Potential of E-Learning Interventions and Artificial Intelligence-Assisted Contouring Skills in Radiotherapy: The ELAISA Study.

IF 3.2 Q2 ONCOLOGY JCO Global Oncology Pub Date : 2024-08-01 DOI:10.1200/GO.24.00173
Mathis Ersted Rasmussen, Kamal Akbarov, Egor Titovich, Jasper Albertus Nijkamp, Wouter Van Elmpt, Hanne Primdahl, Pernille Lassen, Jon Cacicedo, Lisbeth Cordero-Mendez, A F M Kamal Uddin, Ahmed Mohamed, Ben Prajogi, Kartika Erida Brohet, Catherine Nyongesa, Darejan Lomidze, Gisupnikha Prasiko, Gustavo Ferraris, Humera Mahmood, Igor Stojkovski, Isa Isayev, Issa Mohamad, Leivon Shirley, Lotfi Kochbati, Ludmila Eftodiev, Maksim Piatkevich, Maria Matilde Bonilla Jara, Orges Spahiu, Rakhat Aralbayev, Raushan Zakirova, Sandya Subramaniam, Solomon Kibudde, Uranchimeg Tsegmed, Stine Sofia Korreman, Jesper Grau Eriksen
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引用次数: 0

Abstract

Purpose: Most research on artificial intelligence-based auto-contouring as template (AI-assisted contouring) for organs-at-risk (OARs) stem from high-income countries. The effect and safety are, however, likely to depend on local factors. This study aimed to investigate the effects of AI-assisted contouring and teaching on contouring time and contour quality among radiation oncologists (ROs) working in low- and middle-income countries (LMICs).

Materials and methods: Ninety-seven ROs were randomly assigned to either manual or AI-assisted contouring of eight OARs for two head-and-neck cancer cases with an in-between teaching session on contouring guidelines. Thereby, the effect of teaching (yes/no) and AI-assisted contouring (yes/no) was quantified. Second, ROs completed short-term and long-term follow-up cases all using AI assistance. Contour quality was quantified with Dice Similarity Coefficient (DSC) between ROs' contours and expert consensus contours. Groups were compared using absolute differences in medians with 95% CIs.

Results: AI-assisted contouring without previous teaching increased absolute DSC for optic nerve (by 0.05 [0.01; 0.10]), oral cavity (0.10 [0.06; 0.13]), parotid (0.07 [0.05; 0.12]), spinal cord (0.04 [0.01; 0.06]), and mandible (0.02 [0.01; 0.03]). Contouring time decreased for brain stem (-1.41 [-2.44; -0.25]), mandible (-6.60 [-8.09; -3.35]), optic nerve (-0.19 [-0.47; -0.02]), parotid (-1.80 [-2.66; -0.32]), and thyroid (-1.03 [-2.18; -0.05]). Without AI-assisted contouring, teaching increased DSC for oral cavity (0.05 [0.01; 0.09]) and thyroid (0.04 [0.02; 0.07]), and contouring time increased for mandible (2.36 [-0.51; 5.14]), oral cavity (1.42 [-0.08; 4.14]), and thyroid (1.60 [-0.04; 2.22]).

Conclusion: The study suggested that AI-assisted contouring is safe and beneficial to ROs working in LMICs. Prospective clinical trials on AI-assisted contouring should, however, be conducted upon clinical implementation to confirm the effects.

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放疗中电子学习干预和人工智能辅助轮廓塑造技能的潜力:ELAISA 研究。
目的:基于人工智能的以自动轮廓为模板(人工智能辅助轮廓)治疗高危器官(OAR)的研究大多来自高收入国家。然而,其效果和安全性很可能取决于当地因素。本研究旨在调查在中低收入国家(LMICs)工作的放射肿瘤学家(ROs)中,人工智能辅助轮廓绘制和教学对轮廓绘制时间和轮廓质量的影响:97名放射肿瘤科医生被随机分配到两个头颈部癌症病例的8个OAR的手动或人工智能辅助轮廓制作中,并在中间安排了轮廓制作指南的教学课程。因此,对教学(是/否)和人工智能辅助轮廓(是/否)的效果进行了量化。其次,研究人员在人工智能辅助下完成了短期和长期随访病例。轮廓质量通过轮廓师的轮廓与专家共识轮廓之间的骰子相似系数(DSC)进行量化。使用中位数的绝对差异和 95% CIs 对各组进行比较:结果:人工智能辅助轮廓分析增加了视神经(0.05 [0.01; 0.10])、口腔(0.10 [0.06; 0.13])、腮腺(0.07 [0.05; 0.12])、脊髓(0.04 [0.01; 0.06])和下颌骨(0.02 [0.01; 0.03])的绝对 DSC。脑干(-1.41 [-2.44; -0.25])、下颌骨(-6.60 [-8.09; -3.35])、视神经(-0.19 [-0.47; -0.02])、腮腺(-1.80 [-2.66; -0.32])和甲状腺(-1.03 [-2.18; -0.05])的轮廓绘制时间缩短。在没有人工智能辅助轮廓塑形的情况下,教学增加了口腔(0.05 [0.01; 0.09])和甲状腺(0.04 [0.02; 0.07])的DSC,增加了下颌骨(2.36 [-0.51; 5.14])、口腔(1.42 [-0.08; 4.14])和甲状腺(1.60 [-0.04; 2.22])的轮廓塑形时间:该研究表明,人工智能辅助轮廓整形是安全的,对在低收入和中等收入国家工作的区域主任有益。然而,在临床实施人工智能辅助轮廓扫描后,应进行前瞻性临床试验,以确认其效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
JCO Global Oncology
JCO Global Oncology Medicine-Oncology
CiteScore
6.70
自引率
6.70%
发文量
310
审稿时长
7 weeks
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