Background and Objective:Gleason grading system is currently the clinical gold standard for determining prostate cancer aggressiveness. Prostate cancer is typically classified into one of 5 different categories with 1 representing the most indolent disease and 5 reflecting the most aggressive disease. Grades 3 and 4 are the most common and difficult patterns to be discriminated in clinical practice. Even though the degree of gland differentiation is the strongest determinant of Gleason grade, manual grading is subjective and is hampered by substantial inter-reader disagreement, especially with regard to intermediate grade groups.

Methods:To capture the topological characteristics and the degree of connectivity between nuclei around the gland, the concept of Homology Profile (HP) for prostate cancer grading is presented in this paper. HP is an algebraic tool, whereby, certain algebraic invariants are computed based on the structure of a topological space. We utilized the Statistical Representation of Homology Profile (SRHP) features to quantify the extent of glandular differentiation. The quantitative characteristics which represent the image patch are fed into a supervised classifier model for discrimination of grade patterns 3 and 4.

Results:On the basis of the novel homology profile, we evaluated 43 digitized images of prostate biopsy slides annotated for regions corresponding to Grades 3 and 4. The quantitative patch-level evaluation results showed that our approach achieved an Area Under Curve (AUC) of 0.96 and an accuracy of 0.89 in terms of discriminating Grade 3 and 4 patches. Our approach was found to be superior to comparative methods including handcrafted cellular features, Stacked Sparse Autoencoder (SSAE) algorithm and end-to-end supervised learning method (DLGg). Also, slide-level quantitative and qualitative evaluation results reflect the ability of our approach in discriminating Gleason Grade 3 from 4 patterns on H&E tissue images.

Conclusions:We presented a novel Statistical Representation of Homology Profile (SRHP) approach for automated Gleason grading on prostate biopsy slides. The most discriminating topological descriptions of cancerous regions for grade 3 and 4 in prostate cancer were identified. Moreover, these characteristics of homology profile are interpretable, visually meaningful and highly consistent with the rubric employed by pathologists for the task of Gleason grading.

背景与目的：格里森分级系统是目前确定前列腺癌侵袭性的临床金标准。前列腺癌通常分为 5 种不同类别之一，其中 1 类代表最惰性的疾病，而 5 类代表最具侵袭性的疾病。3 级和 4 级是临床实践中最常见和最难区分的模式。尽管腺体分化程度是格里森等级的最强决定因素，但手动评分是主观的，并且受到读者之间大量分歧的阻碍，尤其是对于中级组。

方法：为了捕捉腺体周围细胞核之间的拓扑特征和连接程度，本文提出了用于前列腺癌分级的同源谱（HP）的概念。HP 是一种代数工具，根据拓扑空间的结构计算某些代数不变量。我们利用同源图谱 (SRHP) 特征的统计表示来量化腺体分化的程度。代表图像块的定量特征被输入到监督分类器模型中，用于区分等级模式 3 和 4。

结果：在新的同源性特征的基础上，我们评估了 43 幅前列腺活检载玻片的数字化图像，这些图像标注了对应于 3 级和 4 级的区域。定量斑块级评估结果表明，我们的方法实现了曲线下面积 (AUC) 0.96 和 0.89 在区分 3 级和 4 级补丁方面的准确度。我们的方法被发现优于比较方法，包括手工制作的细胞特征、堆叠稀疏自动编码器 (SSAE) 算法和端到端监督学习方法 (DLGg)。此外，幻灯片级别的定量和定性评估结果反映了我们的方法从 H&E 组织图像上的 4 种模式中区分格里森 3 级的能力。

结论：我们提出了一种新颖的同源图谱统计表示 (SRHP) 方法，用于前列腺活检玻片上的自动格里森分级。确定了前列腺癌中 3 级和 4 级癌变区域最具辨别力的拓扑描述。此外，同源性特征的这些特征是可解释的、视觉上有意义的并且与病理学家用于格里森分级任务的量规高度一致。