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

BackgroundAdaptive 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.MethodsThe 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.ResultsThe 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.ConclusionWe 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

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

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%

Automatic segmentation of the heart in radiotherapy for breast cancer

<div><p></p><p><b>Background.</b> The aim of this study was to evaluate two fully automatic segmentation methods in comparison with manual delineations for their use in delineating the heart on planning computed tomography (CT) used in radiotherapy for breast cancer.</p><p><b>Material and methods.</b> Automatic delineation of heart in 15 breast cancer patients was performed by two different automatic delineation systems. Analysis of accuracy and precision of the differences between manual and automatic delineations were evaluated on volume, mean dose, maximum dose and spatial distance differences. Two sets of manual delineations were used in the evaluation: 1) a set prior to common delineation guidelines; and 2) a second set repeated with a common set of guidelines.</p><p><b>Results.</b> Systematic differences between automatic and manual delineations were small for volume as well as dose. The uncertainty of the difference in volume was smaller than or similar to the inter-observer variation in manual delineations. For dose, the uncertainty was similar to manual delineations performed without common guidelines but slightly higher than the variation in manual delineations with common guidelines. Spatial differences between average manual and automatic delineations were largest at the base of the heart, where also large variations are observed in the manual delineations. Both algorithms could be improved slightly at the apex of the heart where the variation of automatic delineation was larger than for the manual delineations.</p><p><b>Conclusion.</b> Automatic delineation is an equal alternative to manual delineation when compared to the inter-observer variation. The reduction in precision of measured dose was small compared to other uncertainties affecting the estimated heart dose and would for most applications be outweighed by the benefits of fully automated delineations.</p></div

Validation of a new open-source method for automatic delineation and dose assessment of the heart and LADCA in breast radiotherapy with simultaneous uncertainty estimation

Radiotherapy has been shown to increase risks of cardiotoxicities for breast cancer patients. Automated delineation approaches are necessary for consistent and efficient assessment of cardiac doses in large, retrospective datasets, while patient-specific estimation of the uncertainty in these doses provides valuable additional data for modelling and understanding risks. In this work, we aim to validate the consistency of our previously described open-source software model for automatic cardiac delineation in the context of dose assessment, relative to manual contouring. We also extend our software to introduce a novel method to automatically quantify the uncertainty in cardiac doses based on expected inter-observer variability (IOV) in contouring. This method was applied to a cohort of 15 left-sided breast cancer patients treated in Denmark using modern tangential radiotherapy techniques. On each image set, the whole heart and left anterior descending coronary artery (LADCA) were contoured by nine independent experts; the range of doses to these nine volumes provided a reference for the dose uncertainties generated from the automatic method. Local and external atlas sets were used to test the method. Results give confidence in the consistency of automatic segmentations, with mean whole heart dose differences for local and external atlas sets of −0.20 ± 0.17 and −0.10 ± 0.14 Gy, respectively. Automatic estimates of uncertainties in doses are similar to those from IOV for both the whole heart and LADCA. Overall, this study confirms that our automated approach can be used to accurately assess cardiac doses, and the proposed method can provide a useful tool in estimating dose uncertainties

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

Abstract 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

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.</p