Clinical Target Volume Delineation Research Articles

The clinical target volume delineation based on the correlation analysis of neck node levels in oral cavity squamous cell carcinoma (OCSCC)

BackgroundThe delineation of clinical target volume (CTV) base on the correlation analysis between neck node levels of OCSCC has not been reported in detail. This study analyzes the correlations between the neck node levels in 208 cases of OCSCC, and aims to provide preliminary reference for the CTV delineation in OCSCC patients.Materials and methodsThe records of 208 OCSCC patients were retrospectively analyzed. The neck node levels were evaluated according to the 2013 updated guidelines. The Chi-square test and logistic regression model were used to analyze the correlation of each level.ResultsThe most common involved level in OCSCC is Ib (45.7%) and IIa (40.4%). Correlation analysis of OCSCC showed that nodal spread in level Ia is related to levels Ib and VIa. Level Ib is related to levels Ia, IIa, IX. Level IIa is related to levels Ib, IIb, III. Level IIb is related to levels IIa, III, Va. Level III is related to levels IIa, IIb, Va. Level Va is related to levels IIb and III. Level VIa is related to level Ia and T stage. Level IX is only related to level Ib. T stage is only related to level VIa. All the above P values are < 0.05.ConclusionsThe related levels can be considered as high-risk region (HRR) and the unrelated levels as low-risk region (LRR) in the radiotherapy of OCSCC patients. This study can make the CTV delineation more individualized and accurate.

Determining optimal clinical target volume margins based on microscopic extracapsular extension of metastatic nodes in patients with non-small-cell lung cancer after chemotherapy or chemotherapy combined with immunotherapy

BackgroundNo standard has been established for the clinical target volume (CTV) margins of lymph nodes (LNs) in patients with non-small-cell lung cancer (NSCLC) receiving chemotherapy or chemotherapy combined with immunotherapy followed by radiotherapy. This study aimed to discuss the CTV range of NSCLC after chemotherapy or chemotherapy combined with immunotherapy by observing the microscopic extent of tumor spread beyond the LN capsule.MethodsWe retrospectively analyzed the data of 240 patients with stage II and III NSCLC who underwent surgery without neoadjuvant therapy, with neoadjuvant chemotherapy (NAC), or with NAC combined with immunotherapy (NACI). We measured the maximal distance of extracapsular extension (ECE) using a digital microscope, analyzed the correlation between clinicopathological features and ECE distance, and determined the CTV margins of metastatic LN under different treatment methods.ResultsThe ECE distance differed significantly among the three groups (p < 0.001). We determined appropriate margin widths based on a 5% error risk as 3.00, 2.30, and 1.40 mm for direct surgery, NAC, and NACI, respectively. Multivariate analysis revealed that the ECE of metastatic LN correlated with the treatment methods and LN size.ConclusionThe existing CTV delineation standards may increase the radiation toxicity of patients. We believe that different CTV margins should be used for LN in patients with NSCLC receiving different treatments. To ensure 95% coverage of ECE, the gross tumor volume of untreated, chemotherapy-treated, and chemotherapy combined with immunotherapy-treated patients should be expanded by 3.00, 2.30, and 1.40 mm, respectively, to obtain the CTV.

Interobserver variability of clinical target volume delineation in patients undergoing breast-conserving surgery without surgical clips: a pilot study on preoperative magnetic resonance simulation

BackgroundIn patients undergoing breast-conserving therapy without surgical clip implantation, the accuracy of tumor bed identification and the consistency of clinical target volume (CTV) delineation under computed tomography (CT) simulation remain suboptimal. This study aimed to investigate the feasibility of implementing preoperative magnetic resonance (MR) simulation on delineations by assessing interobserver variability (IOV).MethodsPreoperative MR and postoperative CT simulations were performed in patients who underwent breast-conserving surgery with no surgical clips implanted. Custom immobilization pads were used to ensure the same supine position. Three radiation oncologists independently delineated the CTV of tumor bed on the images acquired from MR and CT simulation registration and CT simulation alone. Cavity visualization score (CVS) was assigned to each patient based on the clarity of the tumor bed on CT simulation images. IOV was indicated by generalized conformity index (CIgen), denoted as CIgen−CT and CIgen−MR/CT, and the distance between the centroid of mass (dCOM), denoted as dCOMCT and dCOMMR/CT. The variation of IOV in different CVS subgroups was analyzed.ResultsA total of 10 patients were enrolled in this study. The median and interquartile range (IQR) of maximum pathological diameter of the tumors in all patients were 1.55 (0.80–1.92) cm. No statistical significance was found between the volumes of CTVs on CT simulation and on MR/CT simulation registration images (p = 0.387). CIgen−MR/CT was significantly larger than CIgen−CT (p = 0.005). dCOMMR/CT was significantly smaller than dCOMCT (p = 0.037). The median and IQR of CVS in all patients were 2.34 (2.00–3.08). The difference of CIgen between CIgen−MR/CT and CIgen−CT was larger in the low CVS group (p = 0.016). The difference of dCOM showed a decreasing trend when CVS was lower, although it did not reach statistical significance (p = 0.095).ConclusionsFor patients who underwent breast-conserving surgery without surgical clip implantation, the use of preoperative MR simulation in delineating the CTV of tumor bed decreased the IOV among observers. The consistency of tumor bed identification was improved especially in cases where the margins of tumor bed were challenging to visualize on CT simulation images. The study findings offer potential benefits in reducing local recurrence and minimizing tissue irritation in the surrounding areas. Future investigation in a larger patient cohort to validate our results is warranted.

Artificial intelligence-assisted delineation for postoperative radiotherapy in patients with lung cancer: a prospective, multi-center, cohort study.

Postoperative radiotherapy (PORT) is an important treatment for lung cancer patients with poor prognostic features, but accurate delineation of the clinical target volume (CTV) and organs at risk (OARs) is challenging and time-consuming. Recently, deep learning-based artificial intelligent (AI) algorithms have shown promise in automating this process. To evaluate the clinical utility of a deep learning-based auto-segmentation model for AI-assisted delineating CTV and OARs in patients undergoing PORT, and to compare its accuracy and efficiency with manual delineation by radiation oncology residents from different levels of medical institutions. We previously developed an AI auto-segmentation model in 664 patients and validated its contouring performance in 149 patients. In this multi-center, validation trial, we prospectively involved 55 patients and compared the accuracy and efficiency of 3 contouring methods: (i) unmodified AI auto-segmentation, (ii) fully manual delineation by junior radiation oncology residents from different medical centers, and (iii) manual modifications based on AI segmentation model (AI-assisted delineation). The ground truth of CTV and OARs was delineated by 3 senior radiation oncologists. Contouring accuracy was evaluated by Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean distance of agreement (MDA). Inter-observer consistency was assessed by volume and coefficient of variation (CV). AI-assisted delineation achieved significantly higher accuracy compared to unmodified AI auto-contouring and fully manual delineation by radiation oncologists, with median HD, MDA, and DCS values of 20.03 vs. 21.55 mm, 2.57 vs. 3.06 mm, 0.745 vs. 0.703 (all P<0.05) for CTV, respectively. The results of OARs contours were similar. CV for OARs was reduced by approximately 50%. In addition to better contouring accuracy, the AI-assisted delineation significantly decreased the consuming time and improved the efficiency. AI-assisted CTV and OARs delineation for PORT significantly improves the accuracy and efficiency in the real-world setting, compared with pure AI auto-segmentation or fully manual delineation by junior oncologists. AI-assisted approach has promising clinical potential to enhance the quality of radiotherapy planning and further improve treatment outcomes of patients with lung cancer.

Clinical target volume (CTV) automatic delineation using deep learning network for cervical cancer radiotherapy: A study with external validation.

To explore the accuracy and feasibility of a proposed deep learning (DL) algorithm for clinical target volume (CTV) delineation in cervical cancer radiotherapy and evaluate whether it can perform well in external cervical cancer and endometrial cancer cases for generalization validation. A total of 332 patients were enrolled in this study. A state-of-the-art network called ResCANet, which added the cascademulti-scale convolution in the skip connections to eliminate semantic differences between different feature layers based on ResNet-UNet. The atrous spatial pyramid pooling in the deepest feature layer combined the semantic information of different receptive fields without losing information. A total of 236 cervical cancer cases were randomly grouped into 5-fold cross-training (n=189) and validation (n=47) cohorts. External validations were performed ina separate cohort of 54 cervical cancer and 42 endometrial cancer cases. The performances of the proposed network were evaluated by dice similarity coefficient (DSC), sensitivity (SEN), positive predictive value (PPV), 95% Hausdorff distance (95HD), and oncologist clinical score when comparing them with manual delineation in validation cohorts. In internal validation cohorts, the mean DSC, SEN, PPV, 95HD for ResCANet achieved 74.8%, 81.5%, 73.5%, and 10.5mm. In external independent validation cohorts, ResCANet achieved 73.4%, 72.9%, 75.3%, 12.5mm for cervical cancer cases and 77.1%, 81.1%, 75.5%, 10.3mm for endometrial cancer cases, respectively. The clinical assessment score showed that minor and no revisions (delineation time was shortened to within 30min) accountedforabout 85% of all cases in DL-aided automatic delineation. We demonstrated the problem of model generalizability for DL-based automatic delineation. The proposed network can improve the performance of automatic delineation for cervical cancer and shorten manual delineation time at no expense to quality. The network showed excellent clinical viability, which can also be even generalized for endometrial cancer with excellent performance.

Investigation of the distribution of inguinal lymph nodes and delineation of the inguinal clinical target volume using 18F-FDG PET/CT

ObjectiveRadiotherapy is a crucial treatment modality for pelvic cancers, but uncertainties persist in defining the clinical target volume (CTV) for the inguinal lymphatic drainage region. Suboptimal CTV delineation may compromise treatment efficacy and result in subpar disease control. This study aimed to investigate and map the distribution of lymph node metastases (LNM) in the groin area to facilitate an improved and detailed CTV definition using 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT).MethodsInguinal LNM in patients with biopsy-proven pelvic malignancies were identified using 18F-FDG PET/CT scan. The longitudinally nearest axial plane was determined based on six typical bony landmarks, and the axial direction relative to the femoral artery of LNM was recorded. The distances from the LNM to the nearest edge of the femoral artery were measured on the axial plane. An optimal margin to cover 95% of LNM was estimated to develop contouring recommendations.ResultsIn this study, 500 positive LNM were identified by 18F-FDG PET/CT among 185 patients with primary pelvic malignancies. Relative to the femoral artery, lymph nodes were distributed laterally (10:00–11:00, n = 35), anteriorly (12:00–1:00, n = 213), and medially (2:00–4: 00, n = 252). For CTV delineation, the recommended distances from the femoral artery on the SFH were lateral 19 mm, anterior 19 mm, and medial 25 mm; on the SGT were lateral 26 mm, anterior 20 mm, and medial 25 mm; on the SPS were lateral 28 mm, anterior 29 mm, and medial 26 mm; on the IPS were anterior 29 mm and medial 28 mm; on the IIT were anterior 27 mm and medial 27 mm; on the ILT were anterior 25 mm and medial 23 mm. Use interpolation to contour the area between six axial slices, including any radiographically suspicious LNM.ConclusionsUsing 18F-FDG PET/CT, we investigated the distribution pattern of inguinal LNM and propose a more comprehensive guideline for inguinal CTV delineation.

Validation of different automated segmentation models for target volume contouring in postoperative radiotherapy for breast cancer and regional nodal irradiation

IntroductionTarget volume delineation is routinely performed in postoperative radiotherapy (RT) for breast cancer patients, but it is a time-consuming process. The aim of the present study was to validate the quality, clinical usability and institutional-specific implementation of different auto-segmentation tools into clinical routine. MethodsThree different commercially available, artificial intelligence-, ESTRO-guideline-based segmentation models (M1-3) were applied to fifty consecutive reference patients who received postoperative local RT including regional nodal irradiation for breast cancer for the delineation of clinical target volumes: the residual breast, implant or chestwall, axilla levels 1 and 2, the infra- and supraclavicular regions, the interpectoral and internal mammary nodes. Objective evaluation metrics of the created structures were conducted with the Dice similarity index (DICE) and the Hausdorff distance, and a manual evaluation of usability. ResultsThe resulting geometries of the segmentation models were compared to the reference volumes for each patient and required no or only minor corrections in 72 % (M1), 64 % (M2) and 78 % (M3) of the cases. The median DICE and Hausdorff values for the resulting planning target volumes were 0.87–0.88 and 2.96–3.55, respectively. Clinical usability was significantly correlated with the DICE index, with calculated cut-off values used to define no or minor adjustments of 0.82–0.86. Right or left sided target and breathing method (deep inspiration breath hold vs. free breathing) did not impact the quality of the resulting structures. ConclusionArtificial intelligence-based auto-segmentation programs showed high-quality accuracy and provided standardization and efficient support for guideline-based target volume contouring as a precondition for fully automated workflows in radiotherapy treatment planning.

2603: Onet: Contrast-enhanced CT assisted automatic clinical target volume delineation of prostate cancer

2603: Onet: Contrast-enhanced CT assisted automatic clinical target volume delineation of prostate cancer

517 Delineation of Clinical Target Volumes (CTV) for Nasopharyngeal Carcinoma – Revisited

517 Delineation of Clinical Target Volumes (CTV) for Nasopharyngeal Carcinoma – Revisited

Landmark-based auto-contouring of clinical target volumes for radiotherapy of nasopharyngeal cancer.

The delineation of clinical target volumes (CTVs) for radiotherapy for nasopharyngeal cancer is complex and varies based on the location and extent of disease. The current study aimed to develop an auto-contouring solution following one protocol guidelines (NRG-HN001) that can be adjusted to meet other guidelines, such as RTOG-0225 and the 2018 International guidelines. The study used 2-channel 3-dimensional U-Net and nnU-Net framework to auto-contour 27 normal structures in the head and neck (H&N) region that are used to define CTVs in the protocol. To define the CTV-Expansion (CTV1 and CTV2) and CTV-Overall (the outer envelope of all the CTV contours), we used adjustable morphological geometric landmarks and mimicked physician interpretation of the protocol rules by partially or fully including select anatomic structures. The results were evaluated quantitatively using the dice similarity coefficient (DSC) and mean surface distance (MSD) and qualitatively by independent reviews by two H&N radiation oncologists. The auto-contouring tool showed high accuracy for nasopharyngeal CTVs. Comparison between auto-contours and clinical contours for 19 patients with cancers of various stages showed a DSC of 0.94±0.02 and MSD of 0.4±0.4mm for CTV-Expansion and a DSC of 0.83±0.02 and MSD of 2.4±0.5mm for CTV-Overall. Upon independent review, two H&N physicians found the auto-contours to be usable without edits in 85% and 75% of cases. In 15% of cases, minor edits were required by both physicians. Thus, one physician rated 100% of the auto-contours as usable (use as is, or after minor edits), while the other physician rated 90% as usable. The second physician required major edits in 10% of cases. The study demonstrates the ability of an auto-contouring tool to reliably delineate nasopharyngeal CTVs based on protocol guidelines. The tool was found to be clinically acceptable by two H&N radiation oncology physicians in at least 90% of the cases.

Personalized Deep Learning Model for Clinical Target Volume on Daily Cone Beam Computed Tomography in Breast Cancer Patients

PurposeHerein, we developed a deep learning algorithm to improve the segmentation of the clinical target volume (CTV) on daily cone-beam computed tomography (CBCT) scans in breast cancer radiotherapy. By leveraging the Intentional Deep Overfit Learning (IDOL) framework, we aimed to enhance personalized image-guided radiotherapy (IGRT) based on patient-specific learning. MethodsWe utilized 240 CBCT scans from 100 breast cancer patients and employed a two-stage training approach. The first stage involved training a novel general deep learning model (Swin UNETR, UNET, and SegResNET) on 90 patients. The second stage utilized intentional overfitting on the remaining 10 patients for patient-specific CBCT outputs. Quantitative evaluation was conducted using the Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), mean surface distance (MSD), and independent samples t-test with expert contours on CBCT scans from the 1st to 15th fractions. ResultsIDOL integration significantly improved CTV segmentation, particularly with the Swin UNETR model (p-values <0.05). Using patient-specific data, IDOL enhanced the DSC, HD, and MSD metrics. The average DSC for the 15th fraction improved from 0.9611 to 0.9819, the average HD decreased from 4.0118 mm to 1.3935 mm, and the average MSD decreased from 0.8723 to 0.4603. Incorporating CBCT scans from the initial treatments and 1st to 3rd fractions further improved results, with an average DSC of 0.9850, an average HD of 1.2707 mm, and an average MSD of 0.4076 for the 15th fraction, closely aligned with physician-drawn contours. ConclusionCompared with a general model, our patient-specific deep learning-based training algorithm significantly improved CTV segmentation accuracy of CBCT scans in patients with breast cancer. This approach, coupled with continuous deep learning training using daily CBCT scans, demonstrated enhanced CTV delineation accuracy and efficiency. Future studies should explore the adaptability of the IDOL framework to diverse deep learning models, datasets, and cancer sites.

Optimized target delineation procedure for the radiosurgery treatment of ventricular tachycardia: observer-independent accuracy.

Part of the current stereotactic arrythmia radioablation (STAR) workflow is transfer of findings from the electroanatomic mapping (EAM) to computed tomography (CT). Here, we analyzed inter- and intraobserver variation in a modified EAM-CT registration using automatic registration algorithms designed to yield higher robustness. This work is based on data of 10 patients who had previously undergone STAR. Two observers participated in this study: (1) an electrophysiologist technician (cardiology) with substatial experience in EAM-CT merge, and (2) a clinical engineer (radiotherapy) with minimum experience with EAM-CT merge. EAM-CT merge consists of 3 main steps: segmentation of left ventricle from CT (CT LV), registration of the CT LV and EAM, clinical target volume (CTV) delineation from EAM specific points. Mean Hausdorff distance (MHD), Dice Similarity Coefficient (DSC) and absolute difference in Center of Gravity (CoG) were used to assess intra/interobserver variability. Intraobserver variability: The mean DSC and MHD for 3 CT LVs altogether was 0.92 ± 0.01 and 1.49 ± 0.23 mm. The mean DSC and MHD for 3 CTVs altogether was 0,82 ± 0,06 and 0,71 ± 0,22 mm. Interobserver variability: Segmented CT LVs showed great similarity (mean DSC of 0,91 ± 0,01, MHD of 1,86 ± 0,47 mm). The mean DSC comparing CTVs from both observers was 0,81 ± 0,11 and MHD was 0,87 ± 0,45 mm. The high interobserver similarity of segmented LVs and delineated CTVs confirmed the robustness of the proposed method. Even an inexperienced user can perform a precise EAM-CT merge following workflow instructions.

Localized fine-tuning and clinical evaluation of deep-learning based auto-segmentation (DLAS) model for clinical target volume (CTV) and organs-at-risk (OAR) in rectal cancer radiotherapy

Background and purposeVarious deep learning auto-segmentation (DLAS) models have been proposed, some of which have been commercialized. However, the issue of performance degradation is notable when pretrained models are deployed in the clinic. This study aims to enhance precision of a popular commercial DLAS product in rectal cancer radiotherapy by localized fine-tuning, addressing challenges in practicality and generalizability in real-world clinical settings.Materials and methodsA total of 120 Stage II/III mid-low rectal cancer patients were retrospectively enrolled and divided into three datasets: training (n = 60), external validation (ExVal, n = 30), and generalizability evaluation (GenEva, n = 30) datasets respectively. The patients in the training and ExVal dataset were acquired on the same CT simulator, while those in GenEva were on a different CT simulator. The commercial DLAS software was first localized fine-tuned (LFT) for clinical target volume (CTV) and organs-at-risk (OAR) using the training data, and then validated on ExVal and GenEva respectively. Performance evaluation involved comparing the LFT model with the vendor-provided pretrained model (VPM) against ground truth contours, using metrics like Dice similarity coefficient (DSC), 95th Hausdorff distance (95HD), sensitivity and specificity.ResultsLFT significantly improved CTV delineation accuracy (p < 0.05) with LFT outperforming VPM in target volume, DSC, 95HD and specificity. Both models exhibited adequate accuracy for bladder and femoral heads, and LFT demonstrated significant enhancement in segmenting the more complex small intestine. We did not identify performance degradation when LFT and VPM models were applied in the GenEva dataset.ConclusionsThe necessity and potential benefits of LFT DLAS towards institution-specific model adaption is underscored. The commercial DLAS software exhibits superior accuracy once localized fine-tuned, and is highly robust to imaging equipment changes.

Delineation of Clinical Target Volume of Esophageal Cancer Based on 3D Dense Network with Embedded Capsule Modules

Delineation of Clinical Target Volume of Esophageal Cancer Based on 3D Dense Network with Embedded Capsule Modules

Uncertainty estimation- and attention-based semi-supervised models for automatically delineate clinical target volume in CBCT images of breast cancer

ObjectivesAccurate segmentation of the clinical target volume (CTV) of CBCT images can observe the changes of CTV during patients' radiotherapy, and lay a foundation for the subsequent implementation of adaptive radiotherapy (ART). However, segmentation is challenging due to the poor quality of CBCT images and difficulty in obtaining target volumes. An uncertainty estimation- and attention-based semi-supervised model called residual convolutional block attention-uncertainty aware mean teacher (RCBA-UAMT) was proposed to delineate the CTV in cone-beam computed tomography (CBCT) images of breast cancer automatically.MethodsA total of 60 patients who undergone radiotherapy after breast-conserving surgery were enrolled in this study, which involved 60 planning CTs and 380 CBCTs. RCBA-UAMT was proposed by integrating residual and attention modules in the backbone network 3D UNet. The attention module can adjust channel and spatial weights of the extracted image features. The proposed design can train the model and segment CBCT images with a small amount of labeled data (5%, 10%, and 20%) and a large amount of unlabeled data. Four types of evaluation metrics, namely, dice similarity coefficient (DSC), Jaccard, average surface distance (ASD), and 95% Hausdorff distance (95HD), are used to assess the model segmentation performance quantitatively.ResultsThe proposed method achieved average DSC, Jaccard, 95HD, and ASD of 82%, 70%, 8.93, and 1.49 mm for CTV delineation on CBCT images of breast cancer, respectively. Compared with the three classical methods of mean teacher, uncertainty-aware mean-teacher and uncertainty rectified pyramid consistency, DSC and Jaccard increased by 7.89–9.33% and 14.75–16.67%, respectively, while 95HD and ASD decreased by 33.16–67.81% and 36.05–75.57%, respectively. The comparative experiment results of the labeled data with different proportions (5%, 10% and 20%) showed significant differences in the DSC, Jaccard, and 95HD evaluation indexes in the labeled data with 5% versus 10% and 5% versus 20%. Moreover, no significant differences were observed in the labeled data with 10% versus 20% among all evaluation indexes. Therefore, we can use only 10% labeled data to achieve the experimental objective.ConclusionsUsing the proposed RCBA-UAMT, the CTV of breast cancer CBCT images can be delineated reliably with a small amount of labeled data. These delineated images can be used to observe the changes in CTV and lay the foundation for the follow-up implementation of ART.

Stereotactic arrhythmia radioablation for refractory ventricular tachycardia. A retrospective analysis and optimisation of the planning phase

Abstract Introduction Stereotactic arrhythmia radioablation (STAR) is used as a rescue treatment for refractory ventricular tachycardia (VT). However, the planning phase to accurately delineate the clinical target volume (CTV) still suffers from limitations due to the incompatibility of data formats between 3D electro-anatomical mapping systems (EAM) used for VT ablation and STAR planning software. The concordance between the CTVs as defined by the planning software (CTVSTAR) and the CTV based on EAM data (CTVEAM) remains unknown. Purpose Within the STOPSTORM European consortium, we compared the concordance between CTVSTAR and CTVEAM in our series of 13 patients (pts) treated by STAR for refractory VT. We also evaluated need for redo ablation of VT after STAR. Method Using the software Slicer3D, two protocols were retrospectively compared: Protocol 1 comprised the manual contouring of the CTVSTAR based on 4D-CT SCAN drawn side-by-side using EAM (Carto3, Biosense Webster) screenshots and diagnostic reports. Protocol 2 comprised semiautomatic segmentation of CTVEAM based on 4D-CT SCAN using imported EAMs annotated with landmarks for VT substrate delimitation. The delineation of CTVEAM included 3 steps: (1) semiautomatic segmentation of ventricular myocardium (VM), the blood-pool of the concerned ventricle and at least 2 additional cardiac cavities; (2) 3D-alignment of EAM with the segmented cavities; and (3), 3D-delineation of CTVSTAR identified as the transmural section of VM segment surrounding the EAM’s VT substrate delimited by STAR tags (Figure 1). Volume overlap (Dice coefficient, 100% meaning complete overlap) and volume absolute difference (VAD, 0% meaning similar volumes) between CTVs were compared. The following cumulative averages were computed over time: redo ablation (RedoA) and CTV. Results Figure 1 illustrates the last treated patient (#13) in whom the delineated volumes, CTVSTAR (red) and CTVEAM (blue), displayed good matching (Dice 72% and VAD 4. Figure 2 shows the comparison between CTVSTAR and CTVEAM for the 13 pts (65±7 yo) treated with STAR, resulting in a mean Dice of 47±17% and mean VAD of 40±51%. Figure 3 shows the cumulative RedoA and the cumulative averaged CTVSTAR and CTVEAM over time. Interestingly, the need for redo ablation decreased with increasing CTVs. Conclusion Our study demonstrates the efficacy of STAR treatment for refractory VT. Our confidence in STAR led to higher CTVs and lower RedoA over time. The high variability of CTV delineation using two different protocols suggests that STAR planning phase can be optimised by appointing intraprocedural anatomical landmarks to the VT substrate.

Proposals for the delineation of neck clinical target volume for definitive Radiation therapy in patients with oral/ oropharyngeal squamous cell cancer based on lymph node distribution

Purpose/Objective(s)To establish the distribution pattern of cervical lymph node metastasis (LNM) and propose optimized clinical target volume (CTV) boundaries specific to oral/ oropharyngeal squamous cell cancer (OSCC/OPSCC). Materials/Methods531 patients with pathologically confirmed OSCC/OPSCC were enrolled from January 2013 to June 2022. Patients were stratified into two groups based on the minimal distance from the lesion's edge to the body's midline: ≤1 cm or > 1 cm. The geometric center of cervical metastatic LN was marked on a template CT. LN distribution probability maps were established. The relationships between the LN distribution and consensus guidelines were analyzed to propose modifications for CTV boundaries specific to OSCC/OPSCC. ResultsA total of 1962 positive LNs were enrolled. Compared with the > 1 cm group, the ≤ 1 cm group has following feature tendencies: male smokers, younger, median organs, large gross lesion, infiltrative growth pattern, contralateral LNM. The most frequently involved level of LNM was ipsilateral II, but ipsilateral Ib had the highest involvement rate in the > 1 cm OSCC group. In addition, tongue cancer had a higher incidence of LN extranodal extension (ENE), which mainly distributes in ipsilateral level II. The skip metastasis was prone to from level III to Vb (3.5 %) in LN(+)/ENE (-), and level Ib to VIa (3.7 %) in LN(+)/ENE (+). Accordingly, we proposed the following modifications: 1. only including lateral and posterior margin of submandibular gland within 5 mm; 2. retracting posterior boundary of level II to front edge of levator scapula muscle, and descending the upper boundary to transverse process of C2 vertebra only for OSCC; 3. including posterior third of thyroglossal muscle or anterior edge of sternocleidomastoid muscle; 4. sparing level Va in case of only level II involvement; 5. including upper area of the thyroid cartilage plate in case of level Ib LN(+)/ENE (+); 6. sparing level VIIa is considered. ConclusionThis is the first description of LN topographic spread patterns for OSCC/OPSCC. Modified CTV for prophylactic irradiation was proposed to spare the organs at risk and minimize adverse effects.

Nodal recurrence mapping and clinical target volumes after resection of intrahepatic cholangiocarcinoma or combined hepatocellular-cholangiocarcinoma

BackgroundScarce evidence exists for clinical target volume (CTV) definitions of regional lymph nodes (LNs) in intrahepatic cholangiocarcinoma (iCCA) or combined hepatocellular-cholangiocarcinoma (cHCC-CCA). We investigated the mapping pattern of nodal recurrence after surgery for iCCA and cHCC-CCA and provided evidence for the nodal CTV definition. MethodsWe retrospectively reviewed the medical records of patients with iCCA or cHCC-CCA who underwent surgery between 2010 and 2020. Eligibility criteria included patients pathologically diagnosed with iCCA or cHCC-CCA after surgery and a first recurrent event in regional LNs during follow-up. All recurrent LNs were registered onto reference computed tomography images based on the vascular structures to reconstruct the node mapping. Fifty-three patients were eligible. LN regions were classified into four risk groups. ResultsHepatic hilar and portal vein-vena cava were the most common recurrent regions, with recurrence rates of 62.3 % and 39.6 % (high-risk regions), respectively. Recurrence rates in the left gastric, diaphragmatic, common hepatic, superior mesenteric vessels, celiac trunk, and paracardial regions ranged from 15.1 % to 30.2 % (intermediate-risk regions). There were fewer recurrences in the para-aortic (16a1, a2, b1) and splenic artery and hilum regions, with rates <10 % (low-risk regions). No LN recurrence was observed in the para-oesophageal or para-aortic region (16b2) (very low-risk regions). Based on node mapping, the CTV should include high- and intermediate-risk regions for pathologically negative LN patients during postoperative radiotherapy. Low-risk regions should be included for pathologically positive LN patients. ConclusionWe provide evidence for CTV delineation in patients with iCCA and cHCC-CCA based on recurrent LN mapping.

The individualized delineation of clinical target volume for primary nasopharyngeal carcinoma based on invasion risk of substructures: A prospective, real-world study with a large population

Background and purposeThe delineation of clinical target volume (CTV) for primary nasopharyngeal carcinoma (NPC) is currently controversial and the international guideline still recommend a uniform border for CTV regardless of the tumor extent. We conducted this prospective, real-world study to evaluate the clinical outcomes of our individualized CTV delineation method based on distance plus substructures. Materials and MethodsWe preliminarily investigated the local extension patterns of NPC on 354 newly diagnosed patients and defined the structures surrounding the nasopharynx as Level-1 to Level-4 substructures stratified by the risk of invasion. We then enrolled patients with newly diagnosed NPC without distant metastasis to investigate our individualized CTV delineation protocol. All patients received intensity modulated radiotherapy. CTV1 and CTV2 were prescribed doses of 60 Gy and 54 Gy in 30 ∼ 33 fractions. The primary endpoint was local recurrence-free survival (LRFS); secondary endpoints included regional control and survival, estimated using the Kaplan-Meier method. The local failure patterns were also analyzed. ResultsFrom January 2008 to December 2012 and from January 2013 to September 2019, 356 and 648 patients were enrolled, named as training set and validation set, respectively. With a median follow-up of 104.6 (interquartile, 73.1–126.9) and 51.4 (39.5–78.5) months, 31 (8.7 %) and 38 (5.9 %) patients in training and validation sets experienced local recurrence, and the 5-year LRFS was 93.0 % and 93.2 %, respectively; 63 (17.7 %) and 39 (6 %) patients died in training and validation sets, and the 5-year overall survival (OS) was 88.5 % and 93.4 %, respectively. For the whole study cohort (N = 1004) with a median follow-up of 66.6 (41.5–98.0) months, the 5-year LRFS and OS was 93.2 % and 91.5 %. The grade 3 late toxicities included xerostomia, subcutaneous fibrosis, hearing impairment, trismus, visuality impairment and skin atrophy, with a total incidence of 1.5 %. Sixty-seven of 69 (97.1 %) local recurrence was in high-dose area. ConclusionOur individualized CTV delineation method can achieve favorable local tumor control and long-term survival outcomes with acceptable late toxicities.

Segmentation of 71 Anatomical Structures Necessary for the Evaluation of Guideline-Conforming Clinical Target Volumes in Head and Neck Cancers.

The delineation of the clinical target volumes (CTVs) for radiation therapy is time-consuming, requires intensive training and shows high inter-observer variability. Supervised deep-learning methods depend heavily on consistent training data; thus, State-of-the-Art research focuses on making CTV labels more homogeneous and strictly bounding them to current standards. International consensus expert guidelines standardize CTV delineation by conditioning the extension of the clinical target volume on the surrounding anatomical structures. Training strategies that directly follow the construction rules given in the expert guidelines or the possibility of quantifying the conformance of manually drawn contours to the guidelines are still missing. Seventy-one anatomical structures that are relevant to CTV delineation in head- and neck-cancer patients, according to the expert guidelines, were segmented on 104 computed tomography scans, to assess the possibility of automating their segmentation by State-of-the-Art deep learning methods. All 71 anatomical structures were subdivided into three subsets of non-overlapping structures, and a 3D nnU-Net model with five-fold cross-validation was trained for each subset, to automatically segment the structures on planning computed tomography scans. We report the DICE, Hausdorff distance and surface DICE for 71 + 5 anatomical structures, for most of which no previous segmentation accuracies have been reported. For those structures for which prediction values have been reported, our segmentation accuracy matched or exceeded the reported values. The predictions from our models were always better than those predicted by the TotalSegmentator. The sDICE with 2 mm margin was larger than 80% for almost all the structures. Individual structures with decreased segmentation accuracy are analyzed and discussed with respect to their impact on the CTV delineation following the expert guidelines. No deviation is expected to affect the rule-based automation of the CTV delineation.