furthermore, no differences were seen for all-cause mortality for prevalent or incident hypothyroidism (adjusted HRprevalent 1.02, 95% CI 0.92-1.14, and HRincident 1.08, 95% CI 0.95-1.23, respectively). Stratification by menopausal status, oestrogen receptor status, chemotherapy, or radiotherapy did not alter the estimates. CONCLUSIONS: Hypothyroidism present at diagnosis or during follow-up was not associated with breast cancer recurrence or all-cause mortality in women with breast cancer. Our findings provide reassurance to patients and their physicians that hypothyroidism is unlikely to impact on the clinical course of breast cancer or survival.

Contouring and dose calculation in head and neck cancer radiotherapy after reduction of metal artifacts in CT images.

BACKGROUND: Delineation accuracy of the gross tumor volume (GTV) in radiotherapy planning for head and neck (H&N) cancer is affected by computed tomography (CT) artifacts from metal implants which obscure identification of tumor as well as organs at risk (OAR). This study investigates the impact of metal artifact reduction (MAR) in H&N patients in terms of delineation consistency and dose calculation precision in radiation treatment planning. MATERIAL AND METHODS: Tumor and OAR delineations were evaluated in planning CT scans of eleven oropharynx patients with streaking artifacts in the tumor region preceding curative radiotherapy (RT). The GTV-tumor (GTV-T), GTV-node and parotid glands were contoured by four independent observers on standard CT images and MAR images. Dose calculation was evaluated on thirty H&N patients with dental implants near the treated volume. For each patient, the dose derived from the clinical treatment plan using the standard image set was compared with the recalculated dose on the MAR image dataset. RESULTS: Reduction of metal artifacts resulted in larger volumes of all delineated structures compared to standard reconstruction. The GTV-T and the parotids were on average 22% (p < 0.06) and 7% larger (p = 0.005), respectively, in the MAR image plan compared to the standard image plan. Dice index showed reduced inter-observer variations after reduction of metal artifacts for all structures. The average surface distance between contours of different observers improved using the MAR images for GTV and parotids (p = 0.04 and p = 0.01). The median volume receiving a dose difference larger than ±3% was 2.3 cm3 (range 0-32 cm3). CONCLUSIONS: Delineation of structures in the head and neck were affected by metal artifacts and volumes were generally larger and more consistent after reduction of metal artifacts, however, only small changes were observed in the dose calculations.

Risk of heart disease in relation to radiotherapy and chemotherapy with anthracyclines among 19,464 breast cancer patients in Denmark, 1977-2005.

BACKGROUND AND PURPOSE: The risk of heart disease subsequent to breast cancer radiotherapy was examined with particular focus on women receiving anthracycline-containing chemotherapy. MATERIAL AND METHODS: Women diagnosed with early-stage breast cancer in Denmark, 1977-2005, were identified from the register of the Danish Breast Cancer Cooperative Group, as was information on cancer-directed treatment. Information on heart disease was sought from the Danish National Patient and Cause of Death Registries. Incidence rate ratios were estimated comparing left-sided with right-sided cancer (IRR, LvR), stratified by calendar year, age, and time since breast cancer radiotherapy. RESULTS: Among 19,464 women receiving radiotherapy, the IRR, LvR, was 1.11 (95% CI 1.03-1.20, p=0.005) for all heart disease and among those also receiving anthracyclines the IRR, LvR, was 1.32 (95% CI 1.02-1.70, p=0.03). This risk was highest if the treatment was given before the age of 50years (IRR, LvR, 1.44, (95% CI 1.04-2.01) but there was no significant trend with age or time since treatment. CONCLUSIONS: Radiotherapy for left-sided breast cancer is associated with a higher risk of heart disease than for right-sided with the largest increases seen in women who also received anthracycline-containing chemotherapy.