Morant R, Gräwingholt A, Subelack J, Kuklinski D, Vogel J, Blum M, Eichenberger A, Geissler A
Krebsliga Ostschweiz, Flurhofstrasse 7, 9000, St. Gallen, Schweiz.
Radiologie am Theater, 33098, Paderborn, Deutschland.
Radiologie (Heidelb). 2024 Oct;64(10):773-778. doi: 10.1007/s00117-024-01345-6. Epub 2024 Jul 17.
Mammography screening programs (MSP) have shown that breast cancer can be detected at an earlier stage enabling less invasive treatment and leading to a better survival rate. The considerable numbers of interval breast cancer (IBC) and the additional examinations required, the majority of which turn out not to be cancer, are critically assessed.
In recent years companies and universities have used machine learning (ML) to develop powerful algorithms that demonstrate astonishing abilities to read mammograms. Can such algorithms be used to improve the quality of MSP?
The original screening mammographies of 251 cases with IBC were retrospectively analyzed using the software ProFound AI® (iCAD) and the results were compared (case score, risk score) with a control group. The relevant current literature was also studied.
The distributions of the case scores and the risk scores were markedly shifted to higher risks compared to the control group, comparable to the results of other studies.
Retrospective studies as well as our own data show that artificial intelligence (AI) could change our approach to MSP in the future in the direction of personalized screening and could enable a significant reduction in the workload of radiologists, fewer additional examinations and a reduced number of IBCs; however, the results of prospective studies are needed before implementation.
乳腺钼靶筛查项目（MSP）表明，乳腺癌能够在更早阶段被检测出来，从而可以采用侵入性较小的治疗方法，并提高生存率。对大量的间期乳腺癌（IBC）病例以及所需的额外检查进行了批判性评估，其中大多数检查结果并非癌症。
近年来，公司和大学利用机器学习（ML）开发出了强大的算法，这些算法在解读乳腺钼靶图像方面表现出惊人的能力。这样的算法能否用于提高MSP的质量？
使用ProFound AI®（iCAD）软件对251例IBC病例的原始筛查乳腺钼靶图像进行回顾性分析，并将结果（病例评分、风险评分）与对照组进行比较。同时还研究了相关的当前文献。
与对照组相比，病例评分和风险评分的分布明显向更高风险偏移，与其他研究结果相当。
回顾性研究以及我们自己的数据表明，人工智能（AI）未来可能会改变我们开展MSP的方式，朝着个性化筛查的方向发展，并有可能显著减少放射科医生的工作量、减少额外检查的次数以及减少IBC的数量；然而，在实施之前还需要前瞻性研究的结果。
