Artificial intelligence in pathological anatomy: digitization of the calculation of the proliferation index (Ki-67) in breast carcinoma

Pub Date : 2024-01-06 DOI:10.1007/s10015-023-00923-6
Elmehdi Aniq, Mohamed Chakraoui, Naoual Mouhni
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Abstract

Ki-67 is a non-histone nuclear protein located in the nuclear cortex and is one of the essential biomarkers used to provide the proliferative status of cancer cells. Because of the variability in color, morphology and intensity of the cell nuclei, Ki-67 is sensitive to chemotherapy and radiation therapy. The proliferation index is usually calculated visually by professional pathologists who assess the total percentage of positive (labeled) cells. This semi-quantitative counting can be the source of some inter- and intra-observer variability and is time-consuming. These factors open up a new field of scientific and technological research and development. Artificial intelligence is attracting attention to solve these problems. Our solution is based on deep learning to calculate the percentage of cells labeled by the ki-67 protein. The tumor area with \(\times\)40 magnification is given by the pathologist to segment different types of positive, negative or TIL (tumor infiltrating lymphocytes) cells. The calculation of the percentage comes after cells counting using classical image processing techniques. To give the model our satisfaction, we made a comparison with other datasets of the test and we compared it with the diagnosis of pathologists. Despite the error of our model, KiNet outperforms the best performing models to date in terms of average error measurement.

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病理解剖中的人工智能:乳腺癌增殖指数(Ki-67)计算数字化
Ki-67 是一种位于核皮层的非组蛋白核蛋白,是用于提供癌细胞增殖状态的重要生物标志物之一。由于细胞核的颜色、形态和强度存在差异,Ki-67 对化疗和放疗非常敏感。增殖指数通常由专业病理学家目测计算,评估阳性(标记)细胞的总百分比。这种半定量的计数方法可能会造成观察者之间和观察者内部的一些差异,而且耗费时间。这些因素为科技研发开辟了新的领域。人工智能在解决这些问题方面备受关注。我们的解决方案是基于深度学习来计算被 ki-67 蛋白标记的细胞百分比。病理学家会给出放大到40倍的肿瘤区域,以分割不同类型的阳性、阴性或TIL(肿瘤浸润淋巴细胞)细胞。百分比的计算是在使用经典图像处理技术进行细胞计数后得出的。为了让我们对模型感到满意,我们与其他测试数据集进行了比较,并与病理学家的诊断进行了比较。尽管我们的模型存在误差,但就平均误差测量值而言,KiNet 优于迄今为止表现最好的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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