用普通CT提升肝癌良恶性判断，插件式增强现有模型
PLUS: Plug-and-Play Enhanced Liver Lesion Diagnosis Model on Non-Contrast CT Scans
- 插件式框架，无需重训练即可增强任意3D分割模型
- 在8651例患者中，良恶性分类F1提升超4%至6.26%
- 适合临床肝病筛查，尤其适用于无增强CT的场景
局灶性肝病变（FLL）是体检中常见的发现。早期诊断和干预肝恶性肿瘤对提高患者生存率至关重要。尽管当前3D分割方法能准确检测病灶，但在区分良恶性方面存在局限，主要因难以捕捉病灶间细微差异。此外，现有方法多依赖多期增强CT和MRI等特殊影像，而普通非增强CT（NCCT）更常用于常规腹部检查。为此，我们提出PLUS——一种可插拔的框架，可增强任意3D分割模型在NCCT图像上的肝病分析能力。在涵盖8,651名患者的广泛实验中，PLUS显著提升现有方法性能：病灶级F1分数提升5.66%，恶性患者级提升6.26%，良性患者级提升4.03%。结果表明，PLUS可大幅提升基于常见NCCT影像的恶性FLL筛查能力。
原文摘要 · Abstract (English)
Focal liver lesions (FLL) are common clinical findings during physical examination. Early diagnosis and intervention of liver malignancies are crucial to improving patient survival. Although the current 3D segmentation paradigm can accurately detect lesions, it faces limitations in distinguishing between malignant and benign liver lesions, primarily due to its inability to differentiate subtle variations between different lesions. Furthermore, existing methods predominantly rely on specialized imaging modalities such as multi-phase contrast-enhanced CT and magnetic resonance imaging, whereas non-contrast CT (NCCT) is more prevalent in routine abdominal imaging. To address these limitations, we propose PLUS, a plug-and-play framework that enhances FLL analysis on NCCT images for arbitrary 3D segmentation models. In extensive experiments involving 8,651 patients, PLUS demonstrated a significant improvement with existing methods, improving the lesion-level F1 score by 5.66%, the malignant patient-level F1 score by 6.26%, and the benign patient-level F1 score by 4.03%. Our results demonstrate the potential of PLUS to improve malignant FLL screening using widely available NCCT imaging substantially.
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