Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
Dept. of Pathology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China.
Comput Biol Med. 2022 Jul;146:105520. doi: 10.1016/j.compbiomed.2022.105520. Epub 2022 Apr 27.
Intrahepatic cholangiocarcinoma (ICC) is cancer that originates from the liver's secondary ductal epithelium or branch. Due to the lack of early-stage clinical symptoms and very high mortality, the 5-year postoperative survival rate is only about 35%. A critical step to improve patients' survival is accurately predicting their survival status and giving appropriate treatment. The tumor microenvironment of ICC is the immediate environment on which the tumor cell growth depends. The differentiation of tumor glands, the stroma status, and the tumor-infiltrating lymphocytes in such environments are strictly related to the tumor progress. It is crucial to develop a computerized system for characterizing the tumor environment. This work aims to develop the quantitative histomorphological features that describe lymphocyte density distribution at the cell level and the different components at the tumor's tissue level in H&E-stained whole slide images (WSIs). The goal is to explore whether these features could stratify patients' survival. This study comprised of 127 patients diagnosed with ICC after surgery, where 78 cases were randomly chosen as the modeling set, and the rest of the 49 cases were testing set. Deep learning-based models were developed for tissue segmentation and lymphocyte detection in the WSIs. A total of 107-dimensional features, including different type of graph features on the WSIs were extracted by exploring the histomorphological patterns of these identified tumor tissue and lymphocytes. The top 3 discriminative features were chosen with the mRMR algorithm via 5-fold cross-validation to predict the patient's survival. The model's performance was evaluated on the independent testing set, which achieved an AUC of 0.6818 and the log-rank test p-value of 0.03. The Cox multivariable test was used to control the TNM staging, γ-Glutamytransferase, and the Peritumoral Glisson's Sheath Invasion. It showed that our model could independently predict survival risk with a p-value of 0.048 and HR (95% confidence interval) of 2.90 (1.01-8.32). These results indicated that the composition in tissue-level and global arrangement of lymphocytes in the cell-level could distinguish ICC patients' survival risk.
肝内胆管癌（ICC）是起源于肝脏次级胆管上皮或分支的癌症。由于缺乏早期临床症状和极高的死亡率，术后 5 年生存率仅约为 35%。提高患者生存率的关键步骤是准确预测其生存状况并给予适当的治疗。ICC 的肿瘤微环境是肿瘤细胞生长所依赖的直接环境。肿瘤腺体的分化、基质状态以及肿瘤浸润淋巴细胞在这些环境中的状态与肿瘤的进展严格相关。因此，开发一种用于描述肿瘤环境的计算机系统至关重要。本工作旨在开发定量组织形态学特征，以描述 H&E 染色全切片图像（WSI）中细胞水平上的淋巴细胞密度分布和肿瘤组织水平上的不同成分。目标是探索这些特征是否可以对患者的生存进行分层。本研究纳入了 127 例手术后诊断为 ICC 的患者，其中 78 例随机选择作为建模集，其余 49 例作为测试集。在 WSI 中开发了基于深度学习的组织分割和淋巴细胞检测模型。通过探索这些鉴定的肿瘤组织和淋巴细胞的组织形态模式，提取了 107 维特征，包括 WSI 上不同类型的图特征。通过 5 折交叉验证使用 mRMR 算法选择了前 3 个有鉴别力的特征，以预测患者的生存情况。该模型在独立测试集上的性能评估中，AUC 为 0.6818，对数秩检验 p 值为 0.03。使用 Cox 多变量检验控制 TNM 分期、γ-谷氨酰转移酶和肿瘤周围 Glisson 鞘浸润。结果表明，我们的模型可以独立预测生存风险，p 值为 0.048，HR（95%置信区间）为 2.90（1.01-8.32）。这些结果表明，组织水平上的成分和细胞水平上的淋巴细胞整体排列可以区分 ICC 患者的生存风险。
