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.

Benefit of respiratory gating in the Danish Breast Cancer Group partial breast irradiation trial.

BACKGROUND AND PURPOSE: Partial breast irradiation (PBI)has beenthe Danish Breast Cancer Group(DBCG) standard for selected breast cancer patients since 2016 based onearlyresults from the DBCG PBI trial.During trial accrual, respiratory-gated radiotherapy was introduced in Denmark. This study aims to investigate the effect of respiratory-gating on mean heart dose (MHD). PATIENTS AND METHODS: From 2009 to 2016 the DBCG PBI trial included 230 patientswith left-sided breast cancer receiving external beam PBI, 40 Gy/15 fractions/3 weeks.Localization of the tumor bed on the planning CT scan, the use of respiratory-gating, coverage of the clinical target volume (CTV), and doses to organs at risk were collected. RESULTS: Respiratory-gating was used in 123 patients (53 %). In 176 patients (77 %) the tumor bed was in the upper and in 54 patients (23 %) in the lower breast quadrants. The median MHD was 0.37 Gy (interquartile range 0.26-0.57 Gy), 0.33 Gy (0.23-0.49 Gy) for respiratory-gating, and 0.49 Gy (0.31-0.70 Gy) for free breathing, p < 0.0001. MHD was < 1 Gy in 206 patients (90 %) and < 2 Gy in 221 patients (96 %). Respiratory-gating led to significantly lower MHD for upper-located, but not for lower-located tumor beds, however, all MHD were low irrespective of respiratory-gating. Respiratory-gating did not improve CTV coverage or lower lung doses. CONCLUSIONS: PBI ensured a low MHD for most patients. Adding respiratory-gating further reduced MHD for upper-located but not for lower-located tumor beds but did not influence target coverage or lung doses. Respiratory-gating is no longer DBCG standard for left-sided PBI.

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.

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.

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.

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.

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.

Recommendations for improved reproducibility of ADC derivation on behalf of the Elekta MRI-linac consortium image analysis working group.

BACKGROUND AND PURPOSE: The apparent diffusion coefficient (ADC), a potential imaging biomarker for radiotherapy response, needs to be reproducible before translation into clinical use. The aim of this study was to evaluate the multi-centre delineation- and calculation-related ADC variation and give recommendations to minimize it. MATERIALS AND METHODS: Nine centres received identical diffusion-weighted and anatomical magnetic resonance images of different cancerous tumours (adrenal gland, pelvic oligo metastasis, pancreas, and prostate). All centres delineated the gross tumour volume (GTV), clinical target volume (CTV), and viable tumour volume (VTV), and calculated ADCs using both their local calculation methods and each of the following calculation conditions: b-values 0-500 vs. 150-500 s/mm2, region-of-interest (ROI)-based vs. voxel-based calculation, and mean vs. median. ADC variation was assessed using the mean coefficient of variation across delineations (CVD) and calculation methods (CVC). Absolute ADC differences between calculation conditions were evaluated using Friedman's test. Recommendations for ADC calculation were formulated based on observations and discussions within the Elekta MRI-linac consortium image analysis working group. RESULTS: The median (range) CVD and CVC were 0.06 (0.02-0.32) and 0.17 (0.08-0.26), respectively. The ADC estimates differed 18% between b-value sets and 4% between ROI/voxel-based calculation (p-values < 0.01). No significant difference was observed between mean and median (p = 0.64). Aligning calculation conditions between centres reduced CVC to 0.04 (0.01-0.16). CVD was comparable between ROI types. CONCLUSION: Overall, calculation methods had a larger impact on ADC reproducibility compared to delineation. Based on the results, significant sources of variation were identified, which should be considered when initiating new studies, in particular multi-centre investigations.

Does coronary artery calcium score have an impact on overall survival for locally advanced non-small cell lung cancer treated with definitive radiotherapy.

BACKGROUND AND PURPOSE: Coronary artery calcium score (CACs) is an excellent marker for survival in non-cancer patients, but its role in locally advanced non-small cell lung cancer (LA-NSCLC) patients remains uncertain. In this study, we hypothesize that CACs is a prognostic marker for survival in a competing risk analysis in LA-NSCLC patients treated with definitive radiotherapy. MATERIALS AND METHODS: We included 644 patients with LA-NSCLC treated in 2014-2015 in Denmark. Baseline patient characteristics were derived from the Danish Lung Cancer Registry. Radiotherapy planning CT scans were used for manual CACs measurements, and the patients were divided into four groups, CACs 0, 1-99, 100-399, and ≥400. A multivariable Cox model utilizing bootstrapping for cross-validation modeled overall survival (OS). RESULTS: The median follow-up time was seven years, and the median OS was 26 months (95% CI 24-29). Within each CAC group 0, 1-99, 100-399, and ≥400 were 172, 182, 143, and 147 patients, respectively. In the univariable analysis, the survival decreased with increasing CACs. However, after adjustment for age, PS, radiotherapy dose, and logarithmic GTV, CACs did not have a statistically significant impact on OS with hazard ratios of 1.04 (95% CI 0.85-1.28), 1.11 (95%CI 0.89-1.43), and 1.16 (95%CI 0.92-1.47) for CACs 1-99, CACs 100-399 and ≥400, respectively. Elevated CACs was observed in 73 % of the patients suggesting a high risk of cardiac comorbidity before radiotherapy. CONCLUSION: CACs did not add prognostic information to our population's classical risk factors, such as tumor volume, performance status, and age; the lung cancer has the highest priority despite the risk of baseline cardiac comorbidity.

Breast cancer, breast cancer-directed radiation therapy and risk of hypothyroidism: A systematic review and meta-analysis.

OBJECTIVE: Breast cancer and breast cancer-directed radiation therapy (RT) may increase the risk of late effects, such as hypothyroidism. We conducted a systematic review and meta-analysis to investigate the association between breast cancer, RT, and risk of hypothyroidism in breast cancer survivors. METHODS: Through February 2022, we searched PubMed, EMBASE, and references of relevant articles, to identify papers on breast cancer and breast cancer-directed RT and subsequent risk of hypothyroidism. Articles were screened by title and abstract and reviewed for eligibility. We used a pre-formed data extraction sheet and identified key design elements that could potentially introduce bias. The main outcome was the confounder-adjusted relative risk (RR) of hypothyroidism in breast cancer survivors versus women without breast cancer, and in breast cancer survivors according to the receipt of RT to the supraclavicular lymph nodes. We used a random-effects model to calculate pooled RRs and associated 95% confidence intervals (95% CI). RESULTS: From 951 papers screened by title and abstract, 34 full-text papers were reviewed for eligibility. We included 20 studies published between 1985 and 2021-19 were cohort studies. Compared with women without breast cancer, the pooled RR of hypothyroidism in breast cancer survivors was 1.48 (95% CI: 1.17, 1.87), with highest risk associated with RT to the supraclavicular region (RR = 1.69, 95% CI: 1.16, 2.46). The most important limitations of the studies were small sample size yielding estimates with low precision, and lack of data on potential confounders. CONCLUSION: Breast cancer and radiation therapy to the supraclavicular lymph nodes is associated with an increased risk of hypothyroidism.

Radiation dose to heart and cardiac substructures and risk of coronary artery disease in early breast cancer patients: A DBCG study based on modern radiation therapy techniques.

BACKGROUND: Coronary artery disease (CAD) has been reported as a late effect following radiation therapy (RT) of early breast cancer (BC). This study aims to report individual RT doses to the heart and cardiac substructures in patients treated with CT-based RT and to investigate if a dose-response relationship between RT dose and CAD exists using modern radiation therapy techniques. METHODS: Patients registered in the Danish Breast Cancer Group database from 2005 to 2016 with CT-based RT were eligible. Among 15,765 patients, the study included 204 with CAD after irradiation (cases) and 408 matched controls. Individual planning CTs were retrieved, the heart and cardiac substructures were delineated and dose-volume parameters were extracted. RESULTS: The median follow-up time was 7.3 years (IQR: 4.6-10.0). Among cases, the median mean heart dose was 1.6 Gy (IQR 0.2-6.1) and 0.8 Gy (0.1-2.9) for left-sided and right-sided patients, respectively (p < 0.001). The highest RT doses were observed in the left ventricle and left anterior descending coronary artery for left-sided RT and in the right atrium and the right coronary artery after right-sided RT. The highest left-minus-right dose-difference was located in the distal part of the left anterior descending coronary artery where also the highest left-versus-right ratio of events was observed. However, no significant difference in the distribution of CAD was observed by laterality. Furthermore, no significant differences in the dose-volume parameters were observed for cases versus controls. CONCLUSIONS: CAD tended to occur in the part of the heart with the highest left-minus- right dose difference, however, no significant risk of CAD was observed at 7 years' median follow-up.

Volume matters: Breast induration is associated with irradiated breast volume in the Danish Breast Cancer Group phase III randomized Partial Breast Irradiation trial.

PURPOSE: The relation between breast induration grade 2-3 at 3 years after radiation therapy and irradiated breast volume was investigated for patients in the Danish Breast Cancer Group (DBCG) Partial Breast Irradiation (PBI) trial. METHODS Treatment plan data was obtained from the Danish radiotherapy plan database. Dosimetric parameters for breast and organs at risk were determined. Breast induration data was obtained from the DBCG database. The volume of the whole breast (CTVp_breast) treated to various dose levels was determined for treatment plans in both arms. Logistic regression was used to assess the frequency of induration on breast volume irradiated to ≥40 Gy. RESULTS PBI and WBI was given to 433 and 432 patients, respectively. Median and interquartile ranges (IQR) for CTVp_breast were 710 mL (467-963 mL; PBI) and 666 mL (443-1012 mL; WBI) (p = 0.98). Median and IQR for CTVp_breast treated to ≥40 Gy was 24.9% (18.6-32.6%; PBI) and 59.8% (53.6-68.5%; WBI). Grade 2-3 induration was observed in 5% (PBI) and 10% (WBI) of the patients. A dose-response relationship was established between irradiated breast volume and frequency of breast induration. From the model, 5% and 10% risks of breast induration were observed for ≥40 Gy delivered to CTVp_breast volumes of 177 mL (95%CI, 94-260 mL) and 426 mL (95%CI, 286-567 mL), respectively. CONCLUSION The frequency of breast induration increased significantly with increasing irradiated breast volume, strongly favouring small volumes and PBI. Thus, treated breast volume - not the breast size itself - is the risk factor for induration. This is the first report directly linking the 40 Gy irradiated breast volume to breast induration.

Partial Breast Irradiation Versus Whole Breast Irradiation for Early Breast Cancer Patients in a Randomized Phase III Trial: The Danish Breast Cancer Group Partial Breast Irradiation Trial.

PURPOSE: On the basis of low risk of local recurrence in elderly patients with breast cancer after conservative surgery followed by whole breast irradiation (WBI), the Danish Breast Cancer Group initiated the noninferiority external-beam partial breast irradiation (PBI) trial (ClinicalTrials.gov identifier: NCT00892814). We hypothesized that PBI was noninferior to WBI regarding breast induration. METHODS: Patients operated with breast conservation for relatively low-risk breast cancer were randomly assigned to WBI versus PBI, and all had 40 Gy/15 fractions. The primary end point was 3-year grade 2-3 breast induration. RESULTS: In total, 865 evaluable patients (434 WBI and 431 PBI) were enrolled between 2009 and 2016. Median follow-up was 5.0 years (morbidity) and 7.6 years (locoregional recurrence). The 3-year rate of induration was 9.7% for WBI and 5.1% for PBI (P = .014). Large breast size was significantly associated with induration with a 3-year incidence of 13% (WBI) and 6% (PBI) for large-breasted patients versus 6% (WBI) and 5% (PBI) for small-breasted patients. PBI showed no increased risk of dyspigmentation, telangiectasia, edema, or pain, and patient satisfaction was high. Letrozole and smoking did not increase the risk of radiation-associated morbidity. Sixteen patients had a locoregional recurrence (six WBI and 10 PBI; P = .28), 20 patients had a contralateral breast cancer, and eight patients had distant failure (five WBI and three PBI). A nonbreast second cancer was detected in 73 patients (8.4%), and there was no difference between groups. CONCLUSION: External-beam PBI for patients with low-risk breast cancer was noninferior to WBI in terms of breast induration. Large breast size was a risk factor for radiation-associated induration. Few recurrences were detected and unrelated to PBI.

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.

Atlas-based auto-segmentation for delineating the heart and cardiac substructures in breast cancer radiation therapy.

BACKGROUND: This study aimed to develop and validate an automatic multi-atlas segmentation method for delineating the heart and substructures in breast cancer radiation therapy (RT). MATERIAL AND METHODS: The atlas database consisted of non-contrast-enhanced planning CT scans from 42 breast cancer patients, each with one manual delineation of the heart and 22 cardiac substructures. Half of the patients were scanned during free-breathing, the rest were scanned during a deep inspiration breath-hold. The auto-segmentation was developed in the MIM software system and validated geometrically and dosimetrically in two steps: The first validation in a small dataset to ensure consistency of the atlas. This was succeeded by a final test where multiple manual delineations in CT scans of 12 breast cancer patients were compared to the auto-segmentation. For geometric evaluation, the dice similarity coefficient (DSC) and the mean surface distance (MSD) were used. For dosimetric evaluation, the RT doses to each substructure in the manual and the automatic delineations were compared. RESULTS: In the first validation, a high geometric and dosimetric performance between the automatic and manual delineations was observed for all substructures. The final test confirmed a high agreement between the automatic and manual delineations for the heart (DSC = 0.94) and the cardiac chambers (DSC: 0.75-0.86). The difference in MSD between the automatic and manual delineations was low (<4 mm) in all structures. Finally, a high correlation between mean RT doses for the automatic and the manual delineations was observed for the heart and substructures. CONCLUSIONS: An automatic segmentation tool for delineation of the heart and substructures in breast cancer RT was developed and validated with a high correlation between the automatic and manual delineations. The atlas is pivotal for large-scale evaluations of radiation-associated heart disease.