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

Frontiers in Oncology, Journal Year: 2024, Volume and Issue: 14

Published: Oct. 22, 2024

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

Language: Английский

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Clinical adoption of deep learning target auto-segmentation for radiation therapy: challenges, clinical risks, and mitigation strategies

Deleted Journal, Journal Year: 2024, Volume and Issue: 1(1)

Published: Jan. 1, 2024

Abstract Radiation therapy is a localized cancer treatment that relies on precise delineation of the target to be treated and healthy tissues guarantee optimal effect. This step, known as contouring or segmentation, involves identifying both volumes organs at risk imaging modalities like CT, PET, MRI guide radiation delivery. Manual however, time-consuming highly subjective, despite presence guidelines. In recent years, automated segmentation methods, particularly deep learning models, have shown promise in addressing this task. However, challenges persist their clinical use, including need for robust quality assurance (QA) processes risks associated with use models. review examines considerations adoption auto-segmentation radiotherapy, focused volume. We discuss potential (eg, over- under-segmentation, automation bias, appropriate trust), mitigation strategies human oversight, uncertainty quantification, education professionals), we highlight importance expanding QA include geometric, dose-volume, outcome-based performance monitoring. While offers significant benefits, careful attention rigorous measures are essential its successful integration practice.

Language: Английский

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Artificial Intelligence Uncertainty Quantification in Radiotherapy Applications - A Scoping Review

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: May 13, 2024

Abstract Background/purpose The use of artificial intelligence (AI) in radiotherapy (RT) is expanding rapidly. However, there exists a notable lack clinician trust AI models, underscoring the need for effective uncertainty quantification (UQ) methods. purpose this study was to scope existing literature related UQ RT, identify areas improvement, and determine future directions. Methods We followed PRISMA-ScR scoping review reporting guidelines. utilized population (human cancer patients), concept (utilization UQ), context (radiotherapy applications) framework structure our search screening process. conducted systematic spanning seven databases, supplemented by manual curation, up January 2024. Our yielded total 8980 articles initial review. Manuscript data extraction performed Covidence. Data categories included general characteristics, RT characteristics. Results identified 56 published from 2015-2024. 10 domains applications were represented; most studies evaluated auto-contouring (50%), image-synthesis (13%), multiple simultaneously (11%). 12 disease sites represented, with head neck being common site independent application space (32%). Imaging used 91% studies, while only 13% incorporated dose information. Most focused on failure detection as main (60%), Monte Carlo dropout commonly implemented method (32%) ensembling (16%). 55% did not share code or datasets. Conclusion revealed diversity beyond auto-contouring. Moreover, clear additional methods, such conformal prediction. results may incentivize development guidelines implementation RT.

Language: Английский

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Artificial intelligence for treatment delivery: image-guided radiotherapy

Strahlentherapie und Onkologie, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 13, 2024

Language: Английский

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Comprehensive Clinical Usability-Oriented Contour Quality Evaluation for Deep Learning Auto-segmentation: Combining Multiple Quantitative Metrics Through Machine Learning

Practical Radiation Oncology, Journal Year: 2024, Volume and Issue: 15(1), P. 93 - 102

Published: Sept. 2, 2024

Language: Английский

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Advancing the Collaboration Between Imaging and Radiation Oncology

Seminars in Radiation Oncology, Journal Year: 2024, Volume and Issue: 34(4), P. 402 - 417

Published: Sept. 11, 2024

Language: Английский

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Artificial intelligence and radiotherapy: Evolution or revolution?

Cancer/Radiothérapie, Journal Year: 2024, Volume and Issue: 28(6-7), P. 503 - 509

Published: Oct. 15, 2024

Language: Английский

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Performance of Commercial Deep Learning-Based Auto-Segmentation Software for Breast Cancer Radiation Therapy Planning: A Systematic Review

Multimodal Technologies and Interaction, Journal Year: 2024, Volume and Issue: 8(12), P. 114 - 114

Published: Dec. 20, 2024

As yet, no systematic review on commercial deep learning-based auto-segmentation (DLAS) software for breast cancer radiation therapy (RT) planning has been published, although NRG Oncology highlighted the necessity such. The purpose of this is to investigate performances DLAS packages RT and methods their performance evaluation. A literature search was conducted with use electronic databases. Fifteen papers met selection criteria were included. included studies evaluated eight (Limbus Contour, Manteia AccuLearning, Mirada DLCExpert, MVision.ai Contour+, Radformation AutoContour, RaySearch RayStation, Siemens syngo.via Image Suite/AI-Rad Companion Organs RT, Therapanacea Annotate). Their findings show that could contour ten organs at risk (body, contralateral breast, esophagus-overlapping area, heart, ipsilateral humeral head, left right lungs, liver, sternum trachea) three clinical target volumes (CTVp_breast, CTVp_chestwall, CTVn_L1) up clinically acceptable standard. This can contribute 45.4%–93.7% contouring time reduction per patient. Although NRO suggested every center should conduct its own evaluation before implementation, such testing appears particularly crucial Contour+ as a result methodological weaknesses corresponding studies, small datasets collected retrospectively from single centers

Language: Английский

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An audit of the impact of the introduction of a commercial artificial intelligence driven auto-contouring tool into a radiotherapy department

British Journal of Radiology, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 20, 2024

To audit prospectively the accuracy, time saving and utility of a commercial artificial intelligence auto-contouring tool (AIAC). assess reallocation released by AIAC.

Language: Английский

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