Challenges for Quality Assurance of Target Volume Delineation in Clinical Trials

Where Does Auto-Segmentation for Brain Metastases Radiosurgery Stand Today?

Detection and segmentation of brain metastases (BMs) play a pivotal role in diagnosis, treatment planning, and follow-up evaluations for effective BM management. Given the rising prevalence of BM cases and its predominantly multiple onsets, automated segmentation is becoming necessary in stereotactic radiosurgery. It not only alleviates the clinician's manual workload and improves clinical workflow efficiency but also ensures treatment safety, ultimately improving patient care. Recent strides in machine learning, particularly in deep learning (DL), have revolutionized medical image segmentation, achieving state-of-the-art results. This review aims to analyze auto-segmentation strategies, characterize the utilized data, and assess the performance of cutting-edge BM segmentation methodologies. Additionally, we delve into the challenges confronting BM segmentation and share insights gleaned from our algorithmic and clinical implementation experiences.

Patient-Specific Auto-segmentation on Daily kVCT Images for Adaptive Radiation Therapy

PURPOSE

This study explored deep-learning-based patient-specific auto-segmentation using transfer learning on daily RefleXion kilovoltage computed tomography (kVCT) images to facilitate adaptive radiation therapy, based on data from the first group of patients treated with the innovative RefleXion system.

METHODS AND MATERIALS

For head and neck (HaN) and pelvic cancers, a deep convolutional segmentation network was initially trained on a population data set that contained 67 and 56 patient cases, respectively. Then the pretrained population network was adapted to the specific RefleXion patient by fine-tuning the network weights with a transfer learning method. For each of the 6 collected RefleXion HaN cases and 4 pelvic cases, initial planning computed tomography (CT) scans and 5 to 26 sets of daily kVCT images were used for the patient-specific learning and evaluation separately. The performance of the patient-specific network was compared with the population network and the clinical rigid registration method and evaluated by the Dice similarity coefficient (DSC) with manual contours being the reference. The corresponding dosimetric effects resulting from different auto-segmentation and registration methods were also investigated.

RESULTS

The proposed patient-specific network achieved mean DSC results of 0.88 for 3 HaN organs at risk (OARs) of interest and 0.90 for 8 pelvic target and OARs, outperforming the population network (0.70 and 0.63) and the registration method (0.72 and 0.72). The DSC of the patient-specific network gradually increased with the increment of longitudinal training cases and approached saturation with more than 6 training cases. Compared with using the registration contour, the target and OAR mean doses and dose-volume histograms obtained using the patient-specific auto-segmentation were closer to the results using the manual contour.

CONCLUSIONS

Auto-segmentation of RefleXion kVCT images based on the patient-specific transfer learning could achieve higher accuracy, outperforming a common population network and clinical registration-based method. This approach shows promise in improving dose evaluation accuracy in RefleXion adaptive radiation therapy.

E pluribus unum: prospective acceptability benchmarking from the Contouring Collaborative for Consensus in Radiation Oncology crowdsourced initiative for multiobserver segmentation

Purpose

Contouring Collaborative for Consensus in Radiation Oncology (C3RO) is a crowdsourced challenge engaging radiation oncologists across various expertise levels in segmentation. An obstacle to artificial intelligence (AI) development is the paucity of multiexpert datasets; consequently, we sought to characterize whether aggregate segmentations generated from multiple nonexperts could meet or exceed recognized expert agreement.

Approach

Participants who contoured ≥ 1 region of interest (ROI) for the breast, sarcoma, head and neck (H&N), gynecologic (GYN), or gastrointestinal (GI) cases were identified as a nonexpert or recognized expert. Cohort-specific ROIs were combined into single simultaneous truth and performance level estimation (STAPLE) consensus segmentations.STAPLE nonexpert ROIs were evaluated againstSTAPLE expert contours using Dice similarity coefficient (DSC). The expert interobserver DSC (IODSC expert ) was calculated as an acceptability threshold betweenSTAPLE nonexpert andSTAPLE expert . To determine the number of nonexperts required to match theIODSC expert for each ROI, a single consensus contour was generated using variable numbers of nonexperts and then compared to theIODSC expert .

Results

For all cases, the DSC values forSTAPLE nonexpert versusSTAPLE expert were higher than comparator expertIODSC expert for most ROIs. The minimum number of nonexpert segmentations needed for a consensus ROI to achieveIODSC expert acceptability criteria ranged between 2 and 4 for breast, 3 and 5 for sarcoma, 3 and 5 for H&N, 3 and 5 for GYN, and 3 for GI.

Conclusions

Multiple nonexpert-generated consensus ROIs met or exceeded expert-derived acceptability thresholds. Five nonexperts could potentially generate consensus segmentations for most ROIs with performance approximating experts, suggesting nonexpert segmentations as feasible cost-effective AI inputs.

Automatic gross tumor segmentation of canine head and neck cancer using deep learning and cross-species transfer learning

Background

Radiotherapy (RT) is increasingly being used on dogs with spontaneous head and neck cancer (HNC), which account for a large percentage of veterinary patients treated with RT. Accurate definition of the gross tumor volume (GTV) is a vital part of RT planning, ensuring adequate dose coverage of the tumor while limiting the radiation dose to surrounding tissues. Currently the GTV is contoured manually in medical images, which is a time-consuming and challenging task.

Purpose

The purpose of this study was to evaluate the applicability of deep learning-based automatic segmentation of the GTV in canine patients with HNC.

Materials and methods

Contrast-enhanced computed tomography (CT) images and corresponding manual GTV contours of 36 canine HNC patients and 197 human HNC patients were included. A 3D U-Net convolutional neural network (CNN) was trained to automatically segment the GTV in canine patients using two main approaches: (i) training models from scratch based solely on canine CT images, and (ii) using cross-species transfer learning where models were pretrained on CT images of human patients and then fine-tuned on CT images of canine patients. For the canine patients, automatic segmentations were assessed using the Dice similarity coefficient (Dice), the positive predictive value, the true positive rate, and surface distance metrics, calculated from a four-fold cross-validation strategy where each fold was used as a validation set and test set once in independent model runs.

Results

CNN models trained from scratch on canine data or by using transfer learning obtained mean test set Dice scores of 0.55 and 0.52, respectively, indicating acceptable auto-segmentations, similar to the mean Dice performances reported for CT-based automatic segmentation in human HNC studies. Automatic segmentation of nasal cavity tumors appeared particularly promising, resulting in mean test set Dice scores of 0.69 for both approaches.

Conclusion

In conclusion, deep learning-based automatic segmentation of the GTV using CNN models based on canine data only or a cross-species transfer learning approach shows promise for future application in RT of canine HNC patients.

Deep learning-based automatic delineation of anal cancer gross tumour volume: a multimodality comparison of CT, PET and MRI

BACKGROUND

Accurate target volume delineation is a prerequisite for high-precision radiotherapy. However, manual delineation is resource-demanding and prone to interobserver variation. An automatic delineation approach could potentially save time and increase delineation consistency. In this study, the applicability of deep learning for fully automatic delineation of the gross tumour volume (GTV) in patients with anal squamous cell carcinoma (ASCC) was evaluated for the first time. An extensive comparison of the effects single modality and multimodality combinations of computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) have on automatic delineation quality was conducted.

MATERIAL AND METHODS

18F-fluorodeoxyglucose PET/CT and contrast-enhanced CT (ceCT) images were collected for 86 patients with ASCC. A subset of 36 patients also underwent a study-specific 3T MRI examination including T2- and diffusion-weighted imaging. The resulting two datasets were analysed separately. A two-dimensional U-Net convolutional neural network (CNN) was trained to delineate the GTV in axial image slices based on single or multimodality image input. Manual GTV delineations constituted the ground truth for CNN model training and evaluation. Models were evaluated using the Dice similarity coefficient (Dice) and surface distance metrics computed from five-fold cross-validation.

RESULTS

CNN-generated automatic delineations demonstrated good agreement with the ground truth, resulting in mean Dice scores of 0.65-0.76 and 0.74-0.83 for the 86 and 36-patient datasets, respectively. For both datasets, the highest mean Dice scores were obtained using a multimodal combination of PET and ceCT (0.76-0.83). However, models based on single modality ceCT performed comparably well (0.74-0.81). T2W-only models performed acceptably but were somewhat inferior to the PET/ceCT and ceCT-based models.

CONCLUSION

CNNs provided high-quality automatic GTV delineations for both single and multimodality image input, indicating that deep learning may prove a versatile tool for target volume delineation in future patients with ASCC.

Implementation of web-based open-source radiotherapy delineation software (WORDS) in organs at risk contouring training for newly qualified radiotherapists: quantitative comparison with conventional one-to-one coaching approach

BACKGROUND

Due to the role expansion of radiotherapists in dosimetric aspect, radiotherapists have taken up organs at risk (OARs) contouring work in many clinical settings. However, training of newly qualified radiotherapists in OARs contouring can be time consuming, it may also cause extra burden to experienced radiotherapists. As web-based open-source radiotherapy delineation software (WORDS) has become more readily available, it has provided a free and interactive alternative to conventional one-to-one coaching approach during OARs contouring training. The present study aims to evaluate the effectiveness of WORDS in training OARs contouring skills of newly qualified radiotherapists, compared to those trained by conventional one-to-one coaching approach.

METHODS

Nine newly qualified radiotherapists (licensed in 2017 - 2018) were enrolled to the conventional one-to-one coaching group (control group), while 11 newly qualified radiotherapists (licensed in 2019 - 2021) were assigned to WORDS training group (measured group). Ten OARs were selected to be contoured in this 3-phases quantitative study. Participants were required to undergo phase 1 OARs contouring in the beginning of the training session. Afterwards, conventional one-to-one training or WORDS training session was provided to participants according to their assigned group. Then the participants did phase 2 and 3 OARs contouring which were separated 1 week apart. Phase 1 - 3 OARs contouring aimed to demonstrate participants' pre-training OARs contouring ability, post-training OARs contouring ability and knowledge retention after one-week interval respectively using either training approach. To prevent bias, the computed tomography dataset for OARs contouring in each phase were different. Variations in the contouring scores for the selected OARs were evaluated between 3 phases using Kruskal-Wallis tests with Dunn tests for pairwise comparisons. Variations in the contouring scores between control and measured group in phase 1 - 3 contouring were analyzed using Wilcoxon signed-rank test. A p-value < 0.05 was considered to be statistically significant.

RESULTS

In both control group and measured group, significant improvement (p < 0.05) in phase 2 and 3 contouring scores have been observed comparing to phase 1 contouring scores. In comparison of contouring scores between control group and measured group, no significant differences (p > 0.05) were observed in all OARs between both groups.

CONCLUSIONS

The results in this study have demonstrated that the outcome of OARs contouring training using WORDS is comparable to the conventional training approach. In addition, WORDS can offer flexibility to newly qualified radiotherapists to practice OARs contouring at will, as well as reduce staff training burden of experienced radiotherapists.

Automatic radiotherapy delineation quality assurance on prostate MRI with deep learning in a multicentre clinical trial

Volume delineation quality assurance (QA) is particularly important in clinical trial settings where consistent protocol implementation is required, as outcomes will affect future as well current patients. Currently, where feasible, this is conducted manually, which is time consuming and resource intensive. Although previous studies mostly focused on automating delineation QA on CT, magnetic resonance imaging (MRI) is being increasingly used in radiotherapy treatment. In this work, we propose to perform automatic delineation QA on prostate MRI for both the clinical target volume (CTV) and organs-at-risk (OARs) by using delineations generated by 3D Unet variants as benchmarks for QA. These networks were trained on a small gold standard atlas set and applied on a multicentre radiotherapy clinical trial dataset to generate benchmark delineations. Then, a QA stage was designed to recommend 'pass', 'minor correction' and 'major correction' for each manual delineation in the trial set by thresholding its Dice similarity coefficient to the network generated delineation. Among all 3D Unet variants explored, the Unet with anatomical gates in an AtlasNet architecture performed the best in delineation QA, achieving an area under the receiver operating characteristics curve of 0.97, 0.92, 0.89 and 0.97 for identifying unacceptable (major correction) delineations with a sensitivity of 0.93, 0.73, 0.74 and 0.90 at a specificity of 0.93, 0.86, 0.86 and 0.95 for bladder, prostate CTV, rectum and gel spacer respectively. To the best of our knowledge, this is the first study to propose automated delineation QA for a multicentre radiotherapy clinical trial with treatment planning MRI. The methods proposed in this work can potentially improve the accuracy and consistency of CTV and OAR delineation in radiotherapy treatment planning.

ESTRO ACROP guidelines for the delineation of lymph nodal areas in upper gastrointestinal malignancies

The European SocieTy for Radiation and Oncology -Advisory Committee on Radiation Oncology Practice (ESTRO-ACROP) endorsed a project to provide guidelines (GL) for the identification and delineation of clinically negative lymph-nodal stations (LNs) involved in upper gastrointestinal clinical scenarios. The presented GL is focused on preoperative (or definitive) setting. The project aim is to improve the consistency of clinical target volume (CTV) delineation by providing: a description of the anatomical boundaries of the LNs; a radiological computed tomography-based atlas depicting the LNs areas; a free, web-based, interactive example case for independent training of radiation oncologists on LNs delineation according to the presented GL, by both qualitative and quantitative analysis (through the FALCON EduCase platform). This project was carried out with the intention to facilitate and improve uniformity of future upper gastrointestinal guidelines on nodal CTV delineation. We report methodology and results from the collaboration of a working group panel selected by the ESTRO-ACROP.

FIELDRT: an open-source platform for the assessment of target volume delineation in radiation therapy

Objectives:

Target volume delineation (TVD) has been identified as a weakness in the accuracy of radiotherapy, both within and outside of clinical trials due to the intra/interobserver variations affecting the TVD quality. Sources of variations such as poor compliance or protocol violation may have adverse effect on treatment outcomes. In this paper, we present and describe the FIELD^RT software developed for the ARENA project to improve the quality of TVD through qualitative and quantitative feedbacks and individual and personalized summary of trainee”s performance.

Methods:

For each site-specific clinical case included in the FIELD^RT software, reference volumes, minimum and maximum “acceptable” volumes and organ at risk were derived by outlines of consultants and senior trainees. The software components currently developed include: (a) user-friendly importing interface (b) analysis toolbox to compute quantitative and qualitative (c) visualiser and (d) structured report generator for personalised feedback. The FIELD^RT software was validated by comparing the performance of 63 trainees and by measuring performance over time. In addition, a trainee evaluation day was held in 2019 to collect feedback on FIELD^RT.

Results:

Results show the trainees’ improvement when reoutlining a case after reviewing the feedback generated from the FIELD^RT software. Comments and feedback received after evaluation day were positive and confirmed that FIELD^RT can be a useful application for training purposes.

Conclusion:

We presented a new open-source software to support education in TVD and ongoing continuous professional development for clinical oncology trainees and consultants. ARENA in combination with FIELD^RT implements site-specific modules with reference target and organs at risk volumes and automatically evaluates individual performance using several quantitative and qualitative feedbacks. Pilot results suggests this software could be used as an education tool to reduce variation in TVD so to guarantee high quality in radiotherapy.

Advances in knowledge:

FIELD^RT is a new easy and free to use software aiming at supporting education in TVD and ongoing continuous professional development. The software provides quantitative/qualitative feedback and an exportable report with an individual and personalised summary of trainee’s performance.

Geometric contour variation in clinical target volume of axillary lymph nodes in breast cancer radiotherapy: an AIRO multi-institutional study

OBJECTIVES

To determine interobserver variability in axillary nodal contouring in breast cancer (BC) radiotherapy (RT) by comparing the clinical target volume of participating single centres (SC-CTV) with a gold-standard CTV (GS-CTV).

METHODS

The GS-CTV of three patients (P1, P2, P3) with increasing complexity was created in DICOM format from the median contour of axillary CTVs drawn by BC experts, validated using the simultaneous truth and performance-level estimation and peer-reviewed. GS-CTVs were compared with the correspondent SC-CTVs drawn by radiation oncologists, using validated metrics and a total score (TS) integrating all of them.

RESULTS

Eighteen RT centres participated in the study. Comparative analyses revealed that, on average, the SC-CTVs were smaller than GS-CTV for P1 and P2 (by -29.25% and -27.83%, respectively) and larger for P3 (by +12.53%). The mean Jaccard index was greater for P1 and P2 compared to P3, but the overlap extent value was around 0.50 or less. Regarding nodal levels, L4 showed the highest concordance with the GS. In the intra-patient comparison, L2 and L3 achieved lower TS than L4. Nodal levels showed discrepancy with GS, which was not statistically significant for P1, and negligible for P2, while P3 had the worst agreement. DICE similarity coefficient did not exceed the minimum threshold for agreement of 0.70 in all the measurements.

CONCLUSIONS

Substantial differences were observed between SC- and GS-CTV, especially for P3 with altered arm setup. L2 and L3 were the most critical levels. The study highlighted these key points to address.

ADVANCES IN KNOWLEDGE

The present study compares, by means of validated geometric indexes, manual segmentations of axillary lymph nodes in breast cancer from different observers and different institutions made on radiotherapy planning CT images. Assessing such variability is of paramount importance, as geometric uncertainties might lead to incorrect dosimetry and compromise oncological outcome.

Standardization for oncologic head and neck surgery

The inherent variability in performing specific surgical procedures for head and neck cancer remains a barrier for accurately assessing treatment outcomes, particularly in clinical trials. While non-surgical modalities for cancer therapeutics have evolved to become far more uniform, there remains the challenge to standardize surgery. The purpose of this review is to identify the barriers in achieving uniformity and to highlight efforts by surgical groups to standardize selected operations and nomenclature. While further improvements in standardization will remain a challenge, we must encourage surgical groups to focus on strategies that provide such a level.

Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review

Advances in artificial intelligence-based methods have led to the development and publication of numerous systems for auto-segmentation in radiotherapy. These systems have the potential to decrease contour variability, which has been associated with poor clinical outcomes and increased efficiency in the treatment planning workflow. However, there are no uniform standards for evaluating auto-segmentation platforms to assess their efficacy at meeting these goals. Here, we review the most frequently used evaluation techniques which include geometric overlap, dosimetric parameters, time spent contouring, and clinical rating scales. These data suggest that many of the most commonly used geometric indices, such as the Dice Similarity Coefficient, are not well correlated with clinically meaningful endpoints. As such, a multi-domain evaluation, including composite geometric and/or dosimetric metrics with physician-reported assessment, is necessary to gauge the clinical readiness of auto-segmentation for radiation treatment planning.

Deep learning-based detection and segmentation-assisted management of brain metastases

BACKGROUND

Three-dimensional T1 magnetization prepared rapid acquisition gradient echo (3D-T1-MPRAGE) is preferred in detecting brain metastases (BM) among MRI. We developed an automatic deep learning-based detection and segmentation method for BM (named BMDS net) on 3D-T1-MPRAGE images and evaluated its performance.

METHODS

The BMDS net is a cascaded 3D fully convolution network (FCN) to automatically detect and segment BM. In total, 1652 patients with 3D-T1-MPRAGE images from 3 hospitals (n = 1201, 231, and 220, respectively) were retrospectively included. Manual segmentations were obtained by a neuroradiologist and a radiation oncologist in a consensus reading in 3D-T1-MPRAGE images. Sensitivity, specificity, and dice ratio of the segmentation were evaluated. Specificity and sensitivity measure the fractions of relevant segmented voxels. Dice ratio was used to quantitatively measure the overlap between automatic and manual segmentation results. Paired samples t-tests and analysis of variance were employed for statistical analysis.

RESULTS

The BMDS net can detect all BM, providing a detection result with an accuracy of 100%. Automatic segmentations correlated strongly with manual segmentations through 4-fold cross-validation of the dataset with 1201 patients: the sensitivity was 0.96 ± 0.03 (range, 0.84-0.99), the specificity was 0.99 ± 0.0002 (range, 0.99-1.00), and the dice ratio was 0.85 ± 0.08 (range, 0.62-0.95) for total tumor volume. Similar performances on the other 2 datasets also demonstrate the robustness of BMDS net in correctly detecting and segmenting BM in various settings.

CONCLUSIONS

The BMDS net yields accurate detection and segmentation of BM automatically and could assist stereotactic radiotherapy management for diagnosis, therapy planning, and follow-up.

Challenges in the target volume definition of lung cancer radiotherapy

Radiotherapy, with or without systemic treatment has an important role in the management of lung cancer. In order to deliver the treatment accurately, the clinician must precisely outline the gross tumour volume (GTV), mostly on computed tomography (CT) images. However, due to the limited contrast between tumour and non-malignant changes in the lung tissue, it can be difficult to distinguish the tumour boundaries on CT images leading to large interobserver variation and differences in interpretation. Therefore the definition of the GTV has often been described as the weakest link in radiotherapy with its inaccuracy potentially leading to missing the tumour or unnecessarily irradiating normal tissue. In this article, we review the various techniques that can be used to reduce delineation uncertainties in lung cancer.

Visualizing Glioma Infiltration by the Combination of Multimodality Imaging and Artificial Intelligence, a Systematic Review of the Literature

The aim of this study was to systematically review the literature concerning the integration of multimodality imaging with artificial intelligence methods for visualization of tumor cell infiltration in glioma patients. The review was performed in accordance with the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines. The literature search was conducted in PubMed, Embase, The Cochrane Library and Web of Science and yielded 1304 results. 14 studies were included in the qualitative analysis. The reference standard for tumor infiltration was either histopathology or recurrence on image follow-up. Critical assessment was performed according to the Quality Assessment of Diagnostic Accuracy Studies (QUADAS2). All studies concluded their findings to be of significant value for future clinical practice. Diagnostic test accuracy reached an area under the curve of 0.74–0.91 reported in six studies. There was no consensus with regard to included image modalities, models or training and test strategies. The integration of artificial intelligence with multiparametric imaging shows promise for visualizing tumor cell infiltration in glioma patients. This approach can possibly optimize surgical resection margins and help provide personalized radiotherapy planning.

New target volume delineation and PTV strategies to further personalise radiotherapy

Target volume delineation uncertainty (DU) is arguably one of the largest geometric uncertainties in radiotherapy that are accounted for using planning target volume (PTV) margins. Geometrical uncertainties are typically derived from a limited sample of patients. Consequently, the resultant margins are not tailored to individual patients. Furthermore, standard PTVs cannot account for arbitrary anisotropic extensions of the target volume originating from DU. We address these limitations by developing a method to measure DU for each patient by a single clinician. This information is then used to produce PTVs that account for each patient's unique DU, including any required anisotropic component. We do so using a two-step uncertainty evaluation strategy that does not rely on multiple samples of data to capture the DU of a patient's gross tumour volume (GTV) or clinical target volume. For simplicity, we will just refer to the GTV in the following. First, the clinician delineates two contour sets; one which bounds all voxels believed to have a probability of belonging to the GTV of 1, while the second includes all voxels with a probability greater than 0. Next, one specifies a probability density function for the true GTV boundary position within the boundaries of the two contours. Finally, a patient-specific PTV, designed to account for all systematic errors, is created using this information along with measurements of the other systematic errors. Clinical examples indicate that our margin strategy can produce significantly smaller PTVs than the van Herk margin recipe. Our new radiotherapy target delineation concept allows DUs to be quantified by the clinician for each patient, leading to PTV margins that are tailored to each unique patient, thus paving the way to a greater personalisation of radiotherapy.

Generating High-Quality Lymph Node Clinical Target Volumes for Head and Neck Cancer Radiation Therapy Using a Fully Automated Deep Learning-Based Approach

PURPOSE

To develop a deep learning model that generates consistent, high-quality lymph node clinical target volumes (CTV) contours for head and neck cancer (HNC) patients, as an integral part of a fully automated radiation treatment planning workflow.

METHODS AND MATERIALS

Computed tomography (CT) scans from 71 HNC patients were retrospectively collected and split into training (n = 51), cross-validation (n = 10), and test (n = 10) data sets. All had target volume delineations covering lymph node levels Ia through V (Ia-V), Ib through V (Ib-V), II through IV (II-IV), and retropharyngeal (RP) nodes, which were previously approved by a radiation oncologist specializing in HNC. Volumes of interest (VOIs) about nodal levels were automatically identified using computer vision techniques. The VOI (cropped CT image) and approved contours were used to train a U-Net autosegmentation model. Each lymph node level was trained independently, with model parameters optimized by assessing performance on the cross-validation data set. Once optimal model parameters were identified, overlap and distance metrics were calculated between ground truth and autosegmentations on the test set. Lastly, this final model was used on 32 additional patient scans (not included in original 71 cases) and autosegmentations visually rated by 3 radiation oncologists as being "clinically acceptable without requiring edits," "requiring minor edits," or "requiring major edits."

RESULTS

When comparing ground truths to autosegmentations on the test data set, median Dice Similarity Coefficients were 0.90, 0.90, 0.89, and 0.81, and median mean surface distance values were 1.0 mm, 1.0 mm, 1.1 mm, and 1.3 mm for node levels Ia-V, Ib-V, II-IV, and RP nodes, respectively. Qualitative scoring varied among physicians. Overall, 99% of autosegmented target volumes were either scored as being clinically acceptable or requiring minor edits (ie, stylistic recommendations, <2 minutes).

CONCLUSIONS

We developed a fully automated artificial intelligence approach to autodelineate nodal CTVs for patients with intact HNC. Most autosegmentations were found to be clinically acceptable after qualitative review when considering recommended stylistic edits. This promising work automatically delineates nodal CTVs in a robust and consistent manner; this approach can be implemented in ongoing efforts for fully automated radiation treatment planning.

A Systematic Review of Contouring Guidelines in Radiation Oncology: Analysis of Frequency, Methodology, and Delivery of Consensus Recommendations

PURPOSE

Clinical trials have described variation in radiation therapy plan quality, of which contour delineation is a key component, and linked this to inferior patient outcomes. In response, consensus guidelines have been developed to standardize contour delineation. This investigation assesses trends in contouring guidelines and examines the methodologies used to generate and deliver recommendations.

METHODS AND MATERIALS

We conducted a literature search for contouring guidelines published after 1995. Of 11,124 citations, 332 were identified for full-text review to determine inclusion. We abstracted articles for the intent of the consensus process, key elements of the methodology, and mode of information delivery. A Fisher exact test was used to identify elements that differed among the guidelines generated for clinical trials and routine care.

RESULTS

Overall, 142 guidelines were included, of which 16 (11%) were developed for a clinical trial. There was an increase in guideline publication over time (0 from 1995-1999 vs 65 from 2015- 2019; P = .03), particularly among recommendations for stereotactic radiation and brachytherapy. The most common disease sites were head and neck (24%), gastrointestinal (12%), and gynecologic (12%). Methods used to develop recommendations included literature review (50%) and image-based methods (45%). Panels included a median of 10 physicians (interquartile range, 7-16); 70% of panels represented multidisciplinary expertise. Guidelines developed for a clinical trial were more likely to include an image-based approach, with quantitative analysis of contours submitted by the panel members and to publish a full set of image-based recommendations (P < .005).

CONCLUSIONS

This review highlights an increase in consensus contouring recommendations over time. Guidelines focus on disease sites, such as head and neck, with evidence supporting a correlation between treatment planning and patient outcomes, although variation exists in the approach to the consensus process. Elements that may improve guideline acceptance (ie, image-based consensus contour analysis) and usability (ie, inclusion of a full image set) are more common in guidelines developed for clinical trials.

Impact of Peer Review in Reducing Uncertainty in the Definition of the Lung Target Volume Among Trainee Oncologists

AIMS

To evaluate the impact of peer review and contouring workshops on reducing uncertainty in target volume delineation for lung cancer radiotherapy.

MATERIALS AND METHODS

Data from two lung cancer target volume delineation courses were analysed. In total, 22 trainees in clinical oncology working across different UK centres attended these courses with priori experience in lung cancer radiotherapy. The courses were made up of short presentations and contouring practice sessions. The participants were divided into two groups and asked to first individually delineate (IND) and then individually peer review (IPR) the contours of another participant. The contours were discussed with an expert panel consisting of two consultant clinical oncologists and a consultant radiologist. Contours were analysed quantitatively by measuring the volume and local distance standard deviation (localSD) from the reference expert consensus contour and qualitatively through visual analysis. Feedback from the participants was obtained using a questionnaire.

RESULTS

All participants applied minor editing to the contours during IPR, leading to a non-statistically significant reduction in the mean delineated volume (IND = 140.92 cm3, IPR = 125.26 cm3, P = 0.211). The overall interobserver variation was similar, with a localSD of 0.33 cm and 0.38 cm for the IND and IPR, respectively (P = 0.848). Six participants (29%) carried out correct major changes by either including tumour or excluding healthy tissue. One participant (5%) carried out an incorrect edit by excluding parts of the tumour, while another observer failed to identify a major contour error. The participants' level of confidence in target volume delineation increased following the course and identified the discussions with the radiologist and colleagues as the most important highlights of the course.

CONCLUSION

IPR could improve target volume delineation quality among trainee oncologists by identifying most major contour errors. However, errors were also introduced after IPR, suggesting the need to further discuss major changes with a multidisciplinary team.

Mesorectal radiotherapy for early stage rectal cancer: A novel target volume

With the introduction of population-based bowel cancer screening, rectal cancer is diagnosed at earlier stages, yet standard treatment still requires the same extensive surgery that is used for more advanced stages. Organ preserving treatment is rapidly developing and is subject of investigation in numerous clinical trials. The STAR-TREC trial is an international, multi-centre randomised trial investigating organ preservation using (chemo)radiotherapy. Patients with small mrT1-3bN0V0M0 tumours are randomized between three arms: standard TME, organ preservation with SCRT or with CRT. In this trial, the clinical target volume has been tailored to the early staged disease of the included patients. This mesorectal irradiation volume includes the mesorectum and pre-sacral lymph nodes at the level of the tumour, two centimetres below and cranially up to the S2-3 interspace level. In contrast to conventional irradiation volumes, the lateral lymph nodes and the nodes along the superior rectal artery are excluded. As a result, the dose to the bowel, bladder, anal sphincter and the neurovascular plexus in the lower pelvis is substantially decreased, especially when combined with modern irradiation techniques, such as dynamic arc therapy. These lower doses are expected to lead to decreasing acute and late toxicity and beneficial functional outcomes. The implementation of this novel target volume will be accompanied by an extensive quality assurance program in the STAR-TREC trial. We describe the rationale behind the novel, mesorectal only radiotherapy treatment used in the STAR-TREC trial specifically tailored for early stage disease, with the goal of organ preservation.

Task Group 174 Report: Utilization of [18 F]Fluorodeoxyglucose Positron Emission Tomography ([18 F]FDG-PET) in Radiation Therapy

The use of positron emission tomography (PET) in radiation therapy (RT) is rapidly increasing in the areas of staging, segmentation, treatment planning, and response assessment. The most common radiotracer is ¹⁸ F-fluorodeoxyglucose ([¹⁸ F]FDG), a glucose analog with demonstrated efficacy in cancer diagnosis and staging. However, diagnosis and RT planning are different endeavors with unique requirements, and very little literature is available for guiding physicists and clinicians in the utilization of [¹⁸ F]FDG-PET in RT. The two goals of this report are to educate and provide recommendations. The report provides background and education on current PET imaging systems, PET tracers, intensity quantification, and current utilization in RT (staging, segmentation, image registration, treatment planning, and therapy response assessment). Recommendations are provided on acceptance testing, annual and monthly quality assurance, scanning protocols to ensure consistency between interpatient scans and intrapatient longitudinal scans, reporting of patient and scan parameters in literature, requirements for incorporation of [¹⁸ F]FDG-PET in treatment planning systems, and image registration. The recommendations provided here are minimum requirements and are not meant to cover all aspects of the use of [¹⁸ F]FDG-PET for RT.

Imaging for Target Delineation and Treatment Planning in Radiation Oncology: Current and Emerging Techniques

Imaging in radiation oncology has a wide range of applications. It is necessary not only for tumor staging and treatment response assessment after therapy but also for the treatment planning process, including definition of target and organs at risk, as well as treatment plan calculation. This article provides a comprehensive overview of the main imaging modalities currently used for target delineation and treatment planning and gives insight into new and promising techniques.

Impact of deviations in target volume delineation - Time for a new RTQA approach?

The international radiotherapy community has recognised that non-adherence to RT protocols can influence trial endpoints. However this conclusion is based on studies predominantly assessing the impact of deviations in dosimetric or treatment delivery protocol parameters rather than target volume delineation (TVD). This review evaluates the assessment of TVD within Radiation Therapy Quality Assurance (RTQA) programmes in clinical trials and the clinical impact of TVD protocol deviations. The implications for RTQA programmes are discussed. MEDLINE, PreMEDLINE, Embase, Cochrane Library, Web of Science, OpenGrey, WHO International Clinical Trials Registry Platform portal and ClinicalTrials.gov were searched. Full-length articles and conference abstracts were included to avoid publication bias. 5864 abstracts were screened for relevance; 94 full-length articles were reviewed and 5 relevant trials identified. Various classification systems were used to assess protocol deviations; 'unacceptable' or 'major' deviations in TVD occurred in 2.9-13.4% of assessed RT plans (when reported). It was often not possible to establish deviation rates specifically related to TVD as these were frequently combined with other types of protocol deviations. Details on the nature of unacceptable deviations was also not routinely reported and difficulties in establishing a 'consensus' for appropriate TVD for on-trial patients highlighted. Results suggest that deviations in TVD were associated with poorer outcomes for overall survival, local control and treatment-related toxicity; however the data were heterogeneous. RTQA of TVD was retrospective and feedback on the quality of TVD to recruiting centres was not standard. In summary, few trials have published outcomes on the impact of assessing the quality of TVD in trials. We propose that a new approach is now required. Unacceptable TVD deviations must be clearly defined at the time of protocol development to minimise interobserver variation, thereby promoting consistency in RTQA feedback. Prospective TVD reviews should be implemented for trials involving novel or complex RT techniques to identify deviations that require modification prior to treatment delivery. Furthermore, the consistent reporting of RTQA programme outcomes, both within and across trial groups, is of paramount importance to accelerate the evidence-base for the best RTQA approach when assessing TVD and to enable the impact on clinical outcomes within RT trials to be assessed.