Abstract：
OBJECTIVE: Reticulin stain is used routinely in the histological evaluation of hepatocellular carcinoma (HCC). The goal of this study was to assess whether the histological reticulin proportionate area (RPA) in HCCs predicts tumour-related outcomes.
RESULTS: We developed and validated a supervised artificial intelligence (AI) model that utilises a cloud-based, deep-learning AI platform (Aiforia Technologies, Helsinki, Finland) to specifically recognise and quantify the reticulin framework in normal livers and HCCs using routine reticulin staining. We applied this reticulin AI model to a cohort of consecutive HCC cases from patients undergoing curative resection between 2005 and 2015. A total of 101 HCC resections were included (median age = 68 years, 64 males, median follow-up time = 49.9 months). AI model RPA reduction of > 50% (compared to normal liver tissue) was predictive of metastasis [hazard ratio (HR) = 3.76, P = 0.004, disease-free survival (DFS, HR = 2.48, P < 0.001) and overall survival (OS), HR = 2.80, P = 0.001]. In a Cox regression model, which included clinical and pathological variables, RPA decrease was an independent predictor of DFS and OS and the only independent predictor of metastasis. Similar results were found in the moderately differentiated HCC subgroup (WHO grade 2), in which reticulin quantitative analysis was an independent predictor of metastasis, DFS and OS.
CONCLUSIONS: Our data indicate that decreased RPA is a strong predictor of various HCC-related outcomes, including within the moderately differentiated subgroup. Reticulin, therefore, may represent a novel and important prognostic HCC marker, to be further explored and validated.
RESULTS: We developed and validated a supervised artificial intelligence (AI) model that utilises a cloud-based, deep-learning AI platform (Aiforia Technologies, Helsinki, Finland) to specifically recognise and quantify the reticulin framework in normal livers and HCCs using routine reticulin staining. We applied this reticulin AI model to a cohort of consecutive HCC cases from patients undergoing curative resection between 2005 and 2015. A total of 101 HCC resections were included (median age = 68 years, 64 males, median follow-up time = 49.9 months). AI model RPA reduction of > 50% (compared to normal liver tissue) was predictive of metastasis [hazard ratio (HR) = 3.76, P = 0.004, disease-free survival (DFS, HR = 2.48, P < 0.001) and overall survival (OS), HR = 2.80, P = 0.001]. In a Cox regression model, which included clinical and pathological variables, RPA decrease was an independent predictor of DFS and OS and the only independent predictor of metastasis. Similar results were found in the moderately differentiated HCC subgroup (WHO grade 2), in which reticulin quantitative analysis was an independent predictor of metastasis, DFS and OS.
CONCLUSIONS: Our data indicate that decreased RPA is a strong predictor of various HCC-related outcomes, including within the moderately differentiated subgroup. Reticulin, therefore, may represent a novel and important prognostic HCC marker, to be further explored and validated.
摘要：
目的：网状蛋白染色常规用于肝细胞癌（HCC）的组织学评估。这项研究的目的是评估HCC中的组织学网状蛋白比例面积（RPA）是否可以预测肿瘤相关的结局。
结果：我们开发并验证了一个有监督的人工智能（AI）模型，该模型利用基于云的，深度学习人工智能平台(AiforiaTechnologies，赫尔辛基,芬兰）使用常规网状蛋白染色特异性识别和定量正常肝脏和HCC中的网状蛋白框架。我们将这种网状蛋白AI模型应用于2005年至2015年期间接受根治性切除术的患者的连续HCC病例队列。共纳入101例HCC切除术(中位年龄=68岁，64名男性,中位随访时间=49.9个月)。AI模型RPA降低>50%（与正常肝组织相比）可预测转移[风险比（HR）=3.76，P=0.004，无病生存（DFS，HR=2.48，P<0.001）和总生存期（OS），HR=2.80,P=0.001]。在Cox回归模型中，其中包括临床和病理变量，RPA降低是DFS和OS的独立预测因子，也是转移的唯一独立预测因子。在中分化HCC亚组（WHO2级）中发现了类似的结果，其中网状蛋白定量分析是转移的独立预测因子，DFS和操作系统。
结论：我们的数据表明，降低RPA是各种HCC相关结果的强预测因子，包括在中等分化亚组内。网状蛋白,因此,可能代表一种新颖且重要的HCC预后标志物，有待进一步探索和验证。
结果：我们开发并验证了一个有监督的人工智能（AI）模型，该模型利用基于云的，深度学习人工智能平台(AiforiaTechnologies，赫尔辛基,芬兰）使用常规网状蛋白染色特异性识别和定量正常肝脏和HCC中的网状蛋白框架。我们将这种网状蛋白AI模型应用于2005年至2015年期间接受根治性切除术的患者的连续HCC病例队列。共纳入101例HCC切除术(中位年龄=68岁，64名男性,中位随访时间=49.9个月)。AI模型RPA降低>50%（与正常肝组织相比）可预测转移[风险比（HR）=3.76，P=0.004，无病生存（DFS，HR=2.48，P<0.001）和总生存期（OS），HR=2.80,P=0.001]。在Cox回归模型中，其中包括临床和病理变量，RPA降低是DFS和OS的独立预测因子，也是转移的唯一独立预测因子。在中分化HCC亚组（WHO2级）中发现了类似的结果，其中网状蛋白定量分析是转移的独立预测因子，DFS和操作系统。
结论：我们的数据表明，降低RPA是各种HCC相关结果的强预测因子，包括在中等分化亚组内。网状蛋白,因此,可能代表一种新颖且重要的HCC预后标志物，有待进一步探索和验证。
