Contouring cardiac substructures on average intensity projection 4D-CT for lung cancer radiotherapy: A proposal of a heart valve contouring atlas

Socha, Joanna; Rygielska, A.; Uziębło‐Życzkowska, Beata; Chałubińska-Fendler, Justyna; Jurek, Agnieszka; Maciorowska, Małgorzata; Mielniczuk, Marta; Pawłowski, Paweł; Tyc-Szczepaniak, Dobromira; Krzesiński, Paweł; Kępka, Lucyna

doi:10.1016/j.radonc.2021.12.041

Cited by 11 publications

(9 citation statements)

References 19 publications

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“…Recent dosimetry criteria incite the delineation of more OARs, such as cardiac substructure [ 24 , 25 , 26 ]. Regular implementation of these “new” OARs might be important in the next years and taken into account for AI segmentation tools.…”

Section: Discussionmentioning

confidence: 99%

Excitement and Concerns of Young Radiation Oncologists over Automatic Segmentation: A French Perspective

Bourbonne

Laville

Wagneur

et al. 2023

Cancers

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Introduction: Segmentation of organs at risk (OARs) and target volumes need time and precision but are highly repetitive tasks. Radiation oncology has known tremendous technological advances in recent years, the latest being brought by artificial intelligence (AI). Despite the advantages brought by AI for segmentation, some concerns were raised by academics regarding the impact on young radiation oncologists’ training. A survey was thus conducted on young french radiation oncologists (ROs) by the SFjRO (Société Française des jeunes Radiothérapeutes Oncologues). Methodology: The SFjRO organizes regular webinars focusing on anatomical localization, discussing either segmentation or dosimetry. Completion of the survey was mandatory for registration to a dosimetry webinar dedicated to head and neck (H & N) cancers. The survey was generated in accordance with the CHERRIES guidelines. Quantitative data (e.g., time savings and correction needs) were not measured but determined among the propositions. Results: 117 young ROs from 35 different and mostly academic centers participated. Most centers were either already equipped with such solutions or planning to be equipped in the next two years. AI segmentation software was mostly useful for H & N cases. While for the definition of OARs, participants experienced a significant time gain using AI-proposed delineations, with almost 35% of the participants saving between 50–100% of the segmentation time, time gained for target volumes was significantly lower, with only 8.6% experiencing a 50–100% gain. Contours still needed to be thoroughly checked, especially target volumes for some, and edited. The majority of participants suggested that these tools should be integrated into the training so that future radiation oncologists do not neglect the importance of radioanatomy. Fully aware of this risk, up to one-third of them even suggested that AI tools should be reserved for senior physicians only. Conclusions: We believe this survey on automatic segmentation to be the first to focus on the perception of young radiation oncologists. Software developers should focus on enhancing the quality of proposed segmentations, while young radiation oncologists should become more acquainted with these tools.

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Section: Discussionmentioning

confidence: 99%

Excitement and Concerns of Young Radiation Oncologists over Automatic Segmentation: A French Perspective

Bourbonne

Laville

Wagneur

et al. 2023

Cancers

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“…However, retrospective contouring of structures or sub-structures of interest is not realistic. The manual contouring of heart sub-structures on routine radiotherapy planning CTs is a particularly challenging task as respiratory motion, cardiac motion, as well as the varying extent of co-morbidities can impair visualization and result in large inter-observer variation particularly of small structures, such as the valves or coronary arteries ( 78 ). Even when sub-structures have been prospectively contoured, important variations can be seen due to the different guidelines available, or different interpretation of existing guidelines ( 79 ).…”

Section: Discussionmentioning

confidence: 99%

Role of Real-World Data in Assessing Cardiac Toxicity After Lung Cancer Radiotherapy

et al. 2022

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Radiation-induced heart disease (RIHD) is a recent concern in patients with lung cancer after being treated with radiotherapy. Most of information we have in the field of cardiac toxicity comes from studies utilizing real-world data (RWD) as randomized controlled trials (RCTs) are generally not practical in this field. This article is a narrative review of the literature using RWD to study RIHD in patients with lung cancer following radiotherapy, summarizing heart dosimetric factors associated with outcome, strength, and limitations of the RWD studies, and how RWD can be used to assess a change to cardiac dose constraints.

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“…where these are located relative to observable cardiac structures). This precludes consistent manual contouring of these structures, as is evident in multi-observer contouring studies [ 21 , 24 , 33 ] and is a primary obstacle in the development of automatic segmentation algorithms based on patient imaging. Instead, in the current work, geometric modelling was used to automatically segment the heart valves, providing anatomic consistency and uniform definitions for each patient, and a method that is independent of limitations in imaging information as it relies solely on segmentation of larger cardiac structures.…”

Section: Methodsmentioning

confidence: 99%

“…The AV and PV were defined using a dilation of the respective ventricle, masked by the corresponding great vessel. The thicknesses of the AV and PV were both set to 8 mm following recently published guidelines [ 33 ]. The MV and TV were modelled as a cylinder, which was adjusted to sit at the junction of the respective atrium and ventricle, and rotated to align with the vector directed from the centroids of these chambers (Fig.…”

Section: Methodsmentioning

confidence: 99%

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Open-source, fully-automated hybrid cardiac substructure segmentation: development and optimisation

et al. 2023

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Radiotherapy for thoracic and breast tumours is associated with a range of cardiotoxicities. Emerging evidence suggests cardiac substructure doses may be more predictive of specific outcomes, however, quantitative data necessary to develop clinical planning constraints is lacking. Retrospective analysis of patient data is required, which relies on accurate segmentation of cardiac substructures. In this study, a novel model was designed to deliver reliable, accurate, and anatomically consistent segmentation of 18 cardiac substructures on computed tomography (CT) scans. Thirty manually contoured CT scans were included. The proposed multi-stage method leverages deep learning (DL), multi-atlas mapping, and geometric modelling to automatically segment the whole heart, cardiac chambers, great vessels, heart valves, coronary arteries, and conduction nodes. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), mean distance to agreement (MDA), Hausdorff distance (HD), and volume ratio. Performance was reliable, with no errors observed and acceptable variation in accuracy between cases, including in challenging cases with imaging artefacts and atypical patient anatomy. The median DSC range was 0.81–0.93 for whole heart and cardiac chambers, 0.43–0.76 for great vessels and conduction nodes, and 0.22–0.53 for heart valves. For all structures the median MDA was below 6 mm, median HD ranged 7.7–19.7 mm, and median volume ratio was close to one (0.95–1.49) for all structures except the left main coronary artery (2.07). The fully automatic algorithm takes between 9 and 23 min per case. The proposed fully-automatic method accurately delineates cardiac substructures on radiotherapy planning CT scans. Robust and anatomically consistent segmentations, particularly for smaller structures, represents a major advantage of the proposed segmentation approach. The open-source software will facilitate more precise evaluation of cardiac doses and risks from available clinical datasets. Graphical abstract

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Contouring cardiac substructures on average intensity projection 4D-CT for lung cancer radiotherapy: A proposal of a heart valve contouring atlas

Cited by 11 publications

References 19 publications

Excitement and Concerns of Young Radiation Oncologists over Automatic Segmentation: A French Perspective

Excitement and Concerns of Young Radiation Oncologists over Automatic Segmentation: A French Perspective

Role of Real-World Data in Assessing Cardiac Toxicity After Lung Cancer Radiotherapy

Open-source, fully-automated hybrid cardiac substructure segmentation: development and optimisation

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