Identifying the early-stage colon adenocarcinoma (ECA) patients who have lower risk cancer vs. the higher risk cancer could improve disease prognosis. Our study aimed to explore whether the glandular morphological features determined by computational pathology could identify high risk cancer in ECA via H&E images digitally. 532 ECA patients retrospectively from 2 independent data centers, as well as 113 from The Cancer Genome Atlas (TCGA), were enrolled in this study. Four tissue microarrays (TMAs) were constructed across ECA hematoxylin and eosin (H&E) stained slides. 797 quantitative glandular morphometric features were extracted and 5 most prognostic features were identified using minimum redundancy maximum relevance to construct an image classifier. The image classifier was evaluated on D2/D3 = 223, D4 = 46, D5 = 113. The expression of Ki67 and serum CEA levels were scored on D3, aiming to explore the correlations between image classifier and immunohistochemistry data and serum CEA levels. The roles of clinicopathological data and ECAHBC were evaluated by univariate and multivariate analyses for prognostic value. The image classifier could predict ECA recurrence (accuracy of 88.1%). ECA histomorphometric-based image classifier (ECAHBC) was an independent prognostic factor for poorer disease-specific survival [DSS, (HR = 9.65, 95% CI 2.15–43.12, P = 0.003)]. Significant correlations were observed between ECAHBC-positive patients and positivity of Ki67 labeling index (Ki67Li) and serum CEA. Glandular orientation and shape could predict the high risk cancer in ECA and contribute to precision oncology. Computational pathology is emerging as a viable and objective means of identifying predictive biomarkers for cancer patients.

中文翻译：

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通过计算机病理学确定的腺体方向和形状可以确定早期结肠癌的侵袭性肿瘤：一项三中心研究。

确定具有较低风险癌症相对于较高风险癌症的早期结肠腺癌（ECA）患者可以改善疾病的预后。我们的研究旨在探讨通过计算病理学确定的腺形态学特征是否可以通过H＆E图像以数字方式识别ECA中的高危癌症。回顾性研究了来自2个独立数据中心的532名ECA患者，以及来自癌症基因组图谱（TCGA）的113名ECA患者。跨ECA苏木精和曙红（H＆E）染色的玻片构建了四个组织微阵列（TMA）。提取了797个定量腺形态特征，并使用最小冗余最大相关性确定了5个预后特征，以构建图像分类器。在D2 / D3 = 223，D4 = 46，D5 = 113上评估了图像分类器。在D3上对Ki67的表达和血清CEA水平进行评分，旨在探讨图像分类器和免疫组化数据与血清CEA水平之间的相关性。通过单因素和多因素分析评估临床病理数据和ECAHBC的预后价值。图像分类器可以预测ECA复发（准确性为88.1％）。基于ECA组织形态计量学的图像分类器（ECAHBC）是疾病特异性生存率较差的独立预后因素[DSS，（HR = 9.65，95％CI 2.15–43.12，P = 0.003）]。ECAHBC阳性患者与Ki67标记指数（Ki67Li）和血清CEA阳性之间存在显着相关性。腺体的方向和形状可以预测ECA中的高危癌症并有助于精确的肿瘤学。