To guide the choice of treatment, every new breast cancer is assessed for aggressiveness (i.e., graded) by an experienced histopathologist. Typically, this tumor grade consists of three components, one of which is the nuclear pleomorphism score (the extent of abnormalities in the overall appearance of tumor nuclei). The degree of nuclear pleomorphism is subjectively classified from 1 to 3, where a score of 1 most closely resembles epithelial cells of normal breast epithelium and 3 shows the greatest abnormalities. Establishing numerical criteria for grading nuclear pleomorphism is challenging, and inter-observer agreement is poor. Therefore, we studied the use of deep learning to develop fully automated nuclear pleomorphism scoring in breast cancer. The reference standard used for training the algorithm consisted of the collective knowledge of an international panel of 10 pathologists on a curated set of regions of interest covering the entire spectrum of tumor morphology in breast cancer. To fully exploit the information provided by the pathologists, a first-of-its-kind deep regression model was trained to yield a continuous scoring rather than limiting the pleomorphism scoring to the standard three-tiered system. Our approach preserves the continuum of nuclear pleomorphism without necessitating a large data set with explicit annotations of tumor nuclei. Once translated to the traditional system, our approach achieves top pathologist-level performance in multiple experiments on regions of interest and whole-slide images, compared to a panel of 10 and 4 pathologists, respectively.

为了指导治疗的选择，每个新的乳腺癌都由经验丰富的组织病理学家评估侵袭性（即分级）。通常，该肿瘤等级由三个部分组成，其中之一是核多形性评分（肿瘤细胞核整体外观的异常程度）。核多形性的程度主观上分为 1 到 3 级，其中 1 分与正常乳腺上皮的上皮细胞最相似，3 分表示异常最大。建立核多形性分级的数值标准具有挑战性，而且观察者间的一致性很差。因此，我们研究了使用深度学习来开发全自动的乳腺癌核多形性评分。用于训练该算法的参考标准包括一个由 10 名病理学家组成的国际小组的集体知识，这些小组对一组涵盖乳腺癌肿瘤形态学整个范围的感兴趣区域进行了策划。为了充分利用病理学家提供的信息，我们训练了首个深度回归模型以产生连续评分，而不是将多形性评分限制在标准的三层系统中。我们的方法保留了核多形性的连续统一体，而无需使用带有肿瘤细胞核明确注释的大型数据集。一旦转化为传统系统，与分别由 10 名病理学家和 4 名病理学家组成的小组相比，我们的方法在感兴趣区域和整张幻灯片图像的多个实验中实现了顶级病理学家水平的表现。