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
{"title":"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。","authors":"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","doi":"10.1200/GO.24.00173","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>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).</p><p><strong>Materials and methods: </strong>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.</p><p><strong>Results: </strong>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]).</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":14806,"journal":{"name":"JCO Global Oncology","volume":"10 ","pages":"e2400173"},"PeriodicalIF":3.2000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11404336/pdf/","citationCount":"0","resultStr":"{\"title\":\"Potential of E-Learning Interventions and Artificial Intelligence-Assisted Contouring Skills in Radiotherapy: The ELAISA Study.\",\"authors\":\"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\",\"doi\":\"10.1200/GO.24.00173\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>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).</p><p><strong>Materials and methods: </strong>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.</p><p><strong>Results: </strong>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]).</p><p><strong>Conclusion: </strong>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.</p>\",\"PeriodicalId\":14806,\"journal\":{\"name\":\"JCO Global Oncology\",\"volume\":\"10 \",\"pages\":\"e2400173\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2024-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11404336/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JCO Global Oncology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1200/GO.24.00173\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JCO Global Oncology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1200/GO.24.00173","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
Potential of E-Learning Interventions and Artificial Intelligence-Assisted Contouring Skills in Radiotherapy: The ELAISA Study.
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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