用深度学习分析腺瘤切片，预测患者未来患癌风险
Decoding Future Risk: Deep Learning Analysis of Tubular Adenoma Whole-Slide Images
- 用卷积神经网络分析低级别管状腺瘤的全切片图像
- 可识别传统病理忽略的细微结构特征，预测远期癌变风险
- 适合关注精准筛查与个性化随访的临床研究者
结直肠癌（CRC）仍是癌症相关死亡的主要原因，尽管已广泛实施预防性筛查以检测和切除癌前息肉。虽然筛查能有效降低发病率，但仍有一部分最初被诊断为低级别腺瘤的患者会在未来患上结直肠癌，且无已知高危综合征。识别哪些低风险患者存在更高进展风险，是实现个体化随访与预防性干预的关键未满足需求。传统组织学评估虽为基础手段，但可能无法充分捕捉提示恶性潜能的细微结构或细胞学特征。数字病理与机器学习的发展为全面、客观分析全切片图像（WSIs）提供了可能。本研究探讨卷积神经网络（CNN）能否从低级别管状腺瘤的全切片图像中识别出预测患者长期患癌风险的细微组织学特征。
原文摘要 · Abstract (English)
Colorectal cancer (CRC) remains a significant cause of cancer-related mortality, despite the widespread implementation of prophylactic initiatives aimed at detecting and removing precancerous polyps. Although screening effectively reduces incidence, a notable portion of patients initially diagnosed with low-grade adenomatous polyps will still develop CRC later in life, even without the presence of known high-risk syndromes. Identifying which low-risk patients are at higher risk of progression is a critical unmet need for tailored surveillance and preventative therapeutic strategies. Traditional histological assessment of adenomas, while fundamental, may not fully capture subtle architectural or cytological features indicative of malignant potential. Advancements in digital pathology and machine learning provide an opportunity to analyze whole-slide images (WSIs) comprehensively and objectively. This study investigates whether machine learning algorithms, specifically convolutional neural networks (CNNs), can detect subtle histological features in WSIs of low-grade tubular adenomas that are predictive of a patient's long-term risk of developing colorectal cancer.
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