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放射科医生为何摒弃乳腺热成像?对近期研究及建议的批判性综述。

Why Do Radiologists Disown Breast Thermography? A Critical Review of Recent Studies and Recommendations.

作者信息

Goñi-Arana Ane, Pérez-Martín Jorge, Díez Francisco Javier

机构信息

Department of Artificial Intelligence, Universidad Nacional de Educación a Distancia (UNED), 28040 Madrid, Spain.

出版信息

Cancers (Basel). 2025 Jun 29;17(13):2195. doi: 10.3390/cancers17132195.


DOI:10.3390/cancers17132195
PMID:40647493
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12249184/
Abstract

Thermography was first applied to breast cancer detection in the 1950s but fell out of favor among radiologists due to inconsistent and inconclusive findings in the following decades. Studies conducted in the 21st century using new-generation thermal cameras and computer vision techniques, particularly artificial intelligence, have reported sensitivity and specificity values comparable to those of mammography. However, most radiologists, being unaware of these results, still believe this technique is ineffective, and medical societies advise against using it, even as an adjunct to mammography. In this paper we review recent studies and discuss whether the recommendations of scientific societies are still valid in the light of new evidence. We also propose some ideas for standardizing breast thermography studies that could help make this technique acceptable to the radiology community.

摘要

热成像技术在20世纪50年代首次应用于乳腺癌检测,但在接下来的几十年里,由于结果不一致且无定论,在放射科医生中失宠。21世纪使用新一代热成像相机和计算机视觉技术(特别是人工智能)进行的研究报告称,其灵敏度和特异性值与乳腺X线摄影相当。然而,大多数放射科医生并不知晓这些结果,仍然认为这项技术无效,而且医学协会也建议不要使用它,即使作为乳腺X线摄影的辅助手段。在本文中,我们回顾了近期的研究,并根据新证据讨论科学协会的建议是否仍然有效。我们还提出了一些规范乳腺热成像研究的想法,这可能有助于使这项技术被放射学界接受。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/082c/12249184/7ef8b83cd4e4/cancers-17-02195-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/082c/12249184/a5f79cfd514b/cancers-17-02195-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/082c/12249184/7ef8b83cd4e4/cancers-17-02195-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/082c/12249184/a5f79cfd514b/cancers-17-02195-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/082c/12249184/7ef8b83cd4e4/cancers-17-02195-g002.jpg

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本文引用的文献

[1]
Breast thermography: a systematic review and meta-analysis.

Syst Rev. 2024-11-28

[2]
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods.

BMJ. 2024-4-16

[3]
Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.

CA Cancer J Clin. 2024

[4]
Breast cancer pre-clinical screening using infrared thermography and artificial intelligence: a prospective, multicentre, diagnostic accuracy cohort study.

Int J Surg. 2023-10-1

[5]
Dynamic Vascular Imaging Using Active Breast Thermography.

Sensors (Basel). 2023-3-10

[6]
Quality analysis of a breast thermal images database.

Health Informatics J. 2023

[7]
A prospective evaluation of breast thermography enhanced by a novel machine learning technique for screening breast abnormalities in a general population of women presenting to a secondary care hospital.

Front Artif Intell. 2023-1-4

[8]
Contrast-enhanced Mammography versus Contrast-enhanced Breast MRI: A Systematic Review and Meta-Analysis.

Radiology. 2022-10

[9]
Breast cancer detection using deep learning: Datasets, methods, and challenges ahead.

Comput Biol Med. 2022-10

[10]
Detection of Breast Cancer from Five-View Thermal Images Using Convolutional Neural Networks.

J Healthc Eng. 2022

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