通过人工智能发现良性前列腺活组织检查中的肿瘤指示形态变化

Eduard Chelebian, Christophe Avenel, Helena Järemo, Pernilla Andersson, Anders Bergh, Carolina Wählby
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引用次数: 0

摘要

背景和目的:诊断性针穿活检会漏检有临床意义的前列腺癌(PCa),但很可能取样的是癌症附近的良性组织。这些样本可能含有表明器官其他部位存在癌症的变化。我们的目标是评估人工智能(AI)能否识别PSA升高男性良性活检组织的形态学特征,从而预测未来在30个月的随访中是否能发现有临床意义的PCa。研究方法收集了 232 名 PSA 升高和良性针刺活检患者的回顾性队列,这些患者按年龄、诊断年份和 PSA 水平配对。一半患者在 30 个月内确诊为 PCa,另一半患者至少八年未患癌症。使用接收者操作特征曲线下面积(AUC)评估人工智能模型的性能,并使用注意力图直观显示模型捕捉到的与癌症诊断相关的形态模式。主要发现和局限性:人工智能模型能从最初的良性活检中识别出后来被诊断为 PCa 的患者,AUC 为 0.82。发现了独特的形态模式,如基质胶原蛋白改变和腺上皮细胞组成变化。结论和临床意义:将人工智能应用于标准的血栓素-伊红切片,可识别最初诊断为阴性、但后来发现有临床意义的 PCa 患者。形态学模式有助于深入了解 PCa 对肿瘤器官良性部位的长期影响。患者小结:通过人工智能,我们发现了正常前列腺组织中的微妙变化,这些变化提示前列腺其他部位存在肿瘤。这有助于早期识别潜在的高危肿瘤,限制前列腺活检的过度使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Discovery of tumour indicating morphological changes in benign prostate biopsies through AI
Background and Objective: Diagnostic needle biopsies that miss clinically significant prostate cancers (PCa) likely sample benign tissue adjacent to cancer. Such samples may contain changes indicating the presence of cancer elsewhere in the organ. Our goal is to evaluate if artificial intelligence (AI) can identify morphological characteristics in benign biopsies of men with raised PSA that predict the future detection of clinically significant PCa during a 30-month follow-up. Methods: A retrospective cohort of 232 patients with raised PSA and benign needle biopsies, paired by age, year of diagnosis and PSA levels was collected. Half were diagnosed with PCa within 30 months, while the other half remained cancer-free for at least eight years. AI model performance was assessed using the area under the receiver operating characteristic curve (AUC) and attention maps were used to visualise the morphological patterns relevant for cancer diagnosis as captured by the model. Key findings and Limitations: The AI model could identify patients that were later diagnosed with PCa from their initial benign biopsies with an AUC of 0.82. Distinctive morphological patterns, such as altered stromal collagen and changes in glandular epithelial cell composition, were revealed. Conclusions and Clinical Implications: AI applied to standard haematoxylin-eosin sections identifies patients initially diagnosed as negative but later found to have clinically significant PCa. Morphological patterns offer insights into the long-ranging effects of PCa in the benign parts of the tumour-bearing organ. Patient Summary: Using AI, we identified subtle changes in normal prostate tissue suggesting the presence of tumours elsewhere in the prostate. This could aid in the early identification of potentially high-risk tumours, limiting overuse of prostate biopsies.
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