解码未来风险：深度学习在管状腺瘤全切片图像分析中的应用
Decoding Future Risk: Deep Learning Analysis of Tubular Adenoma Whole-Slide Images
AI总结：
本研究利用深度学习分析管状腺瘤全切片图像，旨在通过检测细微组织学特征预测患者未来患结直肠癌的风险。
摘要：
结直肠癌（CRC）仍然是癌症相关死亡的主要原因之一，尽管广泛实施了旨在检测和移除癌前息肉的预防性措施。尽管筛查有效降低了发病率，但一部分最初被诊断为低级腺瘤的患者仍会在晚年发展为CRC，即使没有已知的高风险综合征。识别哪些低风险患者处于更高风险中是为个性化监测和预防性治疗策略的关键未满足需求。传统组织学评估虽然基础，但可能无法充分捕捉暗示恶性潜力的细微建筑或细胞学特征。数字病理学和机器学习的进步为全面和客观分析全切片图像（WSIs）提供了机会。本研究探讨了机器学习算法，特别是卷积神经网络（CNNs），能否检测出低级别管状腺瘤全切片图像中的细微组织学特征，这些特征可预测患者长期发展为结直肠癌的风险。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
