Search

Accurate delineation of the clinical target volume (CTV) is a crucial prerequisite for safe and effective radiotherapy characterized. This study addresses the integration of magnetic resonance (MR) images to aid in target delineation on computed tomography (CT) images. However, obtaining MR images directly can be challenging. Therefore, we employ AI-based image generation techniques to "intelligentially generate" MR images from CT images to improve CTV delineation based on CT images. To generate high-quality MR images, we propose an attention-guided single-loop image generation model. The model can yield higher-quality images by introducing an attention mechanism in feature extraction and enhancing the loss function. Based on the generated MR images, we propose a CTV segmentation model fusing multi-scale features through image fusion and a hollow space pyramid module to enhance segmentation accuracy. The image generation model used in this study improves the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) from 14.87 and 0.58 to 16.72 and 0.67, respectively, and improves the feature distribution distance and learning-perception image similarity from 180.86 and 0.28 to 110.98 and 0.22, achieving higher quality image generation. The proposed segmentation method demonstrates high accuracy, compared with the FCN method, the intersection over union ratio and the Dice coefficient are improved from 0.8360 and 0.8998 to 0.9043 and 0.9473, respectively. Hausdorff distance and mean surface distance decreased from 5.5573 mm and 2.3269 mm to 4.7204 mm and 0.9397 mm, respectively, achieving clinically acceptable segmentation accuracy. Our method might reduce physicians' manual workload and accelerate the diagnosis and treatment process while decreasing inter-observer variability in identifying anatomical structures. [ABSTRACT FROM AUTHOR]

Simple Summary: In radiation therapy, accurately contouring the volume containing cancerous cells is crucial for effective tumor control. Experts defined this volume by its borders with respect to anatomical structures. This study assesses the feasibility and precision of a deep-learning method in extracting 71 necessary anatomical structures from provided computed tomography scans. For most of these structures, automatically generated outlines are presented for the first time. For other structures, our model improves upon previously reported results. We analyzed the constraints posed by errors in the automatically generated outlines and found none that are relevant to our goal of identifying the entire volume containing cancerous cells. Our research contributes additional and enhanced outlines of anatomical structures, advancing the scientific objective of facilitating the contouring of a human's complete anatomy. Moreover, confirming the effectiveness of automatic contouring techniques signifies a step closer to achieving precise automated contouring of the cancerous volume. 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. [ABSTRACT FROM AUTHOR]

Abstract Background Prophylactic irradiation of supraclavicular lymph node drainage areas can improve the regional control rate of lymph node-positive or lymph node-negative disease but a locally-advanced stage breast cancer, and it can reduce breast cancer-related mortality. However, many controversies exist in the clinical target volume delineation of supraclavicular lymph node drainage in patients with breast cancer. Methods We retrospectively analyzed 42 patients with breast cancer and supraclavicular lymph node metastasis at our hospital between January 2017 and December 2021. Among these cases, 32 were locally advanced and 10 were stage IV at initial treatment. A patient with breast cancer who did not undergo dissection of the supraclavicular and infraclavicular lymph nodes at our hospital was selected as a standard patient. A contrast-enhanced computed tomography (CT) scan for positioning was used as a template image, and blood vessels, muscles, and bony landmarks were used as references for positioning. The metastatic supraclavicular lymph nodes were identified in all enrolled patients and projected into the template CT images. Results The metastastic pattern of supraclavicular lymph node in breast cancer was proposed: distribution along the posterolateral border of the internal jugular vein (medial supraclavicular group) and along the transverse jugular vein (lateral supraclavicular group). We theorized that the lateral and posterior borders of the clinical target volume in the supraclavicular region should include the lymph nodes in the posterior triangle of the neck (level V) in high-risk individuals. If the metastatic axillary lymph node is extensive, then the superior border of the supraclavicular region should be moved upward appropriately. Conclusions This study analyzed patients with breast cancer and supraclavicular lymph node metastasis at initial treatment, explored the metastastic pattern of supraclavicular lymph node, and applied anatomical knowledge to further optimize the target volume delineation of supraclavicular lymph node drainage area in high-risk breast cancer.

Abstract Background To study lymphatic recurrence distribution after radical surgery in the real world and guide clinical tumor volume delineation for regional lymph nodes during postoperative radiotherapy for lower thoracic squamous cell esophageal carcinomas. Methods We enrolled patients who underwent radical esophagectomy, without radiation before or after surgery, at 3 cancer hospitals. Patients were classified into groups according to tumor locations. We included patients with tumors in the lower thoracic segment and analyzed the postoperative lymph node recurrence mode. A cutoff value of 10% was used to differentiate high-risk lymph node drainage areas from others. Results We enrolled 1905 patients in the whole study series, including 652 thoracic esophageal carcinomas that met our inclusion criteria; there were 241 cases of lower thoracic esophageal carcinomas. 1st, 2nd, 4th, 7th, 8th groups of lymph nodes, according to the 8th edition of the AJCC classification, displayed as high-risk recurrence areas, representing 17.8%, 23.9%, 11.7%, 10.9% and 12.2% of lymph node recurrence. Stage III-IV tumors located in the lower segment of the thoracic esophagus showed a tendency to recur in the left gastric nodes (7.9%) and celiac nodes (10.6%). Conclusions According to our results, we recommended including the 4th, 7th and 8th groups of lymph nodes in the radiation field, and for patients with stage III-IV disease, the 17th and 20th groups of nodes should be irradiated during postoperative treatment. Whether including 1st/2nd groups in preventive irradiation needed more proofs.

Background: To study lymphatic recurrence distribution after radical surgery in the real world and guide clinical tumor volume delineation for regional lymph nodes during postoperative radiotherapy for lower thoracic squamous cell esophageal carcinomas.Methods: We enrolled patients who underwent radical esophagectomy, without radiation before or after surgery, at 3 cancer hospitals. Patients were classified into groups according to tumor locations. We included patients with tumors in the lower thoracic segment and analyzed the postoperative lymph node recurrence mode. A cutoff value of 10% was used to differentiate high-risk lymph node drainage areas from others.Results: We enrolled 1905 patients in the whole study series, including 652 thoracic esophageal carcinomas that met our inclusion criteria; there were 241 cases of lower thoracic esophageal carcinomas. 1st, 2nd, 4th, 7th, 8th groups of lymph nodes, according to the 8th edition of the AJCC classification, displayed as high-risk recurrence areas, representing 17.8%, 23.9%, 11.7%, 10.9% and 12.2% of lymph node recurrence. Stage III-IV tumors located in the lower segment of the thoracic esophagus showed a tendency to recur in the left gastric nodes (7.9%) and celiac nodes (10.6%).Conclusions: According to our results, we recommended including the 4th, 7th and 8th groups of lymph nodes in the radiation field, and for patients with stage III-IV disease, the 17th and 20th groups of nodes should be irradiated during postoperative treatment. Whether including 1st/2nd groups in preventive irradiation needed more proofs. [ABSTRACT FROM AUTHOR]

Accurate delineation of the clinical target volume (CTV) is a crucial prerequisite for safe and effective radiotherapy characterized. This study addresses the integration of magnetic resonance (MR) images to aid in target delineation on computed tomography (CT) images. However, obtaining MR images directly can be challenging. Therefore, we employ AI-based image generation techniques to "intelligentially generate" MR images from CT images to improve CTV delineation based on CT images. To generate high-quality MR images, we propose an attention-guided single-loop image generation model. The model can yield higher-quality images by introducing an attention mechanism in feature extraction and enhancing the loss function. Based on the generated MR images, we propose a CTV segmentation model fusing multi-scale features through image fusion and a hollow space pyramid module to enhance segmentation accuracy. The image generation model used in this study improves the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) from 14.87 and 0.58 to 16.72 and 0.67, respectively, and improves the feature distribution distance and learning-perception image similarity from 180.86 and 0.28 to 110.98 and 0.22, achieving higher quality image generation. The proposed segmentation method demonstrates high accuracy, compared with the FCN method, the intersection over union ratio and the Dice coefficient are improved from 0.8360 and 0.8998 to 0.9043 and 0.9473, respectively. Hausdorff distance and mean surface distance decreased from 5.5573 mm and 2.3269 mm to 4.7204 mm and 0.9397 mm, respectively, achieving clinically acceptable segmentation accuracy. Our method might reduce physicians' manual workload and accelerate the diagnosis and treatment process while decreasing inter-observer variability in identifying anatomical structures., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Introduction The delineation of Clinical Target Volume (CTV) is a critical step in radiotherapy. Several guidelines suggest different subvolumes and anatomical boundaries in rectal cancer (RC), potentially leading to a misunderstanding in the CTV definition. International consensus guidelines (CG) are needed to improve uniformity in RC CTV delineation. Material and methods The 7 radiation oncologist experts defined a roadmap to produce RC CG. Step 1: revision of the published guidelines. Step 2: selection of RC cases with different clinical stages. Step 3: delineation of cases using Falcon following previously published guidelines. Step 4: meeting in person to discuss the initial delineation outcome, followed by a CTV proposal based on revised and if needed, adapted anatomical boundaries. Step 5: peer review of the agreed consensus. Step 6: peer review meeting to validate the final outcome. Step 7: completion of RC delineation atlases. Results A new ontology of structure sets was defined and the related table of anatomical boundaries was generated. The major modifications were about the lateral lymph nodes and the ischio-rectal fossa delineation. Seven RC cases were made available online as consultation atlases. Conclusion The definition of international CG for RC delineation endorsed by international experts might support a future homogeneous comparison between clinical trial outcomes. [ABSTRACT FROM AUTHOR]

Copyright of Strahlentherapie und Onkologie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: To define by MRI the local extension patterns in patients presenting with nasopharyngeal carcinoma (NPC) and to improve clinical target volume delineation. Methods and Materials: Consecutive patients (N = 943) with newly diagnosed and untreated NPC were included in this study. All patients underwent MRI of the nasopharynx and neck, which was reviewed by two radiologists. Results: According to the incidence rates of tumor invasion, the anatomic sites surrounding the nasopharynx were initially classified into three risk grades: high risk (≥ 35%), medium risk (≥ 5–35%), and low risk (< 5%). Incidence rates of tumor invasion into anatomic sites at medium risk were increased, reaching 55.2%, when adjacent high-risk anatomic sites were involved. However, the rates were substantially lower, mostly < 10%, when adjacent high-risk sites were not involved. The incidence rates of concurrent tumor invasion into bilateral sites were < 10%, except in the case of prevertebral muscle involvement (13.1%). Among the 178 incidences of cavernous sinus invasion, there were often two or more simultaneous infiltration routes (60.6%); when only one route was involved, the foramen ovale was the most common (26.4%). Conclusions: In patients presenting with NPC, local disease spreads stepwise from proximal sites to more distal sites. Tumors extend quickly through privileged pathways such as neural foramina. The anatomic sites surrounding the nasopharynx are at low risk of concurrent bilateral tumor invasion. Selective radiotherapy of the local disease in NPC may be feasible. [Copyright &y& Elsevier]

Aim: Breast radiotherapy accounts for a significant workload in radiotherapy departments. In 2015 it became clinical practice at the Netherlands Cancer Institute for radiation therapists (RTTs) to delineate the clinical target volume of the breast tissue (CTVbreast) and in 2017 axilla level I-II (CTVln12) according to a delineation atlas. All RTTs were trained and got individual feedback. The aim of this retrospective study was to investigate the variation between the CTVbreast with or without CTVln12 delineated by a trained group of radiation therapists and the clinical adjusted delineations by the radiation oncologist/physician assistant (RO/PA), in a large group of patients treated between January 2017 and June 2020., Materials and Methods: 1012 computer tomography based delineations of CTVbreast and 146 of CTVln12 were collected from January 2017-June 2020. The RTT and RO/PA delineations were compared using the Dice coefficient and the 95th percentile Hausdorff Distance (95%HD). Statistical significance of the differences was tested using a Mann-Whitney test., Results: Differences in CTVbreast delineations were small. A median Dice score of 1.00 for all years, where 83% of the patients had a Dice score > 0.99. For CTVln12 the magnitude of edits made by RO/PAs decreased over time, with the Dice increasing from a median of 0.87 in 2017 to 0.90 in 2020 (p = 0.031). The 95%HD decreased from a median of 0.93 cm in 2017 to 0.61 cm in 2020 (p = 0.051)., Conclusions: This retrospective study shows that trained dedicated RTTs are capable in delivering the same quality delineations as RO/PAs. The low variability supports the increasing role of RTTs in the contouring process, likely making it more time efficient., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Author(s).)