Variability of clinical target volume delineation for rectal cancer patients planned for neoadjuvant radiotherapy with the aid of the platform Anatom-e

Minimizing human interference in an online fully automated daily adaptive radiotherapy workflow for bladder cancer

PURPOSE

The aim was to study the potential for an online fully automated daily adaptive radiotherapy (RT) workflow for bladder cancer, employing a focal boost and fiducial markers. The study focused on comparing the geometric and dosimetric aspects between the simulated automated online adaptive RT (oART) workflow and the clinically performed workflow.

METHODS

Seventeen patients with muscle-invasive bladder cancer were treated with daily Cone Beam CT (CBCT)-guided oART. The bladder and pelvic lymph nodes (CTVelective) received a total dose of 40 Gy in 20 fractions and the tumor bed received an additional simultaneously integrated boost (SIB) of 15 Gy (CTVboost). During the online sessions a CBCT was acquired and used as input for the AI-network to automatically delineate the bladder and rectum, i.e. influencers. These influencers were employed to guide the algorithm utilized in the delineation process of the target. Manual adjustments to the generated contours are common during this clinical workflow prior to plan reoptimization and RT delivery. To study the potential for an online fully automated workflow, the oART workflow was repeated in a simulation environment without manual adjustments. A comparison was made between the clinical and automatic contours and between the treatment plans optimized on these clinical (Dclin) and automatic contours (Dauto).

RESULTS

The bladder and rectum delineated by the AI-network differed from the clinical contours with a median Dice Similarity Coefficient of 0.99 and 0.92, a Mean Distance to Agreement of 1.9 mm and 1.3 mm and a relative volume of 100% and 95%, respectively. For the CTVboost these differences were larger, namely 0.71, 7 mm and 78%. For the CTVboost the median target coverage was 0.42% lower for Dauto compared to Dclin. For CTVelective this difference was 0.03%. The target coverage of Dauto met the clinical requirement of the CTV-coverage in 65% of the sessions for CTVboost and 95% of the sessions for the CTVelective.

CONCLUSIONS

While an online fully automated daily adaptive RT workflow shows promise for bladder treatment, its complexity becomes apparent when incorporating a focal boost, necessitating manual checks to prevent potential underdosage of the target.

Contouring of emerging organs-at-risk (OARS) of the female pelvis and interobserver variability: A study by the Italian association of radiotherapy and clinical oncology (AIRO)

Purpose

To provide straightforward instructions for daily practice in delineating emerging organs-at-risk (OARs) of the female pelvis and to discuss the interobserver variability in a two-step multicenter study.

Methods and materials

A contouring atlas with anatomical boundaries for each emerging OAR was realized by radiation oncologists and radiologists who are experts in pelvic imaging, as per their knowledge and clinical practice. These contours were identified as quality benchmarks for the analysis subsequently carried out. Radiation oncologists not involved in setting the custom-built contouring atlas and interested in the treatment of gynecological cancer were invited to participate in this 2-step trial. In the first step all participants were supplied with a selected clinical case of locally advanced cervical cancer and had to identify emerging OARs (Levator ani muscle; Puborectalis muscle; Internal anal sphincter; External anal sphincter; Bladder base and trigone; Bladder neck; Iliac Bone Marrow; Lower Pelvis Bone Marrow; Lumbosacral Bone Marrow) based on their own personal knowledge of pelvic anatomy and experience. The suggested OARs and the contouring process were then presented at a subsequent webinar meeting with a contouring laboratory. Finally, in the second step, after the webinar meeting, each participant who had joined the study but was not involved in setting the benchmark received the custom-built contouring atlas with anatomical boundaries and was requested to delineate again the OARs using the tool provided. The Dice Similarity Coefficient (DSC) and the Jaccard Similarity Coefficient (JSC) were used to evaluate the spatial overlap accuracy of the different volume delineations and compared with the benchmark; the Hausdorff distance (HD) and the mean distance to agreement (MDA) to explore the distance between contours. All the results were reported as sample mean and standard deviation (SD).

Results

Fifteen radiation oncologists from different Institutions joined the study. The participants had a high agreement degree for pelvic bones sub-structures delineation according to DICE (IBM: 0.9 ± 0.02; LPBM: 0.91 ± 0.01). A moderate degree according to DICE was showed for ovaries (Right: 0.61 ± 0.16, Left: 0.72 ± 0.05), vagina (0.575 ± 0.13), bladder sub-structures (0.515 ± 0.08) and EAS (0.605 ± 0.05), whereas a low degree for the other sub-structures of the anal-rectal sphincter complex (LAM: 0.345 ± 0.07, PRM: 0.41 ± 0.10, and IAS: 0.4 ± 0.07).

Conclusion

This study found a moderate to low level of agreement in the delineation of the female pelvis emerging OARs, with a high degree of variability among observers. The development of delineation tools should be encouraged to improve the routine contouring of these OARs and increase the quality and consistency of radiotherapy planning.

Clinical target volume delineation quality assurance for MRI-guided prostate radiotherapy using deep learning with uncertainty estimation

BACKGROUND AND PURPOSE

Previous studies on automatic delineation quality assurance (QA) have mostly focused on CT-based planning. As MRI-guided radiotherapy is increasingly utilized in prostate cancer treatment, there is a need for more research on MRI-specific automatic QA. This work proposes a clinical target volume (CTV) delineation QA framework based on deep learning (DL) for MRI-guided prostate radiotherapy.

MATERIALS AND METHODS

The proposed workflow utilized a 3D dropblock ResUnet++ (DB-ResUnet++) to generate multiple segmentation predictions via Monte Carlo dropout which were used to compute an average delineation and area of uncertainty. A logistic regression (LR) classifier was employed to classify the manual delineation as pass or discrepancy based on the spatial association between the manual delineation and the network's outputs. This approach was evaluated on a multicentre MRI-only prostate radiotherapy dataset and compared with our previously published QA framework based on AN-AG Unet.

RESULTS

The proposed framework achieved an area under the receiver operating curve (AUROC) of 0.92, a true positive rate (TPR) of 0.92 and a false positive rate of 0.09 with an average processing time per delineation of 1.3 min. Compared with our previous work using AN-AG Unet, this method generated fewer false positive detections at the same TPR with a much faster processing speed.

CONCLUSION

To the best of our knowledge, this is the first study to propose an automatic delineation QA tool using DL with uncertainty estimation for MRI-guided prostate radiotherapy, which can potentially be used for reviewing prostate CTV delineation in multicentre clinical trials.

Clinical target volume and organs at risk segmentation for rectal cancer radiotherapy using the Flex U-Net network

Purpose/Objectives

The aim of this study was to improve the accuracy of the clinical target volume (CTV) and organs at risk (OARs) segmentation for rectal cancer preoperative radiotherapy.

Materials/Methods

Computed tomography (CT) scans from 265 rectal cancer patients treated at our institution were collected to train and validate automatic contouring models. The regions of CTV and OARs were delineated by experienced radiologists as the ground truth. We improved the conventional U-Net and proposed Flex U-Net, which used a register model to correct the noise caused by manual annotation, thus refining the performance of the automatic segmentation model. Then, we compared its performance with that of U-Net and V-Net. The Dice similarity coefficient (DSC), Hausdorff distance (HD), and average symmetric surface distance (ASSD) were calculated for quantitative evaluation purposes. With a Wilcoxon signed-rank test, we found that the differences between our method and the baseline were statistically significant (P< 0.05).

Results

Our proposed framework achieved DSC values of 0.817 ± 0.071, 0.930 ± 0.076, 0.927 ± 0.03, and 0.925 ± 0.03 for CTV, the bladder, Femur head-L and Femur head-R, respectively. Conversely, the baseline results were 0.803 ± 0.082, 0.917 ± 0.105, 0.923 ± 0.03 and 0.917 ± 0.03, respectively.

Conclusion

In conclusion, our proposed Flex U-Net can enable satisfactory CTV and OAR segmentation for rectal cancer and yield superior performance compared to conventional methods. This method provides an automatic, fast and consistent solution for CTV and OAR segmentation and exhibits potential to be widely applied for radiation therapy planning for a variety of cancers.

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.

Large scale crowdsourced radiotherapy segmentations across a variety of cancer anatomic sites

Clinician generated segmentation of tumor and healthy tissue regions of interest (ROIs) on medical images is crucial for radiotherapy. However, interobserver segmentation variability has long been considered a significant detriment to the implementation of high-quality and consistent radiotherapy dose delivery. This has prompted the increasing development of automated segmentation approaches. However, extant segmentation datasets typically only provide segmentations generated by a limited number of annotators with varying, and often unspecified, levels of expertise. In this data descriptor, numerous clinician annotators manually generated segmentations for ROIs on computed tomography images across a variety of cancer sites (breast, sarcoma, head and neck, gynecologic, gastrointestinal; one patient per cancer site) for the Contouring Collaborative for Consensus in Radiation Oncology challenge. In total, over 200 annotators (experts and non-experts) contributed using a standardized annotation platform (ProKnow). Subsequently, we converted Digital Imaging and Communications in Medicine data into Neuroimaging Informatics Technology Initiative format with standardized nomenclature for ease of use. In addition, we generated consensus segmentations for experts and non-experts using the Simultaneous Truth and Performance Level Estimation method. These standardized, structured, and easily accessible data are a valuable resource for systematically studying variability in segmentation applications.

A Feasibility Study of Deep Learning-Based Auto-Segmentation Directly Used in VMAT Planning Design and Optimization for Cervical Cancer

Purpose

To investigate the dosimetric impact on target volumes and organs at risk (OARs) when unmodified auto-segmented OAR contours are directly used in the design of treatment plans.

Materials and Methods

A total of 127 patients with cervical cancer were collected for retrospective analysis, including 105 patients in the training set and 22 patients in the testing set. The 3D U-net architecture was used for model training and auto-segmentation of nine types of organs at risk. The auto-segmented and manually segmented organ contours were used for treatment plan optimization to obtain the AS-VMAT (automatic segmentations VMAT) plan and the MS-VMAT (manual segmentations VMAT) plan, respectively. Geometric accuracy between the manual and predicted contours were evaluated using the Dice similarity coefficient (DSC), mean distance-to-agreement (MDA), and Hausdorff distance (HD). The dose volume histogram (DVH) and the gamma passing rate were used to identify the dose differences between the AS-VMAT plan and the MS-VMAT plan.

Results

Average DSC, MDA and HD₉₅ across all OARs were 0.82–0.96, 0.45–3.21 mm, and 2.30–17.31 mm on the testing set, respectively. The D_99% in the rectum and the Dmean in the spinal cord were 6.04 Gy (P = 0.037) and 0.54 Gy (P = 0.026) higher, respectively, in the AS-VMAT plans than in the MS-VMAT plans. The V₂₀, V₃₀, and V₄₀ in the rectum increased by 1.35% (P = 0.027), 1.73% (P = 0.021), and 1.96% (P = 0.008), respectively, whereas the V₁₀ in the spinal cord increased by 1.93% (P = 0.011). The differences in other dosimetry parameters were not statistically significant. The gamma passing rates in the clinical target volume (CTV) were 92.72% and 98.77%, respectively, using the 2%/2 mm and 3%/3 mm criteria, which satisfied the clinical requirements.

Conclusions

The dose distributions of target volumes were unaffected when auto-segmented organ contours were used in the design of treatment plans, whereas the impact of automated segmentation on the doses to OARs was complicated. We suggest that the auto-segmented contours of tissues in close proximity to the target volume need to be carefully checked and corrected when necessary.

Improving the accuracy and consistency of clinical target volume delineation for rectal cancer by an education program

BACKGROUND

Accurate target volume delineation is the premise for the implementation of precise radiotherapy. Inadequate target volume delineation may diminish tumor control or increase toxicity. Although several clinical target volume (CTV) delineation guidelines for rectal cancer have been published in recent years, significant interobserver variation (IOV) in CTV delineation still exists among radiation oncologists. However, proper education may serve as a bridge that connects complex guidelines with clinical practice.

AIM

To examine whether an education program could improve the accuracy and consistency of preoperative radiotherapy CTV delineation for rectal cancer.

METHODS

The study consisted of a baseline target volume delineation, a 150-min education intervention, and a follow-up evaluation. A 42-year-old man diagnosed with stage IIIC (T3N2bM0) rectal adenocarcinoma was selected for target volume delineation. CTVs obtained before and after the program were compared. Dice similarity coefficient (DSC), inclusiveness index (IncI), conformal index (CI), and relative volume difference [ΔV (%)] were analyzed to quantitatively evaluate the disparities between the participants’ delineation and the standard CTV. Maximum volume ratio (MVR) and coefficient of variation (CV) were calculated to assess the IOV. Qualitative analysis included four common controversies in CTV delineation concerning the upper boundary of the target volume, external iliac area, groin area, and ischiorectal fossa.

RESULTS

Of the 18 radiation oncologists from 10 provinces in China, 13 completed two sets of CTVs. In quantitative analysis, the average CTV volume decreased from 809.82 cm³ to 705.21 cm³ (P = 0.001) after the education program. Regarding the indices for geometric comparison, the mean DSC, IncI, and CI increased significantly, while ΔV (%) decreased remarkably, indicating improved agreement between participants’ delineation and the standard CTV. Moreover, an 11.80% reduction in MVR and 18.19% reduction in CV were noted, demonstrating a smaller IOV in delineation after the education program. Regarding qualitative analysis, the greatest variations in baseline were observed at the external iliac area and ischiorectal fossa; 61.54% (8/13) and 53.85% (7/13) of the participants unnecessarily delineated the external iliac area and the ischiorectal fossa, respectively. However, the education program reduced these variations.

CONCLUSION

Wide variations in CTV delineation for rectal cancer are present among radiation oncologists in mainland China. A well-structured education program could improve delineation accuracy and reduce IOVs.

A deep-learning method for generating synthetic kV-CT and improving tumor segmentation for helical tomotherapy of nasopharyngeal carcinoma

Objective:Megavoltage computed tomography (MV-CT) is used for setup verification and adaptive radiotherapy in tomotherapy. However, its low contrast and high noise lead to poor image quality. This study aimed to develop a deep-learning-based method to generate synthetic kilovoltage CT (skV-CT) and then evaluate its ability to improve image quality and tumor segmentation.Approach:The planning kV-CT and MV-CT images of 270 patients with nasopharyngeal carcinoma (NPC) treated on an Accuray TomoHD system were used. An improved cycle-consistent adversarial network which used residual blocks as its generator was adopted to learn the mapping between MV-CT and kV-CT and then generate skV-CT from MV-CT. A Catphan 700 phantom and 30 patients with NPC were used to evaluate image quality. The quantitative indices included contrast-to-noise ratio (CNR), uniformity and signal-to-noise ratio (SNR) for the phantom and the structural similarity index measure (SSIM), mean absolute error (MAE), and peak signal-to-noise ratio (PSNR) for patients. Next, we trained three models for segmentation of the clinical target volume (CTV): MV-CT, skV-CT, and MV-CT combined with skV-CT. The segmentation accuracy was compared with indices of the dice similarity coefficient (DSC) and mean distance agreement (MDA).Mainresults:Compared with MV-CT, skV-CT showed significant improvement in CNR (184.0%), image uniformity (34.7%), and SNR (199.0%) in the phantom study and improved SSIM (1.7%), MAE (24.7%), and PSNR (7.5%) in the patient study. For CTV segmentation with only MV-CT, only skV-CT, and MV-CT combined with skV-CT, the DSCs were 0.75 ± 0.04, 0.78 ± 0.04, and 0.79 ± 0.03, respectively, and the MDAs (in mm) were 3.69 ± 0.81, 3.14 ± 0.80, and 2.90 ± 0.62, respectively.Significance:The proposed method improved the image quality of MV-CT and thus tumor segmentation in helical tomotherapy. The method potentially can benefit adaptive radiotherapy.

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.

Quality assurance of radiotherapy in the ongoing EORTC 1420 "Best of" trial for early stage oropharyngeal, supraglottic and hypopharyngeal carcinoma: results of the benchmark case procedure

INTRODUCTION

The current phase III EORTC 1420 Best-of trial (NCT02984410) compares the swallowing function after transoral surgery versus intensity modulated radiotherapy (RT) in patients with early-stage carcinoma of the oropharynx, supraglottis and hypopharynx. We report the analysis of the Benchmark Case (BC) procedures before patient recruitment with special attention to dysphagia/aspiration related structures (DARS).

MATERIALS AND METHODS

Submitted RT volumes and plans from participating centers were analyzed and compared against the gold-standard expert delineations and dose distributions. Descriptive analysis of protocol deviations was conducted. Mean Sorensen-Dice similarity index (mDSI) and Hausdorff distance (mHD) were applied to evaluate the inter-observer variability (IOV).

RESULTS

65% (23/35) of the institutions needed more than one submission to achieve Quality assurance (RTQA) clearance. OAR volume delineations were the cause for rejection in 53% (40/76) of cases. IOV could be improved in 5 out of 12 OARs by more than 10 mm after resubmission (mHD). Despite this, final IOV for critical OARs in delineation remained significant among DARS by choosing an aleatory threshold of 0.7 (mDSI) and 15 mm (mHD).

CONCLUSIONS

This is to our knowledge the largest BC analysis among Head and neck RTQA programs performed in the framework of a prospective trial. Benchmarking identified non-common OARs and target delineations errors as the main source of deviations and IOV could be reduced in a significant number of cases after this process. Due to the substantial resources involved with benchmarking, future benchmark analyses should assess fully the impact on patients' clinical outcome.

Interobserver variability in clinical target volume delineation in anal squamous cell carcinoma

We evaluated the inter-physician variability in the target contouring of the radiotherapy for anal squamous cell carcinoma (ASCC). Clinical target volume (CTV) of three patients diagnosed with ASCC was delineated by seven experienced radiation oncologists from multi-institution. These patients were staged as pT1N1a, cT2N0, and cT4N1a, respectively, according to 8^th edition of the American Joint Committee on Cancer staging system. Expert agreement was quantified using an expectation maximization algorithm for Simultaneous Truth and Performance Level Estimation (STAPLE). The maximum distance from the boundaries of the STAPLE generated volume with confidence level of 80% to those of the contour of each CTV in 6 directions was compared. CTV of pelvis which includes primary tumor, perirectal tissue and internal/external iliac lymph node (LN) area (CTV-pelvis) and CTV of inguinal area (CTV-inguinal) were obtained from the seven radiation oncologists. One radiation oncologist did not contain inguinal LN area in the treatment target volume of patient 2 (cT2N0 stage). CTV-inguinal displayed moderate agreement for each patient (overall kappa 0.58, 0.54 and 0.6, respectively), whereas CTV-pelvis showed substantial agreement (overall kappa 0.66, 0.68 and 0.64, respectively). Largest variation among each contour was shown in the inferior margin of the CTV-inguinal. For CTV-pelvis, anterior and superior margin showed the biggest variation. Overall, moderate to substantial agreement was shown for CTV delineation. However, large variations in the anterior and cranial boarder of the CTV-pelvis and the caudal margin of the CTV-inguinal suggest that further studies are needed to establish a clearer target volume delineation guideline.

Three-dimensional (3D) magnetic resonance volume assessment and loco-regional failure in anal cancer: early evaluation case-control study

Background

The primary aim was to test the hypothesis that deriving pre-treatment 3D magnetic resonance tumour volume (mrTV) quantification improves performance characteristics for the prediction of loco-regional failure compared with standard maximal tumour diameter (1D) assessment in patients with squamous cell carcinoma of the anus undergoing chemoradiotherapy.

Methods

We performed an early evaluation case-control study at two UK centres (2007–2014) in 39 patients with loco-regional failure (cases), and 41 patients disease-free at 3 years (controls). mrTV was determined using the summation of areas method (Vol_sum). Reproducibility was assessed using intraclass concordance correlation (ICC) and Bland-Altman limits of agreements. We derived receiver operating curves using logistic regression models and expressed accuracy as area under the curve (ROC_AUC).

Results

The median time per patient for Vol_sum quantification was 7.00 (inter-quartile range, IQR: 0.57–12.48) minutes. Intra and inter-observer reproducibilities were generally good (ICCs from 0.79 to 0.89) but with wide limits of agreement (intra-observer: − 28 to 31%; inter-observer: − 28 to 46%). Median mrTVs were greater for cases (32.6 IQR: 21.5–53.1 cm³) than controls (9.9 IQR: 5.7–18.1 cm³, p < 0.0001). The ROC_AUC for mrT-size predicting loco-regional failure was 0.74 (95% CI: 0.63–0.85) improving to 0.82 (95% CI: 0.72–0.92) when replaced with mrTV (test for ROC differences, p = 0.024).

Conclusion

Preliminary results suggest that the replacement of mrTV for mrT-size improves prediction of loco-regional failure after chemoradiotherapy for squamous cell carcinoma of the anus. However, mrTV calculation is time consuming and variation in its reproducibility are drawbacks with the current technology.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-020-07613-7.

Evaluation of Automatic Segmentation Model With Dosimetric Metrics for Radiotherapy of Esophageal Cancer

Background and Purpose: Automatic segmentation model is proven to be efficient in delineation of organs at risk (OARs) in radiotherapy; its performance is usually evaluated with geometric differences between automatic and manual delineations. However, dosimetric differences attract more interests than geometric differences in the clinic. Therefore, this study aimed to evaluate the performance of automatic segmentation with dosimetric metrics for volumetric modulated arc therapy of esophageal cancer patients.

Methods: Nineteen esophageal cancer cases were included in this study. Clinicians manually delineated the target volumes and the OARs for each case. Another set of OARs was automatically generated using convolutional neural network models. The radiotherapy plans were optimized with the manually delineated targets and the automatically delineated OARs separately. Segmentation accuracy was evaluated by Dice similarity coefficient (DSC) and mean distance to agreement (MDA). Dosimetric metrics of manually and automatically delineated OARs were obtained and compared. The clinically acceptable dose difference and volume difference of OARs between manual and automatic delineations are supposed to be within 1 Gy and 1%, respectively.

Results: Average DSC values were greater than 0.92 except for the spinal cord (0.82), and average MDA values were <0.90 mm except for the heart (1.74 mm). Eleven of the 20 dosimetric metrics of the OARs were not significant (P > 0.05). Although there were significant differences (P < 0.05) for the spinal cord (D2%), left lung (V10, V20, V30, and mean dose), and bilateral lung (V10, V20, V30, and mean dose), their absolute differences were small and acceptable for the clinic. The maximum dosimetric metrics differences of OARs between manual and automatic delineations were ΔD2% = 0.35 Gy for the spinal cord and ΔV30 = 0.4% for the bilateral lung, which were within the clinical criteria in this study.

Conclusion: Dosimetric metrics were proposed to evaluate the automatic delineation in radiotherapy planning of esophageal cancer. Consequently, the automatic delineation could substitute the manual delineation for esophageal cancer radiotherapy planning based on the dosimetric evaluation in this study.

Automatic delineation of the clinical target volume and organs at risk by deep learning for rectal cancer postoperative radiotherapy

BACKGROUND AND PURPOSE

Manual delineation of clinical target volumes (CTVs) and organs at risk (OARs) is time-consuming, and automatic contouring tools lack clinical validation. We aimed to construct and validate the use of convolutional neural networks (CNNs) to set better contouring standards for rectal cancer radiotherapy.

MATERIALS AND METHODS

We retrospectively collected and evaluated computed tomography (CT) scans of 199 rectal cancer patients treated at our hospital from February 2018 to April 2019. Two CNNs-DeepLabv3+ for extracting high-level semantic information and ResUNet for extracting low-level visual features-were used for the CTV and small intestine contouring, and bladder and femoral head contouring, respectively. Contouring quality was compared using the paired t test. Five-point objective grading was performed independently by two experienced radiation oncologists and verified by a third. The CNN manual correction time was recorded.

RESULTS

CTVs calculated using DeepLabv3+ (CTV_DeepLabv3+) had significant quantitative parameter advantages over CTV_ResUNet (volumetric Dice coefficient, 0.88 vs 0.87, P = 0.0005; surface Dice coefficient, 0.79 vs 0.78, P = 0.008). Among 315 graded cases, DeepLabv3+ obtained the highest scores with 284 cases, consistent with the objective criteria, whereas CTV_ResUNet had the minimum mean manual correction time (7.29 min). DeepLabv3+ performed better than ResUNet for small intestine contouring and ResUNet performed better for bladder and femoral head contouring. The manual correction time for OARs was <4 min for both models.

CONCLUSION

CNNs at various feature resolution levels well delineate rectal cancer CTVs and OARs, displaying high quality and requiring shorter computation and manual correction time.

Systematic review of educational interventions to improve contouring in radiotherapy

BACKGROUND AND PURPOSE

Contouring is a critical step in the radiotherapy process, but there is limited research on how to teach it and no consensus about the best method. We summarize the current evidence regarding improvement of contouring skills.

METHODS AND MATERIALS

Comprehensive literature search of the Pubmed-MEDLINE database, EMBASE database and Cochrane Library to identify relevant studies (independently examined by two investigators) that included baseline contouring followed by a re-contouring assessment after an educational intervention.

RESULTS

598 papers were identified. 16 studies met the inclusion criteria representing 370 participants (average number of participants per study of 23; range (4-141). Regarding the teaching methodology, 5/16 used onsite courses, 8/16 online courses, and 2/16 used blended learning. Study quality was heterogenous. There were only 3 randomized studies and only 3 analyzed the dosimetric impact of improving contouring homogeneity. Dice similarity coefficient was the most common evaluation metric (7/16), and in all these studies at least some contours improved significantly post-intervention. The time frame for evaluating the learning effect of the teaching intervention was almost exclusively short-time, with only one study evaluating the long-term utility of the educational program beyond 6 months.

CONCLUSION

The literature on educational interventions designed to improve contouring performance is limited and heterogenous. Onsite, online and blended learning courses have all been shown to be helpful, however, sample sizes are small and impact assessment is almost exclusively short-term and typically does not take into account the effect on treatment planning. The most effective teaching methodology/format is unknown and impact on daily clinical practice is uncertain.

Initial experience with introducing national guidelines for CT- and MRI-based delineation of organs at risk in radiotherapy

A fundamental problem in radiotherapy is the variation of organ at risk (OAR) volumes. Here we present our initial experience in engaging a large Radiation Oncology (RO) community to agree on national guidelines for OAR delineations. Our project builds on associated standardization initiatives and invites professionals from all radiotherapy departments nationwide. Presently, one guideline (rectum) has successfully been agreed on by a majority vote. Reaching out to all relevant parties in a timely manner and motivating funding agencies to support the work represented early challenges. Population-based data and a scalable methodological approach are major strengths of the proposed strategy.