PDF(Pubmed)
Abstract：
UNASSIGNED: The progress in Colorectal cancer (CRC) screening and management has resulted in an unprecedented caseload for histopathological diagnosis. While artificial intelligence (AI) presents a potential solution, the predominant emphasis on slide-level aggregation performance without thorough verification of cancer in each location, impedes both explainability and transparency. Effectively addressing these challenges is crucial to ensuring the reliability and efficacy of AI in histology applications.
UNASSIGNED: In this study, we created an innovative AI algorithm using transfer learning from a polyp segmentation model in endoscopy. The algorithm precisely localized CRC targets within 0.25 mm² grids from whole slide imaging (WSI). We assessed the CRC detection capabilities at this fine granularity and examined the influence of AI on the diagnostic behavior of pathologists. The evaluation utilized an extensive dataset comprising 858 consecutive patient cases with 1418 WSIs obtained from an external center.
UNASSIGNED: Our results underscore a notable sensitivity of 90.25% and specificity of 96.60% at the grid level, accompanied by a commendable area under the curve (AUC) of 0.962. This translates to an impressive 99.39% sensitivity at the slide level, coupled with a negative likelihood ratio of <0.01, signifying the dependability of the AI system to preclude diagnostic considerations. The positive likelihood ratio of 26.54, surpassing 10 at the grid level, underscores the imperative for meticulous scrutiny of any AI-generated highlights. Consequently, all four participating pathologists demonstrated statistically significant diagnostic improvements with AI assistance.
UNASSIGNED: Our transfer learning approach has successfully yielded an algorithm that can be validated for CRC histological localizations in whole slide imaging. The outcome advocates for the integration of the AI system into histopathological diagnosis, serving either as a diagnostic exclusion application or a computer-aided detection (CADe) tool. This integration has the potential to alleviate the workload of pathologists and ultimately benefit patients.
UNASSIGNED: In this study, we created an innovative AI algorithm using transfer learning from a polyp segmentation model in endoscopy. The algorithm precisely localized CRC targets within 0.25 mm² grids from whole slide imaging (WSI). We assessed the CRC detection capabilities at this fine granularity and examined the influence of AI on the diagnostic behavior of pathologists. The evaluation utilized an extensive dataset comprising 858 consecutive patient cases with 1418 WSIs obtained from an external center.
UNASSIGNED: Our results underscore a notable sensitivity of 90.25% and specificity of 96.60% at the grid level, accompanied by a commendable area under the curve (AUC) of 0.962. This translates to an impressive 99.39% sensitivity at the slide level, coupled with a negative likelihood ratio of <0.01, signifying the dependability of the AI system to preclude diagnostic considerations. The positive likelihood ratio of 26.54, surpassing 10 at the grid level, underscores the imperative for meticulous scrutiny of any AI-generated highlights. Consequently, all four participating pathologists demonstrated statistically significant diagnostic improvements with AI assistance.
UNASSIGNED: Our transfer learning approach has successfully yielded an algorithm that can be validated for CRC histological localizations in whole slide imaging. The outcome advocates for the integration of the AI system into histopathological diagnosis, serving either as a diagnostic exclusion application or a computer-aided detection (CADe) tool. This integration has the potential to alleviate the workload of pathologists and ultimately benefit patients.
摘要：
结直肠癌（CRC）筛查和治疗的进展导致了前所未有的组织病理学诊断病例量。虽然人工智能(AI)提出了一个潜在的解决方案，主要强调幻灯片级聚集性能，而不彻底验证每个位置的癌症，阻碍了可解释性和透明度。有效解决这些挑战对于确保AI在组织学应用中的可靠性和有效性至关重要。
■在这项研究中，我们利用内窥镜检查中息肉分割模型的迁移学习,创建了一种创新的AI算法.该算法在整个幻灯片成像（WSI）的0.25mm²网格内精确定位CRC目标。我们以这种精细粒度评估了CRC检测能力，并检查了AI对病理学家诊断行为的影响。评估使用了广泛的数据集，包括858例连续患者病例，其中1418例WSI从外部中心获得。
■我们的结果强调了网格水平的90.25％的显着灵敏度和96.60％的特异性，伴随着0.962的曲线下面积（AUC）。这意味着在幻灯片水平上有令人印象深刻的99.39%的灵敏度，再加上<0.01的负似然比，表明人工智能系统的可靠性，以排除诊断考虑。正似然比为26.54，在网格级别超过10，强调了对任何AI生成的亮点进行细致审查的必要性。因此,所有4名参与研究的病理学家在AI辅助下表现出统计学上显著的诊断改善.
■我们的迁移学习方法已经成功地产生了一种可以在整个载玻片成像中验证CRC组织学定位的算法。结果主张将AI系统集成到组织病理学诊断中，作为诊断排除应用程序或计算机辅助检测（CADe）工具。这种整合有可能减轻病理学家的工作量并最终使患者受益。
■在这项研究中，我们利用内窥镜检查中息肉分割模型的迁移学习,创建了一种创新的AI算法.该算法在整个幻灯片成像（WSI）的0.25mm²网格内精确定位CRC目标。我们以这种精细粒度评估了CRC检测能力，并检查了AI对病理学家诊断行为的影响。评估使用了广泛的数据集，包括858例连续患者病例，其中1418例WSI从外部中心获得。
■我们的结果强调了网格水平的90.25％的显着灵敏度和96.60％的特异性，伴随着0.962的曲线下面积（AUC）。这意味着在幻灯片水平上有令人印象深刻的99.39%的灵敏度，再加上<0.01的负似然比，表明人工智能系统的可靠性，以排除诊断考虑。正似然比为26.54，在网格级别超过10，强调了对任何AI生成的亮点进行细致审查的必要性。因此,所有4名参与研究的病理学家在AI辅助下表现出统计学上显著的诊断改善.
■我们的迁移学习方法已经成功地产生了一种可以在整个载玻片成像中验证CRC组织学定位的算法。结果主张将AI系统集成到组织病理学诊断中，作为诊断排除应用程序或计算机辅助检测（CADe）工具。这种整合有可能减轻病理学家的工作量并最终使患者受益。
