Metabolic tumour volume in Hodgkin lymphoma—A comparison between manual and AI-based analysis

IF 1.3 4区 医学 Q4 PHYSIOLOGY
May Sadik, Sally F. Barrington, Elin Trägårdh, Babak Saboury, Anne L. Nielsen, Annika L. Jakobsen, Jose L. L. Gongora, Jesus L. Urdaneta, Rajender Kumar, Lars Edenbrandt
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引用次数: 0

Abstract

Aim

To compare total metabolic tumour volume (tMTV), calculated using two artificial intelligence (AI)-based tools, with manual segmentation by specialists as the reference.

Methods

Forty-eight consecutive Hodgkin lymphoma (HL) patients staged with [18F] fluorodeoxyglucose positron emission tomography/computed tomography were included. The median age was 35 years (range: 7–75), 46% female. The tMTV was automatically measured using the AI-based tools positron emission tomography assisted reporting system (PARS) (from Siemens) and RECOMIA (recomia.org) without any manual adjustments. A group of eight nuclear medicine specialists manually segmented lesions for tMTV calculations; each patient was independently segmented by two specialists.

Results

The median of the manual tMTV was 146 cm3 (interquartile range [IQR]: 79–568 cm3) and the median difference between two tMTV values segmented by different specialists for the same patient was 26 cm3 (IQR: 10–86 cm3). In 22 of the 48 patients, the manual tMTV value was closer to the RECOMIA tMTV value than to the manual tMTV value segmented by the second specialist. In 11 of the remaining 26 patients, the difference between the RECOMIA tMTV and the manual tMTV was small (<26 cm3, which was the median difference between two manual tMTV values from the same patient). The corresponding numbers for PARS were 18 and 10 patients, respectively.

Conclusion

The results of this study indicate that RECOMIA and Siemens PARS AI tools could be used without any major manual adjustments in 69% (33/48) and 58% (28/48) of HL patients, respectively. This demonstrates the feasibility of using AI tools to support physicians measuring tMTV for assessment of prognosis in clinical practice.

Abstract Image

霍奇金淋巴瘤的代谢肿瘤体积-人工和人工智能分析的比较。
目的:比较两种基于人工智能(AI)的工具计算的总代谢肿瘤体积(tMTV),并以专家人工分割为参考。方法:采用[18F]FDG PET/CT分期的48例连续霍奇金淋巴瘤(HL)患者。中位年龄为35岁(范围7-75岁),46%为女性。使用基于人工智能的工具PARS(来自Siemens)和recoia (recoma.org)自动测量tMTV,无需任何手动调整。一组8名核医学专家手动分割病变进行tMTV计算;每位患者由两位专家独立分割。结果:手工tMTV的中位数为146 cm3(四分位间距(IQR) 79 ~ 568 cm3),不同专科对同一患者分割的两次tMTV值的中位数差为26 cm3 (IQR 10 ~ 86 cm3)。在48例患者中,有22例患者的手动tMTV值更接近recoia tMTV值,而不是第二专科医生分割的手动tMTV值。在其余26例患者中,11例recoia tMTV与手动tMTV之间的差异很小(3,这是同一患者两次手动tMTV值之间的中位数差异)。PARS对应的数字分别为18例和10例。结论:本研究结果表明,69%(33/48)的hl患者可以使用recoia和58%(28/48)的Siemens PARS AI工具,而无需任何人工调整。这证明了在临床实践中使用人工智能工具来支持医生测量tMTV以评估预后的可行性。这篇文章受版权保护。版权所有。
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
CiteScore
3.40
自引率
5.60%
发文量
62
审稿时长
6-12 weeks
期刊介绍: Clinical Physiology and Functional Imaging publishes reports on clinical and experimental research pertinent to human physiology in health and disease. The scope of the Journal is very broad, covering all aspects of the regulatory system in the cardiovascular, renal and pulmonary systems with special emphasis on methodological aspects. The focus for the journal is, however, work that has potential clinical relevance. The Journal also features review articles on recent front-line research within these fields of interest. Covered by the major abstracting services including Current Contents and Science Citation Index, Clinical Physiology and Functional Imaging plays an important role in providing effective and productive communication among clinical physiologists world-wide.
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