PDF(Pubmed)
Abstract：
OBJECTIVE: Radical surgery, the first-line treatment for patients with hepatocellular cancer (HCC), faces the dilemma of high early recurrence rates and the inability to predict effectively. We aim to develop and validate a multimodal model combining clinical, radiomics, and pathomics features to predict the risk of early recurrence.
METHODS: We recruited HCC patients who underwent radical surgery and collected their preoperative clinical information, enhanced computed tomography (CT) images, and whole slide images (WSI) of hematoxylin and eosin (H & E) stained biopsy sections. After feature screening analysis, independent clinical, radiomics, and pathomics features closely associated with early recurrence were identified. Next, we built 16 models using four combination data composed of three type features, four machine learning algorithms, and 5-fold cross-validation to assess the performance and predictive power of the comparative models.
RESULTS: Between January 2016 and December 2020, we recruited 107 HCC patients, of whom 45.8% (49/107) experienced early recurrence. After analysis, we identified two clinical features, two radiomics features, and three pathomics features associated with early recurrence. Multimodal machine learning models showed better predictive performance than bimodal models. Moreover, the SVM algorithm showed the best prediction results among the multimodal models. The average area under the curve (AUC), accuracy (ACC), sensitivity, and specificity were 0.863, 0.784, 0.731, and 0.826, respectively. Finally, we constructed a comprehensive nomogram using clinical features, a radiomics score and a pathomics score to provide a reference for predicting the risk of early recurrence.
CONCLUSIONS: The multimodal models can be used as a primary tool for oncologists to predict the risk of early recurrence after radical HCC surgery, which will help optimize and personalize treatment strategies.
METHODS: We recruited HCC patients who underwent radical surgery and collected their preoperative clinical information, enhanced computed tomography (CT) images, and whole slide images (WSI) of hematoxylin and eosin (H & E) stained biopsy sections. After feature screening analysis, independent clinical, radiomics, and pathomics features closely associated with early recurrence were identified. Next, we built 16 models using four combination data composed of three type features, four machine learning algorithms, and 5-fold cross-validation to assess the performance and predictive power of the comparative models.
RESULTS: Between January 2016 and December 2020, we recruited 107 HCC patients, of whom 45.8% (49/107) experienced early recurrence. After analysis, we identified two clinical features, two radiomics features, and three pathomics features associated with early recurrence. Multimodal machine learning models showed better predictive performance than bimodal models. Moreover, the SVM algorithm showed the best prediction results among the multimodal models. The average area under the curve (AUC), accuracy (ACC), sensitivity, and specificity were 0.863, 0.784, 0.731, and 0.826, respectively. Finally, we constructed a comprehensive nomogram using clinical features, a radiomics score and a pathomics score to provide a reference for predicting the risk of early recurrence.
CONCLUSIONS: The multimodal models can be used as a primary tool for oncologists to predict the risk of early recurrence after radical HCC surgery, which will help optimize and personalize treatment strategies.
摘要：
目标：根治性手术，肝细胞癌（HCC）患者的一线治疗，面临早期复发率高和无法有效预测的困境。我们的目标是开发和验证一个多模式模型结合临床，影像组学，和病理组学特征来预测早期复发的风险。
方法：我们招募了接受根治性手术的HCC患者，并收集了他们的术前临床信息，增强计算机断层扫描（CT）图像，和苏木精和伊红(H&E)染色的活检切片的整个载玻片图像(WSI)。特征筛选分析后,独立的临床，影像组学，并确定了与早期复发密切相关的病理组学特征。接下来,我们使用由三种类型特征组成的四个组合数据构建了16个模型，四种机器学习算法，和5倍交叉验证，以评估比较模型的性能和预测能力。
结果：在2016年1月至2020年12月之间，我们招募了107例HCC患者，其中45.8%(49/107)出现早期复发。经过分析,我们确定了两个临床特征，两个影像组学特征，以及与早期复发相关的三个病理组学特征。多模态机器学习模型显示出比双模模型更好的预测性能。此外，SVM算法在多模态模型中表现出最好的预测结果。曲线下平均面积(AUC),精度（ACC），灵敏度,特异性分别为0.863、0.784、0.731和0.826。最后,我们使用临床特征构建了一个全面的列线图，影像组学评分和病理组学评分为预测早期复发风险提供参考。
结论：多模式模型可作为肿瘤学家预测肝癌根治术后早期复发风险的主要工具，这将有助于优化和个性化治疗策略。
方法：我们招募了接受根治性手术的HCC患者，并收集了他们的术前临床信息，增强计算机断层扫描（CT）图像，和苏木精和伊红(H&E)染色的活检切片的整个载玻片图像(WSI)。特征筛选分析后,独立的临床，影像组学，并确定了与早期复发密切相关的病理组学特征。接下来,我们使用由三种类型特征组成的四个组合数据构建了16个模型，四种机器学习算法，和5倍交叉验证，以评估比较模型的性能和预测能力。
结果：在2016年1月至2020年12月之间，我们招募了107例HCC患者，其中45.8%(49/107)出现早期复发。经过分析,我们确定了两个临床特征，两个影像组学特征，以及与早期复发相关的三个病理组学特征。多模态机器学习模型显示出比双模模型更好的预测性能。此外，SVM算法在多模态模型中表现出最好的预测结果。曲线下平均面积(AUC),精度（ACC），灵敏度,特异性分别为0.863、0.784、0.731和0.826。最后,我们使用临床特征构建了一个全面的列线图，影像组学评分和病理组学评分为预测早期复发风险提供参考。
结论：多模式模型可作为肿瘤学家预测肝癌根治术后早期复发风险的主要工具，这将有助于优化和个性化治疗策略。
