论文标题
DLBCL-MORPH：使用深度学习计算的形态学特征，用于注释的数字DLBCL图像集
DLBCL-Morph: Morphological features computed using deep learning for an annotated digital DLBCL image set
论文作者
论文摘要
弥漫性大B细胞淋巴瘤（DLBCL）是最常见的非霍奇金淋巴瘤。尽管组织学上的DLBCL显示出不同的形态，但尚未证明形态学特征与预后相关。我们提出了来自209例DLBCL病例的组织学切片的形态分析，这些病例与相关的临床和细胞遗传学数据进行了分析。在组织微阵列（TMA）中布置了重复的组织核心切片，并用H＆E和CD10，BCL6，MUM1，BCL2和MYC对复制切片进行染色。 TMA伴随着病理学家注释的利益区域（ROI），这些区域鉴定了代表DLBCL的组织区域。我们使用深度学习模型来分割ROI中的所有肿瘤核，并为每个分段核计算了几种几何特征。我们拟合了COX比例危害模型，以证明这些几何特征在预测生存结果中的实用性，并发现它的C-指数（95％CI）为0.635（0.574,0.691）。我们的发现表明，根据肿瘤核计算得出的几何特征是预后的重要性，应在前瞻性研究中得到验证。
Diffuse Large B-Cell Lymphoma (DLBCL) is the most common non-Hodgkin lymphoma. Though histologically DLBCL shows varying morphologies, no morphologic features have been consistently demonstrated to correlate with prognosis. We present a morphologic analysis of histology sections from 209 DLBCL cases with associated clinical and cytogenetic data. Duplicate tissue core sections were arranged in tissue microarrays (TMAs), and replicate sections were stained with H&E and immunohistochemical stains for CD10, BCL6, MUM1, BCL2, and MYC. The TMAs are accompanied by pathologist-annotated regions-of-interest (ROIs) that identify areas of tissue representative of DLBCL. We used a deep learning model to segment all tumor nuclei in the ROIs, and computed several geometric features for each segmented nucleus. We fit a Cox proportional hazards model to demonstrate the utility of these geometric features in predicting survival outcome, and found that it achieved a C-index (95% CI) of 0.635 (0.574,0.691). Our finding suggests that geometric features computed from tumor nuclei are of prognostic importance, and should be validated in prospective studies.
