Clinical Implementation and Evaluation of Auto-Segmentation Tools for Multi-Site Contouring in Radiotherapy

Heilemann, Gerd; Buschmann, Martin; Lechner, Wolfgang; Dick, Vincent; Eckert, Franziska; Heilmann, Martin; Herrmann, Harald; Moll, Matthias; Knoth, Johannes; Konrad, Stefan; Simek, Inga-Malin; Thiele, Christopher; Zaharie, Alexandru; Georg, Dietmar; Widder, Joachim; Trnkova, Petra

doi:10.1016/j.phro.2023.100515

Cited by 4 publications

(2 citation statements)

References 28 publications

(50 reference statements)

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“…The oncologist noted that PCMs are generally thin (3 mm), but slight widening was observed on number of presented examples. We explored the relationship between DSC metrics and clinical acceptability criteria, building on Heilemann et al.’s ( 44 ) suggestion of a DSC threshold above 0.7 for clinical acceptability. However, due to size-dependent characteristics, smaller ROIs may still be deemed acceptable with DSC below 0.7.…”

Section: Discussionmentioning

confidence: 99%

Clinical acceptance and dosimetric impact of automatically delineated elective target and organs at risk for head and neck MR-Linac patients

Koteva,

Eiben,

Dunlop

et al. 2024

Front. Oncol.

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BackgroundMR-Linac allows for daily online treatment adaptation to the observed geometry of tumor targets and organs at risk (OARs). Manual delineation for head and neck cancer (HNC) patients takes 45-75 minutes, making it unsuitable for online adaptive radiotherapy. This study aims to clinically and dosimetrically validate an in-house developed algorithm which automatically delineates the elective target volume and OARs for HNC patients in under a minute.MethodsAuto-contours were generated by an in-house model with 2D U-Net architecture trained and tested on 52 MRI scans via leave-one-out cross-validation. A randomized selection of 684 automated and manual contours (split half-and-half) was presented to an oncologist to perform a blind test and determine the clinical acceptability. The dosimetric impact was investigated for 13 patients evaluating the differences in dosage for all structures.ResultsAutomated contours were generated in 8 seconds per MRI scan. The blind test concluded that 114 (33%) of auto-contours required adjustments with 85 only minor and 15 (4.4%) of manual contours required adjustments with 12 only minor. Dosimetric analysis showed negligible dosimetric differences between clinically acceptable structures and structures requiring minor changes. The Dice Similarity coefficients for the auto-contours ranged from 0.66 ± 0.11 to 0.88 ± 0.06 across all structures.ConclusionMajority of auto-contours were clinically acceptable and could be used without any adjustments. Majority of structures requiring minor adjustments did not lead to significant dosimetric differences, hence manual adjustments were needed only for structures requiring major changes, which takes no longer than 10 minutes per patient.

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Section: Discussionmentioning

confidence: 99%

Clinical acceptance and dosimetric impact of automatically delineated elective target and organs at risk for head and neck MR-Linac patients

Koteva,

Eiben,

Dunlop

et al. 2024

Front. Oncol.

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“…Medical software must be evaluated by the Food and Drug Administration (FDA) before entering the US market, an untenably slow process prior to the FDA’s amended regulatory protocol in 2019 ( 24 ), and the cost of these systems may also be prohibitive for some less-resourced clinics. Additionally, commercial providers for radiation oncology have been slow to focus efforts on incorporating AI into clinical workflows and their limited access to clinical datasets makes it challenging for them to meet high quality standards ( 2 , 25 ). Consequently, academic centers are largely left to develop their own home-grown solutions.…”

Section: Introductionmentioning

confidence: 99%

Prospective deployment of an automated implementation solution for artificial intelligence translation to clinical radiation oncology

Kehayias,

Yan,

Bontempi

et al. 2024

Front. Oncol.

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IntroductionArtificial intelligence (AI)-based technologies embody countless solutions in radiation oncology, yet translation of AI-assisted software tools to actual clinical environments remains unrealized. We present the Deep Learning On-Demand Assistant (DL-ODA), a fully automated, end-to-end clinical platform that enables AI interventions for any disease site featuring an automated model-training pipeline, auto-segmentations, and QA reporting.Materials and methodsWe developed, tested, and prospectively deployed the DL-ODA system at a large university affiliated hospital center. Medical professionals activate the DL-ODA via two pathways (1): On-Demand, used for immediate AI decision support for a patient-specific treatment plan, and (2) Ambient, in which QA is provided for all daily radiotherapy (RT) plans by comparing DL segmentations with manual delineations and calculating the dosimetric impact. To demonstrate the implementation of a new anatomy segmentation, we used the model-training pipeline to generate a breast segmentation model based on a large clinical dataset. Additionally, the contour QA functionality of existing models was assessed using a retrospective cohort of 3,399 lung and 885 spine RT cases. Ambient QA was performed for various disease sites including spine RT and heart for dosimetric sparing.ResultsSuccessful training of the breast model was completed in less than a day and resulted in clinically viable whole breast contours. For the retrospective analysis, we evaluated manual-versus-AI similarity for the ten most common structures. The DL-ODA detected high similarities in heart, lung, liver, and kidney delineations but lower for esophagus, trachea, stomach, and small bowel due largely to incomplete manual contouring. The deployed Ambient QAs for heart and spine sites have prospectively processed over 2,500 cases and 230 cases over 9 months and 5 months, respectively, automatically alerting the RT personnel.DiscussionThe DL-ODA capabilities in providing universal AI interventions were demonstrated for On-Demand contour QA, DL segmentations, and automated model training, and confirmed successful integration of the system into a large academic radiotherapy department. The novelty of deploying the DL-ODA as a multi-modal, fully automated end-to-end AI clinical implementation solution marks a significant step towards a generalizable framework that leverages AI to improve the efficiency and reliability of RT systems.

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Artificial intelligence for treatment delivery: image-guided radiotherapy

Rabe,

Kurz,

Thummerer

et al. 2024

Strahlenther Onkol

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Clinical Implementation and Evaluation of Auto-Segmentation Tools for Multi-Site Contouring in Radiotherapy

Cited by 4 publications

References 28 publications

Clinical acceptance and dosimetric impact of automatically delineated elective target and organs at risk for head and neck MR-Linac patients

Clinical acceptance and dosimetric impact of automatically delineated elective target and organs at risk for head and neck MR-Linac patients

Prospective deployment of an automated implementation solution for artificial intelligence translation to clinical radiation oncology

Artificial intelligence for treatment delivery: image-guided radiotherapy

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