Martino Francesco, Ilardi Gennaro, Varricchio Silvia, Russo Daniela, Di Crescenzo Rosa Maria, Staibano Stefania, Merolla Francesco
Dedalus HealthCare, Division of Diagnostic Imaging IT, Gertrude-Frohlich-Sandner-Straße 1, Wien 1100, Austria.
Department of Advanced Biomedical Sciences, University of Naples, Via Pansini, 5, Naples 80131, Italy.
J Pathol Inform. 2023 Nov 22;15:100354. doi: 10.1016/j.jpi.2023.100354. eCollection 2024 Dec.
Anatomical pathology is undergoing its third revolution, transitioning from analogical to digital pathology and incorporating new artificial intelligence technologies into clinical practice. Aside from classification, detection, and segmentation models, predictive models are gaining traction since they can impact diagnostic processes and laboratory activity, lowering consumable usage and turnaround time. Our research aimed to create a deep-learning model to generate synthetic Ki-67 immunohistochemistry from Haematoxylin and Eosin (H&E) stained images. We used 175 oral squamous cell carcinoma (OSCC) from the University Federico II's Pathology Unit's archives to train our model to generate 4 Tissue Micro Arrays (TMAs). We sectioned one slide from each TMA, first stained with H&E and then re-stained with anti-Ki-67 immunohistochemistry (IHC). In digitised slides, cores were disarrayed, and the matching cores of the 2 stained were aligned to construct a dataset to train a Pix2Pix algorithm to convert H&E images to IHC. Pathologists could recognise the synthetic images in only half of the cases in a specially designed likelihood test. Hence, our model produced realistic synthetic images. We next used QuPath to quantify IHC positivity, achieving remarkable levels of agreement between genuine and synthetic IHC. Furthermore, a categorical analysis employing 3 Ki-67 positivity cut-offs (5%, 10%, and 15%) revealed high positive-predictive values. Our model is a promising tool for collecting Ki-67 positivity information directly on H&E slides, reducing laboratory demand and improving patient management. It is also a valuable option for smaller laboratories to easily and quickly screen bioptic samples and prioritise them in a digital pathology workflow.
解剖病理学正在经历第三次变革，从传统的类比病理学向数字病理学转变，并将新的人工智能技术融入临床实践。除了分类、检测和分割模型外，预测模型也越来越受到关注，因为它们可以影响诊断过程和实验室活动，减少耗材使用和周转时间。我们的研究旨在创建一个深度学习模型，从苏木精和伊红（H&E）染色图像中生成合成的Ki-67免疫组化图像。我们使用了来自费德里科二世大学病理科档案中的175例口腔鳞状细胞癌（OSCC）来训练我们的模型，以生成4个组织微阵列（TMA）。我们从每个TMA中切取一张切片，首先用H&E染色，然后用抗Ki-67免疫组化（IHC）重新染色。在数字化切片中，核心区域排列混乱，将两张染色切片的匹配核心区域对齐，构建一个数据集来训练Pix2Pix算法，将H&E图像转换为IHC图像。在一项专门设计的似然性测试中，病理学家仅在一半的病例中能够识别合成图像。因此，我们的模型生成了逼真的合成图像。接下来，我们使用QuPath对IHC阳性进行量化，在真实和合成的IHC之间达到了显著的一致性水平。此外，采用3个Ki-67阳性临界值（5%、10%和15%）的分类分析显示出较高的阳性预测值。我们的模型是一种很有前景的工具，可直接在H&E切片上收集Ki-67阳性信息，减少实验室需求并改善患者管理。对于较小的实验室来说，它也是一个有价值的选择，可以轻松快速地筛选活检样本，并在数字病理学工作流程中对其进行优先级排序。
