Despite advances in diagnostic and treatment technologies, predicting outcomes of patients with hepatocellular carcinoma (HCC) remains a challenge. Prognostic models are further obscured by the variable impact of the tumor properties and the remaining liver parenchyma, often affected by cirrhosis or non-alcoholic fatty liver disease that tend to precede HCC. This study investigated the prognostic value of reticulin and collagen microarchitecture in liver resection samples. We analyzed 105 scanned tissue sections that were stained using a Gordon and Sweet’s silver impregnation protocol combined with Picric Acid–Sirius Red. A convolutional neural network was utilized to segment the red-staining collagen and black linear reticulin strands, generating a detailed map of the fiber structure within the HCC and adjacent liver tissue. Subsequent hexagonal grid subsampling coupled with automated epithelial edge detection and computational fiber morphometry provided the foundation for region-specific tissue analysis. Two penalized Cox regression models using LASSO achieved a concordance index (C-index) greater than 0.7. These models incorporated variables such as patient age, tumor multifocality, and fiber-derived features from the epithelial edge in both the tumor and liver compartments. The prognostic value at the tumor edge was derived from the reticulin structure, while collagen characteristics were significant at the epithelial edge of peritumoral liver. The prognostic performance of these models was superior to models solely reliant on conventional clinicopathologic parameters, highlighting the utility of AI-extracted microarchitectural features for the management of HCC.
尽管诊断与治疗技术不断进步，预测肝细胞癌（HCC）患者的预后仍具挑战性。肿瘤特性与残余肝实质（常受肝硬化或非酒精性脂肪肝等HCC前期病变影响）的差异性影响，进一步增加了预后模型的构建难度。本研究探讨了肝切除样本中网状纤维与胶原微结构特征的预后价值。我们分析了105张采用Gordon-Sweet银浸染联合苦味酸-天狼星红双染法的组织切片，运用卷积神经网络对红染胶原纤维与黑染线性网状纤维进行分割，构建了HCC及癌旁肝组织内纤维结构的精细图谱。通过六边形网格亚采样结合自动化上皮边缘检测与计算纤维形态测量技术，实现了区域特异性组织分析。采用LASSO算法的两个惩罚性Cox回归模型获得了一致性指数（C-index）大于0.7的预测效能。这些模型整合了患者年龄、肿瘤多灶性等临床参数，以及肿瘤区域与肝组织区域上皮边缘的纤维衍生特征。研究发现：肿瘤边缘的预后价值主要源于网状纤维结构特征，而胶原特征在癌旁肝组织上皮边缘具有显著预后意义。这些模型的预测性能优于仅依赖传统临床病理参数的模型，凸显了人工智能提取的微结构特征在肝细胞癌临床管理中的应用价值。
肿瘤(癌症)患者之家