| 1. |
- Alkner, Sara, et al.
(författare)
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Protocol for the T-REX-trial: tailored regional external beam radiotherapy in clinically node-negative breast cancer patients with 1-2 sentinel node macrometastases - an open, multicentre, randomised non-inferiority phase 3 trial.
- 2023
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Ingår i: BMJ open. - 2044-6055. ; 13:9
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Tidskriftsartikel (refereegranskat)abstract
- Modern systemic treatment has reduced incidence of regional recurrences and improved survival in breast cancer (BC). It is thus questionable whether regional radiotherapy (RT) is still beneficial in patients with sentinel lymph node (SLN) macrometastasis. Postoperative regional RT is associated with an increased risk of arm morbidity, pneumonitis, cardiac disease and secondary cancer. Therefore, there is a need to individualise regional RT in relation to the risk of recurrence.In this multicentre, prospective randomised trial, clinically node-negative patients with oestrogen receptor-positive, HER2-negative BC and 1-2 SLN macrometastases are eligible. Participants are randomly assigned to receive regional RT (standard arm) or not (intervention arm). Regional RT includes the axilla level I-III, the supraclavicular fossa and in selected patients the internal mammary nodes. Both groups receive RT to the remaining breast. Chest-wall RT after mastectomy is given in the standard arm, but in the intervention arm only in cases of widespread multifocality according to national guidelines. RT quality assurance is an integral part of the trial.The trial aims to include 1350 patients between March 2023 and December 2028 in Sweden and Norway. Primary outcome is recurrence-free survival (RFS) at 5years. Non-inferiority will be declared if outcome in the de-escalation arm is not >4.5percentage units below that with regional RT, corresponding to an HR of 1.41 assuming 88% 5-year RFS with standard treatment. Secondary outcomes include locoregional recurrence, overall survival, patient-reported arm morbidity and health-related quality of life. Gene expression analysis and tumour tissue-based studies to identify prognostic and predictive markers for benefit of regional RT are included.The trial protocol is approved by the Swedish Ethics Authority (Dnr-2022-02178-01, 2022-05093-02, 2023-00826-02, 2023-03035-02). Results will be presented at scientific conferences and in peer-reviewed journals.NCT05634889.
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| 2. |
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| 3. |
- Appelgren, M., et al.
(författare)
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Patient-reported outcomes one year after positive sentinel lymph node biopsy with or without axillary lymph node dissection in the randomized SENOMAC trial
- 2022
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Ingår i: Breast. - : Elsevier BV. - 0960-9776 .- 1532-3080. ; 63, s. 16-23
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: This report evaluates whether health related quality of life (HRQoL) and patient-reported arm morbidity one year after axillary surgery are affected by the omission of axillary lymph node dissection (ALND). Methods: The ongoing international non-inferiority SENOMAC trial randomizes clinically node-negative breast cancer patients (T1-T3) with 1-2 sentinel lymph node (SLN) macrometastases to completion ALND or no further axillary surgery. For this analysis, the first 1181 patients enrolled in Sweden and Denmark between March 2015, and June 2019, were eligible. Data extraction from the trial database was on November 2020. This report covers the secondary outcomes of the SENOMAC trial: HRQoL and patient-reported arm morbidity. The EORTC QLQC30, EORTC QLQ-BR23 and Lymph-ICF questionnaires were completed in the early postoperative phase and at one-year follow-up. Adjusted one-year mean scores and mean differences between the groups are presented corrected for multiple testing.
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| 4. |
- Gorgisyan, Jenny, et al.
(författare)
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Evalutation of two commercial deep learning OAR segmentation models for prostate cancer treatment
- 2022
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Konferensbidrag (refereegranskat)abstract
- Purpose or ObjectiveTo evaluate two commercial, CE labeled deep learning-based models for automatic organs at risk segmentation on planning CT images for prostate cancer radiotherapy. Model evaluation was focused on assessing both geometrical metrics and evaluating a potential time saving.Material and MethodsThe evaluated models consisted of RayStation 10B Deep Learning Segmentation (RaySearch Laboratories AB, Stockholm, Sweden) and MVision AI Segmentation Service (MVision, Helsinki, Finland) and were applied to CT images for a dataset of 54 male pelvis patients. The RaySearch model was re-trained with 44 clinic specific patients (Skåne University Hospital, Lund, Sweden) for the femoral head structures to adjust the model to our specific delineation guidelines. The model was evaluated on 10 patients from the same clinic. Dice similarity coefficient (DSC) and Hausdorff distance (95th percentile) was computed for model evaluation, using an in-house developed Python script. The average time for manual and AI model delineations was recorded.ResultsAverage DSC scores and Hausdorff distances for all patients and both models are presented in Figure 1 and Table 1, respectively. The femoral head segmentations in the re-trained RaySearch model had increased overlap with our clinical data, with a DSC (mean±1 STD) for the right femoral head of 0.55±0.06 (n=53) increasing to 0.91±0.02 (n=10) and mean Hausdorff (mm) decreasing from 55±7 (n=53) to 4±1 (n=10) (similar results for the left femoral head). The deviation in femoral head compared to the RaySearch and MVision original models occurred due to a difference in the femoral head segmentation guideline in the clinic specific data, see Figure 2. Time recording of manual delineation was 13 minutes compared to 0.5 minutes (RaySearch) and 1.4 minutes (MVision) for the AI models, manual correction not included.ConclusionBoth AI models demonstrate good segmentation performance for bladder and rectum. Clinic specific training data (or data that complies to the clinic specific delineation guideline) might be necessary to achieve segmentation results in accordance to the clinical specific standard for some anatomical structures, such as the femoral heads in our case. The time saving was around 90%, not including manual correction.
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| 5. |
- Lempart, Michael, et al.
(författare)
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Pelvic U-Net : multi-label semantic segmentation of pelvic organs at risk for radiation therapy anal cancer patients using a deeply supervised shuffle attention convolutional neural network
- 2022
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Ingår i: Radiation Oncology. - : Springer Science and Business Media LLC. - 1748-717X. ; 17:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Delineation of organs at risk (OAR) for anal cancer radiation therapy treatment planning is a manual and time-consuming process. Deep learning-based methods can accelerate and partially automate this task. The aim of this study was to develop and evaluate a deep learning model for automated and improved segmentations of OAR in the pelvic region. Methods: A 3D, deeply supervised U-Net architecture with shuffle attention, referred to as Pelvic U-Net, was trained on 143 computed tomography (CT) volumes, to segment OAR in the pelvic region, such as total bone marrow, rectum, bladder, and bowel structures. Model predictions were evaluated on an independent test dataset (n = 15) using the Dice similarity coefficient (DSC), the 95th percentile of the Hausdorff distance (HD95), and the mean surface distance (MSD). In addition, three experienced radiation oncologists rated model predictions on a scale between 1–4 (excellent, good, acceptable, not acceptable). Model performance was also evaluated with respect to segmentation time, by comparing complete manual delineation time against model prediction time without and with manual correction of the predictions. Furthermore, dosimetric implications to treatment plans were evaluated using different dose-volume histogram (DVH) indices. Results: Without any manual corrections, mean DSC values of 97%, 87% and 94% were found for total bone marrow, rectum, and bladder. Mean DSC values for bowel cavity, all bowel, small bowel, and large bowel were 95%, 91%, 87% and 81%, respectively. Total bone marrow, bladder, and bowel cavity segmentations derived from our model were rated excellent (89%, 93%, 42%), good (9%, 5%, 42%), or acceptable (2%, 2%, 16%) on average. For almost all the evaluated DVH indices, no significant difference between model predictions and manual delineations was found. Delineation time per patient could be reduced from 40 to 12 min, including manual corrections of model predictions, and to 4 min without corrections. Conclusions: Our Pelvic U-Net led to credible and clinically applicable OAR segmentations and showed improved performance compared to previous studies. Even though manual adjustments were needed for some predicted structures, segmentation time could be reduced by 70% on average. This allows for an accelerated radiation therapy treatment planning workflow for anal cancer patients.
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| 6. |
- Lerner, Minna, et al.
(författare)
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Clinical validation of a commercially available deep learning software for synthetic CT generation for brain
- 2021
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Ingår i: Radiation Oncology. - : Springer Science and Business Media LLC. - 1748-717X. ; 16:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Most studies on synthetic computed tomography (sCT) generation for brain rely on in-house developed methods. They often focus on performance rather than clinical feasibility. Therefore, the aim of this work was to validate sCT images generated using a commercially available software, based on a convolutional neural network (CNN) algorithm, to enable MRI-only treatment planning for the brain in a clinical setting. Methods: This prospective study included 20 patients with brain malignancies of which 14 had areas of resected skull bone due to surgery. A Dixon magnetic resonance (MR) acquisition sequence for sCT generation was added to the clinical brain MR-protocol. The corresponding sCT images were provided by the software MRI Planner (Spectronic Medical AB, Sweden). sCT images were rigidly registered and resampled to CT for each patient. Treatment plans were optimized on CT and recalculated on sCT images for evaluation of dosimetric and geometric endpoints. Further analysis was also performed for the post-surgical cases. Clinical robustness in patient setup verification was assessed by rigidly registering cone beam CT (CBCT) to sCT and CT images, respectively. Results: All sCT images were successfully generated. Areas of bone resection due to surgery were accurately depicted. Mean absolute error of the sCT images within the body contour for all patients was 62.2 ± 4.1 HU. Average absorbed dose differences were below 0.2% for parameters evaluated for both targets and organs at risk. Mean pass rate of global gamma (1%/1 mm) for all patients was 100.0 ± 0.0% within PTV and 99.1 ± 0.6% for the full dose distribution. No clinically relevant deviations were found in the CBCT-sCT vs CBCT-CT image registrations. In addition, mean values of voxel-wise patient specific geometric distortion in the Dixon images for sCT generation were below 0.1 mm for soft tissue, and below 0.2 mm for air and bone. Conclusions: This work successfully validated a commercially available CNN-based software for sCT generation. Results were comparable for sCT and CT images in both dosimetric and geometric evaluation, for both patients with and without anatomical anomalies. Thus, MRI Planner is feasible to use for radiotherapy treatment planning of brain tumours.
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| 7. |
- Lerner, Minna, et al.
(författare)
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MRI-only based treatment with a commercial deep-learning generation method for synthetic CT of brain
- 2020
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Ingår i: ; , s. 47-47
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Konferensbidrag (refereegranskat)abstract
- ObjectivesTo show feasibility of synthetic computed tomography (sCT) images generated using a commerciallyavailable software, enabling MRI-only treatment planning for the brain in a clinical setting.Patients and Methods20 and 16 patients with brain malignancies, including post-surgical cases, were included for validationand treatment, respectively. Dixon MR images of the skull were exported to the MRI Planner software(Spectronic Medical AB), which utilizes convolutional neural network algorithms for sCT generation.In the validation study, sCT images were rigidly registered and resampled to CT geometry for eachpatient. Treatment plans were optimized on CT and retrospectively recalculated on sCT images forevaluation of dosimetric and geometric endpoints. Clinical robustness in patient setup verification wasassessed by rigidly registering cone beam CT (CBCT) to sCT and CT images, respectively.The treatment study was performed on sCT images, using CT solely for QA purposes.ResultsAll sCT images were successfully generated in the validation study. Mean absolute error of the sCTimages within the body contour for all patients was 62.2 ± 4.1 HU. Average absorbed dose differenceswere below 0.2%. Mean pass rate of global gamma (1%/1mm) for all patients was 100.0 ± 0.0 % withinPTV and 99.1 ± 0.6 % for the full dose distribution. No clinically relevant deviations were found in theCBCT-sCT vs CBCT-CT image registrations. Areas of bone resection due to surgery were accuratelydepicted in the sCT images. Finally, treatment success rate was 15/16. One patient was excluded due tosCT artifacts from a haemostatic substance injected during surgery.Conclusion15 patients have successfully received MRI-only RT for brain tumours using the validated commerciallyavailable sCT software. Validation showed comparable results between sCT and CT images for bothdosimetric and geometric endpoints
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| 8. |
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| 9. |
- Lerner, Minna, et al.
(författare)
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Prospective Clinical Feasibility Study for MRI-Only Brain Radiotherapy
- 2022
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Ingår i: Frontiers in Oncology. - : Frontiers Media SA. - 2234-943X. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: MRI-only radiotherapy (RT) provides a workflow to decrease the geometric uncertainty introduced by the image registration process between MRI and CT data and to streamline the RT planning. Despite the recent availability of validated synthetic CT (sCT) methods for the head region, there are no clinical implementations reported for brain tumors. Based on a preceding validation study of sCT, this study aims to investigate MRI-only brain RT through a prospective clinical feasibility study with endpoints for dosimetry and patient setup. Material and Methods: Twenty-one glioma patients were included. MRI Dixon images were used to generate sCT images using a CE-marked deep learning-based software. RT treatment plans were generated based on MRI delineated anatomical structures and sCT for absorbed dose calculations. CT scans were acquired but strictly used for sCT quality assurance (QA). Prospective QA was performed prior to MRI-only treatment approval, comparing sCT and CT image characteristics and calculated dose distributions. Additional retrospective analysis of patient positioning and dose distribution gamma evaluation was performed. Results: Twenty out of 21 patients were treated using the MRI-only workflow. A single patient was excluded due to an MRI artifact caused by a hemostatic substance injected near the target during surgery preceding radiotherapy. All other patients fulfilled the acceptance criteria. Dose deviations in target were within ±1% for all patients in the prospective analysis. Retrospective analysis yielded gamma pass rates (2%, 2 mm) above 99%. Patient positioning using CBCT images was within ± 1 mm for registrations with sCT compared to CT. Conclusion: We report a successful clinical study of MRI-only brain radiotherapy, conducted using both prospective and retrospective analysis. Synthetic CT images generated using the CE-marked deep learning-based software were clinically robust based on endpoints for dosimetry and patient positioning.
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| 10. |
- Salvestrini, Viola, et al.
(författare)
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Safety profile of trastuzumab-emtansine (T-DM1) with concurrent radiation therapy : A systematic review and meta-analysis
- 2023
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Ingår i: Radiotherapy and Oncology. - : ELSEVIER IRELAND LTD. - 0167-8140 .- 1879-0887. ; 186
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Forskningsöversikt (refereegranskat)abstract
- Background and Purpose: In recent years, the treatment landscape for breast cancer has undergone significant advancements, with the introduction of several new anticancer agents. One such agent is trastuzumab emtansine (T-DM1), an antibody drug conjugate that has shown improved outcomes in both early and advanced breast cancer. However, there is currently a lack of comprehensive evidence regarding the safety profile of combining T-DM1 with radiation therapy (RT). In this study, we aim to provide a summary of the available data on the safety of combining RT with T-DM1 in both early and metastatic breast cancer settings. Materials and Methods: This systematic review and meta-analysis project is part of the consensus recommendations by the European Society for Radiotherapy and Oncology (ESTRO) Guidelines Committee on integrating RT with targeted treatments for breast cancer. A thorough literature search was conducted using the PUBMED/MedLine, Embase, and Cochrane databases to identify original studies focusing on the safety profile of combining T-DM1 with RT. Results: After applying eligibility criteria, nine articles were included in the meta-analysis. Pooled data from these studies revealed a high incidence of grade 3 + radionecrosis (17%), while the rates of grade 3 + radiation-related pneumonitis (<1%) and skin toxicity (1%) were found to be very low. Conclusion: Although there is some concern regarding a slight increase in pneumonitis when combining T-DM1 with postoperative RT, the safety profile of this combination was deemed acceptable for locoregional treatment in non-metastatic breast cancer. However, caution is advised when irradiating intracranial sites concurrently with T-DM1. There is a pressing need for international consensus guidelines regarding the safety considerations of combining T-DM1 and RT for breast cancer.
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