Feasibility evaluation of novel AI‐based deep‐learning contouring algorithm for radiotherapy

Maduro Bustos, Luis A.; Sarkar, Abhirup; Doyle, Laura A.; Andreou, Kelly; Noonan, Jodie; Nurbagandova, Diana; Shah, SunJay A.; Irabor, Omoruyi Credit; Mourtada, Firas

doi:10.1002/acm2.14090

J Applied Clin Med Phys

2023

DOI: 10.1002/acm2.14090

|View full text |Cite

Feasibility evaluation of novel AI‐based deep‐learning contouring algorithm for radiotherapy

Luis A. Maduro Bustos,

Abhirup Sarkar,

Laura A. Doyle

et al.

Abstract: PurposeTo evaluate the clinical feasibility of the Siemens Healthineers AI‐Rad Companion Organs RT VA30A (Organs‐RT) auto‐contouring algorithm for organs at risk (OARs) of the pelvis, thorax, and head and neck (H&N).MethodsComputed tomography (CT) datasets from 30 patients (10 pelvis, 10 thorax, and 10 H&N) were collected. Four sets of OARs were generated on each scan, one set by Organs‐RT and the others by three experienced users independently. A physician (expert) then evaluated each contour by assig… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

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

Sjogreen,

Netherton,

Lee

et al. 2024

J Applied Clin Med Phys

View full text Add to dashboard Cite

BackgroundThe delineation of clinical target volumes (CTVs) for radiotherapy for nasopharyngeal cancer is complex and varies based on the location and extent of disease.PurposeThe 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.MethodsThe 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.ResultsThe 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 ± 0.4 mm for CTV‐Expansion and a DSC of 0.83 ± 0.02 and MSD of 2.4 ± 0.5 mm 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.ConclusionsThe 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.

show abstract

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

Sjogreen,

Netherton,

Lee

et al. 2024

J Applied Clin Med Phys

View full text Add to dashboard Cite

show abstract

Development of deep learning-based novel auto-segmentation for the prostatic urethra on planning CT images for prostate cancer radiotherapy

Takagi,

Takeda,

Kadoya

et al. 2024

Radiol Phys Technol

View full text Add to dashboard Cite

Urinary toxicities are one of the serious complications of radiotherapy for prostate cancer, and dose-volume histogram of prostatic urethra has been associated with such toxicities in previous reports. Previous research has focused on estimating the prostatic urethra, which is difficult to delineate in CT images; however, these studies, which are limited in number, mainly focused on cases undergoing brachytherapy uses low-dose-rate sources and do not involve external beam radiation therapy (EBRT). In this study, we aimed to develop a deep learning-based method of determining the position of the prostatic urethra in patients eligible for EBRT. We used contour data from 430 patients with localized prostate cancer. In all cases, a urethral catheter was placed when planning CT to identify the prostatic urethra. We used 2D and 3D U-Net segmentation models. The input images included the bladder and prostate, while the output images focused on the prostatic urethra. The 2D model determined the prostate’s position based on results from both coronal and sagittal directions. Evaluation metrics included the average distance between centerlines. The average centerline distances for the 2D and 3D models were 2.07 ± 0.87 mm and 2.05 ± 0.92 mm, respectively. Increasing the number of cases while maintaining equivalent accuracy as we did in this study suggests the potential for high generalization performance and the feasibility of using deep learning technology for estimating the position of the prostatic urethra.

show abstract

Evaluation of multiple-vendor AI autocontouring solutions

Goddard,

Velten,

Tang

et al. 2024

Radiat Oncol

View full text Add to dashboard Cite

Background Multiple artificial intelligence (AI)-based autocontouring solutions have become available, each promising high accuracy and time savings compared with manual contouring. Before implementing AI-driven autocontouring into clinical practice, three commercially available CT-based solutions were evaluated. Materials and methods The following solutions were evaluated in this work: MIM-ProtégéAI+ (MIM), Radformation-AutoContour (RAD), and Siemens-DirectORGANS (SIE). Sixteen organs were identified that could be contoured by all solutions. For each organ, ten patients that had manually generated contours approved by the treating physician (AP) were identified, totaling forty-seven different patients. CT scans in the supine position were acquired using a Siemens-SOMATOMgo 64-slice helical scanner and used to generate autocontours. Physician scoring of contour accuracy was performed by at least three physicians using a five-point Likert scale. Dice similarity coefficient (DSC), Hausdorff distance (HD) and mean distance to agreement (MDA) were calculated comparing AI contours to “ground truth” AP contours. Results The average physician score ranged from 1.00, indicating that all physicians reviewed the contour as clinically acceptable with no modifications necessary, to 3.70, indicating changes are required and that the time taken to modify the structures would likely take as long or longer than manually generating the contour. When averaged across all sixteen structures, the AP contours had a physician score of 2.02, MIM 2.07, RAD 1.96 and SIE 1.99. DSC ranged from 0.37 to 0.98, with 41/48 (85.4%) contours having an average DSC ≥ 0.7. Average HD ranged from 2.9 to 43.3 mm. Average MDA ranged from 0.6 to 26.1 mm. Conclusions The results of our comparison demonstrate that each vendor’s AI contouring solution exhibited capabilities similar to those of manual contouring. There were a small number of cases where unusual anatomy led to poor scores with one or more of the solutions. The consistency and comparable performance of all three vendors’ solutions suggest that radiation oncology centers can confidently choose any of the evaluated solutions based on individual preferences, resource availability, and compatibility with their existing clinical workflows. Although AI-based contouring may result in high-quality contours for the majority of patients, a minority of patients require manual contouring and more in-depth physician review.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Feasibility evaluation of novel AI‐based deep‐learning contouring algorithm for radiotherapy

Cited by 3 publications

References 29 publications

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

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

Development of deep learning-based novel auto-segmentation for the prostatic urethra on planning CT images for prostate cancer radiotherapy

Evaluation of multiple-vendor AI autocontouring solutions

Contact Info

Product

Resources

About