A review of methods of analysis in contouring studies for radiation oncology

Manual prostate MRI segmentation by readers with different experience: a study of the learning progress

OBJECTIVE

To evaluate the learning progress of less experienced readers in prostate MRI segmentation.

MATERIALS AND METHODS

One hundred bi-parametric prostate MRI scans were retrospectively selected from the Göteborg Prostate Cancer Screening 2 Trial (single center). Nine readers with varying degrees of segmentation experience were involved: one expert radiologist, two experienced radiology residents, two inexperienced radiology residents, and four novices. The task was to segment the whole prostate gland. The expert's segmentations were used as reference. For all other readers except three novices, the 100 MRI scans were divided into five rounds (cases 1-10, 11-25, 26-50, 51-76, 76-100). Three novices segmented only 50 cases (three rounds). After each round, a one-on-one feedback session between the expert and the reader was held, with feedback on systematic errors and potential improvements for the next round. Dice similarity coefficient (DSC) > 0.8 was considered accurate.

RESULTS

Using DSC > 0.8 as the threshold, the novices had a total of 194 accurate segmentations out of 250 (77.6%). The residents had a total of 397/400 (99.2%) accurate segmentations. In round 1, the novices had 19/40 (47.5%) accurate segmentations, in round 2 41/60 (68.3%), and in round 3 84/100 (84.0%) indicating learning progress.

CONCLUSIONS

Radiology residents, regardless of prior experience, showed high segmentation accuracy. Novices showed larger interindividual variation and lower segmentation accuracy than radiology residents. To prepare datasets for artificial intelligence (AI) development, employing radiology residents seems safe and provides a good balance between cost-effectiveness and segmentation accuracy. Employing novices should only be considered on an individual basis.

CLINICAL RELEVANCE STATEMENT

Employing radiology residents for prostate MRI segmentation seems safe and can potentially reduce the workload of expert radiologists. Employing novices should only be considered on an individual basis.

KEY POINTS

• Using less experienced readers for prostate MRI segmentation is cost-effective but may reduce quality. • Radiology residents provided high accuracy segmentations while novices showed large inter-reader variability. • To prepare datasets for AI development, employing radiology residents seems safe and might provide a good balance between cost-effectiveness and segmentation accuracy while novices should only be employed on an individual basis.

Impact of a contouring atlas on radiographer inter-observer variation in male pelvis radiotherapy

PURPOSE/OBJECTIVE

To determine the impact of a MR-based contouring atlas for male pelvis radiotherapy delineation on inter-observer variation to support radiographer led real-time magnetic resonance image guided adaptive radiotherapy (MRgART).

MATERIAL/METHODS

Eight RTTs contoured 25 MR images in the Monaco treatment planning system (Monaco 5.40.01), from 5 patients. The prostate, seminal vesicles, bladder, and rectum were delineated before and after the introduction of an atlas developed through multi-disciplinary consensus. Inter-observer contour variations (volume), time to contour and observer contouring confidence were determined at both time-points using a 5-point Likert scale. Descriptive statistics were used to analyse both continuous and categorical variables. Dice similarity coefficient (DSC), Dice-Jaccard coefficient (DJC) and Hausdorff distance were used to calculate similarity between observers.

RESULTS

Although variation in volume definition decreased for all structures among all observers post intervention, the change was not statistically significant. DSC and DJC measurements remained consistent following the introduction of the atlas for all observers. The highest similarity was found in the bladder and prostate whilst the lowest was the seminal vesicles. The mean contouring time for all observers was reduced by 50% following the introduction of the atlas (53 to 27 minutes, p=0.01). For all structures across all observers, the mean contouring confidence increased significantly from 2.3 to 3.5 out of 5 (p=0.02).

CONCLUSION

Although no significant improvements were observed in contour variation amongst observers, the introduction of the consensus-based contouring atlas improved contouring confidence and speed; key factors for a real-time RTT-led MRgART.

Evaluation of multiple-vendor AI autocontouring solutions

BACKGROUND

Multiple artificial intelligence (AI)-based autocontouring solutions have become available, each promising high accuracy and time savings compared with manual contouring. Before implementing AI-driven autocontouring into clinical practice, three commercially available CT-based solutions were evaluated.

MATERIALS AND METHODS

The following solutions were evaluated in this work: MIM-ProtégéAI+ (MIM), Radformation-AutoContour (RAD), and Siemens-DirectORGANS (SIE). Sixteen organs were identified that could be contoured by all solutions. For each organ, ten patients that had manually generated contours approved by the treating physician (AP) were identified, totaling forty-seven different patients. CT scans in the supine position were acquired using a Siemens-SOMATOMgo 64-slice helical scanner and used to generate autocontours. Physician scoring of contour accuracy was performed by at least three physicians using a five-point Likert scale. Dice similarity coefficient (DSC), Hausdorff distance (HD) and mean distance to agreement (MDA) were calculated comparing AI contours to "ground truth" AP contours.

RESULTS

The average physician score ranged from 1.00, indicating that all physicians reviewed the contour as clinically acceptable with no modifications necessary, to 3.70, indicating changes are required and that the time taken to modify the structures would likely take as long or longer than manually generating the contour. When averaged across all sixteen structures, the AP contours had a physician score of 2.02, MIM 2.07, RAD 1.96 and SIE 1.99. DSC ranged from 0.37 to 0.98, with 41/48 (85.4%) contours having an average DSC ≥ 0.7. Average HD ranged from 2.9 to 43.3 mm. Average MDA ranged from 0.6 to 26.1 mm.

CONCLUSIONS

The results of our comparison demonstrate that each vendor's AI contouring solution exhibited capabilities similar to those of manual contouring. There were a small number of cases where unusual anatomy led to poor scores with one or more of the solutions. The consistency and comparable performance of all three vendors' solutions suggest that radiation oncology centers can confidently choose any of the evaluated solutions based on individual preferences, resource availability, and compatibility with their existing clinical workflows. Although AI-based contouring may result in high-quality contours for the majority of patients, a minority of patients require manual contouring and more in-depth physician review.

Technical Note: Generalizable and Promptable Artificial Intelligence Model to Augment Clinical Delineation in Radiation Oncology

BACKGROUND

Efficient and accurate delineation of organs at risk (OARs) is a critical procedure for treatment planning and dose evaluation. Deep learning-based auto-segmentation of OARs has shown promising results and is increasingly being used in radiation therapy. However, existing deep learning-based auto-segmentation approaches face two challenges in clinical practice: generalizability and human-AI interaction. A generalizable and promptable auto-segmentation model, which segments OARs of multiple disease sites simultaneously and supports on-the-fly human-AI interaction, can significantly enhance the efficiency of radiation therapy treatment planning.

PURPOSE

Meta's segment anything model (SAM) was proposed as a generalizable and promptable model for next-generation natural image segmentation. We further evaluated the performance of SAM in radiotherapy segmentation.

METHODS

Computed tomography (CT) images of clinical cases from four disease sites at our institute were collected: prostate, lung, gastrointestinal, and head & neck. For each case, we selected the OARs important in radiotherapy treatment planning. We then compared both the Dice coefficients and Jaccard indices derived from three distinct methods: manual delineation (ground truth), automatic segmentation using SAM's 'segment anything' mode, and automatic segmentation using SAM's 'box prompt' mode that implements manual interaction via live prompts during segmentation.

RESULTS

Our results indicate that SAM's segment anything mode can achieve clinically acceptable segmentation results in most OARs with Dice scores higher than 0.7. SAM's box prompt mode further improves Dice scores by 0.1∼0.5. Similar results were observed for Jaccard indices. The results show that SAM performs better for prostate and lung, but worse for gastrointestinal and head & neck. When considering the size of organs and the distinctiveness of their boundaries, SAM shows better performance for large organs with distinct boundaries, such as lung and liver, and worse for smaller organs with less distinct boundaries, like parotid and cochlea.

CONCLUSIONS

Our results demonstrate SAM's robust generalizability with consistent accuracy in automatic segmentation for radiotherapy. Furthermore, the advanced box-prompt method enables the users to augment auto-segmentation interactively and dynamically, leading to patient-specific auto-segmentation in radiation therapy. SAM's generalizability across different disease sites and different modalities makes it feasible to develop a generic auto-segmentation model in radiotherapy.

Clinical Use of a Commercial Artificial Intelligence-Based Software for Autocontouring in Radiation Therapy: Geometric Performance and Dosimetric Impact

PURPOSE

When autocontouring based on artificial intelligence (AI) is used in the radiotherapy (RT) workflow, the contours are reviewed and eventually adjusted by a radiation oncologist before an RT treatment plan is generated, with the purpose of improving dosimetry and reducing both interobserver variability and time for contouring. The purpose of this study was to evaluate the results of application of a commercial AI-based autocontouring for RT, assessing both geometric accuracies and the influence on optimized dose from automatically generated contours after review by human operator.

MATERIALS AND METHODS

A commercial autocontouring system was applied to a retrospective database of 40 patients, of which 20 were treated with radiotherapy for prostate cancer (PCa) and 20 for head and neck cancer (HNC). Contours resulting from AI were compared against AI contours reviewed by human operator and human-only contours using Dice similarity coefficient (DSC), Hausdorff distance (HD), and relative volume difference (RVD). Dosimetric indices such as Dmean, D0.03cc, and normalized plan quality metrics were used to compare dose distributions from RT plans generated from structure sets contoured by humans assisted by AI against plans from manual contours. The reduction in contouring time obtained by using automated tools was also assessed. A Wilcoxon rank sum test was computed to assess the significance of differences. Interobserver variability of the comparison of manual vs. AI-assisted contours was also assessed among two radiation oncologists for PCa.

RESULTS

For PCa, AI-assisted segmentation showed good agreement with expert radiation oncologist structures with average DSC among patients ≥ 0.7 for all structures, and minimal radiation oncology adjustment of structures (DSC of adjusted versus AI structures ≥ 0.91). For HNC, results of comparison between manual and AI contouring varied considerably e.g., 0.77 for oral cavity and 0.11-0.13 for brachial plexus, but again, adjustment was generally minimal (DSC of adjusted against AI contours 0.97 for oral cavity, 0.92-0.93 for brachial plexus). The difference in dose for the target and organs at risk were not statistically significant between human and AI-assisted, with the only exceptions of D0.03cc to the anal canal and Dmean to the brachial plexus. The observed average differences in plan quality for PCa and HNC cases were 8% and 6.7%, respectively. The dose parameter changes due to interobserver variability in PCa were small, with the exception of the anal canal, where large dose variations were observed. The reduction in time required for contouring was 72% for PCa and 84% for HNC.

CONCLUSIONS

When an autocontouring system is used in combination with human review, the time of the RT workflow is significantly reduced without affecting dose distribution and plan quality.

Feasibility evaluation of novel AI-based deep-learning contouring algorithm for radiotherapy

PURPOSE

To evaluate the clinical feasibility of the Siemens Healthineers AI-Rad Companion Organs RT VA30A (Organs-RT) auto-contouring algorithm for organs at risk (OARs) of the pelvis, thorax, and head and neck (H&N).

METHODS

Computed tomography (CT) datasets from 30 patients (10 pelvis, 10 thorax, and 10 H&N) were collected. Four sets of OARs were generated on each scan, one set by Organs-RT and the others by three experienced users independently. A physician (expert) then evaluated each contour by assigning a score from the following scale: 1-Must Redo, 2-Major Edits, 3-Minor Edits, 4-Clinically usable. Using the highest-scored OAR from the human users as a reference, the contours generated by Organs-RT were evaluated via Dice Similarity Coefficient (DSC), Hausdorff Distance (HDD), Mean Distance to Agreement (mDTA), Volume comparison, and visual inspection. Additionally, each human user recorded the time to delineate each structure set and time-saving efficiency was measured.

RESULTS

The average DSC obtained for the pelvic OARs ranged between (0.81 ± 0.06)Rectum and (0.94 ± 0.03)Bladder . (0.75 ± 0.09)Esophagus to( 0.96 ± 0.02 ) Rt . Lung ${( {0.96 \pm 0.02} )}_{{\mathrm{Rt}}.{\mathrm{\ Lung}}}$ for the thoracic OARs and (0.66 ± 0.07)Lips to (0.83 ± 0.04)Brainstem for the H&N. The average HDD in cm for the pelvis cohort ranged between (0.95 ± 0.35)Bladder to (3.62 ± 2.50)Rectum , (0.42 ± 0.06)SpinalCord to (2.09 ± 2.00)Esophagus for the thoracic set and( 0.53 ± 0.22 ) Cerv _ SpinalCord ${( {0.53 \pm 0.22} )}_{{\mathrm{Cerv}}\_{\mathrm{SpinalCord}}}$ to (1.50 ± 0.50)Mandible for the H&N region. The time-saving efficiency was 67% for H&N, 83% for pelvis, and 84% for thorax. 72.5%, 82%, and 50% of the pelvis, thorax, and H&N OARs were scored as clinically usable by the expert, respectively.

CONCLUSIONS

The highest agreement registered between OARs generated by Organs-RT and their respective references was for the bladder, heart, lungs, and femoral heads, with an overall DSC≥0.92. The poorest agreement was for the rectum, esophagus, and lips, with an overall DSC⩽0.81. Nonetheless, Organs-RT serves as a reliable auto-contouring tool by minimizing overall contouring time and increasing time-saving efficiency in radiotherapy treatment planning.

Assessing Interobserver Variability in the Delineation of Structures in Radiation Oncology: A Systematic Review

PURPOSE

The delineation of target volumes and organs at risk is the main source of uncertainty in radiation therapy. Numerous interobserver variability (IOV) studies have been conducted, often with unclear methodology and nonstandardized reporting. We aimed to identify the parameters chosen in conducting delineation IOV studies and assess their performances and limits.

METHODS AND MATERIALS

We conducted a systematic literature review to highlight major points of heterogeneity and missing data in IOV studies published between 2018 and 2021. For the main used metrics, we did in silico analyses to assess their limits in specific clinical situations.

RESULTS

All disease sites were represented in the 66 studies examined. Organs at risk were studied independently of tumor site in 29% of reviewed IOV studies. In 65% of studies, statistical analyses were performed. No gold standard (GS; ie, reference) was defined in 36% of studies. A single expert was considered as the GS in 21% of studies, without testing intraobserver variability. All studies reported both absolute and relative indices, including the Dice similarity coefficient (DSC) in 68% and the Hausdorff distance (HD) in 42%. Limitations were shown in silico for small structures when using the DSC and dependence on irregular shapes when using the HD. Variations in DSC values were large between studies, and their thresholds were inconsistent. Most studies (51%) included 1 to 10 cases. The median number of observers or experts was 7 (range, 2-35). The intraclass correlation coefficient was reported in only 9% of cases. Investigating the feasibility of studying IOV in delineation, a minimum of 8 observers with 3 cases, or 11 observers with 2 cases, was required to demonstrate moderate reproducibility.

CONCLUSIONS

Implementation of future IOV studies would benefit from a more standardized methodology: clear definitions of the gold standard and metrics and a justification of the tradeoffs made in the choice of the number of observers and number of delineated cases should be provided.

Analytic calculations and synthetic shapes for validation of quantitative contour comparison software

A high level of variability in reported values was observed in a recent survey of contour similarity measures (CSMs) calculation tools. Such variations in the output measurements prevent meaningful comparison between studies. The purpose of this study was to develop a dataset with analytically calculated gold standard values to facilitate standardization and ensure accuracy of CSM implementations. The dataset was generated in the Digital Imaging and Communications in Medicine (DICOM) format. Both the dataset and the software used for its generation are made publicly available to encourage robust testing of CSM implementations for accuracy, improving consistency between different implementations.

Evaluation of therapeutic radiographer contouring for magnetic resonance image guided online adaptive prostate radiotherapy

BACKGROUND AND PURPOSE

The implementation of MRI-guided online adaptive radiotherapy has facilitated the extension of therapeutic radiographers' roles to include contouring, thus releasing the clinician from attending daily treatment. Following undergoing a specifically designed training programme, an online interobserver variability study was performed.

MATERIALS AND METHODS

117 images from six patients treated on a MR Linac were contoured online by either radiographer or clinician and the same images contoured offline by the alternate profession. Dice similarity coefficient (DSC), mean distance to agreement (MDA), Hausdorff distance (HD) and volume metrics were used to analyse contours. Additionally, the online radiographer contours and optimised plans (n = 59) were analysed using the offline clinician defined contours. After clinical implementation of radiographer contouring, target volume comparison and dose analysis was performed on 20 contours from five patients.

RESULTS

Comparison of the radiographers' and clinicians' contours resulted in a median (range) DSC of 0.92 (0.86 - 0.99), median (range) MDA of 0.98 mm (0.2-1.7) and median (range) HD of 6.3 mm (2.5-11.5) for all 117 fractions. There was no significant difference in volume size between the two groups. Of the 59 plans created with radiographer online contours and overlaid with clinicians' offline contours, 39 met mandatory dose constraints and 12 were acceptable because 95 % of the high dose PTV was covered by 95 % dose, or the high dose PTV was within 3 % of online plan. A clinician blindly reviewed the eight remaining fractions and, using trial quality assurance metrics, deemed all to be acceptable. Following clinical implementation of radiographer contouring, the median (range) DSC of CTV was 0.93 (0.88-1.0), median (range) MDA was 0.8 mm (0.04-1.18) and HD was 5.15 mm (2.09-8.54) respectively. Of the 20 plans created using radiographer online contours overlaid with clinicians' offline contours, 18 met the dosimetric success criteria, the remaining 2 were deemed acceptable by a clinician.

CONCLUSION

Radiographer and clinician prostate and seminal vesicle contours on MRI for an online adaptive workflow are comparable and produce clinically acceptable plans. Radiographer contouring for prostate treatment on a MR-linac can be effectively introduced with appropriate training and evaluation. A DSC threshold for target structures could be implemented to streamline future training.

A Review of the Metrics Used to Assess Auto-Contouring Systems in Radiotherapy

Auto-contouring could revolutionise future planning of radiotherapy treatment. The lack of consensus on how to assess and validate auto-contouring systems currently limits clinical use. This review formally quantifies the assessment metrics used in studies published during one calendar year and assesses the need for standardised practice. A PubMed literature search was undertaken for papers evaluating radiotherapy auto-contouring published during 2021. Papers were assessed for types of metric and the methodology used to generate ground-truth comparators. Our PubMed search identified 212 studies, of which 117 met the criteria for clinical review. Geometric assessment metrics were used in 116 of 117 studies (99.1%). This includes the Dice Similarity Coefficient used in 113 (96.6%) studies. Clinically relevant metrics, such as qualitative, dosimetric and time-saving metrics, were less frequently used in 22 (18.8%), 27 (23.1%) and 18 (15.4%) of 117 studies, respectively. There was heterogeneity within each category of metric. Over 90 different names for geometric measures were used. Methods for qualitative assessment were different in all but two papers. Variation existed in the methods used to generate radiotherapy plans for dosimetric assessment. Consideration of editing time was only given in 11 (9.4%) papers. A single manual contour as a ground-truth comparator was used in 65 (55.6%) studies. Only 31 (26.5%) studies compared auto-contours to usual inter- and/or intra-observer variation. In conclusion, significant variation exists in how research papers currently assess the accuracy of automatically generated contours. Geometric measures are the most popular, however their clinical utility is unknown. There is heterogeneity in the methods used to perform clinical assessment. Considering the different stages of system implementation may provide a framework to decide the most appropriate metrics. This analysis supports the need for a consensus on the clinical implementation of auto-contouring.

Intra- and interobserver assessments of intestinal wall thickness and segmentations from transverse sections of feline abdominal ultrasound images

Measurements of intestinal wall thicknesses from ultrasound imaging (US) are routinely used to support diagnoses of intestinal disorders in cats, however published studies describing observer agreement are currently lacking. The aim of this retrospective, observer agreement study was to quantify inter- and intraobserver repeatability and agreement in the measurement of intestinal wall layer thicknesses and the segmentation of transverse sections of small intestines in US images of 20 cats. Intestinal wall layer thickness measurements of the mucosa, submucosa, muscularis, serosa layer, and total thickness of these layers were performed on five cats with small cell epitheliotropic lymphoma, five with inflammatory bowel disease, and 10 with other conditions. Thickness measurements and the segmentation encompassing the serosa layer were obtained from five observers four times non-sequentially. The average standard deviation in thickness measurements (95% confidence interval) in the mucosa, submucosa, muscularis, serosa, and total thickness were 0.35 (0.07-0.95), 0.24 (0.07-0.52), 0.22 (0.06-0.49), 0.20 (0.05-0.49), and 0.57 (0.11-1.60) mm, respectively. The average intraclass correlation coefficients, which estimates the degree of consistency in thickness measurements and segmentation areas for each observer, ranged from 0.355 to 0.870 and 0.850 to 0.993, respectively. The interclass correlation coefficient, which estimates the degree of consistency when measuring a thickness or segmentation area over all observers ranged from 0.115 to 0.753, and 0.811 to 0.902, respectively. The overall average Dice Coefficient, which estimates the extent of overlap of the segmentations for all observers was 0.957 (0.933 to 0.972). Our results suggest segmentations of small intestines have a higher interobserver agreement than measurements of intestinal wall thicknesses.

Necessity of Pelvic Lymph Node Irradiation in Patients with Recurrent Prostate Cancer after Radical Prostatectomy in the PSMA PET/CT Era: A Narrative Review

The main prostate cancer (PCa) treatments include surgery or radiotherapy (with or without ADT). However, none of the suggested treatments eliminates the risk of lymph node metastases. Conventional imaging methods, including MRI and CT scanning, are not sensitive enough for the diagnosis of lymph node metastases; however, the novel imaging method, PSMA PET/CT scanning, has provided valuable information about the pelvic LN involvement in patients with recurrent PCa (RPCa) after radical prostatectomy. The high sensitivity and negative predictive value enable accurate N staging in PCa patients. In this narrative review, we summarize the evidence on the treatment and extent of radiation in prostate-only or whole-pelvis radiation in patients with positive and negative LN involvement on PSMA PET/CT scans.

Multicenter comparison of measures for quantitative evaluation of contouring in radiotherapy

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There is variation in contour measurement definitions used between institutions.

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Differences in implementation can result in large discrepancies in reported results.

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Many in-house implementations were found to have bugs in at least one measure.

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Extreme care should be taken comparing results from different studies.

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A dataset with known ground truth values would assist in validation of such tools.

Background and Purpose

A wide range of quantitative measures are available to facilitate clinical implementation of auto-contouring software, on-going Quality Assurance (QA) and interobserver contouring variation studies. This study aimed to assess the variation in output when applying different implementations of the measures to the same data in order to investigate how consistently such measures are defined and implemented in radiation oncology.

Materials and Methods

A survey was conducted to assess if there were any differences in definitions of contouring measures or their implementations that would lead to variation in reported results between institutions. This took two forms: a set of computed tomography (CT) image data with “Test” and “Reference” contours was distributed for participants to process using their preferred tools and report results, and a questionnaire regarding the definition of measures and their implementation was completed by the participants.

Results

Thirteen participants completed the survey and submitted results, with one commercial and twelve in-house solutions represented. Excluding outliers, variations of up to 50% in Dice Similarity Coefficient (DSC), 50% in 3D Hausdorff Distance (HD), and 200% in Average Distance (AD) were observed between the participant submitted results. Collaborative investigation with participants revealed a large number of bugs in implementation, confounding the understanding of intentional implementation choices.

Conclusion

Care must be taken when comparing quantitative results between different studies. There is a need for a dataset with clearly defined measures and ground truth for validation of such tools prior to their use.

Contour similarity and its implication on inverse prostate SBRT treatment planning

PURPOSE

Success of auto-segmentation is measured by the similarity between auto and manual contours that is often quantified by Dice coefficient (DC). The dosimetric impact of contour variability on inverse planning has been rarely reported. The main aim of this study is to investigate whether automatically generated organs-at-risk (OARs) could be used in inverse prostate stereotactic body radiation therapy (SBRT) planning and whether the dosimetric parameters are still clinically acceptable after radiation oncologists modify the OARs.

METHODS AND MATERIALS

Planning computed tomography images from 10 patients treated with SBRT for prostate cancer were selected and automatically segmented by commercially available atlas-based software. The automatically generated OAR contours were compared with the manually drawn contours. Two volumetric modulated arc therapy (VMAT) plans, autoRec-VMAT (where only automatically generated rectums were used in optimization) and autoAll-VMAT (where automatically generated OARs were used in inverse optimization) were generated. Dosimetric parameters based on the manually drawn PTV and OARs were compared with the clinically approved plans.

RESULTS

The DCs for the rectum contours varied from 0.55 to 0.74 with a mean value of 0.665. Differences of D₉₅ of the PTV between autoRec-VMAT and manu-VMAT plans varied from 0.03% to -2.85% with a mean value of -0.64%. Differences of D_0.03cc of manual rectum between the two plans varied from -0.86% to 9.94% with a mean value of 2.71%. D₉₅ of PTV between autoAll-VMAT and manu-VMAT plans varied from 0.28% to -2.9% with a mean value -0.83%. Differences of D_0.03cc of manual rectum between the two plans varied from -0.76% to 6.72% with a mean value of 2.62%.

CONCLUSION

Our study implies that it is possible to use unedited automatically generated OARs to perform initial inverse prostate SBRT planning. After radiation oncologists modify/approve the OARs, the plan qualities based on the manually drawn OARs are still clinically acceptable, and a re-optimization may not be needed.

Delineation uncertainties of tumour volumes on MRI of head and neck cancer patients

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Role of target delineation uncertainties in head and neck cancer patients.

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Knowing contouring variations for MRI allows better adaptation of MRLinac for H&N cancers.

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An interobserver variation for GTV among 8 observers was below 2 mm using MRI.

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Variability between observers might improve using other imaging modalities.

Background

During the last decade, radiotherapy using MR Linac has gone from research to clinical implementation for different cancer locations. For head and neck cancer (HNC), target delineation based only on MR images is not yet standard, and the utilisation of MRI instead of PET/CT in radiotherapy planning is not well established. We aimed to analyse the inter-observer variation (IOV) in delineating GTV (gross tumour volume) on MR images only for patients with HNC.

Material/methods

32 HNC patients from two independent departments were included. Four clinical oncologists from Denmark and four radiation oncologists from Australia had independently contoured primary tumour GTVs (GTV-T) and nodal GTVs (GTV-N) on T2-weighted MR images obtained at the time of treatment planning. Observers were provided with sets of images, delineation guidelines and patient synopsis. Simultaneous truth and performance level estimation (STAPLE) reference volumes were generated for each structure using all observer contours. The IOV was assessed using the DICE Similarity Coefficient (DSC) and mean absolute surface distance (MASD).

Results

32 GTV-Ts and 68 GTV-Ns were contoured per observer. The median MASD for GTV-Ts and GTV-Ns across all patients was 0.17 cm (range 0.08–0.39 cm) and 0.07 cm (range 0.04–0.33 cm), respectively. Median DSC relative to a STAPLE volume for GTV-Ts and GTV-Ns across all patients were 0.73 and 0.76, respectively. A significant correlation was seen between median DSCs and median volumes of GTV-Ts (Spearman correlation coefficient 0.76, p < 0.001) and of GTV-Ns (Spearman correlation coefficient 0.55, p < 0.001).

Conclusion

Contouring GTVs in patients with HNC on MRI showed that the median IOV for GTV-T and GTV-N was below 2 mm, based on observes from two separate radiation departments. However, there are still specific regions in tumours that are difficult to resolve as either malignant tissue or oedema that potentially could be improved by further training in MR-only delineation.

Comparative evaluation of a prototype deep learning algorithm for autosegmentation of normal tissues in head and neck radiotherapy

PURPOSE

To introduce and validate a newly developed deep-learning (DL) auto-segmentation algorithm for head and neck (HN) organs at risk (OARs) and to compare its performance with a published commercial algorithm.

METHODS

A total of 864 HN cancer cases were available to train and evaluate a prototype algorithm. The algorithm is based on a fully convolutional network with combined U-Net and V-net. A Dice loss plus Cross-Entropy Loss function with Adam optimizer was used in training. For 75 validation cases, OAR sets were generated with three DL-based models (A: the prototype model trained with gold data, B: a commercial software trained with the same data, and C: the same software trained with data from another institution). The auto-segmented structures were evaluated with Dice similarity coefficient (DSC), Hausdorff distance (HD), voxel-penalty metric (VPM) and DSC of area under dose-volume histograms. A subjective qualitative evaluation was performed on 20 random cases.

RESULTS

Overall trend was for the prototype algorithm to be the closest to the gold data by all five metrics. The average DSC/VPM/HD for algorithms A, B, and C were 0.81/84.1/1.6 mm, 0.74/62.8/3.2 mm, and 0.66/46.8/3.3 mm, respectively. 93% of model A structures were evaluated to be clinically useful.

CONCLUSION

The superior performance of the prototype was validated, even when trained with the same data. In addition to the challenges of perfecting the algorithms, the auto-segmentation results can differ when the same algorithm is trained at different institutions.

International assessment of interobserver reproducibility of flap delineation in head and neck carcinoma

Background: Several reports have suggested that radiotherapy after reconstructive surgery for head and neck cancer (HNC), could have deleterious effects on the flaps with respect to functional outcomes. To predict and prevent toxicities, flap delineation should be accurate and reproducible. The objective of the present study was to evaluate the interobserver variability of frequent types of flaps used in HNC, based on the recent GORTEC atlas.Materials and methods: Each member of an international working group (WG) consisting of 14 experts delineated the flaps on a CT set from six patients. Each patient had one of the five most commonly used flaps in HNC: a regional pedicled pectoralis major myocutaneous flap, a local pedicled rotational soft tissue facial artery musculo-mucosal (FAMM) (2 patients), a fasciocutaneous radial forearm free flap, a soft tissue anterolateral thigh (ALT) free flap, or a fibular free flap. The WG's contours were compared to a reference contour, validated by a surgeon and a radiologist specializing in HNC. Contours were considered as reproducible if the median Dice Similarity Coefficient (DSC) was > 0.7.Results: The median volumes of the six flaps delineated by the WG were close to the reference contour value, with approximately 50 cc for the pectoral, fibula, and ALT flaps, 20 cc for the radial forearm, and up to 10 cc for the FAMM. The volumetric ratio was thus close to the optimal value of 100% for all flaps. The median DSC obtained by the WG compared to the reference for the pectoralis flap, the FAMM, the radial forearm flap, ALT flap, and the fibular flap were 0.82, 0.40, 0.76, 0.81, and 0.76, respectively.Conclusions: This study showed that the delineation of four main flaps used for HNC was reproducible. The delineation of the FAMM, however, requires close cooperation between radiologist, surgeon and radiation oncologist because of the poor visibility of this flap on CT and its small size.

Geometric and Dosimetric Evaluation of the Automatic Delineation of Organs at Risk (OARs) in Non-Small-Cell Lung Cancer Radiotherapy Based on a Modified DenseNet Deep Learning Network

Purpose

To introduce an end-to-end automatic segmentation model for organs at risk (OARs) in thoracic CT images based on modified DenseNet, and reduce the workload of radiation oncologists.

Materials and Methods

The computed tomography (CT) images of 36 lung cancer patients were included in this study, of which 27 patients’ images were randomly selected as the training set, 9 patients’ as the testing set. The validation set was generated by cross validation and 6 patients’ images were randomly selected from the training set during each epoch as the validation set. The autosegmentation task of the left and right lungs, spinal cord, heart, trachea and esophagus was implemented, and the whole training time was approximately 5 hours. Geometric evaluation metrics including the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95) and average surface distance (ASD), were used to assess the autosegmentation performance of OARs based on the proposed model and were compared with those based on U-Net as benchmarks. Then, two sets of treatment plans were optimized based on the manually contoured targets and OARs (Plan1), as well as the manually contours targets and the automatically contoured OARs (Plan2). Dosimetric parameters, including Dmax, Dmean and Vx, of OARs were obtained and compared.

Results

The DSC, HD95 and ASD of the proposed model were better than those of U-Net. The differences in the DSC of the spinal cord and esophagus, differences in the HD95 of the spinal cord, heart, trachea and esophagus, as well as differences in the ASD of the spinal cord were statistically significant between the two models (P<0.05). The differences in the dose-volume parameters of the two sets of plans were not statistically significant (P>0.05). Moreover, compared with manual segmentation, autosegmentation significantly reduced the contouring time by nearly 40.7% (P<0.05).

Conclusions

The bilateral lungs, spinal cord, heart and trachea could be accurately delineated using the proposed model in this study; however, the automatic segmentation effect of the esophagus must still be further improved. The concept of feature map reuse provides a new idea for automatic medical image segmentation.

Evaluation of the impact of teaching on delineation variation during a virtual stereotactic ablative radiotherapy contouring workshop

Introduction:

Variation in delineation of target volumes/organs at risk (OARs) is well recognised in radiotherapy and may be reduced by several methods including teaching. We evaluated the impact of teaching on contouring variation for thoracic/pelvic stereotactic ablative radiotherapy (SABR) during a virtual contouring workshop.

Materials and methods:

Target volume/OAR contours produced by workshop participants for three cases were evaluated against reference contours using DICE similarity coefficient (DSC) and line domain error (LDE) metrics. Pre- and post-workshop DSC results were compared using Wilcoxon signed ranks test to determine the impact of teaching during the workshop.

Results:

Of 50 workshop participants, paired pre- and post-workshop contours were available for 21 (42%), 20 (40%) and 22 (44%) participants for primary lung cancer, pelvic bone metastasis and pelvic node metastasis cases, respectively. Statistically significant improvements post-workshop in median DSC and LDE results were observed for 6 (50%) and 7 (58%) of 12 structures, respectively, although the magnitude of DSC/LDE improvement was modest in most cases. An increase in median DSC post-workshop ≥0·05 was only observed for GTVbone, IGTVlung and SacralPlex, and reduction in median LDE > 1 mm was only observed for GTVbone, CTVbone and SacralPlex. Post-workshop, median DSC values were >0·7 for 75% of structures. For 92% of the structures, post-workshop contours were considered to be acceptable or within acceptable variation following review by the workshop faculty.

Conclusions:

This study has demonstrated that virtual SABR contouring training is feasible and was associated with some improvements in contouring variation for multiple target volumes/OARs.

Automatic Segmentation of Clinical Target Volume and Organs-at-Risk for Breast Conservative Radiotherapy Using a Convolutional Neural Network

Objective

Delineation of clinical target volume (CTV) and organs at risk (OARs) is important for radiotherapy but is time-consuming. We trained and evaluated a U-ResNet model to provide fast and consistent auto-segmentation.

Methods

We collected 160 patients’ CT scans with breast cancer who underwent breast-conserving surgery (BCS) and were treated with radiotherapy. CTV and OARs were delineated manually and were used for model training. The dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (95HD) were used to assess the performance of our model. CTV and OARs were randomly selected as ground truth (GT) masks, and artificial intelligence (AI) masks were generated by the proposed model. Two clinicians randomly compared CTV score differences of the contour. The consistency between two clinicians was tested. Time cost for auto-delineation was evaluated.

Results

The mean DSC values of the proposed method were 0.94, 0.95, 0.94, 0.96, 0.96 and 0.93 for breast CTV, contralateral breast, heart, right lung, left lung and spinal cord, respectively. The mean 95HD values were 4.31mm, 3.59mm, 4.86mm, 3.18mm, 2.79mm and 4.37mm for the above structures, respectively. The average CTV scores for AI and GT were 2.89 versus 2.92 when evaluated by oncologist A (P=0.612), and 2.75 versus 2.83 by oncologist B (P=0.213), with no statistically significant differences. The consistency between two clinicians was poor (kappa=0.282). The time for auto-segmentation of CTV and OARs was 10.03 s.

Conclusion

Our proposed model (U-ResNet) can improve the efficiency and accuracy of delineation compared with U-Net, performing equally well with the segmentation generated by oncologists.

A national study on the inter-observer variability in the delineation of organs at risk in the brain

BACKGROUND

The Danish Neuro Oncology Group (DNOG) has established national consensus guidelines for the delineation of organs at risk (OAR) structures based on published literature. This study was conducted to finalise these guidelines and evaluate the inter-observer variability of the delineated OAR structures by expert observers.

MATERIAL AND METHODS

The DNOG delineation guidelines were formed by participants from all Danish centres that treat brain tumours with radiotherapy. In a two-day workshop, guidelines were discussed and finalised based on a pilot study. Following this, the ten participants contoured the following OARs on T1-weighted gadolinium enhanced MRI from 13 patients with brain tumours: optic tracts, optic nerves, chiasm, spinal cord, brainstem, pituitary gland and hippocampus. The metrics used for comparison were the Dice similarity coefficient (Dice), mean surface distance (MSD) and others.

RESULTS

A total of 968 contours were delineated across the 13 patients. On average eight (range six to nine) individual contour sets were made per patient. Good agreement was found across all structures with a median MSD below 1 mm for most structures, with the chiasm performing the best with a median MSD of 0.45 mm. The Dice was as expected highly volume dependent, the brainstem (the largest structure) had the highest Dice value with a median of 0.89 whereas smaller volumes such as the chiasm had a Dice of 0.71.

CONCLUSION

Except for the caudal definition of the spinal cord, the variances observed in the contours of OARs in the brain were generally low and consistent. Surface mapping revealed sub-regions of higher variance for some organs. The data set is being prepared as a validation data set for auto-segmentation algorithms for use within the Danish Comprehensive Cancer Centre - Radiotherapy and potential collaborators.

Clinically Oriented Target Contour Evaluation Using Geometric and Dosimetric Indices Based on Simple Geometric Transformations

Purpose:

In radiotherapy, geometric indices are often used to evaluate the accuracy of contouring. However, the ability of geometric indices to identify the error of contouring results is limited primarily because they do not consider the clinical background. The purpose of this study is to investigate the relationship between geometric and clinical dosimetric indices.

Methods:

Four different types of targets were selected (C-shaped target, oropharyngeal cancer, metastatic spine cancer, and prostate cancer), and the translation, scaling, rotation, and sine function transformation were performed with the software Python to introduce systematic and random errors. The transformed contours were regarded as reference contours. Dosimetric indices were obtained from the original dose distribution of the radiotherapy plan. The correlations between geometric and dosimetric indices were quantified by linear regression.

Results:

The correlations between the geometric and dosimetric indices were inconsistent. For systematic errors, and with the exception of the sine function transformation (R²: 0.023-0.04, P > 0.05), the geometric transformations of the C-shaped target were correlated with the D98% and D_mean (R²: 0.689-0.988), 80% of which were P < 0.001. For the random errors, the correlations obtained by the all targets were R² > 0.384, P < 0.05. The Wilcoxon signed-rank test was used to compare the spatial direction resolution capability of geometric indices in different directions of the C-shaped target (with systematic errors), and the results showed only the volumetric geometric indices with P < 0.05.

Conclusions:

Clinically, an assessment of the contour accuracy of the region-of-interest is not feasible based on geometric indices alone. Dosimetric indices should be added to the evaluations of the accuracy of the delineation results, which can be helpful for explaining the clinical dose response relationship of delineation more comprehensively and accurately.

Comparison of atlas-based auto-segmentation accuracy for radiotherapy in prostate cancer

•

Auto-contouring accuracy and contouring time were evaluated using two procedures.

•

Dice coefficient was better for the multiple atlases procedure than for one atlas.

•

Contouring time of the multiple atlases procedure is clinically acceptable.

Atlas-based auto-segmentation (ABS) procedure used in radiotherapy can be classified into two groups, one using one atlas per patient (sSM) and the other using multiple atlases (sMM). This study evaluated auto-contouring accuracy and contouring time in patients with prostate cancer using the two procedures. The Dice similarity coefficient of sMM was significantly better than that of sSM (prostate [median, 0.81 (range, 0.66–0.91) vs. 0.64 (0.27–0.71), p < 0.01], seminal vesicles [0.49 (0.31–0.80) vs. 0.18 (0.01–0.60), p < 0.05], and rectum [0.81 (0.37–0.91) vs. 0.57 (0.31–0.77), p < 0.01]). The median contouring times were 2.6 (sMM) and 1.3 min (sSM).

The impact of organ-at-risk contour variations on automatically generated treatment plans for NSCLC

BACKGROUND AND PURPOSE

Quality of automatic contouring is generally assessed by comparison with manual delineations, but the effect of contour differences on the resulting dose distribution remains unknown. This study evaluated dosimetric differences between treatment plans optimized using various organ-at-risk (OAR) contouring methods.

MATERIALS AND METHODS

OARs of twenty lung cancer patients were manually and automatically contoured, after which user-adjustments were made. For each contour set, an automated treatment plan was generated. The dosimetric effect of intra-observer contour variation and the influence of contour variations on treatment plan evaluation and generation were studied using dose-volume histogram (DVH)-parameters for thoracic OARs.

RESULTS

Dosimetric effect of intra-observer contour variability was highest for Heart D_max (3.4 ± 6.8 Gy) and lowest for Lungs-GTV D_mean (0.3 ± 0.4 Gy). The effect of contour variation on treatment plan evaluation was highest for Heart D_max (6.0 ± 13.4 Gy) and Esophagus D_max (8.7 ± 17.2 Gy). Dose differences for the various treatment plans, evaluated on the reference (manual) contour, were on average below 1 Gy/1%. For Heart D_mean, higher dose differences were found for overlap with PTV (median 0.2 Gy, 95% 1.7 Gy) vs. no PTV overlap (median 0 Gy, 95% 0.5 Gy). For D_max-parameters, largest dose difference was found between 0-1 cm distance to PTV (median 1.5 Gy, 95% 4.7 Gy).

CONCLUSION

Dose differences arising from automatic contour variations were of the same magnitude or lower than intra-observer contour variability. For Heart D_mean, we recommend delineation errors to be corrected when the heart overlaps with the PTV. For D_max-parameters, we recommend checking contours if the distance is close to PTV (<5 cm). For the lungs, only obvious large errors need to be adjusted.

The predictive value of segmentation metrics on dosimetry in organs at risk of the brain

BACKGROUND

Fully automatic medical image segmentation has been a long pursuit in radiotherapy (RT). Recent developments involving deep learning show promising results yielding consistent and time efficient contours. In order to train and validate these systems, several geometric based metrics, such as Dice Similarity Coefficient (DSC), Hausdorff, and other related metrics are currently the standard in automated medical image segmentation challenges. However, the relevance of these metrics in RT is questionable. The quality of automated segmentation results needs to reflect clinical relevant treatment outcomes, such as dosimetry and related tumor control and toxicity. In this study, we present results investigating the correlation between popular geometric segmentation metrics and dose parameters for Organs-At-Risk (OAR) in brain tumor patients, and investigate properties that might be predictive for dose changes in brain radiotherapy.

METHODS

A retrospective database of glioblastoma multiforme patients was stratified for planning difficulty, from which 12 cases were selected and reference sets of OARs and radiation targets were defined. In order to assess the relation between segmentation quality -as measured by standard segmentation assessment metrics- and quality of RT plans, clinically realistic, yet alternative contours for each OAR of the selected cases were obtained through three methods: (i) Manual contours by two additional human raters. (ii) Realistic manual manipulations of reference contours. (iii) Through deep learning based segmentation results. On the reference structure set a reference plan was generated that was re-optimized for each corresponding alternative contour set. The correlation between segmentation metrics, and dosimetric changes was obtained and analyzed for each OAR, by means of the mean dose and maximum dose to 1% of the volume (Dmax 1%). Furthermore, we conducted specific experiments to investigate the dosimetric effect of alternative OAR contours with respect to the proximity to the target, size, particular shape and relative location to the target.

RESULTS

We found a low correlation between the DSC, reflecting the alternative OAR contours, and dosimetric changes. The Pearson correlation coefficient between the mean OAR dose effect and the Dice was -0.11. For Dmax 1%, we found a correlation of -0.13. Similar low correlations were found for 22 other segmentation metrics. The organ based analysis showed that there is a better correlation for the larger OARs (i.e. brainstem and eyes) as for the smaller OARs (i.e. optic nerves and chiasm). Furthermore, we found that proximity to the target does not make contour variations more susceptible to the dose effect. However, the direction of the contour variation with respect to the relative location of the target seems to have a strong correlation with the dose effect.

CONCLUSIONS

This study shows a low correlation between segmentation metrics and dosimetric changes for OARs in brain tumor patients. Results suggest that the current metrics for image segmentation in RT, as well as deep learning systems employing such metrics, need to be revisited towards clinically oriented metrics that better reflect how segmentation quality affects dose distribution and related tumor control and toxicity.

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.

Machine learning applications in radiation oncology

Machine learning technology has a growing impact on radiation oncology with an increasing presence in research and industry. The prevalence of diverse data including 3D imaging and the 3D radiation dose delivery presents potential for future automation and scope for treatment improvements for cancer patients. Harnessing this potential requires standardization of tools and data, and focused collaboration between fields of expertise. The rapid advancement of radiation oncology treatment technologies presents opportunities for machine learning integration with investments targeted towards data quality, data extraction, software, and engagement with clinical expertise. In this review, we provide an overview of machine learning concepts before reviewing advances in applying machine learning to radiation oncology and integrating these techniques into the radiation oncology workflows. Several key areas are outlined in the radiation oncology workflow where machine learning has been applied and where it can have a significant impact in terms of efficiency, consistency in treatment and overall treatment outcomes. This review highlights that machine learning has key early applications in radiation oncology due to the repetitive nature of many tasks that also currently have human review. Standardized data management of routinely collected imaging and radiation dose data are also highlighted as enabling engagement in research utilizing machine learning and the ability integrate these technologies into clinical workflow to benefit patients. Physicists need to be part of the conversation to facilitate this technical integration.

Inter-clinician delineation variation for a new highly-conformal flank target volume in children with renal tumors: A SIOP-Renal Tumor Study Group international multicenter exercise

BACKGROUND AND PURPOSE

Recently, the SIOP-RTSG developed a highly-conformal flank target volume definition for children with renal tumors. The aims of this study were to evaluate the inter-clinician delineation variation of this new target volume definition in an international multicenter setting and to explore the necessity of quality assurance.

MATERIALS AND METHODS

Six pediatric renal cancer cases were transferred to ten radiation oncologists from seven European countries ('participants'). These participants delineated the pre- and postoperative Gross Tumor Volume (GTVpre/post), and Clinical Target Volume (CTV) during two test phases (case 1-2 and 3-4), followed by guideline refinement and a quality assurance phase (case 5-6). Reference target volumes (TVref) were established by three experienced radiation oncologists. The Dice Similarity Coefficient between the reference and participants (DSCref/part) was calculated per case. Delineations of case 5-6 were graded by four independent reviewers as 'per protocol' (0-4 mm), 'minor deviation' (5-9 mm) or 'major deviation' (≥10 mm) from the delineation guideline using 18 standardized criteria. Also, a major deviation resulting in underestimation of the CTVref was regarded as an unacceptable variation.

RESULTS

A total of 57/60 delineation sets were completed. The median DSCref/part for the CTV was 0.55 without improvement after sequential cases (case 3-4 vs. case 5-6: p = 0.15). For case 5-6, a major deviation was found for 5/18, 12/17, 18/18 and 4/9 collected delineations of the GTVpre, GTVpost, CTV-T and CTV-N, respectively. An unacceptable variation from the CTVref was found for 7/9 participants for case 5 and 6/9 participants for case 6.

CONCLUSION

This international multicenter delineation exercise demonstrates that the new consensus for highly-conformal postoperative flank target volume delineation leads to geometrical variation among participants. Moreover, standardized review showed an unacceptable delineation variation in the majority of the participants. These findings strongly suggest the need for additional training and centralized pre-treatment review when this target volume delineation approach is implemented on a larger scale.

EANM/SNMMI practice guideline for [18F]FDG PET/CT external beam radiotherapy treatment planning in uterine cervical cancer v1.0

PURPOSE

The aim of this EANM / SNMMI Practice Guideline with ESTRO endorsement is to provide general information and specific considerations about [18F]FDG PET/CT in advanced uterine cervical cancer for external beam radiotherapy planning with emphasis on staging and target definition, mostly in FIGO stages IB3-IVA and IVB, treated with curative intention.

METHODS

Guidelines from related fields, relevant literature and leading experts have been consulted during the development of this guideline. As this field is rapidly evolving, this guideline cannot be seen as definitive, nor is it a summary of all existing protocols. Local variations should be taken into consideration when applying this guideline.

CONCLUSION

The background, common clinical indications, qualifications and responsibilities of personnel, procedure / specifications of the examination, documentation / reporting and equipment specifications, quality control and radiation safety in imaging is discussed with an emphasis on the multidisciplinary approach.

Clinical Acceptability of Automated Radiation Treatment Planning for Head and Neck Cancer Using the Radiation Planning Assistant

PURPOSE

Radiation treatment planning for head and neck cancer is a complex process with much variability; automated treatment planning is a promising option to improve plan quality and efficiency. This study compared radiation plans generated from a fully automated radiation treatment planning system to plans generated manually that had been clinically approved and delivered.

METHODS AND MATERIALS

The study cohort consisted of 50 patients treated by a specialized head and neck cancer team at a tertiary care center. An automated radiation treatment planning system, the Radiation Planning Assistant, was used to create autoplans for all patients using their original, approved contours. Common dose-volume histogram (DVH) criteria were used to compare the quality of autoplans to the clinical plans. Fourteen radiation oncologists, each from a different institution, then reviewed and compared the autoplans and clinical plans in a blinded fashion.

RESULTS

Autoplans and clinical plans were very similar with regard to DVH metrics for coverage and critical structure constraints. Physician reviewers found both the clinical plans and autoplans acceptable for use; overall, 78% of the clinical plans and 88% of the autoplans were found to be usable as is (without any edits). When asked to choose which plan would be preferred for approval, 27% of physician reviewers selected the clinical plan, 47% selected the autoplan, 25% said both were equivalent, and 0% said neither. Hence, overall, 72% of physician reviewers believed the autoplan or either the clinical or autoplan was preferable.

CONCLUSIONS

Automated radiation treatment planning creates consistent, clinically acceptable treatment plans that meet DVH criteria and are found to be appropriate on physician review.

Reduction of inter-observer contouring variability in daily clinical practice through a retrospective, evidence-based intervention

BACKGROUND

Inter-observer variations (IOVs) arising during contouring can potentially impact plan quality and patient outcomes. Regular assessment of contouring IOV is not commonly performed in clinical practice due to the large time commitment required of clinicians from conventional methods. This work uses retrospective information from past treatment plans to facilitate a time-efficient, evidence-based intervention to reduce contouring IOV.

METHODS

The contours of 492 prostate cancer treatment plans created by four radiation oncologists were analyzed in this study. Structure volumes, lengths, and DVHs were extracted from the treatment planning system and stratified based on primary oncologist and inclusion of a pelvic lymph node (PLN) target. Inter-observer variations and their dosimetric consequences were assessed using Student's t-tests. Results of this analysis were presented at an intervention meeting, where new consensus contour definitions were agreed upon. The impact of the intervention was assessed one-year later by repeating the analysis on 152 new plans.

RESULTS

Significant IOV in prostate and PLN target delineation existed pre-intervention between oncologists, impacting dose to nearby OARs. IOV was also present for rectum and penile-bulb structures. Post-intervention, IOV decreased for all previously discordant structures. Dosimetric variations were also reduced. Although target contouring concordance increased significantly, some variations still persisted for PLN structures, highlighting remaining areas for improvement.

CONCLUSION

We detected significant contouring IOV in routine practice using easily accessible retrospective data and successfully decreased IOV in our clinic through a reflective intervention. Continued application of this approach may aid improvements in practice standardization and enhance quality of care.

Using Auto-Segmentation to Reduce Contouring and Dose Inconsistency in Clinical Trials: The Simulated Impact on RTOG 0617

PURPOSE

Contouring inconsistencies are known but understudied in clinical radiation therapy trials. We applied auto-contouring to the Radiation Therapy Oncology Group (RTOG) 0617 dose escalation trial data. We hypothesized that the trial heart doses were higher than reported due to inconsistent and insufficient heart segmentation. We tested our hypothesis by comparing doses between deep-learning (DL) segmented hearts and trial hearts.

METHODS AND MATERIALS

The RTOG 0617 data were downloaded from The Cancer Imaging Archive; the 442 patients with trial hearts and dose distributions were included. All hearts were resegmented using our DL pipeline and quality assured to meet the requirements for clinical implementation. Dose (V5%, V30%, and mean heart dose) was compared between the 2 sets of hearts (Wilcoxon signed-rank test). Each dose metric was associated with overall survival (Cox proportional hazards). Lastly, 18 volume similarity metrics were assessed for the hearts and correlated with |Dose_DL - Dose_RTOG0617| (linear regression; significance: P ≤ .0028; corrected for 18 tests).

RESULTS

Dose metrics were significantly higher for DL hearts compared with trial hearts (eg, mean heart dose: 15 Gy vs 12 Gy; P = 5.8E-16). All 3 DL heart dose metrics were stronger overall survival predictors than those of the trial hearts (median, P = 2.8E-5 vs 2.0E-4). Thirteen similarity metrics explained |Dose_DL - Dose_RTOG0617|; the axial distance between the 2 centers of mass was the strongest predictor (CENT_Axial; median, R² = 0.47; P = 6.1E-62). CENT_Axial agreed with the qualitatively identified inconsistencies in the superior direction. The trial's qualitative heart contouring score was not correlated with |Dose_DL - Dose_RTOG0617| (median, R² = 0.01; P = .02) or with any of the similarity metrics (median, Rs = 0.13 [range, -0.22 to 0.31]).

CONCLUSIONS

Using a coherent heart definition, as enabled through our open-source DL algorithm, the trial heart doses in RTOG 0617 were found to be significantly higher than previously reported, which may have led to an even more rapid mortality accumulation. Auto-segmentation is likely to reduce contouring and dose inconsistencies and increase the quality of clinical RT trials.

Analysis of Geometric Performance and Dosimetric Impact of Using Automatic Contour Segmentation for Radiotherapy Planning

Purpose: To analyze geometric discrepancy and dosimetric impact in using contours generated by auto-segmentation (AS) against manually segmented (MS) clinical contours. Methods: A 48-subject prostate atlas was created and another 15 patients were used for testing. Contours were generated using a commercial atlas-based segmentation tool and compared to their clinical MS counterparts. The geometric correlation was evaluated using the Dice similarity coefficient (DSC) and Hausdorff distance (HD). Dosimetric relevance was evaluated for a subset of patients by assessing the DVH differences derived by optimizing plan dose using the AS and MS contours, respectively, and evaluating with respect to each. A paired t-test was employed for statistical comparison. The discrepancy in plan quality with respect to clinical dosimetric endpoints was evaluated. The analysis was repeated for head/neck (HN) with a 31-subject atlas and 15 test cases. Results: Dice agreement between AS and MS differed significantly across structures: from (L:0.92/R: 0.91) for the femoral heads to seminal vesical of 0.38 in the prostate cohort, and from 0.98 for the brain, to 0.36 for the chiasm of the HN group. Despite the geometric disagreement, the paired t-tests showed the lack of statistical evidence for systematic differences in dosimetric plan quality yielded by the AS and MS approach for the prostate cohort. In HN cases, statistically significant differences in dosimetric endpoints were observed in structures with small volumes or elongated shapes such as cord (p = 0.01) and esophagus (p = 0.04). The largest absolute dose difference of 11 Gy was seen in the mean pharynx dose. Conclusion: Varying AS performance among structures suggests a differential approach of using AS on a subset of structures and focus MS on the rest. The discrepancy between geometric and dosimetric-end-point driven evaluation also indicates the clinical utility of AS contours in optimization and evaluating plan quality despite of suboptimal geometrical accuracy.

Technical note: Atlas-based Auto-segmentation of masticatory muscles for head and neck cancer radiotherapy

PURPOSE

The study aimed to use quantitative geometric and dosimetric metrics to assess the accuracy of atlas-based auto-segmentation of masticatory muscles (MMs) compared to manual drawn contours for head and neck cancer (HNC) radiotherapy (RT).

MATERIALS AND METHODS

Fifty-eight patients with HNC treated with RT were analyzed. Paired MMs (masseter, temporalis, and medial and lateral pterygoids) were manually delineated on planning computed tomography (CT) images for all patients. Twenty-nine patients were used to generate the MM atlas. Using this atlas, automatic segmentation of the MMs was performed for the remaining 29 patients without manual correction. Auto-segmentation accuracy for MMs was compared using dice similarity coefficients (DSCs), Hausdorff distance (HD), HD95, and variation in the center of mass (∆COM). The dosimetric impact on MMs was calculated (∆dose) using dosimetric parameters (D99%, D95%, D50%, and D1%), and compared with the geometric indices to test correlation.

RESULTS

DSC_mean ranges from 0.79 ± 0.04 to 0.85 ± 0.04, HD_mean from 0.43 ± 0.08 to 0.82 ± 0.26 cm, HD95_mean from 0.32 ± 0.08 to 0.42 ± 0.16 cm, and ∆COM_mean from 0.18 ± 0.11 to 0.33 ± 0.23 cm. The mean MM volume difference was < 15%. The correlation coefficient (r) of geometric and dosimetric indices for the four MMs ranges between -0.456 and 0.300.

CONCLUSIONS

Atlas-based auto-segmentation for masticatory muscles provides geometrically accurate contours compared to manual drawn contours. Dose obtained from those auto-segmented contours is comparable to that from manual drawn contours. Atlas-based auto-segmentation strategy for MM in HN radiotherapy is readily availalbe for clinical implementation.

Use CT Imaging to Predict the Short-Term Outcome of Concurrent Chemoradiotherapy in Patients With Locally Advanced Esophageal Squamous Cell Carcinoma

Objective:

To extract the computed tomography (CT) imaging features of the primary lesions in patients with advanced esophageal squamous cell carcinoma (ESCC) and to study whether these imaging features can predict the short-term outcome after concurrent chemoradiotherapy (CCRT).

Methods:

From January 2014 to December 2015, a total of 49 patients with locally advanced ESCC who underwent CCRT were analyzed retrospectively. They were randomly categorized into the training and validation groups. Collection of CT imaging of patients before and intermediate stage undergoing radiotherapy. The correlations between imaging characteristics and short-term outcome were analyzed. The accuracy of cutoff value was verified by imaging characteristics of patients in validation group.

Result:

There were 38 patients in the training group and 11 patients in the validation group. 13 patients in the training group were classified as responders and 25 patients as nonresponders. According to the CT imaging before radiotherapy, there are no significant differences between responders and nonresponders. According to the CT imaging in the middle stage of radiotherapy, responders showed significantly higher Roundness than nonresponders (P = .004, 95% confidence interval [CI] = 0.0419-0.212). The areas under the ROC curves for the ability to predict significantly tumor response were 0.768 for Roundness (P = .001, 95% CI = 0.603-0.889). The cutoff value of Roundness is 0.3099. Roundness showed no significant associations with survival parameters.

Conclusions:

Computed tomography imaging in the middle stage of radiotherapy can predict the short-term outcome of concurrent chemoradiotherapy for patients with locally advanced ESCC but have no predictive effect on the total survival time.

Retrospective Validation and Clinical Implementation of Automated Contouring of Organs at Risk in the Head and Neck: A Step Toward Automated Radiation Treatment Planning for Low- and Middle-Income Countries

Purpose

We assessed automated contouring of normal structures for patients with head-and-neck cancer (HNC) using a multiatlas deformable-image-registration algorithm to better provide a fully automated radiation treatment planning solution for low- and middle-income countries, provide quantitative analysis, and determine acceptability worldwide.

Methods

Autocontours of eight normal structures (brain, brainstem, cochleae, eyes, lungs, mandible, parotid glands, and spinal cord) from 128 patients with HNC were retrospectively scored by a dedicated HNC radiation oncologist. Contours from a 10-patient subset were evaluated by five additional radiation oncologists from international partner institutions, and interphysician variability was assessed. Quantitative agreement of autocontours with independently physician-drawn structures was assessed using the Dice similarity coefficient and mean surface and Hausdorff distances. Automated contouring was then implemented clinically and has been used for 166 patients, and contours were quantitatively compared with the physician-edited autocontours using the same metrics.

Results

Retrospectively, 87% of normal structure contours were rated as acceptable for use in dose-volume-histogram–based planning without edit. Upon clinical implementation, 50% of contours were not edited for use in treatment planning. The mean (± standard deviation) Dice similarity coefficient of autocontours compared with physician-edited autocontours for parotid glands (0.92 ± 0.10), brainstem (0.95 ± 0.09), and spinal cord (0.92 ± 0.12) indicate that only minor edits were performed. The average mean surface and Hausdorff distances for all structures were less than 0.15 mm and 1.8 mm, respectively.

Conclusion

Automated contouring of normal structures generates reliable contours that require only minimal editing, as judged by retrospective ratings from multiple international centers and clinical integration. Autocontours are acceptable for treatment planning with no or, at most, minor edits, suggesting that automated contouring is feasible for clinical use and in the ongoing development of automated radiation treatment planning algorithms.

Automatic detection of contouring errors using convolutional neural networks

PURPOSE

To develop a head and neck normal structures autocontouring tool that could be used to automatically detect the errors in autocontours from a clinically validated autocontouring tool.

METHODS

An autocontouring tool based on convolutional neural networks (CNN) was developed for 16 normal structures of the head and neck and tested to identify the contour errors from a clinically validated multiatlas-based autocontouring system (MACS). The computed tomography (CT) scans and clinical contours from 3495 patients were semiautomatically curated and used to train and validate the CNN-based autocontouring tool. The final accuracy of the tool was evaluated by calculating the Sørensen-Dice similarity coefficients (DSC) and Hausdorff distances between the automatically generated contours and physician-drawn contours on 174 internal and 24 external CT scans. Lastly, the CNN-based tool was evaluated on 60 patients' CT scans to investigate the possibility to detect contouring failures. The contouring failures on these patients were classified as either minor or major errors. The criteria to detect contouring errors were determined by analyzing the DSC between the CNN- and MACS-based contours under two independent scenarios: (a) contours with minor errors are clinically acceptable and (b) contours with minor errors are clinically unacceptable.

RESULTS

The average DSC and Hausdorff distance of our CNN-based tool was 98.4%/1.23 cm for brain, 89.1%/0.42 cm for eyes, 86.8%/1.28 cm for mandible, 86.4%/0.88 cm for brainstem, 83.4%/0.71 cm for spinal cord, 82.7%/1.37 cm for parotids, 80.7%/1.08 cm for esophagus, 71.7%/0.39 cm for lenses, 68.6%/0.72 for optic nerves, 66.4%/0.46 cm for cochleas, and 40.7%/0.96 cm for optic chiasm. With the error detection tool, the proportions of the clinically unacceptable MACS contours that were correctly detected were 0.99/0.80 on average except for the optic chiasm, when contours with minor errors are clinically acceptable/unacceptable, respectively. The proportions of the clinically acceptable MACS contours that were correctly detected were 0.81/0.60 on average except for the optic chiasm, when contours with minor errors are clinically acceptable/unacceptable, respectively.

CONCLUSION

Our CNN-based autocontouring tool performed well on both the publically available and the internal datasets. Furthermore, our results show that CNN-based algorithms are able to identify ill-defined contours from a clinically validated and used multiatlas-based autocontouring tool. Therefore, our CNN-based tool can effectively perform automatic verification of MACS contours.

Inter-observer variability in target delineation increases during adaptive treatment of head-and-neck and lung cancer

Introduction: Inter-observer variability (IOV) in target volume delineation is a well-documented source of geometric uncertainty in radiotherapy. Such variability has not yet been explored in the context of adaptive re-delineation based on imaging data acquired during treatment. We compared IOV in the pre- and mid-treatment setting using expert primary gross tumour volume (GTV) and clinical target volume (CTV) delineations in locoregionally advanced head-and-neck squamous cell carcinoma (HNSCC) and (non-)small cell lung cancer [(N)SCLC]. Material and methods: Five and six observers participated in the HNSCC and (N)SCLC arm, respectively, and provided delineations for five cases each. Imaging data consisted of CT studies partly complemented by FDG-PET and was provided in two separate phases for pre- and mid-treatment. Global delineation compatibility was assessed with a volume overlap metric (the Generalised Conformity Index), while local extremes of IOV were identified through the standard deviation of surface distances from observer delineations to a median consensus delineation. Details of delineation procedures, in particular, GTV to CTV expansion and adaptation strategies, were collected through a questionnaire. Results: Volume overlap analysis revealed a worsening of IOV in all but one case per disease site, which failed to reach significance in this small sample (p-value range .063-.125). Changes in agreement were propagated from GTV to CTV delineations, but correlation could not be formally demonstrated. Surface distance based analysis identified longitudinal target extent as a pervasive source of disagreement for HNSCC. High variability in (N)SCLC was often associated with tumours abutting consolidated lung tissue or potentially invading the mediastinum. Adaptation practices were variable between observers with fewer than half stating that they consistently adapted pre-treatment delineations during treatment. Conclusion: IOV in target volume delineation increases during treatment, where a disparity in institutional adaptation practices adds to the conventional causes of IOV. Consensus guidelines are urgently needed.

Impact of CT reconstruction algorithm on auto-segmentation performance

Model‐based iterative reconstruction (MBIR) reduces CT imaging dose while maintaining image quality. However, MBIR reduces noise while preserving edges which may impact intensity‐based tasks such as auto‐segmentation. This work evaluates the sensitivity of an auto‐contouring prostate atlas across multiple MBIR reconstruction protocols and benchmarks the results against filtered back projection (FBP). Images were created from raw projection data for 11 prostate cancer cases using FBP and nine different MBIR reconstructions (3 protocols/3 noise reduction levels) yielding 10 reconstructions/patient. Five bony structures, bladder, rectum, prostate, and seminal vesicles (SVs) were segmented using an auto‐segmentation pipeline that renders 3D binary masks for analysis. Performance was evaluated for volume percent difference (VPD) and Dice similarity coefficient (DSC), using FBP as the gold standard. Nonparametric Friedman tests plus post hoc all pairwise comparisons were employed to test for significant differences (P < 0.05) for soft tissue organs and protocol/level combinations. A physician performed qualitative grading of 396 MBIR contours across the prostate, bladder, SVs, and rectum in comparison to FBP using a six‐point scale. MBIR contours agreed with FBP for bony anatomy (DSC ≥ 0.98), bladder (DSC ≥ 0.94, VPD < 8.5%), and prostate (DSC = 0.94 ± 0.03, VPD = 4.50 ± 4.77% (range: 0.07–26.39%). Increased variability was observed for rectum (VPD = 7.50 ± 7.56% and DSC = 0.90 ± 0.08) and SVs (VPD and DSC of 8.23 ± 9.86% range (0.00–35.80%) and 0.87 ± 0.11, respectively). Over the all protocol/level comparisons, a significant difference was observed for the prostate VPD between BSPL1 and BSTL2 (adjusted P‐value = 0.039). Nevertheless, 300 of 396 (75.8%) of the four soft tissue structures using MBIR were graded as equivalent or better than FBP, suggesting that MBIR offered potential improvements in auto‐segmentation performance when compared to FBP. Future work may involve tuning organ‐specific MBIR parameters to further improve auto‐segmentation performance. Running title: Impact of CT Reconstruction Algorithm on Auto‐segmentation Performance.

Preliminary Clinical Study of the Differences Between Interobserver Evaluation and Deep Convolutional Neural Network-Based Segmentation of Multiple Organs at Risk in CT Images of Lung Cancer

Background: In this study, publicly datasets with organs at risk (OAR) structures were used as reference data to compare the differences of several observers. Convolutional neural network (CNN)-based auto-contouring was also used in the analysis. We evaluated the variations among observers and the effect of CNN-based auto-contouring in clinical applications. Materials and methods: A total of 60 publicly available lung cancer CT with structures were used; 48 cases were used for training, and the other 12 cases were used for testing. The structures of the datasets were used as reference data. Three observers and a CNN-based program performed contouring for 12 testing cases, and the 3D dice similarity coefficient (DSC) and mean surface distance (MSD) were used to evaluate differences from the reference data. The three observers edited the CNN-based contours, and the results were compared to those of manual contouring. A value of P<0.05 was considered statistically significant. Results: Compared to the reference data, no statistically significant differences were observed for the DSCs and MSDs among the manual contouring performed by the three observers at the same institution for the heart, esophagus, spinal cord, and left and right lungs. The 95% confidence interval (CI) and P-values of the CNN-based auto-contouring results comparing to the manual results for the heart, esophagus, spinal cord, and left and right lungs were as follows: the DSCs were CNN vs. A: 0.914~0.939(P = 0.004), 0.746~0.808(P = 0.002), 0.866~0.887(P = 0.136), 0.952~0.966(P = 0.158) and 0.960~0.972 (P = 0.136); CNN vs. B: 0.913~0.936 (P = 0.002), 0.745~0.807 (P = 0.005), 0.864~0.894 (P = 0.239), 0.952~0.964 (P = 0.308), and 0.959~0.971 (P = 0.272); and CNN vs. C: 0.912~0.933 (P = 0.004), 0.748~0.804(P = 0.002), 0.867~0.890 (P = 0.530), 0.952~0.964 (P = 0.308), and 0.958~0.970 (P = 0.480), respectively. The P-values of MSDs are similar to DSCs. The P-values of heart and esophagus is smaller than 0.05. No significant differences were found between the edited CNN-based auto-contouring results and the manual results. Conclusion: For the spinal cord, both lungs, no statistically significant differences were found between CNN-based auto-contouring and manual contouring. Further modifications to contouring of the heart and esophagus are necessary. Overall, editing based on CNN-based auto-contouring can effectively shorten the contouring time without affecting the results. CNNs have considerable potential for automatic contouring applications.

Auto-segmentation of head and neck organs at risk in radiotherapy and its dependence on anatomic similarity

Purpose

The aim is to study the dependence of deformable based auto-segmentation of head and neck organs-at-risks (OAR) on anatomy matching for a single atlas based system and generate an acceptable set of contours.

Methods

A sample of ten patients in neutral neck position and three atlas sets consisting of ten patients each in different head and neck positions were utilized to generate three scenarios representing poor, average and perfect anatomy matching respectively and auto-segmentation was carried out for each scenario. Brainstem, larynx, mandible, cervical oesophagus, oral cavity, pharyngeal muscles, parotids, spinal cord, and trachea were the structures selected for the study. Automatic and oncologist reference contours were compared using the dice similarity index (DSI), Hausdroff distance and variation in the centre of mass (COM).

Results

The mean DSI scores for brainstem was good irrespective of the anatomy matching scenarios. The scores for mandible, oral cavity, larynx, parotids, spinal cord, and trachea were unacceptable with poor matching but improved with enhanced bony matching whereas cervical oesophagus and pharyngeal muscles had less than acceptable scores for even perfect matching scenario. HD value and variation in COM decreased with better matching for all the structures.

Conclusion

Improved anatomy matching resulted in better segmentation. At least a similar setup can help generate an acceptable set of automatic contours in systems employing single atlas method. Automatic contours from average matching scenario were acceptable for most structures. Importance should be given to head and neck position during atlas generation for a single atlas based system.

Postmastectomy radiotherapy for left-sided breast cancer patients: Comparison of advanced techniques

Postmastectomy radiotherapy (PMRT) has been shown to improve the overall survival for invasive breast cancer patients, and many advanced radiotherapy technologies were adopted for PMRT. The purpose of our study is to compare various advanced PMRT techniques including fixed-beam intensity-modulated radiotherapy (IMRT), non-coplanar volumetric modulated arc therapy (NC-VMAT), multiple arc VMAT (MA-VMAT), and tomotherapy (TOMO). Results of standard VMAT and mixed beam therapy that were published by our group previously were also included in the plan comparisons. Treatment plans were produced for nine PMRT patients previously treated in our clinic. The plans were evaluated based on planning target volume (PTV) coverage, dose homogeneity index (DHI), conformity index (CI), dose to organs at risk (OARs), normal tissue complication probability (NTCP) of pneumonitis, lifetime attributable risk (LAR) of second cancers, and risk of coronary events (RCE). All techniques produced clinically acceptable PMRT plans. Overall, fixed-beam IMRT delivered the lowest mean dose to contralateral breast (1.56 ± 0.4 Gy) and exhibited lowest LAR (0.6 ± 0.2%) of secondary contralateral breast cancer; NC-VMAT delivered the lowest mean dose to lungs (7.5 ± 0.8 Gy), exhibited lowest LAR (5.4 ± 2.8%) of secondary lung cancer and lowest NTCP (2.1 ± 0.4%) of pneumonitis; mixed beam therapy delivered the lowest mean dose to heart (7.1 ± 1.3 Gy) and exhibited lowest RCE (8.6 ± 7.1%); TOMO plans provided the most optimal target coverage while delivering higher dose to OARs than other techniques. Both NC-VMAT and MA-VMAT exhibited lower values of all OARs evaluation metrics compare to standard VMAT. Fixed-beam IMRT, NC-VMAT, and mixed beam therapy could be the optimal radiation technique for certain breast cancer patients after mastectomy.

Automatic image segmentation based on synthetic tissue model for delineating organs at risk in spinal metastasis treatment planning

Purpose

One of the main goals in software solutions for treatment planning is to automatize delineation of organs at risk (OARs). In this pilot feasibility study a clinical validation was made of computed tomography (CT)-based extracranial auto-segmentation (AS) using the Brainlab Anatomical Mapping tool (AM).

Methods

The delineation of nine extracranial OARs (lungs, kidneys, trachea, heart, liver, spinal cord, esophagus) from clinical datasets of 24 treated patients was retrospectively evaluated. Manual delineation of OARs was conducted in clinical routine and compared with AS datasets using AM. The Dice similarity coefficient (DSC) and maximum Hausdorff distance (HD) were used as statistical and geometrical measurements, respectively. Additionally, all AS structures were validated using a subjective qualitative scoring system.

Results

All patient datasets investigated were successfully processed with the evaluated AS software. For the left lung (0.97 ± 0.03), right lung (0.97 ± 0.05), left kidney (0.91 ± 0.07), and trachea (0.93 ± 0.04), the DSC was high with low variability. The DSC scores of other organs (right kidney, heart, liver, spinal cord), except the esophagus, ranged between 0.7 and 0.9. The calculated HD values yielded comparable results. Qualitative assessment showed a general acceptance in more than 85% of AS OARs—except for the esophagus.

Conclusions

The Brainlab AM software is ready for clinical use in most of the OARs evaluated in the thoracic and abdominal region. The software generates highly conformal structure sets compared to manual contouring. The current study design needs revision for further research.