Generalist medical foundation model improves prostate cancer segmentation from multimodal MRI images

IF 12.4 1区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Yuhan Zhang, Xiao Ma, Mingchao Li, Kun Huang, Jie Zhu, Miao Wang, Xi Wang, Menglin Wu, Pheng-Ann Heng
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引用次数: 0

Abstract

Prostate cancer (PCa) is one of the most common types of cancer, seriously affecting adult male health. Accurate and automated PCa segmentation is essential for radiologists to confirm the location of cancer, evaluate its severity, and design appropriate treatments. This paper presents PCaSAM, a fully automated PCa segmentation model that allows us to input multi-modal MRI images into the foundation model to improve performance significantly. We collected multi-center datasets to conduct a comprehensive evaluation. The results showed that PCaSAM outperforms the generalist medical foundation model and the other representative segmentation models, with the average DSC of 0.721 and 0.706 in the internal and external datasets, respectively. Furthermore, with the assistance of segmentation, the PI-RADS scoring of PCa lesions was improved significantly, leading to a substantial increase in average AUC by 8.3–8.9% on two external datasets. Besides, PCaSAM achieved superior efficiency, making it highly suitable for real-world deployment scenarios.

Abstract Image

通才医学基础模型改进了多模态MRI图像中前列腺癌的分割
前列腺癌(PCa)是最常见的癌症之一,严重影响成年男性的健康。准确和自动化的PCa分割对于放射科医生确认癌症的位置、评估其严重程度和设计适当的治疗方法至关重要。本文介绍了一种全自动PCa分割模型PCaSAM,它允许我们将多模态MRI图像输入到基础模型中,以显着提高性能。我们收集多中心数据集进行综合评价。结果表明,PCaSAM优于通用医学基础模型和其他代表性分割模型,在内部和外部数据集上的平均DSC分别为0.721和0.706。此外,在分割的帮助下,PCa病变的PI-RADS评分显著提高,导致两个外部数据集的平均AUC大幅增加8.3-8.9%。此外,PCaSAM实现了卓越的效率,使其非常适合实际部署场景。
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来源期刊
CiteScore
25.10
自引率
3.30%
发文量
170
审稿时长
15 weeks
期刊介绍: npj Digital Medicine is an online open-access journal that focuses on publishing peer-reviewed research in the field of digital medicine. The journal covers various aspects of digital medicine, including the application and implementation of digital and mobile technologies in clinical settings, virtual healthcare, and the use of artificial intelligence and informatics. The primary goal of the journal is to support innovation and the advancement of healthcare through the integration of new digital and mobile technologies. When determining if a manuscript is suitable for publication, the journal considers four important criteria: novelty, clinical relevance, scientific rigor, and digital innovation.
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