EviGUIDE - a tool for evidence-based decision making in image-guided adaptive brachytherapy for cervical cancer.

PURPOSE: To develop a novel decision-support system for radiation oncology that incorporates clinical, treatment and outcome data, as well as outcome models from a large clinical trial on magnetic resonance image-guided adaptive brachytherapy (MR-IGABT) for locally advanced cervical cancer (LACC). METHODS: A system, called EviGUIDE, was developed that combines dosimetric information from the treatment planning system, patient and treatment characteristics, and established tumor control probability (TCP), and normal tissue complication probability (NTCP) models, to predict clinical outcome of radiotherapy treatment of LACC. Six Cox Proportional Hazards models based on data from 1341 patients of the EMBRACE-I study have been integrated. One TCP model for local tumor control, and five NTCP models for OAR morbidities. RESULTS: EviGUIDE incorporates TCP-NTCP graphs to help users visualize the clinical impact of different treatment plans and provides feedback on achievable doses based on a large reference population. It enables holistic assessment of the interplay between multiple clinical endpoints and tumour and treatment variables. Retrospective analysis of 45 patients treated with MR-IGABT showed that there exists a sub-cohort of patients (20%) with increased risk factors, that could greatly benefit from the quantitative and visual feedback. CONCLUSION: A novel digital concept was developed that can enhance clinical decision- making and facilitate personalized treatment. It serves as a proof of concept for a new generation of decision support systems in radiation oncology, which incorporate outcome models and high-quality reference data, and aids the dissemination of evidence-based knowledge about optimal treatment and serve as a blueprint for other sites in radiation oncology.

Tools for large-scale data analytics of an international multi-center study in radiation oncology for cervical cancer.

PURPOSE: To develop and implement a software that enables centers, treating patients with state-of-the-art radiation oncology, to compare their patient, treatment, and outcome data to a reference cohort, and to assess the quality of their treatment approach. MATERIALS AND METHODS: A comprehensive data dashboard was designed, which al- lowed holistic assessment of institutional treatment approaches. The software was tested in the ongoing EMBRACE-II study for locally advanced cervical cancer. The tool created individualized dashboards and automatic analysis scripts, verified pro- tocol compliance and checked data for inconsistencies. Identified quality assurance (QA) events were analysed. A survey among users was conducted to assess usability. RESULTS: The survey indicated favourable feedback to the prototype and highlighted its value for internal monitoring. Overall, 2302 QA events were identified (0.4% of all collected data). 54% were due to missing or incomplete data, and 46% originated from other causes. At least one QA event was found in 519/1001 (52%) of patients. QA events related to primary study endpoints were found in 16% of patients. Sta- tistical methods demonstrated good performance in detecting anomalies, with precisions ranging from 71% to 100%. Most frequent QA event categories were Treatment Technique (27%), Patient Characteristics (22%), Dose Reporting (17%), Outcome 156 (15%), Outliers (12%), and RT Structures (8%). CONCLUSION: A software tool was developed and tested within a clinical trial in radia- tion oncology. It enabled the quantitative and qualitative comparison of institutional patient and treatment parameters with a large multi-center reference cohort. We demonstrated the value of using statistical methods to automatically detect implau- sible data points and highlighted common pitfalls and uncertainties in radiotherapy for cervical cancer.

Risk Factors for Local Failure Following Chemoradiation and Magnetic Resonance Image-Guided Brachytherapy in Locally Advanced Cervical Cancer: Results From the EMBRACE-I Study.

PURPOSE: To report clinical and treatment characteristics, remission and failure patterns, and risk factors for local failure (LF) from the EMBRACE-I study. MATERIALS AND METHODS: EMBRACE-I was a prospective, observational, multicenter cohort study on magnetic resonance imaging-based image-guided adaptive brachytherapy (MR-IGABT) in locally advanced cervical cancer. Treatment consisted of external beam radiotherapy, concurrent chemotherapy, and MR-IGABT. LF was defined as progressive or recurrent disease in the cervix, uterus, parametria, pelvic wall, or vagina. Competing risk analysis was used to estimate local tumor control (LC) and Cox proportional regression models for multivariable analysis and dose-response analysis. RESULTS: One thousand three hundred eighteen patients with a median follow-up of 52 months were available for this analysis. Eighty-one patients had persistent disease 3 months after end of treatment. Of those, 60 patients achieved LC at 6-9 months without further treatment, whereas 21 patients had progressive disease. In addition, 77 patients developed a local recurrence after complete remission comprising a total number of 98 LFs. LFs were located inside the MR-IGABT target volumes in 90% of patients with LF. In multivariable analysis, histology, minimal dose to 90% of high-risk clinical target volume (CTVHR), maximum tumor dimension, CTVHR > 45 cm3, overall treatment time, tumor necrosis on magnetic resonance imaging at diagnosis, uterine corpus infiltration at diagnosis and at MR-IGABT, and mesorectal infiltration at MR-IGABT had significant impact on LF. Dose-response analysis showed that a minimal dose to 90% of 85 Gy to the CTVHR led to 95% (95% CI, 94 to 97) LC 3 years postintervention for squamous cell in comparison to 86% (95% CI, 81 to 90) for adeno/adenosquamous carcinoma histology. CONCLUSION: The present study demonstrates the safety and validity of the GYN GEC-ESTRO/ICRU-89 target concept and provides large-scale evidence for dose prescription and new risk factors for LF in MR-IGABT in locally advanced cervical cancer.

Neural network-assisted automated image registration for MRI-guided adaptive brachytherapy in cervical cancer.

PURPOSE: In image-guided adaptive brachytherapy (IGABT) a quantitative evaluation of the dosimetric changes between fractions due to anatomical variations, can be implemented via rigid registration of images from subsequent fractions based on the applicator as a reference structure. With available treatment planning systems (TPS), this is a manual and time-consuming process. The aim of this retrospective study was to automate this process. A neural network (NN) was trained to predict the applicator structure from MR images. The resulting segmentation was used to automatically register MR-volumes. MATERIAL AND METHODS: DICOM images and plans of 56 patients treated for cervical cancer with high dose-rate (HDR) brachytherapy were used in the study. A 2D and a 3D NN were trained to segment applicator structures on clinical T2-weighted MRI datasets. Different rigid registration algorithms were investigated and compared. To evaluate a fully automatic registration workflow, the NN-predicted applicator segmentations (AS) were used for rigid image registration with the best performing algorithm. The DICE coefficient and mean distance error between dwell positions (MDE) were used to evaluate segmentation and registration performance. RESULTS: The mean DICE coefficient for the predicted AS was 0.70 ±â€¯0.07 and 0.58 ±â€¯0.04 for the 3D NN and 2D NN, respectively. Registration algorithms achieved MDE errors from 8.1 ±â€¯3.7 mm (worst) to 0.7 ±â€¯0.5 mm (best), using ground-truth AS. Using the predicted AS from the 3D NN together with the best registration algorithm, an MDE of 2.7 ±â€¯1.4 mm was achieved. CONCLUSION: Using a combination of deep learning models and state of the art image registration techniques has been demonstrated to be a promising solution for automatic image registration in IGABT. In combination with auto-contouring of organs at risk, the auto-registration workflow from this study could become part of an online-dosimetric interfraction evaluation workflow in the future.

Education and training for image-guided adaptive brachytherapy for cervix cancer-The (GEC)-ESTRO/EMBRACE perspective.

Image-guided adaptive brachytherapy (IGABT) has been shown to improve local/regional control and survival for cervix cancer patients while reducing morbidity. However, the technique is complex involving several conceptual, methodological, and technical innovations compared to conventional brachytherapy. The delivery of high-quality IGABT which will translate into improved outcomes is therefore critically dependent on effective education and training of all health professionals involved in the brachytherapy treatment process. This paper reviews the (GEC)-ESTRO/EMBRACE initiatives for education and training to promote the dissemination and implementation of IGABT for cervix cancer worldwide. The new skills required in different health professionals for successful implementation of IGABT are described. The achievements and challenges of current educational strategies for disseminating IGABT are discussed. Innovations to improve the effectiveness of current and future educational strategies are explored.

Evidence-Based Dose Planning Aims and Dose Prescription in Image-Guided Brachytherapy Combined With Radiochemotherapy in Locally Advanced Cervical Cancer.

The last 2 decades have witnessed the development and broad adoption of image-guided adaptive brachytherapy (IGABT) combined with radiochemotherapy in patients with locally advanced cervical cancer. A variety of brachytherapy techniques and dose/fractionation schedules have been applied, and until recently, there was no strong evidence available for preferring one approach to another. However, large volumes of data have now provided high level clinical evidence for dose-effect relations for both disease and morbidity endpoints. It is therefore now possible to apply evidence based dose planning aims and dose prescription protocols in IGABT for locally advanced cervical cancer. This review gives an overview of targets/organs-at-risk and disease/morbidity endpoints which are relevant in the context of treatment planning and dose prescription in IGABT. The dosimetric and clinical evidence is summarized to support the implementation of dose prescription protocols which include hard and soft constraints for targets and organs at risk.