Artificial intelligence contouring in radiotherapy for organs-at-risk and lymph node areas.

IF 3.3 2区 医学 Q2 ONCOLOGY Radiation Oncology Pub Date : 2024-11-21 DOI:10.1186/s13014-024-02554-y
Céline Meyer, Sandrine Huger, Marie Bruand, Thomas Leroy, Jérémy Palisson, Paul Rétif, Thomas Sarrade, Anais Barateau, Sophie Renard, Maria Jolnerovski, Nicolas Demogeot, Johann Marcel, Nicolas Martz, Anaïs Stefani, Selima Sellami, Juliette Jacques, Emma Agnoux, William Gehin, Ida Trampetti, Agathe Margulies, Constance Golfier, Yassir Khattabi, Olivier Cravéreau, Alizée Renan, Jean-François Py, Jean-Christophe Faivre
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Abstract

Introduction: The delineation of organs-at-risk and lymph node areas is a crucial step in radiotherapy, but it is time-consuming and associated with substantial user-dependent variability in contouring. Artificial intelligence (AI) appears to be the solution to facilitate and standardize this work. The objective of this study is to compare eight available AI software programs in terms of technical aspects and accuracy for contouring organs-at-risk and lymph node areas with current international contouring recommendations.

Material and methods: From January-July 2023, we performed a blinded study of the contour scoring of the organs-at-risk and lymph node areas by eight self-contouring AI programs by 20 radiation oncologists. It was a single-center study conducted in radiation department at the Lorraine Cancer Institute. A qualitative analysis of technical characteristics of the different AI programs was also performed. Three adults (two women and one man) and three children (one girl and two boys) provided six whole-body anonymized CT scans, along with two other adult brain MRI scans. Using a scoring scale from 1 to 3 (best score), radiation oncologists blindly assessed the quality of contouring of organs-at-risk and lymph node areas of all scans and MRI data by the eight AI programs. We have chosen to define the threshold of an average score equal to or greater than 2 to characterize a high-performing AI software, meaning an AI with minimal to moderate corrections but usable in clinical routine.

Results: For adults CT scans: There were two AI programs for which the overall average quality score (that is, all areas tested for OARs and lymph nodes) was higher than 2.0: Limbus (overall average score = 2.03 (0.16)) and MVision (overall average score = 2.13 (0.19)). If we only consider OARs for adults, only Limbus, Therapanacea, MVision and Radformation have an average score above 2. For children CT scan, MVision was the only program to have a average score higher than 2 with overall average score = 2.07 (0.19). If we only consider OARs for children, only Limbus and MVision have an average score above 2. For brain MRIs: TheraPanacea was the only program with an average score over 2, for both brain delineation (2.75 (0.35)) and OARs (2.09 (0.19)). The comparative analysis of the technical aspects highlights the similarities and differences between the software. There is no difference in between senior radiation oncologist and residents for OARs contouring.

Conclusion: For adult CT-scan, two AI programs on the market, MVision and Limbus, delineate most OARs and lymph nodes areas that are useful in clinical routine. For children CT-scan, only one IA, MVision, program is efficient. For adult brain MRI, Therapancea,only one AI program is efficient.

Trial registration: CNIL-MR0004 Number HDH434.

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高危器官和淋巴结区域放疗中的人工智能轮廓设计。
简介危险器官和淋巴结区域的划定是放射治疗的关键步骤,但这项工作耗时较长,而且在轮廓划定方面存在很大的用户依赖性差异。人工智能(AI)似乎是促进这项工作并使之标准化的解决方案。本研究的目的是将现有的八种人工智能软件在技术方面以及风险器官和淋巴结区域轮廓绘制的准确性与当前国际轮廓绘制建议进行比较:2023年1月至7月,我们对20名放射肿瘤科医生使用八种自我轮廓描绘人工智能软件对危险器官和淋巴结区域进行轮廓描绘评分的情况进行了盲法研究。这是一项在洛林癌症研究所放射科进行的单中心研究。研究还对不同人工智能程序的技术特点进行了定性分析。三名成人(两名女性和一名男性)和三名儿童(一名女孩和两名男孩)提供了六次全身匿名 CT 扫描以及另外两次成人脑部 MRI 扫描。放射肿瘤专家采用 1 到 3 分(最佳分数)的评分标准,对八种人工智能程序对所有扫描和核磁共振成像数据中危险器官和淋巴结区域的轮廓描绘质量进行了盲评。我们选择了平均得分等于或大于 2 分的阈值作为高性能人工智能软件的特征,这意味着人工智能只需进行极少或中等程度的修正,但可用于临床常规工作:成人 CT 扫描有两个人工智能程序的总体平均质量得分(即所有检测 OAR 和淋巴结的区域)高于 2.0:Limbus(总平均分 = 2.03 (0.16))和 MVision(总平均分 = 2.13 (0.19))。如果只考虑成人的 OARs,则只有 Limbus、Therapanacea、MVision 和 Radformation 的平均得分高于 2。在儿童 CT 扫描方面,MVision 是唯一平均得分高于 2 分的程序,总平均得分 = 2.07 (0.19)。如果只考虑儿童的 OAR,只有 Limbus 和 MVision 的平均得分高于 2。在脑磁共振成像方面TheraPanacea 是唯一一个在脑部划线(2.75 (0.35))和 OAR(2.09 (0.19))方面平均得分都超过 2 分的程序。技术方面的比较分析突出了软件之间的异同。结论:对于成人 CT 扫描,市场上的两款人工智能软件 MVision 和 Limbus 可以勾画出大多数 OAR 和淋巴结区域,对临床常规工作非常有用。对于儿童 CT 扫描,只有 MVision 这一款人工智能程序是有效的。对于成人脑部核磁共振成像,只有 Therapancea 一种人工智能程序是有效的:试验注册:CNIL-MR0004 编号 HDH434。
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来源期刊
Radiation Oncology
Radiation Oncology ONCOLOGY-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
CiteScore
6.50
自引率
2.80%
发文量
181
审稿时长
3-6 weeks
期刊介绍: Radiation Oncology encompasses all aspects of research that impacts on the treatment of cancer using radiation. It publishes findings in molecular and cellular radiation biology, radiation physics, radiation technology, and clinical oncology.
期刊最新文献
The impact of radiation-related lymphocyte recovery on the prognosis of locally advanced esophageal squamous cell carcinoma patients: a retrospective analysis. Correction: Artificial intelligence contouring in radiotherapy for organs-at-risk and lymph node areas. Deep learning-based synthetic CT for dosimetric monitoring of combined conventional radiotherapy and lattice boost in large lung tumors. Correction: The significance of risk stratification through nomogram-based assessment in determining postmastectomy radiotherapy for patients diagnosed with pT1 - 2N1M0 breast cancer. Sequential or simultaneous-integrated boost in early-stage breast cancer patients: trade-offs between skin toxicity and risk of compromised coverage.
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