Clinical Target Volume Contours Research Articles

A prior-information-based automatic segmentation method for the clinical target volume in adaptive radiotherapy of cervical cancer.

Adaptive planning to accommodate anatomic changes during treatment often requires repeated segmentation. In this study, prior patient-specific data was integrateda into a registration-guided multi-channel multi-path (Rg-MCMP) segmentation framework to improve the accuracy of repeated clinical target volume (CTV) segmentation. This study was based on CT image datasets for a total of 90 cervical cancer patients who received two courses of radiotherapy. A total of 15 patients were selected randomly as the test set. In the Rg-MCMP segmentation framework, the first-course CT images (CT1) were registered to second-course CT images (CT2) to yield aligned CT images (aCT1), and the CTV in the first course (CTV1) was propagated to yield aligned CTV contours (aCTV1). Then, aCT1, aCTV1, and CT2 were combined as the inputs for 3D U-Net consisting of a channel-based multi-path feature extraction network. The performance of the Rg-MCMP segmentation framework was evaluated and compared with the single-channel single-path model (SCSP), the standalone registration methods, and the registration-guided multi-channel single-path (Rg-MCSP) model. The Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and average surface distance (ASD) were used as the metrics. The average DSC of CTV for the deformable image DIR-MCMP model was found to be 0.892, greater than that of the standalone DIR (0.856), SCSP (0.837), and DIR-MCSP (0.877), which were improvements of 4.2%, 6.6%, and 1.7%, respectively. Similarly, the rigid body DIR-MCMP model yielded an average DSC of 0.875, which exceeded standalone RB (0.787), SCSP (0.837), and registration-guided multi-channel single-path (0.848), which were improvements of 11.2%, 4.5%, and 3.2%, respectively. These improvements in DSC were statistically significant (p<0.05). The proposed Rg-MCMP framework achieved excellent accuracy in CTV segmentation as part of the adaptive radiotherapy workflow.

Automatic end-to-end VMAT treatment planning for rectal cancers.

The treatment planning process from segmentation to producing a deliverable plan is time-consuming and labor-intensive. Existing solutions automate the segmentation and planning processes individually. The feasibility of combining auto-segmentation and auto-planning for volumetric modulated arc therapy (VMAT) for rectal cancers in an end-to-end process is not clear. To create and clinically evaluate a complete end-to-end process for auto-segmentation and auto-planning of VMAT for rectal cancer requiring only the gross tumor volume contour and a CT scan as inputs. Patient scans and data were retrospectively selected from our institutional records for patients treated for malignant neoplasm of the rectum. We trained, validated, and tested deep learning auto-segmentation models using nnU-Net architecture for clinical target volume (CTV), bowel bag, large bowel, small bowel, total bowel, femurs, bladder, bone marrow, and female and male genitalia. For the CTV, we identified 174 patients with clinically drawn CTVs. We used data for 18 patients for all structures other than the CTV. The structures were contoured under the guidance of and reviewed by a gastrointestinal (GI) radiation oncologist. The predicted results for CTV in 35 patients and organs at risk (OAR) in six patients were scored by the GI radiation oncologist using a five-point Likert scale. For auto-planning, a RapidPlan knowledge-based planning solution was modeled for VMAT delivery with a prescription of 25Gy in five fractions. The model was trained and tested on 20 and 34 patients, respectively. The resulting plans were scored by two GI radiation oncologists using a five-point Likert scale. Finally, the end-to-end pipeline was evaluated on 16 patients, and the resulting plans were scored by two GI radiation oncologists. In 31 of 35 patients, CTV contours were clinically acceptable without necessary modifications. The CTV achieved a Dice similarity coefficient of 0.85 (±0.05) and 95% Hausdorff distance of 15.25 (±5.59)mm. All OAR contours were clinically acceptable without edits, except for large and small bowel which were challenging to differentiate. However, contours for total, large, and small bowel were clinically acceptable. The two physicians accepted 100% and 91% of the auto-plans. For the end-to-end pipeline, the two physicians accepted 88% and 62% of the auto-plans. This study demonstrated that the VMAT treatment planning technique for rectal cancer can be automated to generate clinically acceptable and safe plans with minimal human interventions.

Integration of electro-anatomical maps in the planning phase of stereotactic arrhythmia radioablation for refractory ventricular tachycardia

Abstract Introduction Despite the efficacy of stereotactic arrhythmia radioablation (STAR) for the treatment of refractory ventricular tachycardia (VT), the planning phase to precisely delineate the arrhythmogenic substrate still suffers from limitations due to the incompatibility of data formats between 3D electro-anatomical mapping systems (EAM) and STAR planning software (CyberKnife). Purpose Within the STOPSTORM European consortium, we present a workflow to integrate all 3D EAM data (e.g. voltage, activation time, anatomical tags) into the STAR planning software using the open source software Slicer3D to improve targeting of the arrhythmogenic substrate. Method Two protocols were compared retrospectively. Protocol 1 (STAR gold-standard) comprised the manual contouring of the clinical target volume (CTV) on planning 4D-CAT SCAN drawn side-by-side using EAM (Carto3, Biosense Webster) screen-shots and diagnostic reports. Protocol 2 comprised semiautomatic segmentation of CTV on planning 4D-CAT SCAN using imported EAMs annotated with landmarks for VT target delimitation. The semiautomatic segmentation of CTVs included three steps: (1) semiautomatic segmentation of ventricular myocardium (VM), the blood-pool of the concerned ventricle and at least two additional cardiac cavities; (2) 3D alignment of EAM with the segmented cavities; and (3), 3D delineation of CTV identified as the transmural section of VM segment surrounding the EAM’s VT target delimited by the annotated landmarks. The volume overlap (Dice coefficient) and the absolute difference of the CTVs obtained with the two protocols were compared. Both measurements were expressed in percentage as follows: Dice coefficient 0% means no overlap and 100% complete overlap; absolute difference of volumes 0% means no difference and 100% means that one CTV was twice the size of the CTV generated by the other protocol. Results Both protocols were applied to four patients (65.5 ± 8.5 yo) with refractory VT successfully treated with STAR. Figure 1 shows an example (Patient 1) of the CTV contouring using the CARTO EAMs aligned with the CT images in Slicer3D. Figure 2 illustrates significant mismatch between the segmented CTVs of the two protocols. The median volume overlap of the CTVs was 44%, while the median volume absolute difference was 35%. Table 1 reports detailed results. Conclusion Our study reveals a high variability of CTV delineation for STAR using two different protocols. The intraprocedural assignment of anatomical landmarks for VT target delimitation on EAM appears promising to improve CTV delineation. Further research is needed to evaluate the correlation between STAR outcome and CTV delineation accuracy.

Patient-Specific Deep Learning Model for Clinical Target Volume Delineation on Daily CBCT of Breast Cancer Patients based on Intentional Deep Overfit Learning (IDOL) Framework

Increasingly complex target volumes and the use of modern irradiation techniques emphasize the importance of daily image guidance more than ever. Significant progress has been made in adjuvant breast cancer radiotherapy (RT) and the need for optimized image guidance is growing. Furthermore, the position of the breast during RT after breast-conserving surgery is highly variable than expected. In this context, cone beam computed tomography (CBCT) is a very effective tool enabling prompt and accurate adaptive radiation therapy (ART). In this study, we aim to develop a deep learning (DL)-based algorithm to segment clinical target volume (CTV) from daily CBCT scans. Also, we validate the optimization of further learning when applying the Intentional Deep Overfit Learning (IDOL) framework. A total of 240 different CBCT scans obtained from 100 breast cancer patients were used for this study. CTV was defined as whole breast plus margin in all patients. The workflow consists of two training stages: (1) training a novel 'generalized' DL model (Swin_UNETR) to identify and delineate breast CTV on CBCT scans using 90 breast cancer patient cases (2) applying an 'intentional overfitting' to the 'generalized' DL model to generate a 'patient-specific' model using the remaining 10 breast cancer patients. In this study, for the intentionally overfitting stage, we additionally trained with CBCT scans from the patient's 1st fraction to the 14th fractions cases. The results of the proposed method were compared quantitatively with the expert's contours on 1st-15th fractions CBCT scans using Dice Similarity Coefficient (DSC). The average DSC between the 'generalized' DL model-based breast CTV contours and reference contours for the patient's 15th fraction was 0.9672. When implementing the IDOL framework with the CBCT scan obtained during the patient's 1st treatment, the average DSC was improved to 0.9809. When additional CBCT scans taken during each of the 1st to 6th fractions were used for training, the average DSC could be most effectively raised to 0.9835. The p-value comparison between the 'generalized' DL model and the 1st fraction was found to be 3.62E-04, while the comparison with the 6th fractions resulted in a p-value of 8.36E-05. The average time required for IDOL training using one CBCT scan and six CBCT scans was 107 seconds and 127 seconds, respectively. In this study, we developed a patient-specific DL-based training algorithm to segment CTV in CBCT scans for breast cancer patients. The performance improvement was relatively significant and was confirmed that using continual DL with additional CBCT scans, which are taken every day, can be more accurate and efficient than drawing breast CTV using a general model. Our novel patient-specific model can be effectively applied to various ARTs by not only reducing labor and time but also increasing accuracy.

Clinical evaluation of deep learning-based automatic clinical target volume segmentation: a single-institution multi-site tumor experience.

The large variability in tumor appearance and shape makes manual delineation of the clinical target volume (CTV) time-consuming, and the results depend on the oncologists' experience. Whereas deep learning techniques have allowed oncologists to automate the CTV delineation, multi-site tumor analysis is often lacking in the literature. This study aimed to evaluate the deep learning models that automatically contour CTVs of tumors at various sites on computed tomography (CT) images from objective and subjective perspectives. 577 patients were selected for the present study, including nasopharyngeal (n = 34), esophageal (n = 40), breast-conserving surgery (BCS) (left-sided, n = 71; right-sided, n = 71), breast-radical mastectomy (BRM) (left-sided, n = 43; right-sided, n = 37), cervical (radical radiotherapy, n = 45; postoperative, n = 85), prostate (n = 42), and rectal (n = 109) carcinomas. Manually delineated CTV contours by radiation oncologists are served as ground truth. Four models were evaluated: Flexnet, Unet, Vnet, and Segresnet, which are commercially available in the medical product "AccuLearning AI model training platform". The data were divided into the training, validation, and testing set at a ratio of 5:1:4. The geometric metrics, including Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD), were calculated for objective evaluation. For subjective assessment, oncologists rated the segmentation contours of the testing set visually. High correlations were observed between automatic and manual contours. Based on the results of the independent test group, most of the patients achieved satisfactory quantitative results (DSC > 0.8), except for patients with esophageal carcinoma (DSC: 0.62-0.64). The subjective review indicated that 82.65% of predicted CTVs scored either as clinically accepting (8.68%) or requiring minor revision (73.97%), and no patients were scored as rejected. This experimental work demonstrated that auto-generated contours could serve as an initial template to help oncologists save time in CTV delineation. The deep learning-based auto-segmentations achieve acceptable accuracy and show the potential to improve clinical efficiency for radiotherapy of a variety of cancer.

Inguinal nodal clinical target volume delineation based on analysis of anatomical locations of normal and metastatic lymph nodes in pelvic malignant tumors

PurposeTo optimize inguinal nodal clinical target volume (CTV) delineation based on analysis of the anatomical locations and embryonic development of normal and metastatic inguinal lymph nodes (ILNs) in patients with pelvic malignant tumors, including cervical, vaginal, vulvar, and anal tumors. Materials and methodsOne hundred and eighty-one patients with pelvic malignancies and 415 involved ILNs treated with intensity-modulated radiation therapy were selected. First, the inguinal nodal CTV was divided into three fields as follows: I, horizontal superficial inguinal field; II, vertical superficial inguinal field; and III, deep inguinal field. Initial CTV delineation of each field was based on analysis of the anatomical locations and embryonic development of normal ILNs. Subsequently, the positions of metastatic ILNs relative to the proper anatomical landmarks or vessels were determined to optimally delineate the final ILN CTV contours. Eighty percent of the data acquired (n = 145) were used as test data for optimization and analysis, the remaining 20% (n = 36) were used for delineation validation. ResultsIn total, 252 positive ILNs in 103 cervical cancer patients, 94 positive ILNs in 41 vulvar cancer patients, 8 positive ILNs in 3 vaginal cancer patients, and 61 positive ILNs in 34 anal cancer patients were enrolled. Detailed target volume contouring guidelines for the three divisions were determined on images. All positive ILNs from the remaining 20% patients were located in the CTV boundaries delineated based on analysis of 80% of the data acquired. Importantly, the final inguinal nodal CTV field determined using our method was substantially smaller than defined by existing atlases, and the femoral vessels were excluded in the delineation. ConclusionsThis study provided anatomical, embryonic, surgical, and statistical evidence to facilitate ILN CTV delineation in radiotherapy planning for patients with pelvic malignancies.

Dosimetric Comparison of Intensity-Modulated Radiation Therapy (IMRT) and Intensity-Modulated Proton Therapy (IMPT) for a Novel Oral Tongue Avoidance Concept in Low-Risk Squamous Cell Carcinoma of the Oral Tongue.

After adequate surgical resection, early-stage oral tongue cancer patients can harbor a low risk of local recurrence but remain at risk of regional recurrence. Oral tongue avoidance during adjuvant radiation therapy is an attractive potential treatment strategy to mitigate treatment toxicity. We sought to quantify the dosimetric advantages of this approach and hypothesized that intensity-modulated proton therapy (IMPT) may further reduce organs at risk doses compared with intensity-modulated radiation therapy (IMRT). Five patients with oral tongue cancer treated with postoperative radiation therapy from August 2020 to September 2021 were retrospectively reviewed. Novel clinical target volume contours, excluding the oral tongue, were generated while maintaining coverage of bilateral at-risk lymph nodes. Comparison IMRT (X) and IMPT (PBT) plans were generated using standard treatment volumes (control) and avoidance volumes (study) (n = 4 plans/patient). Dosimetric variables for organs at risk were compared using the paired t test. The prescribed dose was 60 Gy in 30 fractions. D95% clinical target volume coverage was similar between X and PBT plans for both control and study clinical target volumes. Comparing control with study plans, both X (58.9 Gy vs 38.3 Gy, P = .007) and PBT (60.2 Gy vs 26.1 Gy, P < .001) decreased the oral cavity dosemean. The pharyngeal constrictor dosemean was also reduced (P < .003). There was no difference between control and study plans for larynx (P = .19), parotid (P = .11), or mandible dose (P = .59). For study plans, PBT significantly reduced oral cavity dosemean (38.3 Gy vs 26.1 Gy, P = .007) and parotid dosemean (23.3 Gy vs 19.3 Gy, P = .03) compared with X. For control plans, there was no difference in oral cavity dosemean using PBT compared with X, but PBT did improve the parotid dosemean (26.6 Gy vs 19.7 Gy, P = .02). This study quantifies the feasibility and dosimetric advantages of oral tongue avoidance while still treating the at-risk lymph nodes for oral tongue cancer. The dosimetric difference between PBT and X was most prominent with an oral tongue-avoidance strategy.

Clinical target volume segmentation based on gross tumor volume using deep learning for head and neck cancer treatment.

Accurate clinical target volume (CTV) delineation is important for head and neck intensity-modulated radiation therapy. However, delineation is time-consuming and susceptible to interobserver variability (IOV). Based on a manual contouring process commonly used in clinical practice, we developed a deep learning (DL)-based method to delineate a low-risk CTV with computed tomography (CT) and gross tumor volume (GTV) input and compared it with a CT-only input. A total of 310 patients with oropharynx cancer were randomly divided into the training set (250) and test set (60). The low-risk CTV and primary GTV contours were used to generate label data for the input and ground truth. A 3D U-Net with a two-channel input of CT and GTV (U-NetGTV) was proposed and its performance was compared with a U-Net with only CT input (U-NetCT). The Dice similarity coefficient (DSC) and average Hausdorff distance (AHD) were evaluated. The time required to predict the CTV was 0.86 s per patient. U-NetGTV showed a significantly higher mean DSC value than U-NetCT (0.80 ± 0.03 and 0.76 ± 0.05) and a significantly lower mean AHD value (3.0 ± 0.5 mm vs 3.5 ± 0.7 mm). Compared to the existing DL method with only CT input, the proposed GTV-based segmentation using DL showed a more precise low-risk CTV segmentation for head and neck cancer. Our findings suggest that the proposed method could reduce the contouring time of a low-risk CTV, allowing the standardization of target delineations for head and neck cancer.

Lymph drainage and cervical fascia anatomy-oriented differential nodal CTV delineation at the supraclavicular region for esophageal cancer and nasopharyngeal cancer

PurposeTo determine the differences in supraclavicular lymph node metastasis between esophageal cancer (EC) and nasopharyngeal cancer (NPC) and explore the feasibility of differential supraclavicular clinical target volume (CTV) contouring between these two diseases based on the involvement of different fascial spaces. Materials and methodsOne hundred patients with supraclavicular nodes positive for EC or NPC were enrolled, and their pre-treatment images were reviewed. The distribution patterns of nodes between the two diseases were compared in the context of node levels defined by the 2017 Japanese Esophageal Society and 2013 International Consensus on Cervical Lymph Node Level Classification. Grouping supraclavicular nodes based on sub-compartments formed by the cervical fascia was discussed, and the feasibility of differential CTV contouring based on the differences in the involvement of these sub-compartments between EC and NPC was explored. ResultsThe 2013 Consensus on cervical node levels and 2017 Japanese Esophageal Society node station could not practically guide supraclavicular CTV contouring. We divided the supraclavicular space into six sub-compartments: the para-esophageal space (PES), carotid sheath space (CSS), sub-thyroid pre-trachea space (STPTS), pre-vascular space (PVS), and vascular lateral space (VLS) I and II. EC mainly spread to the PES, STPTS, CSS, and VLS I, whereas NPC tended to spread to the CSS, VLS I, and VLS II. These combinations of sub-compartments may help constitute the supraclavicular CTVs for EC and NPC. ConclusionsThe fascia anatomy-based sub-compartments sufficiently distinguished metastasis to the supraclavicular space between EC and NPC, thus facilitating differential CTV contouring between these two diseases.

Evaluation of a Dedicated Software "Elements™ Spine SRS, Brainlab®" for Target Volume Definition in the Treatment of Spinal Bone Metastases With Stereotactic Body Radiotherapy.

IntroductionStereotactic body radiotherapy (SBRT) is a treatment option for spine metastases. The International Spine Radiosurgery Consortium (ISRC) has published consensus guidelines for target delineation in spine SBRT. A new software called Elements™ Spine SRS by Brainlab® that includes the module Elements SmartBrush Spine (v3.0, Munich, Germany) has been developed specifically for SBRT treatment of spine metastases, and the latter provides the ability to perform semiautomatic clinical target volume (CTV) generation based on gross tumor volume (GTV) localization and guidelines. The aims of our study were to evaluate this software by studying differences in volumes between semiautomatic CTV contours compared to manual contouring performed by an expert radiation oncologist and to determine the dosimetric impact of these differences on treatment plans.MethodsA total of 35 volumes (“Expert GTV” and “Expert CTV”) from 30 patients were defined by a single expert. A semiautomatic definition of these 35 CTVs based on the location of “Expert GTV” and following ISRC guidelines was also performed in Elements SmartBrush Spine (“Brainlab CTV”). The spatial overlap between “Brainlab” and “Expert” CTVs was calculated using the Dice similarity coefficient (DSC). We considered a threshold of 0.80 or above to indicate that Elements SmartBrush Spine performed very well with adequate contours for clinical use. Two dosimetric treatment plans, each corresponding to a specific planning target volume (PTV; Expert PTV, Brainlab PTV), were created for 11 patients.ResultsWe showed that “Brainlab CTV” and “Expert CTV” mean volumes were 29.8 ± 16.1 and 28.7 ± 15.7 cm3, respectively (p = 0.23). We also showed that the mean DSC for semiautomatic contouring relative to expert manual contouring was 0.85 ± 0.08 and less than 0.80 in five cases. For metastases involving the vertebral body only (n = 13,37%), the mean DSC was 0.90 ± 0.03, and for ones involving other or several vertebral regions (n = 22.63%), the mean DSC was 0.81 ± 0.08 (p < 0.001). The comparison of dosimetric treatment plans was performed for equivalent PTV coverage. There were no differences between doses received by organs at risk (spinal cord and esophagus) for Expert and Brainlab PTVs, respectively.ConclusionThe results showed that the semiautomatic method had quite good accuracy and can be used in clinical routine even for complex lesions.

Clinical Evaluation of an Auto-Segmentation Tool for Spine SBRT Treatment

PurposeSpine SBRT target delineation is time-consuming due to the complex bone structure. Recently, Elements SmartBrush Spine (ESS) was developed by Brainlab to automatically generate a clinical target volume (CTV) based on gross tumor volume (GTV). The aim of this project is to evaluate the accuracy and efficiency of ESS auto-segmentation.MethodsTwenty spine SBRT patients with 21 target sites treated at our institution were used for this retrospective comparison study. Planning CT/MRI images and physician-drawn GTVs were inputs for ESS. ESS can automatically segment the vertebra, split the vertebra into 6 sectors, and generate a CTV based on the GTV location, according to the International Spine Radiosurgery Consortium (ISRC) Consensus guidelines. The auto-segmented CTV can be edited by including/excluding sectors of the vertebra, if necessary. The ESS-generated CTV contour was then compared to the clinically used CTV using qualitative and quantitative methods. The CTV contours were compared using visual assessment by the clinicians, relative volume differences (RVD), distance of center of mass (DCM), and three other common contour similarity measurements such as dice similarity coefficient (DICE), Hausdorff distance (HD), and 95% Hausdorff distance (HD95).ResultsQualitatively, the study showed that ESS can segment vertebra more accurately and consistently than humans at normal curvature conditions. The accuracy of CTV delineation can be improved significantly if the auto-segmentation is used as the first step. Conversely, ESS may mistakenly split or join different vertebrae when large curvatures in anatomy exist. In this study, human interactions were needed in 7 of 21 cases to generate the final CTVs by including/excluding sectors of the vertebra. In 90% of cases, the RVD were within ±15%. The RVD, DCM, DICE, HD, and HD95 for the 21 cases were 3% ± 12%, 1.9 ± 1.5 mm, 0.86 ± 0.06, 13.34 ± 7.47 mm, and 4.67 ± 2.21 mm, respectively.ConclusionESS can auto-segment a CTV quickly and accurately and has a good agreement with clinically used CTV. Inter-person variation and contouring time can be reduced with ESS. Physician editing is needed for some occasions. Our study supports the idea of using ESS as the first step for spine SBRT target delineation to improve the contouring consistency as well as to reduce the contouring time.

Deep Learning-Aided Automatic Contouring of Clinical Target Volumes for Radiotherapy in Breast Cancer After Modified Radical Mastectomy

Purpose: The aim of this study is to develop a practicable automatic clinical target volume (CTV) delineation method for radiotherapy of breast cancer after modified radical mastectomy.Methods: Unlike breast conserving surgery, the radiotherapy CTV for modified radical mastectomy involves several regions, including CTV in the chest wall (CTVcw), supra- and infra-clavicular region (CTVsc), and internal mammary lymphatic region (CTVim). For accurate and efficient segmentation of the CTVs in radiotherapy of breast cancer after modified radical mastectomy, a multi-scale convolutional neural network with an orientation attention mechanism is proposed to capture the corresponding features in different perception fields. A channel-specific local Dice loss, alongside several data augmentation methods, is also designed specifically to stabilize the model training and improve the generalization performance of the model. The segmentation performance is quantitatively evaluated by statistical metrics and qualitatively evaluated by clinicians in terms of consistency and time efficiency.Results: The proposed method is trained and evaluated on the self-collected dataset, which contains 110 computed tomography scans from patients with breast cancer who underwent modified mastectomy. The experimental results show that the proposed segmentation method achieved superior performance in terms of Dice similarity coefficient (DSC), Hausdorff distance (HD) and Average symmetric surface distance (ASSD) compared with baseline approaches.Conclusion: Both quantitative and qualitative evaluation results demonstrated that the specifically designed method is practical and effective in automatic contouring of CTVs for radiotherapy of breast cancer after modified radical mastectomy. Clinicians can significantly save time on manual delineation while obtaining contouring results with high consistency by employing this method.

A blind randomized validated convolutional neural network for auto-segmentation of clinical target volume in rectal cancer patients receiving neoadjuvant radiotherapy.

BackgroundDelineation of clinical target volume (CTV) for radiotherapy is a time‐consuming and labor‐intensive work. This study aims to propose a novel convolutional neural network (CNN)‐based model for fast auto‐segmentation of CTV. To evaluate its performance and clinical utility, a blind randomized validation method was used.MethodsOur proposed model was based on the generally accepted U‐Net architecture using computed tomography slices with CTV contours delineated by experienced radiation clinicians from 135 rectal patients receiving neoadjuvant radiotherapy. The Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (95HD) were used to measure segmentation performance. The validated dataset of additional 20 patients for clinical evaluation by 10 experienced oncology clinicians from 7 centers was randomly and blindly divided into two groups for clinicians' scoring and Turing test, respectively. Second evaluation was performed with different randomization after 2 weeks.ResultsThe mean DSC and 95HD values of the proposed model were 0.90 ± 0.02 and 8.11 ± 1.93 mm for CTV of rectal cancer patients, respectively. The average time for automatic segmentation in the validation groups was 15 s per patient. By clinicians' scoring, the AI model performed better than manually delineating, though the differences were not significant (Week 0: 2.59 vs. 2.52, p = 0.086; Week 2: 2.55 vs. 2.47, p = 0.115). Additionally, the mean positive rates in the Turing test were 40.5% in Week 0 and 45.2% in Week 2, which demonstrated the great intelligence of our model.ConclusionsOur proposed model can be used clinically for assisting contouring of CTVs in rectal cancer patients receiving neoadjuvant radiotherapy, which improves the efficiency and consistency of radiation clinicians' work.

Continuing Education Needs Identified During the Initial Experience of Delivering 3D Conformal Radiotherapy for Cervical Cancer Patients in Tanzania

In resource-limited settings where experience with 3D conformal radiation therapy (3DCRT) is still growing, review of simulation CTs and radiation therapy plans may illuminate topics for continuing education. We conducted a review of an early cohort of cervical cancer patients treated with 3DCRT in Tanzania to identify education needs that may be generalizable to similar resource-limited settings.In Tanzania, 3DCRT capabilities began in August 2018. A retrospective review of 30 consecutive patients who received definitive whole pelvis 3DCRT for cervical cancer from 2019 to 2020 was conducted after IRB approval. Each patient's simulation CT was evaluated for evidence of advanced disease not detected on initial diagnostic work-up, including pelvic or para-aortic lymphadenopathy (LAD) defined as axial dimension > = 1 cm. An independent set of contours was generated according to United States national consensus guidelines. DICE similarity coefficient (DSC) agreement analysis was conducted between contours. As non-systematic variations in planning target volume (PTV) labelling, summations, and expansions precluded direct comparisons, only clinical target volume (CTV) contours were compared in this analysis. Clinically treated 3DCRT plans were evaluated using original and consensus-based contours, and dosimetric coverage was compared using Wilcoxon signed-rank testing.Simulation CT was the sole means of pre-treatment cross-sectional abdominopelvic imaging in 24/30 patients (80%). The pre-simulation clinical stages were FIGO IB (n = 6), IIA (n = 10), IIB (n = 12), IIIA (n = 1), and IVA (n = 1). Simulation CT upstaged 20 of 30 patients (66.7%) to IIIC1 or IIIC2 disease, identifying eight patients with para-aortic LAD and 13 patients with pelvic LAD. Agreement analysis between clinical and consensus-based contours revealed high agreement in the primary CTV (DSC median 0.83, IQR 0.68 - 0.87), bladder (0.93, IQR 0.88 - 0.95), and femur (0.86) contours, but lower agreement in lymph node CTV (CTVn) contours (DSC 0.59, IQR 0.46 - 0.67). The median volume of clinical-CTVn covered by 45 Gy was 99.7% (IQR 97.5 - 100) while the median volume of consensus-CTVn covered by 45 Gy was 83.3% (IQR 80.1 - 90.1) (P < 0.001). The most frequently undercovered regions were the superior and inferior pre-sacral nodal regions.In resource-limited settings, early case review after 3DCRT initiation may reveal key elements in radiation planning that can guide continuing education topics. In our setting, high yield interventions include systematic evaluation of pelvic and para-aortic LAD on simulation CTs, educational refreshers about nodal contouring, and development of resource-appropriate treatment paradigms when advanced nodal disease is detected.

Clinical application and improvement of a CNN-based autosegmentation model for clinical target volumes in cervical cancer radiotherapy.

ObjectiveClinical target volume (CTV) autosegmentation for cervical cancer is desirable for radiation therapy. Data heterogeneity and interobserver variability (IOV) limit the clinical adaptability of such methods. The adaptive method is proposed to improve the adaptability of CNN‐based autosegmentation of CTV contours in cervical cancer.MethodsThis study included 400 cervical cancer treatment planning cases with CTV delineated by radiation oncologists from three hospitals. The datasets were divided into five subdatasets (80 cases each). The cases in datasets 1, 2, and 3 were delineated by physicians A, B, and C, respectively. The cases in datasets 4 and 5 were delineated by multiple physicians. Dataset 1 was divided into training (50 cases), validation (10 cases), and testing (20 cases) cohorts, and they were used to construct the pretrained model. Datasets 2–5 were regarded as host datasets to evaluate the accuracy of the pretrained model. In the adaptive process, the pretrained model was fine‐tuned to measure improvements by gradually adding more training cases selected from the host datasets. The accuracy of the autosegmentation model on each host dataset was evaluated using the corresponding test cases. The Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD_95) were used to evaluate the accuracy.ResultsBefore and after adaptive improvements, the average DSC values on the host datasets were 0.818 versus 0.882, 0.763 versus 0.810, 0.727 versus 0.772, and 0.679 versus 0.789, which are improvements of 7.82%, 6.16%, 6.19%, and 16.05%, respectively. The average HD_95 values were 11.143 mm versus 6.853 mm, 22.402 mm versus 14.076 mm, 28.145 mm versus 16.437 mm, and 33.034 mm versus 16.441 mm, which are improvements of 37.94%, 37.17%, 41.60%, and 50.23%, respectively.ConclusionThe proposed method improved the adaptability of the CNN‐based autosegmentation model when applied to host datasets.

International Multi-institutional Patterns of Contouring Practice and Clinical Target Volume Recommendations for Stereotactic Body Radiation Therapy for Non-Spine Bone Metastases

Despite the increasing use of stereotactic body radiation therapy for non-spine bone metastases (NSBM), there is no established standard for target delineation. The objective of this study was to provide consensus recommendations on clinical target volume (CTV) delineation based on international expert contours. Eleven cases of NSBM were contoured by 9 international radiation oncologists. For each case, the gross tumor volume was provided on the simulation computed tomography scans with accompanying magnetic resonance imaging. Participants contoured the CTV and completed a clinical survey. Agreement between CTV contours were analyzed with simultaneous truth and performance level estimation using the kappa coefficient and the Dice similarity coefficient (DSC) and summarized to establish contouring recommendations. A direction-dependent analysis was applied to the consensus contours to quantify margins. All CTV contours were completed. Six participants used a single-dose level, whereas 3 used a 2-dose level simultaneous integrated boost (SIB) technique. For the SIB cases, the largest volume receiving a stereotactic body radiation therapy (SBRT) dose was used for contour analysis. There was substantial agreement between contours across cases with a mean kappa of 0.72 (mean sensitivity 0.85, mean specificity 0.97). The mean DSC value was 0.77 (range, 0.67-0.87). Consensus CTV contouring recommendations were (1) an intraosseous CTV margin of 5 to 10 mm should be strongly considered within contiguous bone; (2) an extraosseous margin of 5 to 10 mm should be strongly considered where there is soft tissue disease or cortical bone disruption; (3) CTVs should be manually cropped to respect anatomic barriers to spread (eg, peritoneal cavity, pleura, uninvolved joint space and cortical bone). CTV contouring recommendations for NSBM-SBRT were established based on analysis of international expert consensus contours with a high level of agreement. These principles may provide guidance to treating physicians and inform future study until prospective clinical data can provide further refinement.

Accounting for uncertainties in the position of anatomical barriers used to define the clinical target volume

The definition of the clinical target volume (CTV) is becoming the weakest link in the radiotherapy chain. CTV definition consensus guidelines include the geometric expansion beyond the visible gross tumor volume, while avoiding anatomical barriers. In a previous publication we described how to implement these consensus guidelines using deep learning and graph search techniques in a computerized CTV auto-delineation process. In this paper we address the remaining problem of how to deal with uncertainties in positions of the anatomical barriers. The objective was to develop an algorithm that implements the consensus guidelines on considering barrier uncertainties. Our approach is to perform multiple expansions using the fast marching method with barriers in place or removed at different stages of the expansion. We validate the algorithm in a computational phantom and compare manually generated with automated CTV contours, both taking barrier uncertainties into account.

Dosimetric Impact of Physician Style Variations in Contouring CTV for Postoperative Prostate Cancer: A Deep Learning—Based Simulation Study

Inter-observer variation is a significant problem in clinical target volume (CTV) segmentation in postoperative settings, where there is no gross tumor present. In this scenario, the CTV is not an anatomically established structure, but one determined by the physician based on the clinical guideline used, the preferred trade-off between tumor control and toxicity, their experience and training background, and other factors. This results in high inter-observer variability between physicians. This variability has been considered an issue, but the absence of multiple physician CTV contours for each patient and the significant amount of time required for dose planning based on each physician CTV have made it impractical to study its dosimetric consequences. In this study, we analyze the impact that variations in physician style have on dose to organs-at-risk (OAR) by simulating the clinical workflow via deep learning. For a given patient previously treated by one physician, we use deep learning–based tools to simulate how other physicians would contour the CTV and how the corresponding dose distributions would look for other physicians. To simulate multiple physician styles, we use a previously developed in-house CTV segmentation model that can produce physician style–aware segmentations. The corresponding dose distribution is predicted using another in-house deep learning tool, which, can predict dose within 3% of the prescription dose, on average, on the test data. For every test patient, four different physician style CTVs are considered, and four different dose distributions are analyzed. OAR dose metrics are compared, showing that even though physician style variations result in organs getting different doses, all the important dose metrics except maximum dose point are within the clinically acceptable limit.

A deep learning-based framework for segmenting invisible clinical target volumes with estimated uncertainties for post-operative prostate cancer radiotherapy.

In post-operative radiotherapy for prostate cancer, precisely contouring the clinical target volume (CTV) to be irradiated is challenging, because the cancerous prostate gland has been surgically removed, so the CTV encompasses the microscopic spread of tumor cells, which cannot be visualized in clinical images like computed tomography or magnetic resonance imaging. In current clinical practice, physicians' segment CTVs manually based on their relationship with nearby organs and other clinical information, but this allows large inter-physician variability. Automating post-operative prostate CTV segmentation with traditional image segmentation methods has yielded suboptimal results. We propose using deep learning to accurately segment post-operative prostate CTVs. The model proposed is trained using labels that were clinically approved and used for patient treatment. To segment the CTV, we segment nearby organs first, then use their relationship with the CTV to assist CTV segmentation. To ease the encoding of distance-based features, which are important for learning both the CTV contours' overlap with the surrounding OARs and the distance from their borders, we add distance prediction as an auxiliary task to the CTV network. To make the DL model practical for clinical use, we use Monte Carlo dropout (MCDO) to estimate model uncertainty. Using MCDO, we estimate and visualize the 95% upper and lower confidence bounds for each prediction which informs the physicians of areas that might require correction. The model proposed achieves an average Dice similarity coefficient (DSC) of 0.87 on a holdout test dataset, much better than established methods, such as atlas-based methods (DSC<0.7). The predicted contours agree with physician contours better than medical resident contours do. A reader study showed that the clinical acceptability of the automatically segmented CTV contours is equal to that of approved clinical contours manually drawn by physicians. Our deep learning model can accurately segment CTVs with the help of surrounding organ masks. Because the DL framework can outperform residents, it can be implemented practically in a clinical workflow to generate initial CTV contours or to guide residents in generating these contours for physicians to review and revise. Providing physicians with the 95% confidence bounds could streamline the review process for an efficient clinical workflow as this would enable physicians to concentrate their inspecting and editing efforts on the large uncertain areas.

Automated Clinical Target Volume Delineation for Non-Small Cell Lung Cancer Patients Using Deep 3D Networks

Patient-specific clinical target volume (CTV) delineation is the most difficult step in defining the planning target volume from the gross tumor volume. Delineating patient specific CTVs is a labor-intensive process, prone to human error, with poor inter-observer consistency. Automated volumetric expansions have been used to reduce these concerns, but these ignore patient specific anatomic and clinical variables, as well as the clinical experience of the radiation oncologist. We developed an automated CTV delineation system for lung cancer, which was trained to learn the physician’s CTV contour pattern using a deep 3D neural network. Our network took both patients’ CT images and their corresponding internal target volume (ITV) as input, and output CTV delineation. ITVs were contoured by physicians after studying the internal motion of the target from the 4D CT scans. In our study, the network was trained from twenty-five NSCLC patients in right upper lung lobe (RUL). Then, the trained network was tested on four additional datasets from RUL NSCLC patients. Each CTV delineation output by the trained network was compared with the corresponding CTV contoured by physicians. One challenge to train a 3D network for CTV delineation is the high imbalance between the CTV volume and the background volume. Instead of using hyperparameters to balance as existing methods did, we directly optimize a loss function robust to high imbalance. This improves the stability of the model, especially when the balance dynamically changes in images. Three out of four CTV contours that were automatically delineated by our trained model agree very well with the physician’s CTV contour. Only one has visible discrepancies near the apex of the upper lobe, but still looks reasonable. As shown in Table 1, the performance of the trained neural network was also quantitatively evaluated using precision and recall between the predicted CTVs and the ground truth CTVs. The average precision is 0.902, and recall is 0.871. The recall and precision are the fraction of true volume among the total ground-truth and the fraction of true volume among the predicted volume, respectively. The automated CTV delineation was utilized for RUL NSCLC patients using the deep learning method solely based on CT images with manually contoured ITV. On the limited testing samples, the model contoured CTVs reasonably well. This approach would potentially combine the ease of automated volumetric expansions while incorporating the patient specific anatomic and clinical variables along with the clinical expertise of the radiation oncologist.Tabled 1Abstract 2782; Table; A quantitative evaluation of our automated CTV delineation system on four testing patients.Test #1Test #2Test #3Test #4Recall0.9420.7270.9090.907Precision0.8150.8890.9550.950 Open table in a new tab