Chelebian Eduard, Avenel Christophe, Järemo Helena, Andersson Pernilla, Bergh Anders, Wählby Carolina
Department of Information Technology and SciLifeLab, Uppsala University, 75237, Uppsala, Sweden.
Department of Medical Biosciences, Pathology, Umeå University, 90187, Umeå, Sweden.
Sci Rep. 2025 Aug 21;15(1):30770. doi: 10.1038/s41598-025-15105-6.
Diagnostic needle biopsies that miss clinically significant prostate cancer (PCa) often sample benign tissue near hidden cancers. Such benign samples might still display subtle morphological signs of cancer elsewhere in the prostate. This study examined if artificial intelligence (AI) could detect these morphological clues in benign biopsies from men with elevated prostate-specific antigen (PSA) levels to predict subsequent diagnosis of clinically significant PCa within 30 months. We analysed biopsies from 232 men initially diagnosed as benign, matched for age, diagnosis year, and PSA levels-half were later diagnosed with PCa, while the rest remained cancer-free for at least eight years. The AI model accurately predicted future PCa diagnosis from initial benign biopsies (AUC = 0.82), highlighting patterns such as changes in stromal collagen and altered glandular epithelial cells. This demonstrates that AI analysis of routine haematoxylin-eosin biopsy sections can detect subtle signs indicating clinically significant PCa before it becomes histologically apparent. Such morphological patterns shed light on the broader tissue alterations induced by prostate cancer, even in benign tissue, potentially enhancing early detection and clinical decision-making.
错过具有临床意义的前列腺癌（PCa）的诊断性针吸活检通常采集的是隐匿性癌症附近的良性组织。此类良性样本在前列腺其他部位仍可能显示出癌症的细微形态学迹象。本研究调查了人工智能（AI）能否在前列腺特异性抗原（PSA）水平升高的男性的良性活检样本中检测到这些形态学线索，以预测30个月内具有临床意义的PCa的后续诊断。我们分析了232名最初诊断为良性的男性的活检样本，这些样本在年龄、诊断年份和PSA水平上进行了匹配，其中一半后来被诊断为PCa，而其余的至少八年无癌。AI模型从最初的良性活检样本中准确预测了未来的PCa诊断（AUC = 0.82），突出了诸如基质胶原变化和腺上皮细胞改变等模式。这表明，对常规苏木精-伊红活检切片进行AI分析可以在具有临床意义的PCa在组织学上显现之前检测到表明其存在的细微迹象。此类形态学模式揭示了前列腺癌引起的更广泛的组织改变，即使在良性组织中也是如此，这可能会加强早期检测和临床决策。
