Potential of E-Learning Interventions and Artificial Intelligence–Assisted Contouring Skills in Radiotherapy: The ELAISA Study-Reference-Cited by-同舟云学术

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

Published:2024-08 Issue:10 Volume: Page:
ISSN:2687-8941
Container-title:JCO Global Oncology
language:en
Short-container-title:JCO Glob Oncol

Author:

Rasmussen Mathis Ersted¹^ORCID,Akbarov Kamal²^ORCID,Titovich Egor²^ORCID,Nijkamp Jasper Albertus³^ORCID,Van Elmpt Wouter⁴^ORCID,Primdahl Hanne⁵,Lassen Pernille⁵,Cacicedo Jon⁶,Cordero-Mendez Lisbeth²,Uddin A.F.M. Kamal⁷,Mohamed Ahmed⁸^ORCID,Prajogi Ben⁹^ORCID,Brohet Kartika Erida¹⁰^ORCID,Nyongesa Catherine¹¹,Lomidze Darejan¹²,Prasiko Gisupnikha¹³,Ferraris Gustavo¹⁴^ORCID,Mahmood Humera¹⁵,Stojkovski Igor¹⁶^ORCID,Isayev Isa¹⁷,Mohamad Issa¹⁸^ORCID,Shirley Leivon¹⁹^ORCID,Kochbati Lotfi²⁰,Eftodiev Ludmila²¹,Piatkevich Maksim²²^ORCID,Bonilla Jara Maria Matilde²³,Spahiu Orges²⁴^ORCID,Aralbayev Rakhat²⁵,Zakirova Raushan²⁶^ORCID,Subramaniam Sandya²⁷^ORCID,Kibudde Solomon²⁸^ORCID,Tsegmed Uranchimeg²⁹,Korreman Stine Sofia³^ORCID,Eriksen Jesper Grau¹^ORCID

Affiliation:

1. Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark

2. International Atomic Energy Agency, Vienna, Austria

3. Department of Clinical Medicine, Aarhus University, Aarhus, Denmark

4. MAASTRO clinic, Maastricht University Medical Centre, Maastricht, the Netherlands

5. Department of Oncology, Aarhus University Hospital, Aarhus, Denmark

6. Department of Radiation Oncology, Cruces University Hospital, Bilbao, Spain

7. Labaid Cancer Hospital and Super Speciality Centre, Dhaka, Bangladesh

8. National Cancer Institute, University of Gezira, Wad Madani, Sudan

9. Cipto Mangunkusumo Hospital, Jakarta, Indonesia

10. Dharmais Cancer Hospital, Jakarta, Indonesia

11. Kenyatta National Hospital, Nairobi, Kenya

12. Tbilisi State Medical University and Ingorokva High Medical Technology University Clinic, Tbilisi, Georgia

13. Nepal Cancer Hospital and Research Center, Lalitpur, Nepal

14. Centro de Radioterapiya dean Funes, Cordoba, Argentina

15. Atomic Energy Cancer Hospital NORI, Islamabad, Pakistan

16. University Clinic of Radiotherapy and Oncology, Skopje, Macedonia

17. National Center of Oncology, Baku, Azerbaijan

18. King Hussein Cancer Center, Amman, Jordan

19. Christian Institute of Health Science and Research, Dimapur, India

20. Hospital Abderrahmen Mami, Ariana, Tunesia

21. Moldavian Oncology Institute, Chisinau, Moldova

22. N. N. Alexandrov National Cancer Centre of Belarus, Minsk, Belarus

23. Hospital México, San José, Costa Rica

24. Mother Tereza Hospital, Tirana, Albania

25. National Centre of Oncology and Hematology, Bishkek, Kyrgyzstan

26. Center of Nuclear Medicine and Oncology, Semey, Kazakhstan

27. Hospital Kuala Lumpur, Kuala Lumpur, Malaysia

28. Uganda Cancer Institute, Kampala, Uganda

29. National Cancer Center of Mongolia, Ulaanbaatar, Mongolia

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.

Publisher

American Society of Clinical Oncology (ASCO)

Link

https://ascopubs.org/doi/pdfdirect/10.1200/GO.24.00173

Reference33 articles.

1. Expanding global access to radiotherapy

2. Cost-effectiveness of stereotactic body radiotherapy in the treatment of non–small-cell lung cancer (NSCLC): a systematic review

3. Adjuvant breast radiotherapy: How to trade-off cost and effectiveness?

4. A systematic review of the cost and cost-effectiveness studies of proton radiotherapy

5. Model-based cost-effectiveness analysis of external beam radiation therapy for the treatment of localized prostate cancer: a systematic review