Target Volume Delineation Research Articles

Impact of Varying Chest Wall Target Volume Delineation on Postmastectomy Radiation Therapy Outcomes in Breast Cancer Patients with Implant-Based Reconstruction

Background: The target volume for post-mastectomy radiation therapy (PMRT) in breast cancer patients with reconstruction has been a subject of debate. Traditionally, the RT chest wall (CW) volume encompasses the entire implant. For patients with retropectoral implants, the deep lymphatic plexus dorsal part of the implant is no longer considered high risk and can be omitted. This study aimed to assess the radiation dose distribution and treatment outcomes associated with different CW delineation according to ESTRO ACROP guideline for patients who have undergone implant-based reconstruction. Methods: We conducted a retrospective review of breast cancer patients who underwent a mastectomy followed by two-stage implant-based breast reconstruction and adjuvant radiation therapy (RT) between 2007 and 2022. The expanders/implants were positioned retropectorally. The chest wall target volumes were categorized into two groups: the prepectoral group, which excluded the deep lymphatic plexus, and the whole expander group. Results: The study included 26 patients, with 15 in the prepectoral group and 11 in the whole expander group. No significant differences were observed in normal organ exposure between the two groups. There was a trend toward a lower ipsilateral lung mean dose in the prepectoral group (10.2 vs. 11.1 Gy, p = 0.06). Both groups exhibited limited instances of reconstruction failure and local recurrence. Conclusions: For patients undergoing two-stage expander/implant retropectoral breast reconstruction and PMRT, our data provided comparable outcomes and normal organ exposure for those omitting the deep lymphatic plexus.

Artificial Intelligence–Based Autosegmentation: Advantages in Delineation, Absorbed Dose-Distribution, and Logistics

PurposeTo compare the performance of artificial intelligence (AI) in auto-contouring compared to a human practitioner in terms of precision, differences in dose distribution and time consumption. Methods and MaterialsDatasets of previously irradiated patients in three different segments (head and neck- (HNC), breast- (BC) and prostate cancer (PC)) were retrospectively collected. An experienced radiation oncologist (MD) performed organs-at-risk (OARs) and standard clinical target volume (CTV) delineations as baseline structures for comparison. AI-based auto-contours were generated in two additional CT copies; therefore, three groups were assessed: MD alone, AI alone, AI plus MD corrections (AI+C). Differences in Dice similarity coefficient (DSC) and person-hour burden were assessed. Furthermore, changes in clinically relevant dose-volume parameters were evaluated and compared. ResultsSeventy-five previously treated cases were collected (25 per segment) for the analysis. When compared to MD contours, the mean DSC scores were higher than 0.7 for 74% and 80% of AI and AI+C, respectively. After corrections, 17.1% structures presented DSC score deviations higher than 0.1 and 10.4% dose-volume parameters significantly changed in AI-contoured structures. The time consumption assessment yielded mean person-hour reductions of 68%, 51% and 71% for BC, PC and HNC, respectively. ConclusionIn great extent, AI yielded clinically acceptable OARs and certain CTVs in the explored anatomical segments. Sparse correction and assessment requirements place AI+C as a standard workflow. Minimal clinically relevant differences in OAR-exposure were identified. A substantial amount of person-hours could be repurposed with this technology.

Prior information guided auto-segmentation of clinical target volume of tumor bed in postoperative breast cancer radiotherapy

BackgroundAccurate delineation of clinical target volume of tumor bed (CTV-TB) is important but it is also challenging due to surgical effects and soft tissue contrast. Recently a few auto-segmentation methods were developed to improve the process. However, those methods had comparatively low segmentation accuracy. In this study the prior information was introduced to aid auto-segmentation of CTV-TB based on a deep-learning model.MethodsTo aid the delineation of CTV-TB, the tumor contour on preoperative CT was transformed onto postoperative CT via deformable image registration. Both original and transformed tumor contours were used for prior information in training an auto-segmentation model. Then, the CTV-TB contour on postoperative CT was predicted by the model. 110 pairs of preoperative and postoperative CT images were used with a 5-fold cross-validation strategy. The predicted contour was compared with the clinically approved contour for accuracy evaluation using dice similarity coefficient (DSC) and Hausdorff distance.ResultsThe average DSC of the deep-learning model with prior information was improved than the one without prior information (0.808 vs. 0.734, P < 0.05). The average DSC of the deep-learning model with prior information was higher than that of the traditional method (0.808 vs. 0.622, P < 0.05).ConclusionsThe introduction of prior information in deep-learning model can improve segmentation accuracy of CTV-TB. The proposed method provided an effective way to automatically delineate CTV-TB in postoperative breast cancer radiotherapy.

Clinical outcome after high dose rate intracavitary brachytherapy with traditional point 'A' dose prescription in locally advanced carcinoma of uterine cervix: dosimetric analysis from the perspective of computed tomography imaging-based 3-dimensional treatment planning.

To analyze tumour response and toxicity with respect to cumulative radiotherapy dose to target and organs at risk (OARs) with computed tomography (CT)-based image guided adaptive brachytherapy planning for locally advanced carcinoma cervix. Patients were treated with two-dimensional concurrent chemoradiotherapy to whole pelvis followed by intracavitary brachytherapy (ICBT) with dose prescription to point 'A'. CT image-based delineation of high-risk clinical target volume (HR-CTV), urinary bladder, rectum and sigmoid colon was done with generation of dose-volume histogram (DVH) data and optimization of doses to target and OARs. Follow up assessments were done for response of disease and toxicity with generation of data for statistical analysis. One hundred thirty-six patients were enrolled in the study. Delineated volume of HR-CTV ranged from 20.9 to 37.1 mL, with median value of 30.2 mL. The equivalent dose in 2 Gy per fraction (EQD2) for point 'A' ranged from 71.31 to 79.75 Gy with median value of 75.1 Gy and EQD2 HR-CTV D90 ranged from 71.9 to 89.7 Gy with median value of 85.1 Gy. 69.2% of patients showed complete response and after median follow-up of 25 months, 50 patients remained disease free, of whom, 74.0% had received ≥85 Gy to HR-CTV D90 versus 26.0% receiving <85 Gy to HR-CTV D90. s Amidst the unavailability of magnetic resonance imaging facilities in low middle income countries, incorporation of CT-image based treatment planning into routine practice for ICBT provides the scope to delineate volumes of target and OARs and to generate DVH data, which can prove to be a better surrogate for disease response and toxicity.

Towards deep-learning (DL) based fully automated target delineation for rectal cancer neoadjuvant radiotherapy using a divide-and-conquer strategy: a study with multicenter blind and randomized validation

PurposeManual clinical target volume (CTV) and gross tumor volume (GTV) delineation for rectal cancer neoadjuvant radiotherapy is pivotal but labor-intensive. This study aims to propose a deep learning (DL)-based workflow towards fully automated clinical target volume (CTV) and gross tumor volume (GTV) delineation for rectal cancer neoadjuvant radiotherapy.Materials & methodsWe retrospectively included 141 patients with Stage II-III mid-low rectal cancer and randomly grouped them into training (n = 121) and testing (n = 20) cohorts. We adopted a divide-and-conquer strategy to address CTV and GTV segmentation using two separate DL models with DpuUnet as backend-one model for CTV segmentation in the CT domain, and the other for GTV in the MRI domain. The workflow was validated using a three-level multicenter-involved blind and randomized evaluation scheme. Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (95HD) metrics were calculated in Level 1, four-grade expert scoring was performed in Level 2, and head-to-head Turing test in Level 3.ResultsFor the DL-based CTV contours over the testing cohort, the DSC and 95HD (mean ± SD) were 0.85 ± 0.06 and 7.75 ± 6.42 mm respectively, and 96.4% cases achieved clinical viable scores (≥ 2). The positive rate in the Turing test was 52.3%. For GTV, the DSC and 95HD were 0.87 ± 0.07 and 4.07 ± 1.67 mm respectively, and 100% of the DL-based contours achieved clinical viable scores (≥ 2). The positive rate in the Turing test was 52.0%.ConclusionThe proposed DL-based workflow exhibited promising accuracy and excellent clinical viability towards automated CTV and GTV delineation for rectal cancer neoadjuvant radiotherapy.

A Prospective, Multi-Institutional Study of Problematic Plan Detection during Physician Chart Rounds.

We performed a multi-institutional prospective study to determine the detection rate of problematic treatment plans (PP) at physician chart rounds (CR), and to identify factors associated with PP detection. Curative intent PPs with simulated errors (representative of the most common targets of peer review) were generated. Two breast specialists selected twenty appropriate plans for inclusion and assigned them American Association of Physicists in Medicine (AAPM) Task Group 100 severity and detectability scores. The PPs were blinded and embedded at weekly virtual CR at 2 institutions over 12 months. At site A, both breast and lung cases were reviewed by a mix of breast and lung specialists during CR, and at site B, only breast cases were presented and reviewed by breast specialists. At both sites, breast plans were reviewed via slice-by-slice review in the treatment planning system (TPS), and both used a color-coded tool from the TPS to assess adherence to planning directives. Both sites had systematic approaches to case presentation (without a checklist). Site A was usually prospective CR, while site B was exclusively prospective. The following CR elements were recorded: PP detection, time of detection, length of CR, total number of cases presented, plan elements displayed, number and roles of attendees, and detector's role. Analysis was performed using simple statistics with chi-square testing. By PP error type classification, 55.0% pertained to "target volume delineation," 25% to "non-target volume delineation or normal tissue sparing," and 20.0% to "dose prescription or written directives." Detectability was rated ≤5 (<5% likelihood of going undetected) for 60% of PPs, and severity was rated ≥7 ("at least potentially serious toxicity or tumor underdose") for only 30% of PPs. CR lasted a median of 64 minutes at site A (IQR 55-82.5) and 70 minutes at site B (IQR 52.5-81.5). PPs were presented at a median of 34 minutes (IQR 22.5-43, site A) and 41.5 minutes (IQR 23.5-56, site B) after CR start. A median of 16 cases (IQR 13-19) at site A and 32 cases (IQR 25-34.5) at site B were presented per CR session, with a median of 1 PP (site A and B) presented per session (range 1-2). The median time spent per case was 4.0 minutes (Site A) and 2.2 minutes (Site B). The median number of attendings at CR was 4 for site A (range 2-6) and 6.5 for Site B (range 5-10). PP detection rate at site A was 20% (n = 4) and at site B was 70% (n = 14) (p = 0.001). Detections were made by an attending physician in 100% (site A, n = 4) and 92.9% (Site B, n = 13) of PP detections. There were no differences in detection rate by PP error type (p = 0.78), detectability (p = 0.60) or severity score (p = 0.68), or by time PP presented after CR start (p = 0.39). The effectiveness of PP detection at chart rounds can vary greatly between institutions. The study suggests possible areas for improvement but further study is needed to determine best practices.

Development and Validation of a Deep Learning-Based Auto-Delineation of Target Volume and Organs at Risk in Pancreatic Cancer Radiotherapy.

The delineation of the clinical target volume (CTV), gross target volume (GTV) and organs at risk (OARs) is a crucial and laborious in pancreatic cancer radiotherapy. In this work, we propose and evaluate a three-dimensional (3D) novel convolutional neural network (CNN) for automatic and accurate CTV, GTV and OARs in pancreatic cancer. A total of 120 computed tomography (CT) scans patients with pancreatic cancer were collected. A novel 3D CNN network, called ResUNet3D, was developed to achieve auto-delineation. 96 patients chosen randomly were used for training, 12 patients for validation, and 12 patients for testing. Meanwhile, the Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95%) were used to assess the performance. The DSC values for the test data were 80.9±8.6%, 77.5±5.6%, 94.5±1.3%, 66.2±13.4%, 73.6±7.6%, 79.0±8.7%, 94.1±1.9%, 94.6±1.4%, 87.3±5.8% for CTV, GTV, liver, duodenum, spinal cord, bowel, kidney left, kidney right, stomach. The corresponding HD95% values were 10.7±6.9mm, 7.8±5.7mm, 11.6±5.6mm, 18.6±5.6mm, 2.7±0.7mm, 17.7±8.6mm, 3.9±1.4mm, 3.7±1.9mm, 13.4±5.7mm, respectively. The average delineation time for one patient's CT images was within 5 seconds. The experimental results demonstrate that the CTV, GTV and OARs delineated for pancreatic cancer by ResUNet3D achieved a close agreement with the ground truth. ResUNet3D could significantly reduce the radiation oncologists' contouring time.

Objective Boundary Generation for Gross Target Volume and Organs at Risk Using 3D Multi-Modal Medical Images.

Accurate delineation of Gross Target Volume (GTV) and Organs at Risk (OARs) in medical images is an essential but challenging step in radiotherapy. Deep-learning based automated delineation methods, which learn from manual annotations, are currently prevalent in academic research. However, the limited resolution of medical images and the fuzzy boundaries of lesions and organs present a challenge to the precision of manual annotations. By leveraging the complementary information from multi-modal medical images, this study proposed a novel method to generate objective boundaries of GTV and OARs. We present a novel method of objective boundary generation, inspired by image matting primarily used for 2D RGB natural images, to process 3D grayscale medical images. The proposed method has the following advantages. 1) It allows for flexible input modalities and assigns weights to each modality according to their relative significance when computing information flows in the matting algorithm. 2) It computes 3D spatial information flow among voxels, which has more advantages over its 2D counterpart. 3) It has a closed-form solution that generates deterministic results. To evaluate the characteristics of the generated boundaries, patients with stage I nasopharyngeal carcinoma (NPC) were studied, with CT images and multi-modal MR images (T1, T1C, T2) aligned using deformable registration. Region of Interests (ROIs), i.e., GTV and parotid gland, were used, with a rough trimap marking extremely few foreground voxels, many background voxels, and a large unknown region. The proposed algorithm leverages the connection between each voxel and its nearest neighbors in the feature space, to propagate the opacity information. We evaluated the results by employing both qualitative and quantitative methods. Using qualitative evaluation, experienced clinicians confirmed that the results were in agreement with the input data, especially for areas where borders were visible in most modalities (e.g., between air and tumor). For more challenging regions, where boundaries were unclear in the images, the results displayed fine-grained opacity transitions indicating the confidence of each voxel belonging to the ROI. When compared to the delineations made by clinicians, we found our results are usually more compact. We define a precision metric that evaluates the ratio of the matted foreground inside clinicians' delineations versus the entire matted foreground. Using a threshold of 0.4, our binarized result scored 0.95 for GTV and 0.92 for parotid gland. The proposed method demonstrated satisfactory results on challenging ROIs. The objective boundaries generated by this method have advantages in many aspects, including improvement of delineation protocols, enhancement of manual annotation consistency, and increase of deep-learning based automated delineation accuracy.

Art for Advanced Radiation Oncology – Synchronizing the Past and the Future for a Better Patient Care

"Professor Maria Antonietta Gambacorta is a specialist in Radiation Oncology with a PhD in Brachytherapy. She is responsible for the Radiotherapy Oncology hospitalizations and the Interventional Oncology Center and the coordinator of the Multidisciplinary Group for the treatment of rectal tumors within the Comprehensive Cancer Centre of the Fondazione Policlinico Agostino Gemelli, Rome. Due to her level of expertise, she was elected member of the Board of Directors of the Italian Association of Radiation Oncology (AIRO); and member of the board for the drafting of the Guidelines on rectal cancer of the Italian Association of Medical Oncology. Professor Gambacorta is a member of European Society of Radiation Oncology (ESTRO) where she carries out teaching activities in international courses of the ESTRO school. She has published more than 170 scientific publications in peer-reviewed journals, including the International consensus guidelines for Clinical Target Volume delineation in rectal cancer. In this interview, Professor Gambacorta shares some insights about the Art for ART project, implemented in the Radiotherapy department of Policinico Agostino Gemelli, Rome."

Dose Escalation in Esophageal Cancer: Comparing Pre-Accrual and On-Trial Target Volume Delineation in the UK SCOPE2 Trial

The ongoing UK SCOPE2 trial evaluates radiotherapy (RT) dose escalation and PET-guided systemic therapy in esophageal cancer, and has an accompanying RT trials quality assurance (RTTQA) program, evolved through the preceding SCOPE trials. We compare pre-accrual with on-trial individual case review (ICR) target volume delineation (TVD). Prior to recruitment, centers were required to undertake TVD exercises using 3D/4D DICOM datasets with relevant clinical details and a RT planning guidance document (RPGD) provided. Contours were then compared against the RTTQA team-defined gold standard. Exceptions were those who had satisfied QA requirements for a previous esophageal RT trial (NeoSCOPE). For ICRs, prospective reviews (prior RT start, PRs) were undertaken for each center's first submission, plus high-dose cases submitted pending formal safety review. Additional PRs were undertaken at the RTTQA team's discretion. Timely retrospective reviews (within 2 weeks of RT start, TRR) were also undertaken for a random 10% sample. TVDs were assessed for compliance using predefined criteria and the RPGD. Resubmission was requested at reviewer's discretion, usually due to unacceptable variation (UV) from protocol. Clarification was sought before contour approval/resubmission request if appropriate. Review outcomes were then evaluated. PTV6000 was new to SCOPE2, along with a greater emphasis on use of 4DCT than in prior SCOPE trials. A total of 85 pre-accrual cases from 33 UK centers were reviewed, of which 20 (24%) were resubmissions, and 50 (59%) were accepted. 99 TVD UVs were observed in 49 cases, most commonly in CTVB (42/99, 42%), which included editing for normal structures and elective lymph node regions, followed by ITV (4D cases only, 14/52, 27%) and PTV6000 (13/99, 13%). 121 ICRs from 31 UK centers were available for review. 87 (72%) were PRs and 34 (28%) TRRs. 43 (36%) completed the relevant SCOPE2 exercise. 19 (16%) were resubmissions, and 82 (68%) were accepted. 72 UVs were observed in 45 ICRs; again, most commonly in CTVB (34/72, 48%), PTV6000 (high dose arm only, 11/46, 24%) and ITV (4D only, 5/26, 19%). Of the 45 cases where a UV was recorded, 16 (36%) had completed the relevant SCOPE2 pre-accrual. Comparing area of UV on SCOPE2 pre-accrual cases and ICRs, 3 (19%) contours contained the same (2 = CTVB, 1 = PTV6000), 5 (31%) contained different and 8 (50%) had no UVs at pre-accrual. The rate of UV was significantly lower for ICR than for pre-accrual submissions (0.60 and 1.16 respectively, p = 0.001). Significantly fewer UVs in ICR compared with pre-accrual supports a robust, educational RTTQA program through national collaboration and evolving trial series. CTVB, along with newer volumes of ITV and PTV6000, were recurring UV domains and should inform RPGD development and RTTQA for ongoing recruitment and future trials.

Efficacy and Toxicity of Different Target Volume Delineations of Radiotherapy Based on the Updated RTOG/NRG and EORTC Guidelines in Patients with High Grade Glioma: A Randomized, Controlled Clinical Trial

Postoperative radiotherapy with concomitant and adjuvant temozolomide (TMZ) is the standard of care for newly diagnosed high grade glioma, but the optimal method for target volume delineations for intensity modulated radiation therapy (IMRT) is still unclear. We hypothesized that compared with the EORTC guidelines, IMRT based on the updated RTOG/NRG guidelines was equally effective, without increasing toxicities for patients with high-grade glioma. The purpose of this randomized phase 2 study was to compare the efficacy and toxicity of IMRT based on different target volume delineations (updated RTOG/NRG versus EORTC guidelines) with concomitant and adjuvant TMZ for patients with high grade glioma. A total of 302 patients with newly diagnosed high-grade glioma (WHO grade 3-4) were randomly assigned (1:1) to receive postoperative IMRT based on either updated RTOG/NRG guidelines (RTOG/NRG group, n = 151) or EORTC guideline (EORTC group, n = 151), with concomitant and adjuvant TMZ. In the RTOG/NRG group, an initial volume consisting of enhancement, postoperative cavity, plus surrounding edema (or fluid-attenuated inversion recovery [FLAIR] abnormality defined by magnetic resonance imaging [MRI]) and a 2-cm margin received 46 Gy in 23 fractions followed by a boost of 14 Gy in 7 fractions to the area of enhancement plus the cavity and a 2-cm margin. In the EORTC group, a single planning volume was used to deliver 60 Gy in 30 fractions to the area of enhancement and the cavity with a 2-cm margin. The primary end point was overall survival (OS). Secondary end points included progression-free survival (PFS) and toxicities associated with each treatment. No statistically significant differences were observed between groups for 1-year OS (71.8% for RTOG/NRG group and 69.9% for EORTC group, respectively; P = 0.759) or 1-year PFS (46.7% for RTOG/NRG group and 43.6% for EORTC group, respectively; P = 0.674). Efficacy did not differ by MGMT methylation status. There were no differences in grade 3-4 toxicities (leukopenia, lymphopenia, neutropenia, thrombocytopenia, fatigue, nausea and vomiting) between the two groups. No grade 5 toxicities were observed in both groups. Multivariate analyses showed that tumor MGMT status (methylated vs unmethylated) and WHO grade (grade 3 vs grade 4) were associated with OS and PFS. However, radiation type (RTOG/NRG group vs EORTC), sex, age, and Karnofsky scale did not significantly influence OS or PFS. Compared with EORTC guidelines for postoperative radiotherapy, IMRT based on RTOG/NRG guidelines was equally effective, without increasing toxicities for patients with high-grade glioma. This trial is registered with chictr.org.cn, number ChiCTR2100046667.

Stereotactic Body Radiotherapy (SBRT) for Sacral Metastases: Deviation from Recommended Target Volume Delineation Predicts Higher Risk of Local Failure

A international consensus recommendation was published to guide target volume delineation specific to sacral stereotactic body radiotherapy (SBRT). We report outcomes after sacrum SBRT, focusing on the impact of contouring deviation on local failure (LF) risk, with an aim to validate this guideline. All patients who underwent SBRT to any level between S1 and S5 from 2010 to 2021 were identified from a prospectively maintained institutional database. The primary outcome was magnetic resonance-based LF. Secondary outcomes included vertebral compression fracture (VCF) and overall survival (OS). Cumulative LF and VCF rates were calculated per segment using the competing risk analysis method. Kaplan Meier analysis was used to estimate OS per patient. Cox proportional hazards model was used to assess predictive factors of LF, VCF, and OS. A total of 215 treated sacral segments in 112 patients were retrospectively reviewed. The median follow-up was 13 months (range, 0.4-116.9). The median age was 64 years (range, 18-86), and 56% were male. Most patients (52%) had treatment to a single segment. The median clinical target volume (CTV) was 129.2 cc (range, 5.8-753.5). Most segments were treated with 30 Gy/4 fractions (51%), 24 Gy/2 fractions (31%), or 30 Gy/5 fractions (10%). Thirty-one percent of segments were of radioresistant histology (gastrointestinal, kidney, melanoma, sarcoma, or thyroid primary), and 51% had extraosseous disease. Sixteen percent of segments were under-contoured per consensus guidelines, with incomplete coverage of the involved sector (71%), omission of the adjacent uninvolved sector (17%), or both (11%) as the causes for deviation. The cumulative incidence of LF was 18.4% (95% CI 13.5-24.0) at 12-months and 23.1% (95% CI 17.6-29.0) at 24-months. On multivariate analysis (MVA), under-contouring (HR 2.4, 95% CI 1.3-4.7, p = 0.008), radioresistant histology (HR 2.4, 95% CI 1,4-4.1, p = 0.001), and extraosseous extension (HR 2.5, 95% CI 1.3-4.7, p = 0.005) were predictors of increased risk of LF. The LF rates at 12/24-months were 15.1%/18.8% for segments contoured per guideline versus 31.4%/40.0% for those under-contoured. The cumulative incidence of VCF was 7.1% (95% CI 4.1-11.1) at 12-months and 12.3% (95% CI 8.2-17.2) at 24-months. On MVA, female gender was the only risk factor for VCF (HR 2.3, 95% CI 1.1-5.2, p = 0.04). The median OS was 29.5 months (95% CI 17.5-59.2). On MVA, primary kidney (HR 4.7, 95% CI 1.7-12.5, p = 0.002) or lung histology (HR 3.4, 95% CI 1.3-8.5, p = 0.010), the presence of liver (HR 2.8, 95% CI 1.2-6.4, p = 0.016) or lung (HR 2.5, 95% CI 1.3-5.1, p = 0.008) metastases, ECOG performance status 2 or 3 (HR 3.3, 95% CI 1.2-8.2, p = 0.013), and the presence of sensory or motor deficit (HR 2.6, 95% CI 1.2-5.4, p = 0.012) were prognostic for worse OS. Sacral SBRT is associated with high rates of efficacy and an acceptable VCF risk. Adherence to target volume delineation consensus guidelines reduces the risk of LF.

Clinical prior Knowledge-Based One-Shot Learning for Automatic Delineation of Clinical Target Volumes in Adaptation Radiotherapy of Cervical Cancer

Rapid and accurate delineation of clinical target volumes (CTV) of cervical cancer is the crux to ensure the efficiency and benefits of adaptation radiotherapy (ART). However, contour propagation using deformation image registration (DIR) is difficult to ensure the accuracy of CTV contours due to the significant tumor recession in next fraction, and the tumor progress in each fraction is not considered by conventional automatic delineation methods based on deep learning (DL). Currently, one-shot learning (OSL) is feasible to learn the tumor progress from former fractions to improve the accuracy of automatically delineating CTV. We retrospectively collected 45 patients with cervical cancer from January 2021 to May 2022 in our department. All patients consist of a pair of planning CT and daily CT in ART. A personalized automatic delineation method based on one-shot learning was developed to delineate CTV in daily CT by learning the clinical prior knowledge from the CTV contours and images of planning CT. The performance of our proposed method was evaluated by dice similarity coefficient (DSC), 95% Harsdorff distance (95HD) and average surface distance (ASD) with human experts, and its automatic delineation performance were compared with DIR and DL in daily CT. Our automatic delineation method OSL performed the best results in all evaluation metrics (denoted by mean ± standard deviation) as shown in Table 1, it is superior to method DL: 0.92 & 0.90 of DSC, 2.33 mm & 2.68 mm of HD95, 0.68 mm & 0.82 mm of ASD, P < 0.05 for DSC and ASD. Specifically, our method is significantly superior to the automatic delineation results by method DIR: 0.92 & 0.84 of DSC, 2.33 mm & 4.11 mm of HD95, 0.68 mm & 1.52 mm of ASD, P < 0.05 for all. In addition, OSL can significantly overcome the delineation problems in fuzzy boundary and delineation missing and perform better generalization for some unusual images, compared with DIR and DL. We proposed an automatic delineation method based on one-shot learning for CTV of cervical cancer in ART, the results demonstrated that the proposed method could improve the precision and generalization of automatically delineating CTV compared against current popular methods. Therefore, it is potential to improve the quality and efficiency of ART for personalized patients and have a positive impact on tumor control and patient survival.

GTV Based Automatic Delineation of Clinical Target Volume for Cervical Cancer

The delineation of gross tumor volume (GTV) and clinical target volume (CTV) are two critical steps in the radiotherapy planning for cervical cancer. GTV defines the primary treatment region for the gross tumor, while CTV is the area surrounding GTV that includes a certain probability (5% to 10%) of subclinical lesions. In contrast to GTV, CTV delineation relies on predefined and judgment-based boundaries, and the high variability among users makes this task particularly challenging. In this study, we evaluated the potential relationship between GTV and CTV and developed an automatic CTV delineation algorithm for cervical cancer based on the fusion of GTV information. We introduced position and shape constraints of GTV to improve the accuracy of CTV delineation. The GTV-Net deep learning method was used to segment the CTV images of cervical cancer. The method aimed to use the delineation results of the GTV region for one-hot coding and add human anatomy experience in the clinical field to guide the CTV segmentation. This retrospective study included 545 cervical cancer patients who received radiation therapy from June 2017 to May 2019, including postoperative and radical treatment groups. The CTV and GTV regions were manually delineated by human experts. Numerous experiments were conducted to evaluate the performance of the network. First, compared with different network architectures, the Dice similarity coefficient (DSC) and 95% Hausdorff distance (95HD) of GTV-Net were both improved. Then, we compared the GTV-Net method with two resident physicians. Our GTV-Net method outperformed both resident physicians. In the postoperative group, our method improved the DSC by 4% compared to 3D-UNet, reaching 76.55%, and increased by about 2.57% compared to V-Net's 73.98%, with an improvement of approximately 1.23% compared to the two resident physicians. In the radical treatment group, compared to 3D-UNet's 78.76%, our method increased the DSC by about 3.25%, reaching 82%, and increased by approximately 2.08% compared to V-Net's 79.92%, with an improvement of about 1.35% compared to the two resident physicians. Compared with 3D-UNet, the average 95HD in the postoperative group decreased from 1.489 to 1.457, and in the radical treatment group, it decreased from 1.454 to 1.433. The results of 95HD also showed some improvement compared to V-Net. This study is the first to introduce GTV information for automatic segmentation of the clinical target area for cervical cancer. In this experiment, we observed a positive gain in CTV target automatic delineation guided by GTV information compared to solely performing CTV segmentation, with an improvement in Dice similarity of more than 4% and Hausdorff distance of more than 6% in the experimental dataset. In addition, GTV-guided CTV automatic delineation has also shown promising results on multicenter data, which will better serve the clinical field.

A Multi-Institutional Analysis of MRI-Based Brachytherapy for Medically Inoperable Endometrial Cancer

Patients with medically inoperable endometrial cancer (MIEC) are curable with brachytherapy (BT)-based treatment yet have competing comorbidities making definitive treatment challenging. MRI demonstrates superior soft tissue anatomy and target volume delineation but with limited data in MIEC patients. We aim to report disease and toxicity outcomes with MRI-based BT and identify dose-volume relationships for toxicities in the treatment of MIEC patients treated with MRI-based BT. We conducted a retrospective multi-institutional analysis of MIEC patients undergoing definitive MRI-based BT (+/- EBRT). MRI-based BT was delivered with the applicator in situ or coregistered to a planning CT. We identified patient, tumor, and dosimetric factors associated with disease and toxicity outcomes. Kaplan-Meier method was used for survival estimates. Log rank test and Cox proportional hazards were used for univariate and multivariate analyses, respectively. T-test was used for dose-volume toxicity analysis. A total of 120 patients were included with a median follow up of 28.0 months. Median age was 68.5 years. ECOG PS was 0-1 in 70%. Clinical stage I was 83.3% and II-IV, 16.7%. Most patients (91.7%) were node negative. Endometrioid and high risk histologies comprised 83.3%, and 16.7%, respectively. EBRT + BT was delivered in 97 patients (80.8%) and BT alone in 23 patients (19.2%). Chemotherapy or hormonal therapy was delivered during treatment in 10 (8.3%) and 11 (9.2%) patients, respectively. Estimated 3-year freedom from local, nodal, and distant recurrence were 88.0%, 96.0%, and 89.1% respectively. Estimated 3-year PFS and OS were 60.9% and 62.9%, respectively. On UVA, older age, PS ≥2, high risk histology, higher grade, and larger GTV at BT were significant (p<0.1). On MVA, older age, higher grade, and larger GTV at BT (p<0.05) predicted for inferior PFS. Fifteen late grade ≥3 toxicities were experienced in 14 (11.6%) patients, 13 of whom received EBRT and BT and 1 who received BT alone. Grade ≥3 toxicities were rectal (2, 1.7%), sigmoid (8, 6.7%), bowel (1, 0.8%), bladder (3, 2.5%), and osseous (1, 0.8%). EBRT was delivered in 7 of 8 sigmoid toxicities. Median sigmoid doses (EQD2a/b = 3Gy) for patients with and without late grade ≥3 sigmoid toxicity were 69.6 Gy and 64.3 Gy, respectively (p = 0.009). MRI-based BT for MIEC patients results in high rates of local control and favorable rates of late grade ≥3 morbidity. Older age, higher grade, and larger GTV at BT predicted for poorer PFS. Sigmoid colon was the predominant organ at risk for grade ≥3 toxicity with a dose -volume relationship observed. Attention to the location of the sigmoid throughout the treatment course may add insight into its predilection for risk. Future work will include additional institutions and dose-volume relationships of target volumes and normal tissues for further disease control and toxicity analysis.

Impact of the New ESTRO-ACROP Target Volume Delineation Guideline on Breast-Related Complications after Implant-Based Reconstruction and Postmastectomy Radiotherapy

The European Society for Radiotherapy and Oncology Advisory Committee in Radiation Oncology Practice (ESTRO-ACROP) recently updated a new target volume delineation guideline for postmastectomy radiotherapy (PMRT) after implant-based reconstruction. This study aimed whether this change has impact on breast-related complications. We retrospectively reviewed patients who underwent PMRT after mastectomy with tissue expander or permanent implant insertion from 2016 to 2021. In total, 412 patients were included; 277 received RT by the new ESTRO-ACROP target delineation (ESTRO-T), and 135 received RT by conventional target delineation (CONV-T). The primary endpoint was comparison between the target groups of major breast-related complication, including infection, capsular contracture, deformity and necrosis requiring re-operation or re-hospitalization during follow-up after RT or delayed implant replacement. Complications were evaluated according to the Common Terminology Criteria for Adverse Events (CTCAE) version 5.0., and capsular contracture was graded by the Baker Classification. The median follow-up was 29.5 months (range, 0.3-76.8). The 1-, 2-, and 3-year incidence rates of major breast-related complication were 5.7%, 10.0%, and 11.6% in the ESTRO-T group, and 8.2%, 13.8%, and 14.7% in the CONV-T groups; it did not show a difference between the groups (P = 0.55). In multivariate analyses, target delineation is not significantly associated with the major complications (hazard ratio [HR] = 0.93; P = 0.83, Table 1). There was no significant difference between the ESTRO-T and CONV-T groups in the incidence of any breast-related complications (3-year cumulative incidence, 37.3% vs. 29.4%, respectively; P = 0.28). Symptomatic RT-induced pneumonitis rates were 2.7% in the ESTRO-T group (7 patients) and 2.2% in the CONV-T group (3 patients). Only one local recurrence event occurred in the ESTRO-T group, which was within the ESTRO-target volume. Target volume delineation according to the new ESTRO-ACROP guideline did not reduce the risk of major or any breast-related complications. As the dosimetric benefits of heart and lung have been reported, further analyses with long-term follow-up are necessary to evaluate whether it could be connected to better clinical outcomes.

Stereotactic Body Radiotherapy for Posterior Element Only Spinal Metastases: Outcomes and Validation of Recommended Clinical Target Volume Delineation Practice

Spine stereotactic body radiotherapy (SBRT) results in improved local control and pain response compared to conventional external beam radiotherapy. Consensus stipulates MRI-based delineation of the clinical target volume (CTV) is critical and is based on spine segment sector involvement. The applicability of these contouring guidelines to metastases confined to the posterior elements is unknown. The purpose of this study was to determine the patterns of failure, as well as the safety of treating posterior element metastases when the vertebral body was intentionally excluded from the CTV. A retrospective review of a prospectively maintained database of 605 patients and 1412 spine segments treated with spine SBRT was performed. Only treated segments involving the posterior elements alone were included for the analyses. The primary outcome was local failure, as per SPINO recommendations, and secondary outcomes included patterns of failure, toxicities. Clinical and tumor factors were reported with descriptive statistics. The cumulative risk of local failure was estimated using the Fine-Gray method, accounting for death before local failure as a competing risk. A total of 24/605 patients and 31/1412 segments within the database were treated to the posterior elements only. Local failure occurred in 11/31 segments. The cumulative rate of local recurrence was 9.7% at 12 months and 30.8% at 24 months. Amongst local failures, the most common histologies were renal cell carcinoma (36.4%) and non-small cell lung cancer (36.4%). At baseline, 4/11 (36.4%) segments with local failure (36.4%) had epidural disease and 8/11 (72.7%) had paraspinal disease. Most local failures were treated in the de novo setting (8/11, 72.7%). 6/11 (54.5%) failed exclusively within treated CTV sectors and 5/11 (45.5%) with both treated and adjacent untreated sectors. Of these five, four had disease progression within the untreated vertebral body. No failures occurred exclusively within the untreated vertebral body. One patient (4.2%) experienced a grade 4 skin toxicity and one patient (4.2%) developed an iatrogenic Grade 1 vertebral compression fracture. Posterior element alone metastases are rare. Our analyses support SBRT consensus contouring guidelines such that the vertebral body can be excluded from CTV in spinal metastases confined to the posterior elements.

Selectively Irradiation of the Supraclavicular Area in Nasopharyngeal Carcinoma Treated with Intensity Modulated Radiotherapy

The application of intensity-modulated radiotherapy (IMRT) has significantly improved the locoregional control and overall survival of patients with nasopharyngeal carcinoma (NPC), but how to deescalate the intensity of treatment to further reduce the side effects while ensuring the efficacy is the main concern of clinicians. Decreasing radiation volume is one of the hot research topics. Whole-neck irradiation is recommended for patients with cervical lymph node involvement, which includes levels IVb and Vc. Since the implementation of IMRT in our clinic, patients with only upper cervical lymph node invasion have not been irradiated in the levels IVb and Vc, and the regional control rate is comparable to that of other centers. This study intends to retrospectively analyze the feasibility of omitting levels IVb and Vc in patients with only upper cervical lymph node invasion, and provide a basis for the revision of international guideline for the delineation of the clinical target volume (CTV). Patients with histologically confirmed NPC who completed IMRT successfully at our attending group between January 2014 to March 2018 were candidates for this study. Other eligibility criteria for analysis were as follows: (1) The first patient without distant metastasis; (2) Only patients with upper cervical lymph node invasion (only N1, N2, because the maximum diameter of the upper neck 6cm diagnosed as N3 patients were not included) (AJCC staging system, 8th edition); (3) Having complete baseline imaging data; (4) Over 3 months of follow-up; (5) Patients without previous history of malignant tumor and secondary cancer. According to the delineation of CTV in our protocol for patients with stage N1 and N2, levels IVb and Vc are not irradiated (i.e., retaining the original supraclavicular area). The main endpoints of the study were regional recurrence-free survival (RRFS) and recurrence rates in levels IVb and Vc. In all, 363 patients with NPC and median follow-up of 61.9 months (range, 3.67-93.3) met the study inclusion criteria and comprise the dataset for the analysis. The overall survival, survival without local recurrence, survival without regional recurrence, and survival without distant metastasis at 3 and 5 years were 94.6% and 91.1%, 97.7% and 96.5%, 98.6% and 98.0%, 93.5% and 91.1%. At the final follow-up, 11 cases showed regional recurrence, and only one case (0.28%) showed recurrence in regions IVb and Vc, which were field recurrences, while the rest were intra-field recurrences. Omitting levels Ⅳb and Ⅴc IMRT should be safe and feasible for patients who only had involvement of upper neck LNs. Further well-designed multicenter prospective trials should be conducted to confirm our results and further optimize the recommendation of the International Guideline.

Proposals for the Delineation of Neck Clinical Target Volume Specific to Oral Squamous Cell Cancer Based on Lymph Node Distribution

To establish the regional lymph node (LN) distribution probability map and draw the neck clinical target volume specific to oral squamous cell cancer (OSCC). A total of 293 patients with pathologically proven OSCC were enrolled from Jan 2014 to Jun 2022. The patients were divided into two groups according to the ≤1 cm or>1 cm of minimal distance from the lesion's edge to the midline of body. The center point of the LNs with a minimal axial diameter of ≥10 mm was marked on a single treatment planning computed tomography scan. The neck LN levels I to X were based on 2013 updated international consensus guidelines. LN distribution probability maps were established. The relationships between the LN distribution and consensus guidelines were analyzed to propose modifications for clinical target volume boundaries specific to OSCC. A total of 1033 LNs were marked. Compared with the >1 cm group, The ≤1 cm group has following feature tendencies: strong addiction to smoking and drinking, predilection for tongue and oral floor cancer, larger primary lesion with mainly infiltrative growth pattern, more frequent extra-nodal extension with mainly contralateral LNs. For the ≤1cm group, the distribution probability of LNs was: ipsilateral II (42.67%), ipsilateral Ib (17.35%), contralateral II (11.18%), ipsilateral III (8.74%), contralateral Ib (7.46%), Ia (6.43%). The other group was: ipsilateral Ib (43.14%), ipsilateral II (37.64%), contralateral Ib (4.31%), ipsilateral III (3.92%), contralateral II (3.53%), Ia (0.78%). Accordingly, the proposals for the delineation of neck clinical target volume were: 1. the inferior boundary of level Ia extends until the level of the thyroid cartilage plate; 2. the upper boundary of level Ib descends to the level of the mandibular angle, and the inner posterior boundary only includes the lateral and posterior margin of the submandibular gland within 5mm; 3. the upper boundary of level II changes to the transverse process of the second cervical vertebra, and the posterior boundary retracts to the posterior edge of the levator scapula. 4. the anterior inner border of level IVa contains only the internal jugular arteriovenous sheath. This is the first description of LN topographic spread patterns for OSCC. Optimization of CTV delineation is helpful to improve the treatment outcome and reduce adverse events.

A Multicenter Study on Deep Learning for Glioblastoma Auto-Segmentation with Prior Knowledge in Multimodal Imaging

A precise radiotherapy plan is required to ensure accurate delineation of gross tumor volumes (GTV) and clinical target volumes (CTV1 and CTV2) of glioblastomas (GBMs). However, traditional manual delineation is labor intensive and highly dependent on oncologists' experience. To construct and evaluate a deep learning-based automatic delineation method using prior knowledge in multimodal medical imaging to automate precise GTV, CTV1 and CTV2 contouring in GBM patients. We retrospectively collected the CT and MRI scans of 55 eligible patients with histologically proven high-grade glioma (HGG) from an institute, these scans were performed with non-enhanced CT (CT), contrast-enhanced T1-weighted (T1C) and T2-FLAIR (T2F) sequences. We proposed a two-stage automatic segmentation framework (PKMI-Net) for GTV, CTV1 and CTV2 based on deep learning using prior knowledge in multimodal medical imaging, and its segmentation performance was evaluated with dice similarity coefficient (DSC), 95% Harsdorff distance (HD95), average surface distance (ASD) and relative volume difference (RVD). To further investigate the generalizability of our method, we designed and conducted two evaluation strategies (Mix and Cross) on four multicenter datasets (including 55 patients, 37 patients, 21 patients and 35 patients). The evaluation results with an 11-patient test set from the single institute were summarized in Table 1, the proposed method demonstrated the best accuracy in segmenting, respectively, GTV, CTV1 and CTV, achieving a DSC of 0.94, 0.95 and 0.92; HD95 of 2.07 mm, 1.18 mm and 3.80 mm; ASD of 0.69 mm, 0.39 mm and 1.13 mm and RVE of 5.50%, 3.97% and 9.68%. In the multicenter evaluation, the segmentation performance of our method implemented with the Cross strategy was comparable to that with the Mix strategy, demonstrating that our method had high and stable generalizability across multicenter datasets in automatically segmenting GTV, CTV1 and CTV2. Our proposed method achieved promising results in automatically segmenting gliomas across various datasets, which could improve the quality and efficiency of glioblastoma radiotherapy.